ATTENTION TO IRREGULAR VERBS BY BEGINNING LEARNERS OF GERMAN:
AN EYE-MOVEMENT STUDY
By
Maren Schierloh

A DISSERTATION
Submitted to
Michigan State University
In partial fulfillment of the requirements
For the degree of
DOCTOR OF PHILOSOPHY
Second Language Studies
2011

ABSTRACT
ATTENTION TO IRREGULAR VERBS BY BEGINNING LEARNERS OF GERMAN:
AN EYE-MOVEMENT STUDY
By
Maren Schierloh
In this study I examine the noticing of verbs with stem vowel changes by beginning adult
learners of L2 German who have not been formally introduced to this linguistic feature. Two
research questions (RQs) guided the experimental design and the empirical analyses in this
study: (1) Do adult beginning learners of German who are unfamiliar with stem-changing verbs
attend to those irregularities during reading? (2) Is increased attention to irregular verbs
associated with subsequent learning of them? Regarding RQ1, I hypothesized that during the
reading, learners would fixate on vowel-stem-changing verbs significantly longer than regular
verbs in which the stem does not change, with fixations measured through eye-tracking
technology. Regarding RQ2, I hypothesized that variations in eye fixation durations would not
be predictive of the learning of stem-changes in that there would be no statistically significant
correlation between pre- and posttest improvements and fixation times. I predicted that learners
would improve on the inflections and overall spelling of verbs due to the exposure to the verbs
during the reading activity.
The participants, 43 beginning learners enrolled in a first semester German course, took a
picture-based sentence production test to estimate their initial command of stem-changing verbs.
Then, they read sentences with stem-changing verbs (experimental condition) and regular verbs
(control condition) on a computer screen. The sentences appeared in pairs of two, one baseline
sentence (no-stem change, regardless of condition) and one critical sentence (stem-change in the

nd

experiment condition). The sentence endings as well as the person (2

and 3

rd

person singular)

were counterbalanced across 24 trials, which were also randomized for each participant. As they
read, participants’’ eye movements were recorded. Last, participants took a post-test, which was
identical to the pre-test with the difference being the order in which the stimuli-pictures were
presented.
I used multivariate ANOVA modeling to investigate whether the novel verb irregularities
(i.e., vowel stem changes) had an effect on learners’’ eye fixations (total time, first fixation
duration, and gaze duration). Statistical and visual inspection of the data revealed outlying
observations, which were cut from the regular data set (truncated) and analyzed separately via
Chi-square tests and logistic regressions. Regarding total time, there was a significant effect of
verb type, which was also evident in the analysis of the outlier data. For first fixation, statistical
significance was only found with the outlier observations, revealing a trend similar to the
findings with total time. The findings regarding gaze duration were also not statistically
significant. The outliers evidenced the same but weaker pattern. Overall, there was a notable
trend showing that irregular verbs were fixated on longer, which was statistically significant in
half of all statistical analyses. The correlation analyses between total time gains (i.e., the fixation
time critical condition subtracted from the baseline condition) and learning gains revealed that
noticing (longer fixations on irregular verbs) was related to holistic learning gains (not
necessarily associated with stem-changes).
I interpret the results from multiple perspectives with reference to previous research on
noticing and language learning. The results provide for a more nuanced yet more empiricallybased understanding of the noticing construct.

To My Parents

iv

ACKNOWLEDGEMENTS
A great many people have been indispensible——not only in the pursuit of this dissertation
project, but also throughout my graduate career. The conduct of this dissertation project would
not have been possible without the help of my dissertation guidance committee (Drs. Aline
Godfroid, Paula Winke, Susan Gass, and Debra Hardison), Dr. Marcus Johnson from SR
Research, the volunteers at the Department of Statistics and Probability, Shelee-Ann Flemmings,
the German instructors and students, my colleagues, my friends, and my family. It gives me
great pleasure to take this opportunity to express my heart-felt gratitude to all.
Aline Godfroid has been an irreplaceable source of inspiration and encouragement; it was
her enthusiasm and interest in this project that motivated me to follow through. Aline’’s
exceptional expertise in experimental design, eye-tracking research and statistics, her thorough
feedback, in-depth thinking and eye for detail have helped me tremendously. I feel fortunate
being her student, colleague, and friend——a privilege I will always treasure.
In uncountable ways, I am indebted to my advisor, Paula Winke, who has become a lifelong role model for me. I thank her for motivating me, and for increasing my awareness for
seeing the bigger picture, not only in respect to my dissertation. I also owe much gratitude to
Susan Gass, whose genuine care on so many levels gave me confidence and enthusiasm to power
through. I am honored having been mentored by a Distinguished Professor of her caliber. Last
but not least, I am grateful for Debra Hardison’’s service on my dissertation committee, but even
more so, for the great impact she has had on my development as a university-level teacher.
The programming of the eye-tracking project would not have been possible without
Marcus Johnson from SR research, whose detailed instructions and many screen-shots helped me
v

navigate the world of computer programming, a terrain I never thought I would understand. My
sincere thanks are also due to Vineet Bansal whose Excel-script-writing skills saved me many
hours of cut-and-paste, and, more importantly, from human error. Similarly, I would like to
extend my gratitude to the volunteers at the Department of Statistics and Probability, who kindly
welcomed my frequent inquiries and showed great excitement for my data. I also would like to
acknowledge and express my gratitude for the assistance of Shelee-Ann Flemmings during my
data collection time.
While I feel grateful for being among many fine colleagues in the MSU Second
Language Studies Program, I have been incredibly blessed with the friendship that grew from my
teamwork with Luke Plonsky. Everyone needs a friend like Luke.
I cannot imagine having arrived at this point in my life or academic career without the
love and support of my family. My future mother and father in law have given me great support
throughout; it is overwhelming to know that people I admire so much are so proud of me. My
parents in Germany, Rainer and Ulrike Schierloh, are the ones to whom this dissertation is
dedicated. Thank you for instilling in me the values of perseverance, discipline, and love for
learning. Thank you for supporting the pursuit of my dreams, and for always being there for me.
And finally, I am thankful beyond measure to my future husband, Dr. Christopher Uggen,
for his love and care, encouragement and support, and his genuine interest in my studies - while
also reminding me that there is more to life than ANOVAs and Logistic Regressions! Thank you
for bringing home Apollo, our dear yellow-lab puppy, whose everlasting happiness and faithful
company I cherish every day, especially during long hours of writing. Thank you, Chris, for
believing in me——and for encouraging me to never do any less than my very, very best.
vi

TABLE OF CONTENTS
LIST OF TABLES .......................................................................................................................... x
LIST OF FIGURES ....................................................................................................................... xi
INTRODUCTION .......................................................................................................................... 1
CHAPTER 1 TAKING A COGNITIVE PERSPECTIVE ON SLA .............................................. 4
Attention and Awareness ............................................................................................................ 4
Schmidt’’s Noticing Hypothesis. ........................................................................................... 10
Tomlin and Villa’’s Theory of Attention ............................................................................... 10
VanPatten’’s Input Processing Theory................................................................................... 11
Robinson’’s (2003) Theorizing of Attention .......................................................................... 14
Implicit versus Explicit Learning.............................................................................................. 25
Intentional versus Incidental Learning...................................................................................... 29
CHAPTER 2 NOTICING ............................................................................................................. 33
Noticing and Learning .............................................................................................................. 37
The Measurement of Noticing .................................................................................................. 40
Eye-Tracking as a Measurement of Noticing ........................................................................... 44
Word Frequency.................................................................................................................... 45
Word Predictability ............................................................................................................... 46
Word Length ......................................................................................................................... 47
The E-Z Reader Model ......................................................................................................... 49
CHAPTER 3 GERMAN STEM-CHANGING VERBS ............................................................... 54
L2 Morphology and Its Noticability ......................................................................................... 54
German Stem Changing Verbs ................................................................................................. 55
The Difficulty and Noticability of L2 Forms ............................................................................ 57
CHAPTER 4 METHODOLOGY ................................................................................................. 61
Research Questions and Hypotheses ........................................................................................ 61
Materials ................................................................................................................................... 64
The Pre- and Post-test ........................................................................................................... 64
The Reading Task ................................................................................................................. 65
The Vocabulary Familiarity Test. ......................................................................................... 70
Procedures ................................................................................................................................. 70
Analyses .................................................................................................................................... 73
Analysis of the Eye-tracking Data ........................................................................................ 74
Analysis of the Test Scores ................................................................................................... 76
CHAPTER 5 RESULTS ............................................................................................................... 78
vii

Results Addressing RQ 1 .......................................................................................................... 79
Total Time ............................................................................................................................. 79
Total time ANOVA............................................................................................................... 81
Post Hoc Tests................................................................................................................... 82
Pair-wise Comparisons ..................................................................................................... 83
Nested ANOVA Assumptions .............................................................................................. 84
Homogeneity of Residuals Variances ............................................................................... 84
Normality of the Residuals ............................................................................................... 85
Analysis of Omitted Outliers ................................................................................................ 85
First Fixation Duration .............................................................................................................. 93
First Fixation Duration ANOVA .......................................................................................... 93
Nested ANOVA Assumptions .............................................................................................. 94
Analysis of Outliers .............................................................................................................. 94
Gaze Duration. .......................................................................................................................... 99
Gaze Duration ANOVA ...................................................................................................... 100
Assumptions........................................................................................................................ 101
Analysis of Outliers ............................................................................................................ 101
Summary of Findings for Research Question 1. ................................................................. 105
Results Addressing RQ 2 ........................................................................................................ 106
Correlation of Fixation Times with Learning Gains ........................................................... 110
Partial Correlation of Fixation Times with Learning Gains ............................................... 111
Summary of Findings for RQ 2 .............................................................................................. 117
CHAPTER 6 DISCUSSION ....................................................................................................... 118
Research Question One ........................................................................................................... 118
Attention to Irregular Verbs ................................................................................................ 119
““Outlier Cases”” with Extreme Fixation Times ................................................................... 121
Research Question Two .......................................................................................................... 124
Non-saliency of Irregular Verb Morphology. ..................................................................... 125
The Developmental Level of the Learner and Attention .................................................... 131
Rule-based versus Item-based Learning. ............................................................................ 132
Parabolic Relationship ........................................................................................................ 134
The Verbs ““Sehen”” and ““Fahren”” ....................................................................................... 136
Limitations and Directions for Future Research ..................................................................... 137
Limitations .......................................................................................................................... 137
Suggestions for Future Research ........................................................................................ 142
CHAPTER 7 CONCLUSION..................................................................................................... 148
Pedagogical Suggestions ......................................................................................................... 149
The Contributions of This Study to SLA Theory ................................................................... 150
APPENDICES ............................................................................................................................ 151

viii

REFERENCES ........................................................................................................................... 159

ix

LIST OF TABLES
Table 1: Descriptive Statistics...................................................................................................... 64
Table 2: Counterbalancing of Sentence Endings and Persons ..................................................... 69
Table 3: Variables in the Interest Area Report ............................................................................ 74
Table 4: Logistic Regression with Total Time............................................................................. 91
Table 5: Logistic Regression with First Fixation Duration.......................................................... 99
Table 6: Logistic Regression with Gaze Duration ..................................................................... 105
Table 7: Verbs with Stem Changes from e

i/ie ..................................................................... 153

Table 8: Verbs with Stem Changes from a

ä ........................................................................ 153

Table 9: Sentence Stimuli by Group .......................................................................................... 154
Table 10: Sentence Stimuli by Group (a)................................................................................... 155
Table 11: Sentence Stimuli by Group (b) .................................................................................. 156
Table 12: Sentence Stimuli by Group (c)................................................................................... 157
Table 13: Frequency of the Verbs Used in this Study ............................................................... 158

x

LIST OF FIGURES
Figure 1. Partial Display of Gass’’s (1997) Model of Apperception .............................................. 8
Figure 2. A Generic Model of Human Information Processing with Three Memory Systems ... 15
Figure 3. Wickens’’s (2007) Model of Information Processing ................................................... 17
Figure 4. Wickens’’s Proposed Structure of Processing Resources (2007, p. 186) ...................... 19
Figure 5. Continuum with Different Levels of Awareness .......................................................... 22
Figure 6. Graphic Representation of the Noticing Hypothesis .................................................... 33
Figure 7. Simple Illustration of the Experimental Design ........................................................... 66
Figure 8. Example of Eye-tracker Screens................................................................................... 68
Figure 9. Nested Design .............................................................................................................. 79
Figure 10. Post-Hoc Results........................................................................................................ 83
Figure 11. Verb Type and Total Time Gain (Regular versus Irregular) ...................................... 87
Figure 12. Verb Type and Total Time Gain (by Stem-change) ................................................... 89
Figure 13. Verb Type and First Fixation (Regular versus Irregular) ........................................... 97
Figure 14. Verb Type and First Fixation (by Stem-change) ........................................................ 98
Figure 15. Verb Type and Gaze Duration (Regular versus Irregular) ....................................... 103
Figure 16. Verb Type and Gaze Duration (by Stem-change) .................................................... 104
Figure 17. Design of the Study .................................................................................................. 107
Figure 19. Illustration of a Semi Partial Correlation (Controlling for Reading Speed) ............. 110
Figure 20. Simplistic Depiction of the Parabolic Relationship .................................................. 114
Figure 21. Verb and Mean Scores Stem-change ........................................................................ 115

xi

Figure 22. Verb and Mean Scores Holistic ................................................................................ 116

xii

INTRODUCTION
This Ph.D. dissertation reports on a study investigating whether adult beginning learners
of German (mostly native speakers of English) attend to or notice irregular, stem-changing verbs,
given that they do not know the feature of verb stem-changes in German. I was interested not
only in the learners’’ attention to irregularities, but also whether increased attention to these
features is associated with improvements in learners’’ production of stem-changing verbs. To
measure attention, I used eye-tracking technology, a measurement tool recently utilized to
investigate the noticing construct in second language acquisition (SLA).
My interest in language learners’’ noticing of novel linguistic features and its relationship
to learning is grounded in the ongoing and intense debate in SLA centering on the following
question: Does incidental attention to a particular language form promote the acquisition of that
form? (e.g., Doughty & Williams, 1998; Norris & Ortega, 2000; Skehan & Foster, 2001). This
question, and the role of noticing in particular, have played crucial roles in a vast number of SLA
studies. As supported by Robinson (1995; 1996; 2003; 2008), Schmidt (1995; 2001; 2010),
Skehan (1998) and other scholars in the field (e.g., Adams, 2003; Carr & Curran, 1994; Carrell,
1992; DeKeyser, 1997; R. Ellis, 1994; Fotos, 1993, 1994; Gass & Alvarez-Torres, 2005; Hama
& Leow, 2010; Izumi, 2002; Izumi & Bigelow, 2000; Izumi, Bigelow, Fujiwara, & Fearnow,
1999; Izumi & Izumi, 2004; Leow et al., 2008; Leow & Morgan-Short, 2004; Leow, 1997b,
1998, 2000; Long, 1996; Mackey, 2006; Mackey, Gass, & McDonough, 2000; Norris & Ortega,
2000; Roberts, 1995; Rosa & Leow, 2004; Rosa & O'Neill, 1999; Sachs & Suh, 2007; Swain,
1995, 1998, 2005; Swain & Lapkin, 1995; Van Hell & De Groot, 1999), successful language
learning by adults seems to be tightly linked to learners’’ noticing of words, phrases, and
grammar structures.
1

The notion that noticing is essential for the acquisition of new linguistic systems was
introduced and developed by Schmidt (1990, 1993a, 1994, 1995, 2001), and it has since been
subject to intense study (e.g., Leow, Hsieh, & Moreno, 2008; Leow, 1997, 2000; Robinson,
1995; 2003; 2008) and much scrutiny (e.g., Carroll, 2006a, 2006b; Schachter, 1998; Truscott,
1998; Williams, 2005). Much of the argumentation is grounded in the difficulty of
operationalizing and measuring L2 learners’’ internal cognitive processes (e.g., Egi, 2004; Leow,
2000, 2002; Simard & Wong, 2001). Researchers in SLA and cognitive science have attempted
to overcome this challenge by introducing and testing several hypotheses related to the type and
amount of attention necessary for language learning (Godfroid, 2010; Godfroid, Housen &
Boers, 2010; Simard & Wong, 2001; Tomlin & Villa, 1994), and the specific aspects of language
that are most likely to be noticed (Schierloh, in press; Carroll, 2006a, 2006b; N. Ellis & Sagarra,
2010).
In this dissertation, I operationalize noticing as focal attention with some awareness
(Schmidt, 1995, 2001; Robinson, 2003). I use eye-tracking methodology to monitor where and
for how long beginning learners look when they read simple sentences in their second language
(L2). As such, eye-tracking allowed me to tap into the learners’’ attentional/visual foci during
reading and to investigate whether increased attention to new a morphological feature yields
uptake or learning of that word or feature.
Research on the relationship between noticing and learning is of theoretical and practical
importance. As several scholars have observed (see, for example, Godfroid, 2010), there is a
need to refine the theoretical foundations of the noticing construct, and investigations into the
extent to which eye-tracking methodology can provide an accurate, empirically-based
measurement of the noticing construct is of utmost interest in this respect. On a practical level,
2

there is a need for researchers to investigate whether beginning learners pay attention to new
linguistic features when they first come across them while reading. Such information can help,
for example, instructors of foreign languages understand how their students process (or do not
process) new linguistic information, and whether explicit instruction and interventions are
needed to enhance students’’ attention to and learning of form.
This dissertation is organized in the following way: In the first chapter, relevant areas of
the literature pertaining to three areas of SLA research are discussed: (a) attention and
awareness; (b) implicit versus explicit learning; and (c) incidental versus intentional learning.
The second chapter discusses the concept of noticing, the central issue of this dissertation. After
discussing the Noticing Hypothesis and the three types of noticing, methodological issues are
reviewed and the methodology of this studyeye-trackingas well as this study’’s
operationalization of noticing are discussed. In the third chapter I describe the linguistic
characteristics of German stem-changing verbs, their underlying rules, and their acquisition by
foreign language learners. The fourth chapter outlines the methodology of this study. The
following chapter reports the empirical findings. In chapter six I discuss the findings in light of
1

the research questions and address the observed relationships among attention and learning. In
the final chapter I summarize the findings and outline limitations and suggestions for future
research. I conclude by providing pedagogical implications.

1

In regard to this dissertation study, the term ‘‘learning’’ corresponds to gains in performance
from pre-test to post-test.
3

CHAPTER 1 TAKING A COGNITIVE PERSPECTIVE ON SLA
In this chapter I present literature on some of the main cognitive issues in SLA and how
they have been conceptualized, researched, and debated. In particular, I discuss how the notions
of attention and awareness have been theorized, discussed, and operationalized in contexts of
SLA and cognitive psychology. In this discussion, I explain the concepts of implicit (i.e.,
unaware) and explicit (i.e., aware) learning, and the roles these concepts play in my dissertation
research. Closely related to the issues surrounding implicit and explicit learning are the notions
of incidental and intentional learning; those will be briefly discussed in the last sub-section of
Chapter 1.
Attention and Awareness
In the SLA literature, Schmidt was the first scholar who conceptualized and discussed
different levels of awareness/consciousness in relation to SLA. Schmidt (1990) used the notions
2

of ‘‘awareness’’ and ‘‘consciousness’’ interchangeably, and in this paper I use the single term
‘‘awareness’’ to maintain clarity. Although Schmidt did not suggest a dichotomy of attention and
awareness, he suggested that there are different levels of conscious learning, ranging on the
awareness scale from intentional learningwith the highest level of awareness involvedto
implicit learning, and to subliminal learningwith the least amount of awareness involved.
With this conceptualization, Schmidt is also the founder of the well-known Noticing Hypothesis,
which will be discussed in Chapter 2. His differentiation of the umbrella term of consciousness
or awareness (in this paper termed awareness) and what it means for the study of SLA is

2

In Schmidt (1990) awareness is one of the three senses in which the term consciousness is
commonly used when discussing the possibility of unconscious learning.
4

important because it discusses the involvement of different levels of consciousness in the L2
learning process.
Schmidt’’s investigations into awareness originated as a reaction to Krashen (1981, 1985)
and Krashen and Terrell (1983) who hypothesized that adults acquire an L2 unconsciously, just
as children learn their first language (L1). Schmidt (1990) argued that Krashen over-simplified
the role of consciousness in the acquisition process. According to Schmidt (1990),
consciousness plays an essential role in adult L2 acquisition, and there are three senses of
consciousness: (1) consciousness as awareness; (2) consciousness as intention; and (3)
consciousness as knowledge or understanding (see also Godfroid, 2010). When referring to the
first sense, consciousness as awareness, he further distinguished three levels.
1. Perception
2. Noticing (i.e., here: awareness and attention)
3. Understanding
The crucial part of this conceptualization is that noticing does not require awareness; it is
a type or a quality of awareness (sometimes also referred to by Schmidt as ‘‘awareness at the
level of noticing’’). In 2001, Schmidt proposed that a low level of awarenessat the level of
noticing (i.e., focal attention)is necessary for input to become intake for learning. The details
and impact of this Noticing Hypothesis are discussed in Chapter 2.
Schmidt’’s (2001) argument that ‘‘perception’’ is below the level of conscientious noticing
has been criticized because is impossible to investigate the absolute absence of awareness in
learners (cf. Hama & Leow, 2010) who cannot report this type of perception verbally (Godfroid,
2010). If language learners merely perceive input, without awareness, it is highly unlikely that
they will learn from it (Sharwood-Smith & Truscott, 2010; Sharwood-Smith & Truscott, in
5

press; Truscott, 1998). The question is whether there are categorical distinctions on the
awareness scale, and, if so, where do these boundaries, or thresholds, lie in our consciousness
that spans from perception (i.e., non-conscious registration) to noticing (i.e., conscious
3

perception or perception that involves some level of awareness) to understanding (i.e., full
awareness of the learning process and of what is being learned). Sharwood-Smith and Truscott
(in press) similarly argued, ““the concept of noticing can only be understood if there is a
principled means of drawing these boundaries. But no such means have yet been offered”” (p. 8).
In the field of cognitive science, however, researchers have been occupied with very similar, if
not the same, question of how we can determine whether a participant has consciously perceived
certain visual stimuli (e.g., Dehaene & Changeux, 2004; Dehaene et al., 2001). This marks the
difference between subliminal processing, on the one hand, and conscious perception on the
other.
In referring to research on the involvement of awareness in learning, the cognitive
psychologists Merikle, Smilek, and Eastwood (2001) distinguished two different threshold areas
within research on unconscious perception: (a) a subjective threshold, which is a participant’’s
failure to report conscious awareness of a stimulus, and (b) an objective threshold, which is a

3

In the cognitive science literature, there is agreement that (visual) perception involves
awareness, simply because no study has shown clear evidence of zero consciousness/awareness
given the difficulty or impossibility of supporting the null hypothesis. It is important to note here
that in the field of cognitive psychology, functional neuroimaging studies indicated that visual
processing is possible without conscious awareness with blind sight patients (see, for example,
Overgaard, Fehl, Mouridsen, Bergholt, & Cleermans, 2008). Thus, we cannot state for certain
that all perception involves awareness.
6

4

participant’’s inability to make accurate forced-choice decisions about a stimulus. According to
Merikle et al. (2001), research participants often demonstrate awareness assessed by the
objective threshold, even if there is a lack of evidence of awareness at the subjective threshold.
This view corresponds to work by Lamme (2003), who maintains that ““attention does not
determine whether stimuli reach a conscious state but determines whether a (conscious) report
about stimuli is possible”” (p. 13, highlights are mine). Cognitive psychologists, unlike most SLA
researchers, usually favor the reliance on an objective threshold for validating reports of attention
compared to presenting possibly biased or inaccurate reports of conscious experience (at the
subjective level).
Regardless of the unsolved question as to the involvement of awareness in foreign
language processing and learning, SLA researchers agree that Schmidt’’s distinction of
perception, noticing (i.e., focal attention in reading), and understanding relies on the extent to
which awareness is involved. Thus, Schmidt made a crucial contribution to the field of SLA.
Yet, even more valuable is his emphasis on the essence of attention, which he believes to involve
5

some level of awareness, for language learning to occur.

In 2001, he directly addressed the role

of attention:

4

In an experiment this decision-making could be to guess with above chance level whether a
word is a word or not, or, in reference to this dissertation study, whether a word is fixated and refixated (i.e., attended to) or not.
5
While in the SLA literature researchers widely believe that consciousness goes hand in hand
with attention, it is important to acknowledge that recently psychological and neurophysiological
researchers put forward that attention and consciousness are distinct (not isomorphic, as Schmidt
suggested) because they have distinct neuronal mechanisms (B. J. Baars, 1997; B. J. Baars,
2002; Block, 2005; Dehaene, Changeux, Naccache, Sackur, & Sergent, 2006; Koch, 2004; Koch
& Tsuchiya, 2007; Lamme, 2003; Naccache, Blandin, & Dehaene, 2002; Woodman & Luck,
2003).
7

““[……] the concept of attention is necessary in order to understand virtually every aspect of
second language acquisition, including the development of interlanguages (IL) over time,
variation within IL at particular points in time, the development of L2 fluency, the role of
individual differences, such as motivation, aptitude, and learning strategies in L2
learning, and the ways in which negotiation for meaning, and all forms of instruction
contribute to language learning”” (p. 3).
While admitting that it is nearly impossible to truly separate attention and awareness (p.
4), Schmidt argued that his concept of noticing is equivalent to Gass’’s ‘‘apperception’’ of novel
input that involves some level of awareness. According to Gass (1997), ““apperception is an
internal cognitive act in which a linguistic form is related to some bit of existing knowledge (or
gap in knowledge)”” (p. 4). Figure 1 demonstrates one part of Gass’’s (1997) model that illustrates
a conceptualization of apperception.

Figure 1. Partial Display of Gass’’s (1997) Model of Apperception

8

According to this model, apperceived input is input that has been attended to with some
level of awareness, perhaps because the input is recognized due to its frequency, the learner’’s
prior knowledge or experience, or because there is something particular in the input that does not
match the current representation of the language in the learner’’s mind. This concept of noticing
as focal attention, or apperception, provides an important theoretical foundation for this
dissertation study. The important point to keep in mind is that I do not equalize attended input
(i.e., ‘‘intake’’) with acquisition (not all attended input enters long-term memory), but I will use
the concept of apperceived input to discuss some findings in this study.
It is important to acknowledge that, with few exceptions (e.g., Hama & Leow, 2010),
SLA researchers have moved away from debating the role of awareness in the SLA process,
mainly due to the impossibility of investigating the complete absence of awareness in language
learners (e.g., Block, 1995; Godfroid, 2010; Rosa & Leow, 2004; Rosa & O’’Neill, 1999; but cf.
Williams, 2005). More recently, researchers have designed studies to investigate how attention
and which types of attention function in adult language acquisition (e.g., Gass & Alvarez-Torres,
2005; Gass, Svetics, & Lemelin, 2003; Godfroid, Housen, & Boers, 2010b; Leow, 1997, 2000;
Robinson, 2003), questions which are also central to this dissertation research. The literature in
cognitive psychology largely refers to selective processing when referring to one of the main
functions of attention and distinguishes two types of attention: focused attention and divided
attention. Individuals employ focused attention when they only attend to selected parts of the
stimuli input while other stimuli are ignored (therefore, it is also known as selective attention).
Divided attention is when more than one stimulus or type of information is attended to
simultaneously (Eysenck & Keane, 2010). Robinson (2008) used the same conceptual
distinction, but he used different terminology. He called divided attention perceptual attention
9

and focused attention focal attention. According to Robinson, focal attention is ““under some
degree of voluntary executive control”” and therefore a ““precondition for awareness”” (p. 133). I
adopt the terminology by Robinson in this dissertation. With the distinction of perceptual
attention and focused attention in mind, I will explain how the role of attention has been
theorized in SLA. I will elaborate on attention in (1) Schmidt’’s Noticing Hypothesis, (2) Tomlin
and Villa’’s Theory of Attention, (3) VanPatten’’s Input Processing Theory, and (4) Robinson’’s
(2003) Theory of Attention.
6

Schmidt’s Noticing Hypothesis

In drawing on work in cognitive psychology, Schmidt (2001) concluded that attention is
selective and only partially under voluntary control. Attention controls the access to awareness
and is essential for learning, but the amount of attention needed differs depending on the
linguistic aspects (syntax, morphology, pragmatics, etc.) at hand. Although he never stated it
explicitly, it can be assumed that Schmidt is theorizing on what Robinson (2003) calls focal
attention.
Tomlin and Villa’s Theory of Attention
Tomlin and Villa’’s view of the role of attention in SLA (1994) differed from the
mainstream theories in SLA, which attracted opposition (e.g., Schmidt, 2001; Simard & Wong,
2001). They divided attention into three distinct processes: (1) alertness, which deals with the
learner’’s affective and motivational state, specifically the learner’’s overall readiness to pay
attention to the input features they encounter either through reading or listening; (2) orientation,
which refers to the concentration of attention to a specific feature in the input at the expense of
6

Schmidt’’s Noticing Hypothesis is the focus of Chapter 3, hence the succinct summary.
10

others; and (3) detection, the stage at which the cognitive processing of the focused input takes
places. Detection is also the stage at which the specific language feature is registered in working
memory. Tomlin and Villa claimed that the last component, detection, does not require the first
two components to take place. That is, learners can detect a specific part of the incoming stimuli
without alertness or orientation. They further claimed that none of the three processes of
attention requires awareness, but attention is a prerequisite for awareness. These claims have
received scrutiny but also interest from others. Most notably, Leow (1998) conducted a study in
which he assigned language learners to four conditions, all of which were categorized as +
awareness, but differed as to whether they were + or –– orientation and + or –– detection.
According to Leow’’s findings, detection, but not orientation, appeared to be a crucial attentional
mechanism that allows L2 development to occur.
Simard and Wong (2001) criticized Leow’’s research as well as Tomlin and Villa’’s
theorizing on the basis that alertness, orientation, and detection constitute one process in the
mind——they not separable. They criticized Loew’’s research because it did not measure what it
intended to (i.e., alertness, orientation, and detection), and that a much more fine-grained
analysis of attention is needed if one is to tease apart these attentional mechanisms. In addition
to criticizing Leow (1998), Simard and Wong challenged Tomlin and Villa (1994) for their claim
that alertness, orientation, and detection can take place without awareness. According to Simard
and Wong, the involvement or non-involvement of awareness in attentional processes is an issue
that has not been resolved to date, so strong claims such as those by Tomlin and Villa as well as
Leow, should not be made. The role of awareness will resurface in my discussion of explicit and
implicit learning.
VanPatten’s Input Processing Theory
11

VanPatten’’s Input Processing (IP) theory makes claims regarding the nature of attention,
language processing, and the structure of attentional resources. VanPatten (1990, 1994) was
interested in how learners allocate their attention when processing input and asked the following
question: ““What happens when learners are asked to attend to meaning and form together or just
to meaning or form?”” His research findings suggested that both dimensions of the linguistic
input, meaning and form, compete for learners’’ attention. Because of limited working memory
capacity, VanPatten claims that learners have to prioritize one over the other and they can only
attend to form when they can easily understand the meaning of the input. Based on these
findings, VanPatten (1996, pp. 24-26) formulated his IP Principles, which pedagogically
underpin his theory:
P1:
P1a:
P1b:
P1c:
P2:

Learners process input for meaning before they process it for form.
Learners process content words in the input before anything else.
Learners prefer processing lexical items to grammatical items (for example,
morphological markings) for semantic information.
Learners prefer processing ‘‘more meaningful’’ morphology before ‘‘less or nonmeaningful’’ morphology
For learners to process form that is not meaningful, they must be able to process
information or communicate content at no (or little) cost to attention.

Based on these principles, learners might only be able to pay attention to form if the
overall meaning of the input is understood effortlessly. If this is not the case, learners focus their
attention to the meaning, usually content words in the input, as their working memory capacity is
limited. That is, meaning is primary, and form processing is secondary. I will turn to the issue of
whether or not language learners can be described as this type of ““limited capacity processors”” in
Chapter 3.

12

DeKeyser, Salaberry, Robinson, and Harrington (2002) criticized VanPatten’’s IP Theory
in relation to three areas: (1) the validity of the limited-capacity processing concept, (2) the
consideration of only the single-resource model of attention, and (3) the details of the
mechanisms involved in L2 processing. Concerning the first two areas, DeKeyser et al.
criticized the nature of VanPatten’’s research (VanPatten, 1994, 1996; VanPatten & Cadierno,
1993; VanPatten & Oikkenon, 1996) where VanPatten and his colleagues manipulate the
7

linguistic input so learners are likely to trade off attention to form to attention to meaning,

which is then explained within the framework of the what DeKeyser et al. consider an outdated
single-resource, limited-capacity model of attention. In summary, DeKeyser et al. wrote that
they believe attention in the IP model is too vaguely defined and not in accordance with existing
cognitive theory.
In reference to VanPatten’’s IP Theory, information on bottom-up and top-down
processing adds to the completeness of the theory. When learners process input, they naturally
process the language from the top down (i.e., relying on contextual cues to aid in the
comprehension of the information) as well as from the bottom up (i.e., relying on linguistic cues)
(Bell, 2006; Kintsch, 2005; Kurby, Britt, & Magliano, 2005). Tsui and Fullilove (1998), for
example, examined the processing skills used by skilled and less-skilled L2 readers. They
observed that skilled readers are better able to engage in top-down processing. In light of
attentional processes, skilled L2 readers——those who favor top-down processing——might pay
more attention to linguistic cues, such as morphology and syntax in the L2. Yet, both top-down

7

DeKeyser et al. (2002) also find fault with the stimuli (target words) selected in VanPatten’’s
study, so the results do not reflect ““a form-meaning processing distinction but rather an easydifficult scanning distinction”” (p. 813).
13

and bottom-up processes jointly influence the mental representations of linguistic stimuli that are
8

formed during or as a result of comprehension (Kintsch, 2005).

Beginning learners who experience processing difficulties in their L2 are assumed to
depend more on contextual cues that are present elsewhere in the sentence or come from other
sources, such as pictures, the situational context, or the learners’’ background knowledge.
Robinson’s (2003) Theorizing of Attention
In 2003, Robinson provided a very comprehensive synthesis of the work on attention in
the field of SLA. He differentiated three types of attention:
(1) Attention as selection (focused attention)
(2) Attention as capacity
(3) Attention as effort
Regarding the first type, attention as selection, which is relevant in light of the present study,
Robinson relied on Wickens, Gordon, and Liu’’s (1997) information processing framework to
elucidate where and how attention as selection takes place during information processing.

8

In the cognitive science literature, where attention is typically investigated with reference to
visual perception rather than language processing, Corbetta and Shulman (2002) and Yantis
(2008), for example, referred to top-down (““active””) and bottom-up (““passive””) modes of
attention. Simply put, top-down attention is controlled by the individuals’’ objectives, while
bottom-up attention is driven by external stimuli, or stimuli that are not part of the individual’’s
immediate goals. Similarly, Ruz and Nobre (2008) studied how attention modulates initial stages
of visual word processing (with native speakers). They found that selective attention to linguistic
attributes (i.e., top-down processing) enhances the initial stages of visual word processing.
14

Figure 2. A Generic Model of Human Information Processing with Three Memory Systems

15

Attention is a major resource during central processing. It influences the perception and
the selection of stimuli; that is, it controls what part of the perceived input enters into an
individual’’s working memory. In Robinson’’s (2003) words, ““focal attention and noticing are
selective of input”” (p. 637).
In 2007, Wickens proposed a modified model of information processing that elucidates
the allocation of attentional resources in every aspect of information processing. Attentional
resources are specifically allocated to the selection and the perception, the latter of which is also
influenced by already established representations in long-term memory. In Wickens’’s 2007
model, attentional selection is hypothesized to happen early in this model. Working memory,
which is an intermediary step to long-term memory, is also under attentional control.

16

Figure 3. Wickens’’s (2007) Model of Information Processing

17

Thus, attentional selection is likely to be the result of focal attention; it happens when
input is attended to during the first stage of the information-processing model shown above.
According to Robinson, attention as selection does not require but is likely to involve awareness.
Robinson equated attention as selection with Schmidt’’s noticing; it happens when input is
initially attended and later, after it is further processed and stored in working memory. As such,
selection also happens after the information has been analyzed. Unlike VanPatten’’s direct
connection between the two, selection is independent of working memory capacity limits.
Selection is a consequence of the nature of the task demands and the learner’’s associated
objectives. This conceptualization reflects what Cowan suggested in 1995: ““[T]here
theoretically could be selective attention only because the subject would rather not encode
useless information, and not because selection is needed in order to conserve a limited attentional
resource”” (p. 203).
Although Robinson does not view attention as selection as a result of limits in working
memory capacity, he elaborates on working memory limits when referring to attention as
capacity. With regard to the latter, Robinson discussed three theoretical models. The singlecapacity model holds that ““complex and less automatized tasks consume more attentional
capacity, and require greater effort”” (p.634). The multiple-resource model, adapted from
Wickens (1992), takes the single capacity model a step further, stating that attention can be
allocated to different tasks (i.e., divided attention), as long as the tasks require different
processing mechanisms (e.g., auditory versus visual). Figure 4 below, which is adapted from
Wickens (2007), shows that ““resource competition is argued to exist within, but not between,
separate attentional pools”” (p. 644). In this sense, when language stimuli are processed via one
modality only, as in reading (i.e., visually), there supposedly is no divide of attentional resources
18

and hence the model predicts there will be competition. Recall that VanPatten, however,
claimed that if learners allocate attention to content words that convey the sentence meaning,
they usually do not allocate their attention to form. Thus, the VanPatten’’s IP Theory and the
Resource Competition Model convey differing concepts.

Figure 4. Wickens’’s Proposed Structure of Processing Resources (2007, p. 186)
The multiple-resource model——as used to theorize attention during L2 learning——received
some criticism as researchers were unable to specify where and how attention differentiates
between inputs from two language modalities and what happens if one modality requires more
attentional resources than others (Rosa & O'Neill, 1999).
19

The last model that Robinson discusses in his account on attention as capacity is the
interference model. The model demonstrates that learners’’ low performance on certain language
tasks does not result from limited short-term memory capacity (e.g., McLaughlin, 1965, 1990;
McLaughlin & Heredia, 1996), but from ““involuntary attention shifts”” (p. 645) induced by
conflicting codes (of L1, interlanguage, and L2 syntax, morphology, semantics, and
phonology/orthography).
Last, attention as effort refers to the extent to which a learner can focus his or her
attention on a certain language task over period of time, which related to the notion of sustained
attention. If attention as effort, or sustained attention, falls short, a learner’’s processing of
incoming stimuli may lack the concomitant selection of information from input (i.e., a function
of focused attention), or, in terms of output, a learner’’s oral production of language may include
fewer self-repairs and less overall monitoring and planning of the output. Attention as effort
thereby corresponds to Swain’’s (2005) notion of pushed output and Dörnyei’’s (2009) discussion
of the influence of motivation on language processing.
Robinson (2003) agrees with Schmidt’’s (2001) view that a learner’’s focal attention
selects which stimuli gets processed more deeply, or, in Schmidt’’s (2001) words ““which parts of
the input gain access to the learner’’s awareness”” (p. 6). According to Robinson, the allocation of
focal attention is a prerequisite for the linguistic form (word or structure) to enter into one’’s
9

working memory, where the attended form will be processed further. In Robinson’’s words,
““memory processes, such as maintenance and elaborative rehearsal, which allocation of focal
attention activates, are co-responsible for noticing and the durability and extent of awareness that
9

This is the point where Schmidt assumes awareness to be involved.
20

noticing is accompanied by”” (p. 637). Eventually, but not a guarantee, the linguistic feature will
be stored in long-term memory. According to Robinson (1995), ““more permanent encoding in
long-term memory is a consequence of the level of activation in short-term memory and the
10

result of rehearsal and elaboration.

This process is what Robinson and other scholars have

defined as ““learning.””
In this study focal attention (i.e., noticing) was measured via eye-movement recordings
during reading. Thereby, I could determine whether the stimuli (regular and irregular verbs in
German sentences) that received focal attention——stimuli that were noticed——might be processed
more deeply. Since this study operationalized noticing as the learners’’ focal attention, I cannot
make strong claims about their awareness. Yet, based on prior eye-movement research,
11

especially disappearing text studies

(e.g., Rayner, 2009; Rayner, Liversedge, White, &

Vergilino-Perez, 2003; Rayner, Liversedge, & White, 2006), it is safe to assume that people are
aware of (i.e., consciously perceive) any stimulus that they look at or attend to for more than 60
12

milliseconds (Godfroid, 2010; Liversedge & Findlay, 2000).

In this dissertation, I

conceptualize noticing as follows: awareness is inherent in focal attention (i.e., noticing), and
constitutes a continuum from perception (no or little awareness) to understanding on the lower
end (full awareness) on the upper end. Following Schmidt (1995, 2001) my conceptualization of

10

According to Cowan (2001), short-term memory needs to be distinguished from working
memory which is responsible for processes used for temporarily storing and manipulating
information.
11
The disappearing text studies will be explained in more detail in Chapter 2.
12

The complexity of the relationship between attention and awareness has been explicated in
various models in the field of cognitive science (e.g., Dehaene et al., 2006; Koch & Tsuchiya,
2007; Lamme, 2003), with the current consensus being that attention and awareness correspond
to different cognitive circuits, whereby awareness influences attention.
21

13

noticing does not involve attention to abstract or complex rules or principles,

but mostly focal

attention to surface structures and lexical items that might be a novelty for the learner. Thus, I
do not aim to measure awareness at the level of understanding (Schmidt, 1990) where learners
are aware of generalizations of rules. Rather, my conceptualization is in line with Schmidt’’s
distinction between noticing and metalinguistic awareness. Whether focal attention leads to
deeper processing that gives rise to learning depends on where attention on the awareness
continuum is located (see Figure 5). This idea is partly in line with Sharwood-Smith and
Truscott (in press) who suggested the existence of noticing thresholds.

Figure 5. Continuum with Different Levels of Awareness
Leow’’s research (Leow et al., 2008; Leow, 1997, 2000) collectively suggested that
learners who showed higher levels of awareness learned more than those who merely attended
and noticed. His studies (1997; 2000) examined the learning of Spanish irregular verbs by adult
native speakers of English. The participants were exposed to these forms via crossword puzzle
tasks, and they were instructed to think aloud while working through the puzzles. Half of the
participants noticed the verbs’’ stem-vowel changes; that is, they commented on them in their
concurrent verbal reports. The learners who verbally reported on the stem-vowel changes

13

But I do not deny that there is the possibility of noticing at the level of metalinguistic
understanding (Schmidt, 2001, p. 19)
22

displayed statistically significant gains in accuracy on producing stem-vowel changing verbs on
their posttests (compared to a pretest). Those participants who did not comment any noticing of
stem changes did not improve on the posttests.
14

In this study, I assume awareness is a complex process, and a quality of noticing.

However, it is important to note that Williams (2004, 2005) found evidence for learning without
awareness. Williams (2005) explored whether adult language learners can implicitly learn
associations between grammatical morphemes and the underlying meaning. The study
participants, native and non-native speakers of English, read English sentences in which the
article ‘‘the’’ or ‘‘a/an’’ were replaced by gi, ul, ro, or ne. They were instructed that the choice of
the determiner was based on whether the object was ““near”” or ““far”” from the subject of the
sentence. Yet, in addition to the distance criterion, the choice of the determiner was also
dependent on whether the object was animate or not. Williams aimed to find out whether the
participants became aware or remained unaware of this extra dimension of the rule, and how
their level of awareness was reflected in their learning gains. He found that some participants
were able to correctly generalize the rule of animacy to new determiner-noun combinations,
while being unaware of the actual rule or the relevance of animacy. Williams concluded that
learning without awareness is possible.
Hama and Leow (2010) conceptually replicated William’’s (2005) study with
methodological extensions regarding the measurement of ‘‘awareness,’’ employing what they
termed a ‘‘hybrid research design.’’ They qualitatively measured learners’’ awareness during the
experiment via think-aloud protocols (online) and after the experiment via questionnaires
(offline). The online data showed that some participants were aware of the animacy rule during
14

Note that I do not state that noticing does not require awareness, but is a quality of awareness.
23

task performance. Those learners improved on posttests, while ““unaware participants did not
appear to demonstrate significant learning of the animacy feature of the novel words at the
production level of trained items or at the level of generalizations to new items”” (p. 482). Thus,
Hama and Leow concluded that there is ““no dissociation between awareness and learning”” (p.
482).
To conclude, the exact roles of attention and, especially, awareness in language learning
remain unresolved to date. While we can assume that some form of attention is necessary for the
integration of new linguistic information (whether it may lead to learning or not), SLA
researchers have yet to establish models that capture the complex interaction of attention and
15

working memory, as well as attention and awareness,

and awareness and learning. Godfroid

(2010) provides the probably most useful account to date: ““Noticing refers to a sequence of
events involving attention and awareness, the quantity (duration) and quality (nature of mental
operations) of which will determine the durability of the memory trace created [……]”” (p. 183,
highlights are mine). In this study, I am defining noticing as a process where attention and
awareness (two distinct processes in the mind) merge, whereby the attention paid to a new
linguistic structure exceeds a threshold (Godfroid, 2010, p. 111) so that this novelty is held in
working memory for long enough to increase the (initially low) level of awareness. If this
noticing process (attention and awareness) occurs with sufficient frequency, the linguistic item
will eventually build a representation/become a stored exemplar in long-term memory. While, in
my view, attention plays a more prominent role than awareness, the latter also plays an important
15

However, recall from an earlier footnote that researchers in neurophysiology and cognitive
science argue that attention and consciousness are distinct (not isomorphic, as Schmidt
suggested) because they have distinct functions neuronal mechanisms (B. J. Baars, 1997; B. J.
Baars, 2002; Block, 2005; Dehaene et al., 2006; Koch, 2004; Koch & Tsuchiya, 2007; Lamme,
2003; Naccache et al., 2002; Woodman & Luck, 2003 Dehaene et al., 2006).
24

role as it is likely to reflect the ““nature of mental operations”” that learners engage in while the
linguistic form is in focus of their attention (Godfroid, 2010, p. 183).
Implicit versus Explicit Learning
The constructs of awareness and attention, as discussed above, lay the foundation of a
critical understanding of the concepts of explicit and implicit learning in SLA. The notions of
awareness and consciousness are intrinsically linked to the implicit/explicit learning distinction.
In the field of cognitive linguistics and SLA, the role of explicit and implicit knowledge has
evolved as an important area of investigation (e.g., DeKeyser, 2003; Dörnyei, 2009; N. Ellis,
1994a, 1994b, 2002, 2005, 2007; R. Ellis, 2002; Hulstijn, 1989, 2003, 2005; Reber, Allen, &
Reber, 1999; Robinson, 1996, 1997a, 1997b, 2005; Schmidt, 1994b; Shanks, 2005). DeKeyser
(2003) defined implicit learning as ““learning without awareness of what is being learned”” (p.
314). His definition goes a step beyond the work of Reberthe pioneer of implicit learning
researchwho defined implicit learning as ““the process whereby knowledge is acquired largely
16

independently of awareness”” (1999, p. 475).

Reber rejected the possibility that that there can

be a complete lack of awareness, suggesting that both implicit and explicit processes are
involved in ““virtually everything interesting that human beings do”” (p. 488). Similarly, J. N.
Williams (2005) considered the explicit/implicit distinction as a continuum as opposed to a
dichotomy. At the implicit end of the spectrum, awareness is not involved in the learning
process and product, while at the explicit end of the spectrum, learners are entirely conscious of
16

In reviewing empirical research that targets artificial grammas, sequence learning, and control
of complex systems, DeKeyser (2003) concluded that ““implicit learning is necessarily rather
concrete and really abstract learning is necessarily explicit”” (p. 333). This points to the
importance of clearly defining the objects of learning in implicit learning research; that is, are
they concrete items or abstract rules?
25

their internal input comparisons and hypothesis testing (p. 271). In other words, explicit
knowledge can be brought to awareness and can be verbalized, while implicit knowledge cannot
(Hulstijn, 2002, 2003, 2005). The current consensus is that although implicit and explicit
learning are separable and distinct, humans engage with them in interplay, so implicit and
explicit learning mechanisms can influence one another (N. Ellis, 2005, 2011; Segalowitz, 2003;
Dörnyei, 2009).
The cognitive science literature (e.g., Anderson, 2005; Cleermans & Destrebecqz, 2005;
Hampton, 2005; E. Smith, 2005; Taylor, 2003) is much in line with N. Ellis’’s (2005, 2011)
views. Cognitive psychologists usually contrast explicit and implicit processing mechanisms as
follows: Explicit processing is conscious rule-based processing, while contextual variations are
neglected. Implicit processing is similarity-based processing; the input is matched with
properties of existing representations.
To give a practical example, when learners use their L2 fluently, they draw on implicit
processes and their attention is focused on meaning rather than on form (R. Ellis, 2005;
VanPatten, 1990; 2003). Explicit processes usually take over when learners experience
comprehension and production difficulties (N. Ellis, 2005). In such cases, learners deliberately
pay attention to language form; that is, they analyze the input they receive and/or the output they
produce. In doing so they utilize their explicit knowledge and engage explicit processes.
According to N. Ellis (2011), L2 learners engage in explicit learning when it comes to the
””fragile”” aspects of grammar (p. 1), which are not acquired in spite of their frequent occurrence
in the L2. Irregular verbs are an example of such fragile aspects or grammar. ““[L]earners fail to
notice cues which are lacking in salience and redundant in cuing meaning, or because of
interference where the features need to be processed in a different way from that usual in their
26

L1”” (p. 1). N. Ellis emphasized the crucial role of noticing and attention in the acquisition of
non-salient L2 forms and the importance of form-focused, explicit instruction in this respect. He
provided the example of the frequency effect; in particular, learners might implicitly acquire
knowledge of associations between language elements because they encounter these elements
together so frequently (albeit incidentally) in the input and because learners acquire ‘‘probabilistic
knowledge’’ (p. 11). The latter refers to knowledge of the probabilities of occurrence of words
that are like to follow of precede other words (transitional probabilities), and form-function
mappings. Yet, there is an interface between explicit and implicit knowledge. ““Many aspects of
language are unlearnable, or at best only very slowly acquirable, from implicit processes alone.
Which is why an attentive focus on the form-meaning relation is also necessary in the initial
registration of pattern recognizers for constructions”” (p. 12). Learners might not attend or
register new patterns or forms because they are not salient or because learners falsely process
them just as they process their L1, not noticing the relevant particular cues.
In German, for example, morphological verb inflection in the present tense is redundant
because verbs are accompanied by the sentence-subject that indicates the person and number.
Thus, the actual verb inflection (ending and vowel stem change for irregular verbs) is redundant
in the sense that it is not the only source that indicates whether the subject is singular, plural, or
whether the person corresponds to I, you, he/she, etc. Thus, this structure is likely not salient to
learners (especially if their L1 morphology lacks this type of inflection), or they process the
inflected verbs just as they process verbs in their L1, English——without paying attention to
morphological changes. In cases like these, explicit learning plays a crucial role (N. Ellis, 2005).
To help the learner to ‘‘notice’’ the cue in the first place, form-focused instruction or consciousraising tasks are needed (Fotos, 1993, 1994; Sharwood-Smith & Truscott, 2010). In fact, there is
27

strong empiric evidence that instruction that guides learners’’ attentional foci to morphological
cues in the input is effective (Doughty and Williams 1998; Ellis and Laporte 1997; Hulstijn and
DeKeyser 1997; Lightbown, Spada, and White 1993; Long 1983; Spada 1997). Norris and
Ortega (2000) conducted a comprehensive meta-analysis revealing that explicit L2 instruction is
generally more effective than implicit instruction.
If learners receive instruction on a particular grammatical structure, they will build up
explicit memory of that structure. When learners then encounter a certain structure on which
they have already received instruction, it is likely that the learners engage in a conscious process
of remembering their prior learning experience of that structure, so they are aware of the
knowledge they have. In contrast, learners show evidence of implicit memory ““when there is
facilitation of the processing of a stimulus as a function of a prior encounter with an identical or
related stimulus but where the subject at no point has to consciously recall the prior event”” (N.
Ellis, 2010, p. 18). For example, learners of L2 German would have implicit memory or
knowledge when they inflect an irregular verb correctly without being able to consciously recall
they had ever seen this verb form before. These facts about implicit memory notwithstanding,
implicit learning still requires some attention to the formal features of the language. While this
seems apparent for the learning of content words; implicit learning is also likely to involve some
attention to form, even if learners are not conscious or aware of the relevant aspects of the target
form in terms of the underlying structure or ““rule””.
To date, SLA researchers (e.g., DeKeyser, 2003; Dörnyei, 2009; N. Ellis, 1994a, 1994b,
2002, 2005, 2007, 2010; R. Ellis, 2002; Hulstijn, 1989, 2003, 2005; Perruchet & Pacteau, 1990;
2006; Reber et al., 1999; Robinson, 1996, 1997a, 1997b, 2005; Schmidt, 1994b; Segalowitz,
2003; Williams, 2004, 2009) and cognitive psychologists (Cleermans & Destrebecqz, 2005;
28

Mathews et al., 1989; Paradis, 1994; Shanks & St. John, 1994; Shanks, 2005) have generally
concluded that much more research into the complementary systems of explicit and implicit
learning is needed in order to obtain a clearer picture of the contribution of attention and
awareness in the establishment of coherent representations of language structures in the mind. In
Ellis’’s (2011) words, more research is needed into the ““ways that linguistic constructions are first
noticed and registered, and thence figured and tuned into the system”” (p. 20). The notion of
explicit and implicit learning processes will resurface in the remaining chapters of this
dissertation. In particular, I will discuss the noticing construct in regard to specific linguistic
structures they may function as targets for noticing. The notion of implicit and explicit language
processing is important in regard to the learners’’ memory representations of the noticed forms.
Specifically, different types of L2 forms (e.g., lexical items or morphosyntactic target structures)
may be represented differently in the brain——either symbolically or associatively. Consequently,
different L2 structures invite different levels of engagement of explicit and implicit processes.
Intentional versus Incidental Learning
The construct of implicit versus explicit learning is intrinsically related to the construct of
intentional versus incidental learning. Intentional learning involves a deliberate attempt to learn;
and this is very likely to involve awareness. For example, a learner might read a text with the
intent to increase his/her vocabulary and is therefore likely to consciously attend to unfamiliar
words in the text. In contrast, incidental learning is marked by the absence of a conscious intent
to learn, but it might involve unplanned conscious attention to words or formal features of the
L2. Precisely, ““[t]he term incidental learning is used, in applied linguistics, to refer to the
acquisition of a word or expression without the conscious intention to commit the element to

29

memory, such as ““picking up”” an unknown word from listening to someone or from reading a
text”” (Hulstijn, in press, p. 1).
Explicit learning, as explained in the previous section, overlaps with intentional learning
to the extent that it is a conscious process and a likely intentional one as well. Yet, explicit
attention to some feature can occur incidentally without planning, for example, when a learner
experiences difficulties or attends to some unknown words or features in the L2 input just
because he or she is eager to learn. In such a case, explicit and intentional learning diverge.
There is a paucity of research that investigates incidental/intentional learning of L2
grammar. Yet, there is a wealth of SLA literature on incidental L2 vocabulary acquisition (see
Hulstijn, 2003, for a review). Incidental focus on L2 grammar structures usually occurs when
learners naturally focus on meaning, but then experience difficulty with a certain linguistic
structure and incidentally attend to form (e.g., Loewen, 2005). Such an experience might
facilitate ““the cognitive mapping among forms, meaning, and use that is fundamental to language
learning”” (Doughty & Williams, 2001, p. 221).
Studies on incidental learning of grammar (not vocabulary) in the spoken modality have
largely analyzed L2 learners’’ spoken interaction with teachers or native-speaking interlocutors.
In contrast, most studies on the learning of a grammatical form during reading are input
enhancement studies. Although it could be argued that input enhancement studies——due to the
very purpose of the input manipulation——do not tap into incidental learning, many input
enhancements studies have had a control group that received the texts without enhancement, so
incidental grammar learning was indirectly examined with those learners. The meta-analysis by
Lee and Huang (2008) yielded that ““learners exposed to enhanced texts outperformed learners

30

who read unenhanced texts by a very small-sized effect (d = 0.22)”” (p. 323).

17

Lee and Huang

suggested that the learners who read unenhanced texts might have improved their grammar
knowledge due to the input flood, ““which in itself is thought to have small but detectable effects
on grammar learning”” (p. 323). In other words, the learning of L2 grammar structures during
reading seems quite feasible if the structure occurs with sufficient frequency. Yet, the input
enhancement studies provide limited insight into the cognitive processes that occur during
reading, and how the learners process the enhanced or unenhanced forms, as well as what types
of learning mechanisms account for improvements on posttests. Research on incidental learning
targeting oral interactions were better able to tap into cognitive (especially attentional) processes,
as verbal data can provide of incidental attention to form, the effects of which can be tested with
post-tests (e.g., Ellis, Basturkmen, & Loewen, 2002; Loewen, 2005; Swain & Lapkin, 2002; J.
Williams, 2001; J. N. Williams, 2004). This dissertation study investigates——via eye-movement
measures——how L2 structures are processed in real time during reading, so it probes into the
incidental learning of L2 verb morphology.
In Robinson’’s view (2005), working memory and the capacity to rehearse sub-vocally
forms that have been attended to jointly determine the possibility and extent of incidental
learning. ““[W]orking memory is the strongest individual difference predictor of incidental SLA;
[it] forms part of the aptitude complex for learning from incidental exposure to written input”” (p.
261). Thus, learners with higher working memory capacity might be more apt to incidentally
and implicitly acquire underlying L2 systems than learners with more limited working memory
capacity.
17

In another meta-analysis on input enhancement by Han, Park and Combs (2008), similar
results were obtained.
31

In summary, Schmidt (1990) put forth key arguments on incidental, implicit, and
subliminal learning (the first two will be discussed in detail in the following sections).
Concerning the first notion, he claims ““incidental learning is certainly possible when task
demands focus attention on relevant features of the input”” (p. 149). The issue of implicit
learning is more complex. He stated that implicit learning is possible, but ““there is no reason to
accept [……] that awareness does not affect language learning, [……] or that most language learning
is implicit”” (p. 149). In this sense, Schmidt supported his critical notion that ““subliminal
learning is impossible, and that intake is what learners consciously notice”” (p. 149). As such,
learners must consciously notice aspects of language (including vocabulary, phonology,
grammatical form, and pragmatics) in the input in order for it to be registered in memory. This
premise is the heart of the Noticing Hypothesis, which is detailed in the following chapter.

32

CHAPTER 2 NOTICING
The Noticing Hypothesis––––as proposed in the 1990s by Schmidt (1990; 1993a, 1993b,
1994, 1995)––––is that noticing requires awareness and is a necessary condition for second
language acquisition. The Noticing Hypothesis rejects subliminal learning (i.e., learning for
acquisition). Figure 6 demonstrates the commonly used visualization of the Noticing Hypothesis.

Figure 6. Graphic Representation of the Noticing Hypothesis
(adopted with permission from Godfroid, 2010)
According to Schmidt, only what has been noticed (i.e., consciously registered) in the
input can become intake. More precisely, in 1993 Schmidt specified noticing as ““the necessary
and sufficient condition for the conversion of input to intake”” (p. 209). Yet, ““this
characterization does not specify exactly what intake is, other than that it is the product of
noticing and an intermediary step in the acquisition process”” (Godfroid, Housen, & Boers, 2010,
p. 170).
In his latest essay (2010), Schmidt maintained that it is common sense that learning is
driven by attention, as we ““don’’t learn much about things we don’’t attend to”” (p. 721).
Regardless of the intuitive plausibility of this notion, the early version of the noticing hypothesis
has been objected to on three major grounds: (1) that noticing is a prerequisite for learning; (2)

33

that noticing does involve awareness; and (3) noticing is not related to linguistic knowledge,
18

(competence), but appertains to metalinguistc knowledge.

Regarding the first objection, Gass (1997) and Leow (1997), among others, disagreed
with Schmidt’’s premise that conscious attention is a necessary prerequisite for L2 acquisition.
According to their view, attention is not a prerequisite for all learning as learners are able to, for
example, generalize a certain grammatical structures to new ones. Schachter (1998) claimed that
only language features pertaining to lexical semantics (individual words) and phonetics (novel
phonetic inventories) require focused attention and that all other linguistic features do not. A
majority of SLA and cognitive psychologists have evidenced that Schachter’’s claim is not valid
(Perruchet & Pacteau, 1990; Perruchet & Pacton, 2006; Shanks, 2005; J. N. Williams, 2009).
Gass, Svetics, and Lemelin (2003) discredited the premise that noticing is a necessary
condition for learning. Their research findings demonstrated that L2 acquisition can take place
without focused attention. They assigned Italian learners at different proficiency levels to one of
two conditions ([+focused attention] and [ focused attention]) for each of the following
linguistic features: syntactical, morphosyntatical, and lexical. Their hypothesis that focused
attention would have the least effect on syntactical acquisition and the largest effect on the
lexical acquisition was rejected. In fact, their findings revealed the opposite effect. Contrary to

18

A third objection came from Carroll (Carroll, 2001, 2006a, 2006b, 2006c). Carroll criticized
the Noticing Hypothesis in that the characteristics of input available for noticing are unspecified,
and that linguistic representations exist in the mind to begin with and are not mediated by
environmental stimuli. Carroll’’s (2006a) and (2006b) publications, which deal with phonetic and
phonological perception (prosody, specifically), are best summarized by Godfroid (2010).
““[According to Carroll,] language-specific coalitions of [prosodic] properties are acquired wellbelow the threshold of awareness. On this account, they constitute a case of unaware phonetic
and phonological learning and may thus count as evidence against the Noticing Hypothesis”” (p.
80, insertions are mine).
34

their predictions, learners in the non-focused-attention condition showed greater focus on lexis
than syntax. With regard to proficiency, focused attention was most beneficial for beginning
learners and least beneficial for intermediate learners.
The second main objection to the Noticing Hypothesis targeted the claim that noticing
involves awareness; that is, while there is subliminal, unconscious perception, there is no
subliminal, unconscious learning (Schmidt, 1995, 2001). As explained in Chapter 1, Tomlin and
Villa (1994) proposed that alertness, orientation, and detection (i.e., the closest equivalent to
Schmidt’’s noticing) do not require awareness. Similarly, Williams (2005) found that language
learning might happen without awareness. This claim was addressed and largely refuted by a
series of studies and reviews by Leow and co-authors (Hama & Leow, 2010; Leow et al., 2008;
Leow, 1997b, 1998, 2000, 2002; Rosa & Leow, 2004). In addition, from a theoretical
perspective, Robinson (1995, 2003, 2009) and Schmidt (1990, 1994a, 1995, 2001, 2010)
maintained that the representation of novel linguistic forms involves focal attention and
awareness.
Schmidt has acknowledged that the objections to the Noticing Hypothesis (mentioned
above) have merit and cannot be dismissed easily. In 2001, Schmidt formulated a weaker
version of the Noticing Hypothesis, arguing that noticing may not be necessary for, but certainly
is facilitative of L2 learning in that ““more noticing leads to more learning”” (2001, p. 8).
Last, the Noticing Hypothesis has been criticized because it does not relate to
unconscious linguistic representations (competence); rather it relates to the acquisition of
metalinguistc knowledge (Truscott, 1998). Truscott’’s opposition stems from the conception that
attention is a ““very confused”” and ““poorly defined commonsensical”” concept (p. 105). As
noticing, in Truscott’’s view, merely relates to metalinguistc knowledge, and not to unconscious
35

language competence, it is ““not only unnecessary but also unhelpful”” (p. 126). In his 2000 essay
““Unconscious second language acquisition: Alive and well”” Truscott maintained that SLA
research on attention and awareness ““rejects unconscious acquisition”” (Truscott, 2000, p. 115).
Clearly, no SLA researcher has rejected the possibility of implicit acquisition; in fact, there is a
wealth of literature on the intricate relationships among implicit and explicit processes in adult
SLA (e.g., DeKeyser, 2003; Dörnyei, 2009; N. Ellis, 1994a, 1994b, 2002, 2005, 2007, 2010; R.
Ellis, 2002; Hulstijn, 1989, 2003, 2005; Perruchet & Pacton, 1990; 2006; Reber et al., 1999;
Robinson, 1996, 1997a, 1997b, 2005; Schmidt, 1994b; Segalowitz, 2003; Williams, 2004, 2009)
but, at the same time, there is substantial L2 research data supporting the idea of noticing as a
facilitating factor in L2 learning (e.g., Adams, 2003; Carr & Curran, 1994; Carrell, 1992;
DeKeyser, 1997; R. Ellis, 1994; Fotos, 1993, 1994; Gass & Alvarez-Torres, 2005; Hama &
Leow, 2010; Izumi, 2002; Izumi & Bigelow, 2000; Izumi, Bigelow, Fujiwara, & Fearnow, 1999;
Izumi & Izumi, 2004; Leow et al., 2008; Leow & Morgan-Short, 2004; Leow, 1997b, 1998,
2000; Long, 1996; Mackey, 2006; Mackey, Gass, & McDonough, 2000; Norris & Ortega, 2000;
Roberts, 1995; Rosa & Leow, 2004; Rosa & O'Neill, 1999; Sachs & Suh, 2007; Swain, 1995,
1998, 2005; Swain & Lapkin, 1995; Van Hell & De Groot, 1999). Thus, the prevalent notion to
date is that noticing, which involves attention and some level of awareness, greatly facilitates
language learning, but may not be absolutely necessary for learning to occur; at least some types
of learning can also occur implicitly, without awareness.
Swain (1998) suggested that there are several levels of noticing: Learners may simply
notice a structure in the target language due to the frequency or salience of that form (see also
Bardovi-Harlig, 1987; Jourdenais, Ota, Stauffer, Boyson, & Doughty, 1995) or they may notice
that the target language is different from their own interlanguage (i.e., noticing the gap, see also
36

Doughty & Williams, 1998; Gass, Mackey, & Pica, 1998; Philp, 2003). Godfroid (2010) did not
agree with Swain’’s point that noticing and noticing the gap refers to different levels of noticing,
but noticing and noticing the gap are different kinds or types of noticing. According to Godfroid,
awareness may have different levels, one of which corresponds to noticing as proposed by
Schmidt. For SLA researchers it is more useful to distinguish between two types of processes:
(1) noticing per se during input processing and (2) noticing the gap during output processing.
The mechanisms underlying these two processes may not be the same; noticing the gap may
function as a priming device for noticing (Godfroid, 2010, see pp. 70-72 in particular).
As mentioned in the previous chapter, Schmidt (2001) and Leow (1997) proposed a
distinction between noticing and metalinguistic awareness. They suggested two levels of
noticing: simple noticing (i.e., registration with awareness) and noticing with metalinguistic
awareness (understanding). Noticing might be a necessary and sufficient condition for learning,
but understanding is not. In other words, learners can learn a new language structure without
necessarily understanding the underlying rules of that specific structure. Recall from Chapter 1
that Schmidt (1990) claimed that unconscious learning can refer to either (1) incidental (i.e. nonintentional) learning, (2) learning without understanding (i.e. implicit learning) or (3) learning
without awareness (i.e. subliminal learning). It follows from the above that Schmidt
acknowledges the possibility of (2), but rejects the notion of (3).
Noticing and Learning
Usually, reports of experimental research concerned with language acquisition leave the
reader to wonder whether the improvements on posttests reflect learning. Even more important,
the experiment results do not provide information on which type of learning takes please inside
the learners’’ mind. Just based on the results reported in the research articles (Alanen, 1995;
37

Leow, 1997, 2000; Long, 1991; Rosa & O’’Neill, 1999; Sachs & Polio, 2007; Sharwood Smith,
1993; VanPatten, 1990, 1996, 2002), we do not know whether learners were able to generalize
the target structure across items and apply their acquired knowledge to new situations. Leow
(1997, 2000), for example, found that aware learners significantly improved their production of
correct Spanish verb-morphology. According to Leow (2000), ““[t]hese findings provide further
empirical evidence for the association between awareness and subsequent processing of L2 data””
(p. 568). Yet, did these learners ““learn”” the stem-changing Spanish verb forms as items (similar
to the way that they would learn a vocabulary item) or did they learn the underlying system (i.e.,
the morphologic rule) that governs Spanish verb morphology in the past tense? In J. N.
William’’s (2005) words, ““we must be careful in defining learning”” (p. 271). In his research
(1997, 2000), Leow assumed that his participants were equally aware of all the items (i.e., verb
forms they filled out in the cross-word puzzle task). This operationalization of one level of
awareness (i.e., either aware or unaware) for all stimuli has implications for how learning can be
traced back to the processing of the input. That is, based on this operationalization, we do not
know (a) whether the learners’’ awareness of the structure increased with the completion of the
cross-word puzzle task, and consequently, (b) whether the learners’’ processing shifted from
noticing (attending with a low level of awareness) to understanding (attending with a high level
of awareness) over the course of the task. Thus, it remains unknown whether the learners
established a foundation or data base of consciously stored examplars, based on which they
derived the understanding of the morphological rule. This is what usage-based accounts of
language learning (e.g., N. Ellis, 2002a) would predict. When Leow’’s (aware) learners
demonstrated their learning, they only showed evidence of item-based learning. As such, the
notion of item-based learning and system-based learning is quite relevant (Shanks, 2005),
38

especially when it comes to studying the fine details of the learning process. System learning
involves the understanding of underlying rules so that rule generalizations to new items are
possible and likely. Item learning, in contrast, refers to the rote memory of items.
Shanks and St. John (1994) investigated the issue of item and rule learning and, thereby,
the possible existence of independent explicit and implicit learning systems. Their research also
had ramifications regarding the involvement of awareness and the depth and type of learning.
Shanks and St. John inquired whether learning mainly involve the encoding of language
fragments (which we may think of as ‘‘items’’) or, whether it goes deeper in that it involves the
induction of abstract rules? Based on the premise that implicit learning equals unconscious rule
learning, Shanks and St. John found, among other facts, that unconscious learning has not been
satisfactorily established in artificial grammar learning (see also DeKeyser, 2003). ““When
subjects cannot report the ““implicitly learned”” rules [……], this is often because their knowledge
consists of instances or fragments of the training stimuli rather than rules”” (p. 367, highlights are
mine). ““Human learning is almost invariably accompanied by conscious awareness, and in tasks
such as artificial grammar learning, where learning is frequently thought to involve rule
abstraction, performance is most often based on the acquisition of instances or fragments from
the training stage”” (p. 394, highlights are mine). Shanks and St. John therefore suggested that
the distinction between (conscious) instance learning and (conscious) rule learning is more
theoretically sound and useful than the distinction between conscious and unconscious learning.
19

In contrast, according to Robinson (1995), once specific linguistic input is encoded,
generalizations across instances can happen without the learners being aware of those
19

Recall that according to Robinson (1995, 2000), noticing means that specific linguistic input
is encoded in working memory
39

generalizations. If learners are aware of their generalizations, according to Schmidt (1990,
2008), this corresponds to ““awareness at the level of understanding”” (1990, p. 145). Leow
(1997, 2000) concluded that awareness at the level of understanding produces notably more
learning than awareness at the level of noticing (see also Qi & Lapkin, 2001; Rosa & Leow,
2004; Rosa & O'Neill, 1999). However, as established by Leow, Johnson, and Zárate-Sández
(2011), it is both conceptually and practically very difficult to determine different levels of
awareness and/or a complete lack thereof.
It is important to note that none of the noticing studies cited in this dissertation claims
that there is a causal relationship between noticing and learning. It is unlikely that any learning is
caused by one single factor (e.g., noticing) or one instance of the occurrence of this very factor.
We can, however, safely assume that there is a positive relationship between noticing and
learning, and that noticing is a complex concept composed of and interacting with a range of
other cognitive factors (e.g., attention, awareness, aptitude, developmental level, the specific
language form, social and linguistic context, etc.). Although there is a general consensus among
prominent SLA researchers that noticing is at least facilitative of learning, the operationalisation
of noticing and the research methods used to tap into the assumed relationship between noticing
and learning have been subject to much debate (e.g., Bowles & Leow, 2005; Bowles, 2008,
2010; Yoshida, 2008). The following section addresses the issue of operationalization.
The Measurement of Noticing
The scrutiny concerning the operationalization of noticing is largely due to the difficulty
in defining noticing so that it can be measured. Operationalizing noticing is difficult because it
requires introspection——the assessment of learner-internal cognitive activities. (For a detailed
overview of L2 introspection studies, see Bowles, 2010; Gass & Mackey, 2000.) However, not
40

all measures of learner-internal cognitive activities are introspective: the registration of eyemovements (i.e., by means of eye-tracking technology) is not, for instance.
Schmidt (1995) proposed to operationalize noticing in terms of learners’’ self-reports
either during or immediately after exposure to input. However, a lack of self-reporting should not
be interpreted as lack of awareness, as some thought processes during exposure to input are
difficult to verbalize (e.g., Jourdenais, 2001; Schmidt, 1995, 2001). In addition, noticing
incidences might involve a low level of awareness that is not ‘‘reportable’’. The challenge facing
the measurement of noticing is to accurately link observable and/or self-reported behaviors by
language learners to the construct of noticing.
Methods used to qualitatively and quantitatively account for learners’’ noticing of specific
target language features, words, or phrases fall into two categories: online, which measures
language learners’’ noticing during performance of a certain language task, and offline, which
employs a post-treatment assessment of noticing. However, neither online nor offline
methodologies enable an absolute account of the learners’’ attentional processes (e.g., Egi, 2004;
Robinson, 1997a, 1997b; Williams, 2005). Nevertheless, each has some value: Offline measures
do not interfere with the language task completion by the learners, while online measures offer
instantaneous assessment of cognitive processes.
Think-aloud protocols, which require the participants to monitor (in most cases) and
orally self-report their mental processes, are the most common online method (see Bowles, 2010,
for a thorough review and meta-analysis of studies that have investigated the reactivity of the
think-aloud method). Some research has attempted to measure language learners’’ noticing
through written online tasks such as note taking (Hanaoka, 2007; Izumi & Bigelow, 2000; Izumi,
Bigelow, Fujiwara, & Fearnow, 1999), circling of words (Leow, Hsieh, & Moreno, 2008) and
41

underlining (Izumi & Bigelow, 2000; Schierloh, in press). The advantage of online measures, as
opposed to post-exposure measures, is their instantaneous access to L2 processing, thus
minimizing the risk of possible memory decay by the L2 learner (Gass & Mackey, 2000, 2007).
Stimulated recall has evolved as a frequently used post-exposure method of obtaining data of the
language learners’’ thought processes (e.g., Mackey, 2006; Mackey et al., 2000; Roberts, 1995;
Swain & Lapkin, 2002). During stimulated recall, learners are prompted with a stimulus (e.g., the
learner’’s written product or a video displaying the learner’’s actions while engaging in a language
task), and they are asked to report on their thought processes during the performance of the
language task.
The problem with protocol analyses such as think-aloud protocols and stimulated recall is
the reactivity (Bowles, 2010). Reactivity refers to the extent to which the observed participant
performance (e.g., their mental engagement with a task or their posttest performance) is a result
of or caused by the actual research treatment (i.e., the verbalization).
Think-aloud protocols, for example, might be reactive; that is, the verbalization may
change the nature of actual cognitive processes (e.g., R. Ellis, 2001; Jourdenais, 2001). In
addition to interfering with cognitive processes, thinking aloud may add an additional processing
load on the learners. Therefore, verbal reports might not reflect real thought processes. The think
aloud data method itself acts as an additional task which must be considered carefully when
examining learner performance”” (Jourdenais, 2001, p. 373).
Godfroid (2010) identified eight studies on the reactivity issue associated with thinkaloud protocols in SLA: Bowles & Leow, 2005; Bowles, 2008; Leow & Morgan-Short, 2004;
Rossomondo, 2007; Sachs & Polio, 2007; Sachs & Suh, 2007; Sanz, 2009; and Yoshida, 2008.
As described by Godfroid, the findings of these studies are mixed. Leow and Morgan-Short
42

(2004) provided empirical data showing no reactivity of thinking aloud on text comprehension,
intake and production with beginners. Bowles and Leow (2005) explored the reactivity issue by
investigating the differential effects of type of verbalization (non-metalinguistic vs.
metalinguistic) with advanced language learners, showing that neither type of think-aloud
protocol caused reactivity. Yet, the learners’’ overall text comprehension was negatively affected
by metalinguistc verbalization. Similarly, Bowles (2008) found that metalinguistic verbalization
interferes with item learning and extends the time needed for completion of the task. Sachs and
Polio (2007) investigated the reactivity of think-alouds as a research tool as a secondary question
in their study on learners’’ uses of different types of written feedback on an L2 writing revision
task. The think-aloud protocols appeared to be reactive, so Sachs and Polio emphasized that
think-aloud protocols need to be implemented and interpreted with caution. Rossomondo (2007)
had her participants engage in either silent reading or think-alouds to investigate the role of
lexical cues to infer meaning in the incidental acquisition of the Spanish future tense. The thinkaloud protocol appeared to have an effect on the recognition and production of the Spanish future
tense. The findings were interpreted in that thinking aloud, in contrast to reading silently,
encouraged the learners to pay attention to the target structure. Last, Sachs and Suh (2008)
evidenced a reactivity effect of thinking-aloud on a text completion task, but not on subsequent
learning.
Most recently, Goo (2010) investigated the issue of reactivity in an examination of the
relationship between working memory capacity and think-alouds, focusing on the issue of
reactivity. His data evidenced that working memory capacity is related to reactivity because it
interacts with the cognitive load consumed by verbalization. The think-aloud protocols were
therefore associated with either more or less reactive effects on learner comprehension.
43

In conclusion, the problem of reactivity is inevitable when employing think-aloud or
stimulated recall methods to measure ‘‘noticing’’ (or the level of awareness involved in focal
attention). Reactivity is also likely to play a role, albeit minor, in other research methodologies
that investigate ‘‘noticing’’ by learners, such as underlining, circling, or note taking. Thus, any
research employing these methods to investigate ‘‘noticing’’ needs to be interpreted with caution.
The following section discusses a new method, eye-tracking, that is not belabored by reactivity
problems.
Eye-Tracking as a Measurement of Noticing
The technique of eye-tracking——the registration and recording of eye-positions and eyemovement——is a suitable way to tap into learners cognitive and, particularly, attentional
processes. While eye-tracking has been used since the mid-seventies in cognitive psychology
and the cognitive sciences to investigate cognitive processes during scene perception, reading,
and visual search (Rayner, 1998, 2007, 2009), it has only been recently that SLA researchers
have utilized eye-movement recordings to study language learners’’ ongoing cognitive processes
during reading (for a review of syntactic processing studies, see Frenck-Mestre, 2005). ““One
great virtue of eye-movement data is that they give a good moment-to-moment indication of
cognitive processes during reading”” (Rayner, 2009, p. 1461). Studies investigating language
(lexical, morphological, syntactic, and discourse) processing during reading collectively rely on
the premise that there is a close link between overt attention (i.e., eye-location) and covert
attention, the mental focus and processing of what is attended overtly (for a detailed discussion
of overt and covert attention, see Wright & Ward, 2008). Godfroid (2010, p. 99) coined the term
““eye-mind assumption”” based on Just and Carpenter (1980) and Morrison (1984) who
convincingly theorized that there is a tight link between the eyes and the mind: Our cognition
44

controls our eye-movement during reading. According to Godfroid, the ‘‘eye-mind assumption’’
essentially corresponds to ““what Reichle, Pollatsek and Rayner (2006) coined the ‘‘eye-mind
link’’ (p. 4) and what Engbert, Nuthman, Richter and Kliegl (2005) call the ‘‘immediacy-ofprocessing-assumption’’ (p. 796)”” (2010, p. 99). In conclusion, where an individuals’’ eyes fixate
most likely coincides with his or her focused attention.
Eye fixation times during reading can be influenced by any or all of the following
variables: (1) frequency, (2) familiarity, (3), plausibility/predictability, (4) length, (5) number of
meanings, (6) morphological complexity, (7) contextual constraint, and (8) age-of-acquisition
(for overviews and reviews, see Liversedge & Findlay, 2000; Rayner, 1998, 2007, 2009; Starr &
Rayner, 2001).

20

The most influential factors, the ‘‘big three’’ predictors, are frequency,

predictability, and word length (Kliegl, Grabner, Rolfs, & Engbert, 2004; Kliegl, Nuthmann, &
Engbert, 2006), which are described below.
Word Frequency
Numerous studies have shown frequency effects on the different fixation time measures:
low-frequency words (as determined based on corpus data) are usually fixated on longer as they
are perceived as ‘‘difficult, and high-frequency words are usually fixated on less as they are
perceived as ‘‘easy’’ (see Rayner, 1998; Rayner, Sereno, & Raney, 1996; Reichle, Rayner &
Pollatsek, 2003 for summaries). Interestingly, the frequency effect is attenuated when words are
encountered several times in a reading passage. By the third encounter of a high or low
frequency word, there is no difference between the two in terms of fixation times. That is, the

20

For citations of all studies examining each of the variables, see Rayner, 2009, p. 1472.
45

durations of fixations on low frequency words decreases considerably with repetition21 (Rayner,
Raney, & Pollatsek, 1995).
Disappearing text studies have corroborated the frequency effect: Even if a word
disappears after 60 milliseconds, the eyes remain fixating on the empty space (K. Rayner, 2009;
K. Rayner, Liversedge, et al., 2003; K. Rayner et al., 2006; K. Rayner, Liversedge, S.P., White,
S. J., & Vergilino-Perez, D, 2003). How long the eyes keep fixating on the empty space is
determined by the frequency of the word that disappeared. This phenomenon also supports the
eye-mind assumption. Although the (low frequency) words that disappeared were most likely
encoded within 60 milliseconds, they were probably not yet processed in the reader’’s mind, so
the eyes remain on the empty space.22 According to Rayner (2009), this is ““very compelling
evidence that the cognitive processing associated with a fixated word is the engine driving the
eyes through the text”” (p. 1473).
Word Predictability
In addition to the frequency of a word, the predictability of a word in context influences
the time needed to process that word. That is, the more predictable or plausible a word is given
the textual context, the easier or faster the processing of the word. Thus, predictability is often
defined in terms of the ‘‘contextual constraint’’ (Frisson, Rayner, & Pickering, 2005, p. 862). To
explain the issue of contextual constraint and predictability, I use an example from Frissom,
Rayner and Pickering (2005): ““For example, stamp is highly predictable in the (high21

Durations of fixations on high frequency words actually also decreases, but not as
dramatically as for low frequency words.
22
This is yet another example that underscores the major flaws in design and in the
operationalisation of ‘‘noticing’’ in Smith’’s (2010) study. Smith claimed that noticing happens
when the eyes fixate on the target item 500 ms or longer, and no noticing occurs that if readers
fixate for less than 500 ms on the target item. This is pure non-sense given the overwhelming
eye-tracking literature that speaks of lexical processing within as little as 60msec.
46

constraining) context, He mailed the letter without a ____, but low predictable in the (lowconstraining) con- text He saw a beautiful _____.”” (p. 862).
A large amount of eye movement research has shown that high-predictable words are
read faster than low-predictable words. This effect has been evidenced with different types of
eye-fixations, including first fixation duration, gaze duration, and total time, the variables
analyzed in this dissertation study. Most important for this dissertation study, word
predictability has an effect on the first fixation duration (Altarriba, Kroll, Sholl, & Rayner, 1996;
Rayner, Ashby, Pollatsek, & Reichle, 2004) as well as on gaze duration, the sum of all fixations
on a word before fixating on another word (Rayner & Well, 1996) and on total time spent
reading the target word (Calvo & Meseguer, 2002; Rayner & Well, 1996). More predictable
words are also skipped more often compared to less predictable words (Altarriba et al., 1996;
Brysbaert, Drieghe, & Vitu, 2005; Drieghe, Brysbaert, Desmet, & De Baecke, 2004) and
individuals rarely fixate on them more than once (Calvo & Meseguer, 2002). The issue of
predictability is important and I will return to it in the Discussion Chapter.
Word Length
The last influential factor as part of the ‘‘big three’’ predictors is word length. Rayner
(2009) simply describes this phenomenon: ““As word length increases, the probability of fixating
a word increases”” (p. 1461). Just as the predictability of a word increases the probability of it
being skipped, so does the length of the word (Brysbaert et al., 2005). Words composed of eight
or more letters are rarely skipped, whereas shorter words are fixated on less frequently, only 25%
of the time (Rayner, 2009, p. 1461, citing McConkie, Kerr, Reddix, Zola, & Jacobs, 1989;
Vergilino & Beauvillain, 2000, 2001; Vergilino-Perez, Collins, & Dore-Mazars, 2004).

47

In light of this dissertation study, the appeal of eye-tracking is based on the relationship
that has been found between eye movements and ongoing language processing during reading
(Rayner, Liversedge, et al., 2003; Rayner et al., 2006). By means of eye-tracking, focal attention
can be measured through an increase in fixation times, that is, longer gazes and more regressions
(e.g., Godfroid, et al., 2010). Noticed, or more specifically, attended features (here verb stemvowel changes) are likely to be marked by greater viewing activity (longer fixations and more
23

regressions),

relative to unnoticed features. Thus, eye-tracking data yield information on the

readers’’ (language learners’’) attentional foci and their general processing of written input
(Rayner, 1998, 2007). Clahsen, Felser, Neubauer and Sato (2010) similarly emphasize the great
benefit of eye-movement recordings. This method ““can provide a window on how linguistic
representations are constructed in real time during language comprehension and production and
reduce the possibility of participants relying on their explicit or metalinguistc knowledge,
compared to commonly used offline tasks”” (p. 22). Thus, the great advantage of studying eyemovements of language learners is that compared to other online measurements of noticing (e.g.,
underlining, circling, or thinking aloud) there is no potential interference that may increase
learners’’ cognitive load, distract them, or decrease what is being noticed. Furthermore, reading
sentences or passages on the screen during an eye-tracking experiment is relatively natural (e.g.,
Duyck, Van Assche, Drieghe, & Hartsuiker, 2007). Thus, eye movement recordings have
become very appealing for SLA researchers trying to tap into the learners’’ ongoing cognitive
processes as they read the L2.
23

Some caution is necessary for the interpretation of fixations on the irregular verb as evidence
for attention. Words can be recognized and processed when the fixation lands few characters
before the critical word, which provide a parafoveal preview of the target word (Rayner, 1998;
Clifton, Staub and Rayner, 2007).
48

The next section briefly describes models of ocular movements as they relate to
cognition: the E-Z reader model, followed by a brief description of the Saccade-generation with
Inhibition by Foveal Targets (SWIFT) model. The E-Z Reader model has more relevance to this
dissertation study than the SWIFT model, therefore the shortened explanation of the SWIFT
model. Future elaboration of the other eye movement control models that have been proposed
(i.e., several occulomotor and cognitive control models) would go well beyond the scope of this
study and does not contribute to the theoretical foundation and/or the discussion appropriate for
this study.
The E-Z Reader Model
The intuitive notion that readers move their eyes through a text to acquire information
about its content inspired the establishment of the E-Z Reader Model. The E-Z Reader is a
computational model that simulates eye-movement during reading. It works on the premise that
cognitive processes associated with processing a fixated word serve as the engine driving the
eyes through the text and that words are fixated in a serial fashion, word by word (a different
view is presented by the SWIFT model). A wealth of literature has described and tested or used
the E-Z reader model (most notably Pollatsek & Rayner, 2009; Rayner, Ashby, Pollatsek, &
Reichle, 2004; Rayner, Pollatsek, & Reichle, 2003; Reichle et al., 2006).
According to the E-Z Reader Model, the identification of a word is completed in three
stages: an early visual processing stage (V) that is pre-attentive, and later lexical processing
which has two sub-stages. These two sub stages require different types of attention. There are
two ‘‘events’’ that occur during these later, attention-demanding stages: (a) a familiarity check

49

24

(level 1) that signals the occulomotor system (eye-muscles) to program a saccade

to the next

word, and (b) the lexical access (level 2) that signals the reader’’s mental ‘‘attention system’’ to
move attention to the next word. It has been assumed that brain fires signals to move the eye
(when needed) and also fires signals to attend to the input at the same time. Pollatsek, Reichle
and Rayner (2006) claimed that the brain mediating a familiarity check triggers the programming
of the saccade. Likewise, the completion of the lexical access might subsume the latency of an
attention shift. ““[T]he completion of lexical access is a point when processing of a word is
complete enough so that the processing system can shift attention to the next word so that lexical
processing of that word can begin with no ‘‘‘‘crosstalk’’’’ from the processing of the prior word””
(Reichle et al., p. 11). Based on this account, Godfroid (2010) convincingly argued that ““the
dependence of the saccadic program from the successful completion of the lexical familiarity
check initiates the eye-mind link in E-Z Reader”” (p. 105, italics are mine). In summary, the E-Z
Reader Model stipulates that an eye-movement is programmed or primed to occur when only
part of the processing (i.e., familiarity check) has occurred. As such, the ““time”” (a few
milliseconds) that elapses between the completion of the processing of the current word and
movement of the eyes to the next word, is influenced by the completion of the familiarity check.
If the next word is highly predictable by the sentence context, it might be skipped. Researchers
concerned with eye-movements during reading usually view the E-Z Reader Model as a useful
and appropriate way to account for and predict eye-movements by readers (e.g., Reichle et al.,
2003).

24

A saccade is the rapid movement of the pupil as it jumps from fixation on one point to another,
for example in reading (Rayner, 1998).
50

While the E-Z Reader Model suggests that words are fixated on one at a time, the SWIFT
model suggests that words are fixated on in a parallel fashion. For example, the characteristics
of the following word influence the fixation time and processing of the currently fixated-on
word; they are processed nearly simultaneously, in an analogous fashion. Studies evaluating
both, the E-Z Reader Model as well as the SWIFT model (e.g., Kennedy & Pynte, 2008;
Kennedy, Pynte, & Ducrot, 2002; White, 2008) revealed that both models are valid to some
25

extent,

yet the E-Z Reader Model provides ““a more transparent theoretical framework than

SWIFT and has, importantly, been proven highly successful in accounting for well-established
phenomena of eye-movement control”” (Godfroid, 2010, p. 109-110).
The majority of SLA studies have used eye-tracking to investigate the effects of language
transfer, either from (linguistic knowledge of) the L1 to the L2 or vice versa (N. Ellis and
Sagarra, 2010; Roberts, Gullberg & Indefrey, 2008). The eye-tracking technique has also been
adopted to study aspects of vocabulary acquisition, more specifically, idiom processing
(Siyanova-Chanturia, Conklin & Schmitt, 2011) or the noticing of novel lexical items (Godfroid,
2010; Godfroid, Housen, & Boers, 2010b). Smith (2010) also used eye-tracking to explore the
relationship between recasts, noticing, and performance during computer-mediated
communication (CMC). However, there are substantial problems with the design of Smith’’s
study, particularly with the operationalization of noticing (i.e., fixations of longer than 500
millisecond = noticing; fixations shorter than 500 milliseconds = no noticing). Choosing a cut-

25

The E-Z Reader Model has been criticized (1) as it does not take into account for sentencelevel or discourse processing, which are also important in reading, (2) for the assumption of
serial processing, and (3) for its reliance on word-frequency as a predictor for fixation length, as
predictability might be even more important than frequency (see Rayner, 2009 for a review).
51

off point (500 milliseconds) is completely random and an inappropriate way of operationalizing
26

the complex construct of noticing.

Only two studies (that did not employ eye-tracking) have investigated learners’’ noticing
of (Spanish) verbs with a stem-vowel change (Leow, 1997, 2000). Thus, the investigation of
attention to stem-vowel changes utilizing eye-tracking technology is a novelty in the field of
SLA. Since a vast number of research studies has supported that noticing plays an important role
in the acquisition process, this study explored (a) whether learning can occur based on a single
27

exposure to a novel morphological structure,

and (b) how important the amount of attention is

for possible learning gains. Godfroid (2010) inspired this research as she pursued an almost
identical question (see also Godfroid, Housen & Boers, 2010). However, her eye-tracking
research focused on vocabulary acquisition. She examined the processing of novel words in
texts by advanced learners of English.

28

They read English paragraphs, half of which contained

unknown (pseudo) words in different conditions. The words were either preceded or followed by
a synonym, or they occurred without any contextual clues. The learners allocated more attention

26

As stated by Godfroid (2010), ““[t]he definition and operationalisation of noticing [……]
requires a good understanding of its component processes, i.e. attention and awareness, the
theoretical details which belong to the fields of cognitive psychology and neuroscience rather
than to traditional language acquisition research”” (pp. 177-178). The study of eye-movement in
reading is also challenging and complex, as eye-fixations and movements are influenced by a
large variety of factors. Smith (2010) appears to be inconsiderate of both, a) how attention and
awareness have been operationalized and researched in cognitive psychology, and b) the wealth
of eye-tracking research that informs future eye-tracking studies in the field of SLA.
27
Note that the participants in this study were exposed to many exemplars illustrating the same
underlying phenomenon (i.e., the stem-vowel change) but they only saw each exemplar once.
28
This is an important difference to the current study. Godfroid’’s participants were English
language majors at a university who had learned English in secondary school in Brussels or
Flanders since the age of 13. In this study, the learners had been exposed to the target language,
German, for only for 4 to5 weeks, so they can be characterized as ““true beginning learners.””
52

to the novel words (as measured by eye fixation times), and this effect was enhanced when the
context clarified the possible meaning of the word. The learners’’ total fixation time on a new
word predicted vocabulary learning, as measured by an immediate recognition post-test. That is,
the longer a participant looked at a word X during the reading task, the more likely he/she was to
recognize word X in its original context on a subsequent test. The study showed that incidental
noticing during reading might be an important step in advanced learners’’ integration of new
words into their L2 mental lexicon.
To date, no eye-tracking research has investigated the noticing of verb morphology by
German learners. Research into the attention to, and the processing and acquisition of verb
morphology, such as vowel stem changes, will add much to the understanding of how adult
learners attend to and become aware of irregularities that may or may not be a feature in their
native language. As such, this dissertation project has theoretical and practical significance: It
will inform SLA theory by using a new and empirically-based operationalisation of noticing, and
it will inform foreign language pedagogy as this study will add to the knowledge of how learners
of German process morphological irregularities when they first encounter them.

53

CHAPTER 3 GERMAN STEM-CHANGING VERBS
L2 Morphology and Its Noticeability
As explained in Chapter 2, the majority of noticing studies have addressed learners’’
noticing of specific syntactic, morphological, or morphosyntactical features (Alanen, 1995;
Fotos, 1993; Hama & Leow, 2010; Hanaoka, 2007; Izumi & Bigelow, 2000; Izumi & Izumi,
2004; Jourdenais et al., 1995; Leow, 1997a, 1997b, 2000; A Mackey, 2006; Philp, 2003;
Robinson, 1997a, 1997b; Rosa & Leow, 2004; Song & Suh, 2008). Two researchers (J.
Williams, 2001; Hanoka, 2007) looked through a broader lens and examined what learners
naturally notice. J. Williams investigated whether learners initiate attention to form, and if so,
what types of forms they notice. The results suggest that——though relatively infrequently——
learners do attend to form. Learners’’ proficiency, the nature of the language activity and
learners’’ associated objectives played an important role in learner-generated attention to form.
More important, however, Williams found evidence that learner-generated attention to form is
usually related to questions on lexis. ““More than anything else, learners want to know about
words”” (J. Williams, 2001, p. 304). Although conducted in a different context, Hanaoka’’s
(2007) study similarly found that Japanese learners of L2 English by and large attended to lexical
features. The proficiency level of the learners also played a role: Proficient learners noticed
more features than less proficient learners. The role of the proficiency level, or the
developmental level of the learners, will resurface in the discussion chapter of this dissertation.
These two studies report that L2 learners primarily notice lexis as opposed to
morphological features. Thus, this dissertation study builds upon these findings and investigates
the extent to which beginning learners attend to content words, such as verbs, that also carry

54

morphological information. In this study, stem-vowel changes of irregular verbs in German
constituted the morphological structure under investigation.
German Stem Changing Verbs
In German, several verbs change their stem-vowels (i.e., irregular verbs, also called
““strong verbs””) in the second and third person singular, for example:
ich sehe du siehst (I see, you see)
ich sehe → er sieht (I see, he sees)
ich esse du isst (I eat, you eat)
ich fahre er fährt (I drive, he drives)
ich laufe er läuft (I run, he runs)
Research investigating the acquisition of this irregularity has shown that adult learners,
regardless of their proficiency level, have difficulty learning the inflectional morphology of
verbs, particularly those with stem-vowel changes (e.g., Leow, 1997, 2000, for Spanish), which
might be related to the fact that most stem-change patterns are lexically arbitrary and therefore
unpredictable (Bybee & Newman, 1995). The acquisition of verbal morphology poses a
challenge even to advanced learners of German (e.g., Clahsen, 1999; Neubauer & Clahsen, 2009;
Prévost & White, 2000) learners of Spanish (VanPatten, Keating, & Leeser, in press), and
learners of English (Haznedar, 2001; Lardiere, 2007). Just recently, in her 2010 review article
on ‘‘L2 Morpheme Processing and Acquisition,’’ Larsen-Freeman addressed the difficulty that
(verb) morphology poses to language learners:
Learning inflectional morphology is a vexing problem for second language (L2) learners.
Children acquiring their native language also experience some difficulty, which results in
their committing overgeneralization errors. However, relatively quickly, children sort out
the regulars from the irregulars and one allomorph from another. This is not the case for
learners of L2s, at least not for older learners. Long after individuals have achieved a
high level of proficiency in the L2, they are still plagued by uncertainty when it comes to
grammatical inflections (Todeva, 2010), and their production in the L2 is still
characterized by morphological omissions, commissions, and substitutions of one
allomorph for another. (p. 221)
55

Larsen-Freeman then addressed the fundamental question that Pinker (1999) brought up
over a decade ago: ““Is language acquisition rule based or item based?”” (p. 222); that is, in regard
to this dissertation study, are irregular verbs learned as items or as rules? When learning
irregular verbs, do learners use their declarative or their procedural memory?

29

Obviously, there

is no ready answer to such a general question because the type of learning is likely to depend on
the particular grammar structure in focus, the learners’’ L1, learning contexts and situations, etc.
In concluding her review article, Larsen-Freeman cautions that ““we are susceptible of falling into
the same trap again by using the acquisition of morphology to decide between a rule-based and
associative learning account on SLA”” (p. 228). Results from single experimental studies are not
in the position of evidencing whether learners generally engage in item or rule-based learning.
In this respect, with this dissertation study I do not attempt to probe into whether irregular verbs
are learned item or rule based, but the study findings might allow me to consider and speculate
on the design and possible results from future longitudinal research studies that investigate this
issue.
Although descriptive linguistics has developed a system of rules for verb inflection and
stem-vowel changes based on morphological and phonological regularities, it is not clear to what
extent and when these regularities start to correspond to the L2 learners’’ mental storage,
processing, and production (Neubauer & Clahsen, 2009) and to what extent the learners’’
attentional foci and levels of awareness relate to the acquisition of verb morphology.
Considering the relative importance of the reliable inflection of regular and irregular verbs
29

Declarative knowledge corresponds to the storage of facts and events. In contrast, procedural
knowledge corresponds knowing how to perform a certain task or completing an activity
(Segalowitz, 2003).
56

coupled with the difficulty of the learning task, the question arises as to how L2 learners acquire
correct verb inflection. In the context of noticing novel words or morphological features, no
study has investigated whether learners of German who have not been formally introduced to
these irregularities attend to these irregular features during reading. A recent review article by
Clahsen, Felser, Neubauer and Sato (2010) synthesized the findings of different experiments that
investigated native and non-native morphological processing. The overarching synthesis was that
““L2 learners are less sensitive to morphological structure than native speakers and rely more on
lexical storage than morphological parsing during processing”” (p. 21, highlights are mine). If L2
learners are less sensitive to morphological structures, do they notice morphological changes, for
example, in verbs? What exact roles do attention and awareness play in the acquisition of novel
L2 forms, and do these cognitive mechanisms depend on the nature of the L2 structure? The
following section addresses the latter question.
The Difficulty and Noticeability of L2 Forms
DeKeyser, in his 2005 review article, addressed the following important question: ““What
makes learning a second language grammar difficult?”” He identified three major factors: (1)
complexity of form, (2) complexity of meaning, and (3) the complexity of the form-meaning
relationship. Regarding the third factor, DeKeyser argued ““it is the transparency of formmeaning relationships to a learner who is processing a language for meaning that determines the
difficulty of acquisition, at least for learners who are left to their own resources instead of
presented with a reasonably complete set of rules about form-meaning relationships”” (p. 3).
SLA researchers have debated connections between the difficulty level of the target
language structure and the learner’’s attentional resources (e.g., DeKeyser, 2005; Ellis, 1994;
Gass et al., 2003; Long, 1996; Philp, 2003; Robinson, 2003; VanPatten, 1996).4 The attentional
57

resources consumed by the learner depend on the nature of the L2 task, because language tasks
requiring more complex structures require greater processing (and thus greater processing
capacities) than tasks requiring simple structures (e.g., McLaughlin, Rossman & McLeod, 1983;
McLaughlin & Heredia, 1996). Complex structures may also capture more attentiveness by the
learner and may therefore be more noticeable (White, 1998).
In the SLA literature there are two dominant views on how the difficulty level of the L2
task or the complexity of the target structure may influence the learners’’ attentional demands and
noticing. One possible view is premised on the Noticing Hypothesis by Schmidt: the view is that
the difficulty level of the structure may draw the learner’’s attention; that is, a more complex
structure may have more saliency and, therefore, might more likely be noticed and learned
(DeKeyser, 2005). Conversely, the higher complexity of a structure may increase its perceptual
saliency or noticability and, therefore, stimulate processing for form, which may result in
language learning gains (Skehan, 1998). Beyond that, the complexity of the L2 structure or task
may not only increase its saliency, but may also promote the noticing of one’’s linguistic
limitations (i.e., noticing the gap by the learner) (Gass & Varonis, 1994; Schmidt, 1990). From
an information processing/cognitive theory of attention (e.g., McLaughlin et al., 1983;
McLaughlin & Heredia, 1996), however, L2 learners may experience attentional constraints in
specific processing conditions. In other words, if learners are limited-capacity processors (where
their capacity for complex or difficult processing is limited), then attention to complex, relatively
new structures may result in a cognitive overload that negatively impacts L2 gains. That is, if an
L2 structure or L2 task is too complex, the attentional demands may result in a cognitive
overload and impede learning processes. Izumi (2003) further elaborated on the interplay among
cognitive overload, existing knowledge, and noticing during reading in that ““the restricted L2
58

knowledge of the learners may make them rely on certain strategies (e.g., use of semantic
information or contextual cues) more than others (e.g., syntactical cues)”” (p. 184). Thus, such
learners’’ attention to form might be limited.
As described in Chapter 1, VanPatten (1996) also addressed the issue of attentional
constraints within the notion of learners as limited-capacity processors and proposed that
learners’’ processing of the input depends on the attentional resources they currently have.
Notwithstanding the criticism VanPatten’’s IP Principles have received, they still made a
significant contribution to the field. According to VanPatten (2004), processing is equalized with
““making form-meaning/function connections during real time comprehension”” and ““an online
phenomenon that takes place in working memory”” (p. 4). As such, if learners are limitedcapacity processors (as suggested by McLaughlin et al. 1983), their simultaneous attention to
meaning and form may result in a cognitive overload. Thus, they may not attend to and notice a
particular structure if they are processing the L2 input primarily for meaning.
This dissertation study has implications many of the above raised issues. It will provide a
fine-grained examination of the role of focused attention in the learning of verb morphology. If
learners pay attention to these features, do they use their selective attention? Do they rehearse a
novel feature in working memory? To what extent is awareness involved in their focused
attention? How well does eye-tracking methodology gauge learners’’ L2 processing? Are
beginning learners able to process sentences from the top down, or do they mainly rely on
bottom-up processes? Does their learning of irregular verbs suggest item or rule-based learning?
And last, does the particular grammar structure in focus determine whether learners pay attention
to form or not? The dissertation study lends toward the answers to some pieces of information to

59

several, but possibly not all, of these questions, and the results from this study are likely to raise
new questions and pave ways for new eye-tracking research agendas.
The strength and uniqueness of this dissertation study lies in the fact that it is the first
eye-tracking study that has been conducted with true beginning adult learners. It is also the first
eye-tracking study to investigate attention to verb morphology in the L2.

60

CHAPTER 4 METHODOLOGY
Research Questions and Hypotheses
This study was motivated by the following research questions and hypotheses.
1. Do adult beginning learners of German who are unfamiliar with stem-changing verbs pay
attention to those irregularities during reading?
2. Is increased attention to irregular verbs associated with subsequent learning of them?
I addressed the first question via eye-tracking methodology (measurement of fixation times)
and mixed factorial ANOVAs. I hypothesized that during the reading of simple sentences,
learners would fixate on vowel-stem-changing verbs significantly longer than regular verbs in
which the stem does not change. In other words, I expected to find a significant effect of verbtype (regularity) on reading. This prediction was based on previous research demonstrating that
more visual attention is deployed on unknown words or structures (e.g., Godfroid, 2010) and on
theories suggesting that eye-movements are governed by cognitive processing (Engbert et al.,
2005; Just & Carpenter, 1980; Morrison, 1984; Rayner, 1998, 2007; Reichle et al., 2006).
I addressed the second question using pre- and post-production test gain scores and
correlating gain scores and with total time gains. For this second question, I hypothesized that
the eye fixation durations would not be predictive of the uptake of stem-changes in that there
would be no statistically significant improvement of accuracy in producing (writing) stemchanging verbs after learners had been exposed to them through written input. The
understanding of a partially rule-governed target structure (i.e., a vague underlying system) such
as stem changes may not develop immediately but may develop gradually over time with
exposure to several exemplars of the same underlying rule (e.g., N. Ellis, 2005). I predicted,
61

however, that learners would improve on the inflections (second person) and overall spelling of
verbs due to the exposure to the verbs during the reading activity.
Participants.
Forty-seven adult learners of German enrolled in German 101 courses at Michigan State
30

University (MSU) participated in the study on a volunteer basis.

Six German 101 courses

were offered in the Fall semester of 2010. Every semester, students are enrolled in German 101
31

courses if they have no previous experience in German or receive a low score

on the German

Placement Test (administered at MSU). The German placement test is administered online by
MSU’’s Center for Language Teaching Advancement. The test consists of 24 listening, 24
reading, 12 vocabulary, and 16 grammar items. The test items were developed by German
department professors who based the design of the items on the ACTFL Proficiency Guidelines
(www.actfl.org) and department course materials and instructional objectives. The test’’s
reliability is only moderate, but the professors in the department consider the placement tests to
be adequate for placing students into the courses. One limitation is that students may
30
31

The data of 43 participants was included in the analysis of this study.

Based on personal communication (February 25, 2011) with Dr. Dan Reed, head of the
language testing office at Michigan State University, the specific cut scores (for German 101,
102, 201, and 202) are regularly reviewed by the department and programmed into a system, so
they are not easily accessible. The language test consists of 39
multiple-choice items that each student responds to (and these are selected from a pool, so not
the same for each student). The 39-item test can be expected to have an average standard error of
about 3. In 2007, Rasch-based item reliabilities averaged in the mid 70s, but the test has had
some improvements since then, so the numbers are likely better to date. It is important to note
that the multiple choice test is only part of the placement system, which begins with a
background questionnaire and ends with the advisor making decisions based on all available
information. So asking the reliability of the MC part is like asking about the reliability of a
subset of the test and underestimates the reliability of the whole placement system. Nonetheless,
the system as a whole works relatively well due to all the checks and balances (background
information collected, MC results, essay results for higher-level placement, and advisor
recommendations).
62

intentionally perform below the level of their true abilities in order to place into an ““easier””
course; it remains unknown how typical that is (Personal Communication with Tom Lovik,
Department Chair, August 26, 2010).
Based on this information, I assumed that the 43 participants had comparable German
proficiency. Based on consultations with the German course instructors and the German program
coordinator, and based on my analysis of the Fall semester’’s syllabus, I confirmed that the stem
changing verbs would not have been introduced to the students, so none of the students had
explicitly learned that stem-changes existed or how to use stem-changes accurately. The
Teaching Assistants of the German 101 courses as well as the coordinator of the German
undergraduate program supported my class visits for recruitment purposes.
The 43 participants consisted of 24 females and 19 males. Their age ranged from 17 to
36, with an average of 20 years. Thirty-eight participants reported that English was their native
language, and the remaining five participants reported that their native language was Mandarin
(one participant), Uzbek and Kazah (one participant), and two participants stated they were
bilingual, one in French and English and another in Malay and English. Twenty-five participants
stated they were of German heritage (but not German heritage-speakers); two reported having
family members with whom they occasionally exchange words and phrases in German. I
followed up by email and learned that these two learners’’ German interactions were quite
insignificant; for example, one stated, ““my Grandma taught me to count in German.”” Only one
student reported having had prior German instruction in middle school, but he did not attend
regularly, nor did he remember any German from his middle school time. Nine students reported
that they had ““some German”” in high school. After a follow-up via phone and email, I learned
that they had either dropped out of class early in the school year or ““not remembered anything.””
63

None of the participants had received any private instruction or tutoring. Thirty-four participants
indicated that they have studied other foreign languages; 24 had studied Spanish or a
combination of Spanish and French, one student had also studied Thai and one student reported
having studied Latin. Ten participants reported that they had been to Germany with the length of
stay ranging from one to 14 days. The questionnaire inquired whether the participants had
studied or interacted in German during their stay, which was unanimously negated. The students
varied notably in terms their indication of weekly hours spent on homework assignments or just
studying German: It ranged from zero to 10 hours per week with an average of 3.09 hours. The
participants assessed their German proficiency in the four language skills (listening, speaking,
reading, and writing). The descriptive statistics are summarized in Table 1.
Table 1: Descriptive Statistics
Beginner
Low Int.
Intermediate
High Int.
Advanced
Listening
27*
11
5
0
0
Speaking
31
9
3
0
0
Reading
27
10
6
0
0
Writing
28
11
4
0
0
*Number of students who indicated the proficiency level on the background questionnaire.
Materials
The materials used in this study consisted of the background questionnaire (Appendix A),
the pre- and the post-test, and the four counterbalanced versions of the reading task (i.e.,
sentences and pictures) on the eye-tracker screen and a vocabulary familiarity test.
The Pre- and Post-test
The pre- and the post-test were identical with the only difference being the order in which
the pictures were presented to the students. Both tests required the participant to write down a

64

sentence using the action verb that was depicted on the picture. The exact procedures are
described under the ‘‘procedures’’ section.
The Reading Task
The target words (i.e., the regular and irregular verbs in the sentences) were elements of
short, simple sentences that the participants were instructed to read for meaning. For a
comprehensive list of all verbs, their inflection, and translation please see Appendix B. The
learnability of the regular and irregular verbs used in this experiment was assumed to be equal,
as the verbs have similar frequency (see Appendix D) and number of syllables, that is, exactly
two. All verbs (regular and irregular) occurred as the second element of the sentence, which is
the word order used in main clauses. The vocabulary familiarity test established that students had
32

at least ““seen the word”” (i.e., the relevant verb) before.

The sentences were also matched by

number of words, with an exact average of 4.5 words per sentence. All stimuli sentences were
designed with the learners’’ proficiency level in mind, and they have also been shared with
another experienced German foreign language teacher, who confirmed that the learners would
not experience difficulty comprehending the gist or the overall meaning of the sentences. As the
words used in the sentences were covered by the textbook by the time the data collection started,
it was safe to assume that the vocabulary items used in the sentence were familiar to the
participants. Every sentence was preceded by a fixation cross in front of the sentence.
I informed students that the study’’s purpose was to investigate how beginning learners
read from a computer screen, and this information may help language learning software
developers such as Rosetta Stone. The reading task included two sentences, one baseline
32

Three verbs were not covered by the textbook, so the Teaching Assistants introduced the
infinitive and the meaning of the verbs in their instruction.
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sentence (starting with ich (I) = the first sentence, termed the ‘‘baseline sentence’’) and a critical
sentence (starting with ‘‘du’’(you) or ‘‘er’’ (he), ‘‘sie’’ (she) = the second sentence termed the
‘‘critical sentence’’) which used the same verb but with different inflections due to the first and
second/third person difference. In the experiment condition the difference was not only
established by inflection, but also by the stem-vowel change; thus, the term critical condition.
The participants worked through 24 verbs and saw three screens per verb (trial). On the
first screen of each trail, participants saw a picture of an action verb. When they clicked to the
next screen, they saw two sentences, which they read. On he third screen, participants saw three
pictures, one of which, again, depicted the action verb of that trail. Then, the participants looked
at a fixation cross (drift correct) before they proceeded to the next trial, which again contained
three screens. On the whole, the participants saw 12 experimental trials (irregular verbs) and 12
control trials (regular verbs).

Figure 7. Simple Illustration of the Experimental Design

66

The purpose of the fixation cross between the trails was to perform a drift correct, which
provides additional security to avoid a drift in the gaze position. Although the calibration
procedure at the beginning of the experiment notices any drifting problems, additional drift
corrections via fixation-crosses add an additional safety measure.
Another type of fixation-cross appeared right at the beginning of the two sentences.
Before the participants read the sentence, they briefly fixated the cross. This ensured an
additional recalibration of their eye fixation coordinates on the screen. The sentences appeared in
Tahoma 16-point font on a Power Point slide in presentation mode with a subtle yellow fading to
blue background (for eye comfort). The font was kept at a regular size to make the reading
exercise appear as natural as possible. A picture showing the action (e.g., a woman running)
preceded the screen that showed the two sentences (i.e., baseline and critical sentence) using the
action verb. The purpose of showing a picture depicting the action was to ease the overall
reading task for the beginning learners. Following the reading of the two sentences, the third
screen showed three pictures. One of those pictures, again, depicted the action verb described by
the sentences shown on the previous screen (but this picture was different from the initial picture
depicting the action verb). The other two pictures depicted other actions. The participants were
instructed to look at the correct picture, that is, the picture that corresponded to the preceding
sentences. Figure 8 illustrates what a trial looked like on the eye-tracker screen.

67

Figure 8. Example of Eye-tracker Screens
For interpretation of the references to color in this and all other figures, the reader is referred to
the electronic version of this dissertation.

68

The stimuli sentences that occurred in the experiment were all counterbalanced by
sentence endings and by the person (‘‘du’’ or ‘‘er’’/’’sie’’) used for the critical condition. That is, all
sentences occurred with either stimuli endings A or with stimuli ending B and they contained
nd

either the 2

rd
person or the 3 person singular. Table 2 below illustrates the counterbalancing

of sentence endings and persons:
Table 2: Counterbalancing of Sentence Endings and Persons
Infinitive

Sehen

Sprechen

Essen

Ich sehe das Meer.
I see the ocean.
Du siehst die Natur.
You see nature.

Ich spreche mit dem Lehrer.
I speak with the teacher.
Du sprichst am Telefon.
You speak on the telephone.

Ich esse gern Brot.
I like to eat bread.
Du isst oft Müsli.
You often eat cereal.

Group II:

Ich sehe die Natur.
Du siehst das Meer.

Ich spreche am Telefon.
Du sprichst mit dem Lehrer.

Ich esse oft Müsli.
Du isst gern Brot.

Group III:

Ich sehe das Meer.
Er sieht die Natur.

Ich spreche mit dem Lehrer.
Er spricht am Telefon.

Ich esse gern Brot.
Sie isst oft Müsli.

Ich spreche am Telefon.
Sie spricht mit dem Lehrer.

Ich esse oft Müsli.
Er isst gern Brot.

Group I:

Group IV:

Ich sehe die Natur.
Sie sieht das Meer.
Second person stimuli ending A
Second person stimuli ending B
Third person stimuli ending A
Third person stimuli ending B

Recall from Chapter 2 that fixation times are likely to be influenced by the words after
the target word (Kliegl, Nuthmann and Engbert, 2006). Therefore, I controlled for that by
counterbalancing the sentence endings across the groups to which the participants were assigned.
Note that these groups did not constitute experimental groups and control groups; rather, they
dictated what counterbalancing condition the participant would be assigned to.
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Due to the counterbalancing of stimuli endings across groups, I assumed that the
experimental variance was not influenced by the words that followed the target word. In other
words, any sentence ending’’s effect on processing time was controlled. During the actual
experiment, the stimuli were presented in a fully randomized order, that is, a random mix of
trails containing regular or irregular verbs. However, each experiment included all 24 trials (i.e.,
all 24 possible verbs).
The Vocabulary Familiarity Test
The vocabulary familiarity test included all words that appeared in the reading task. The
participants indicated on a scale from one (don't know the word) to seven (know the word and its
meaning).
Procedures
To ensure that the participants had not been formally introduced to the phenomenon of
stem-changing verbs in German, all data were collected early in the fall semester, specifically,
during the fourth and fifth week of the semester. According to the German 101 syllabus and the
coordinator of the German undergraduate program, the stem-changing verbs were not formally
introduced to the students until the end of the 6th week of the fall semester.
Participation in the study was voluntary. I recruited the participants by briefly visiting the
rd
th
six 101 German classes on the first days of classes (September 3 and 4 ) to inform students

about the opportunity to participate in the study. Without revealing the actual research goals and
purpose of the study, I informed the students about the procedures of the study and the risks and
benefits involved. Students who were interested in participating were asked to document their

70

names and email addresses on a sign-up sheet so I could subsequently email them to confirm
their appointment and provide guidance to the location of the research room.
The German 101 volunteers participated individually in a quiet room (the eye-tracking
research laboratory) for about one hour, depending on the participant’’s pace of completing the
research tasks. Before the research procedure started, the participants carefully read and signed
the consent form. Each participant was informed of the sequence of the activities prior to
completing the pre-test, the reading activity, and the post-test. As the first step in the research
procedures, the participants completed a background questionnaire that elicited information
about the participants’’ demography, language learning background, prior German exposure, and
a self-assessment of their German proficiency.
Following the completion of the background questionnaire, the participants took the pretest. As the entire experiment followed a pre- and posttest design, the purpose of the pre-test was
to establish a baseline against which the post-test scores can be assessed. To avoid test anxiety,
the participants were told that the pre-test was a warm-up activity, but that they should do their
very best. A typical testing session proceeded as follows: I sat down with the participant and I
had a stack of 14 pictures illustrating 14 verbs, 12 irregular verbs that are used in the experiment
as well as two regular verbs (distractors). The participant had a sheet of paper with 14 lines and a
pencil in front of him or her. Then I held up a picture and asked the participant in English what
the man or the woman in the picture was doing. If the picture showed a woman talking on the
phone, the participants usually responded: ““She is talking on the phone.”” After positive
acknowledgement I encouraged the student to say the sentence in German. Typically, the
student then said ““Sie sprecht am/in/auf Telefon”” (sic.). I asked the student to write down the
sentence on the paper. I also informed them that I wouldn’’t provide them with any feedback on
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whether their sentences were correct or incorrect, but I encouraged them to write down the
sentence just as they said it. The whole procedure was repeated until all fourteen pictures had
been shown. The advantage of this form of pre- and post-testing lies in the vagueness of the
testing purpose. That is, the participants did not know whether the test was targeting grammar,
vocabulary items, pronunciation, or their ability to form simple sentences. Thus, the participants
did not get the idea that I was looking for their use of stem-changes in inflected verbs. There was
no time pressure to complete the pre- or posttest; the time spent on the pre- or posttest ranged
from ten minutes to eighteen minutes.
Upon students’’ completion of the pre-tests, the participants sat in front of a desk with a
computer screen approximately 80 cm in front of them. The participants placed their chin on a
forehead rest (head supported as opposed to remote/head free). A camera attached to the
computer screen captured the participants’’ pupils’’ movements as they read the sentences and
looked at pictures presented on the screen. The participants had a game controller in their hands
that allowed them to click through the slides at their own pace. The experiment session began
with a short built-in calibration exercise on the computer screen. To explain the calibration
exercise, the participants looked at a moving dot presented on different parts of the computer
screen to calibrate the Eye-Link camera. Godfroid (2010) explains the calibration exercise
precisely: The calibration ““adjust(s) the graze position measurement for date collection. During
calibration of the EyeLink II, a participant is required to fixate on [……] points that appear at
various locations on the screen. Thus, the system knows a participant’’s exact gaze position on
the screen and can adjust the pupil position as measured by the cameras to this benchmark.
Hence, calibration is the process of adjusting the eye gaze measurement with a view to
subsequent data collection”” (p. 133).
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I explained some practical aspects of the experiment, for example how to use the game
controller to move from one screen to the next, how to successfully complete the calibration
exercise, and that they had to focus their eyes on the fixation cross (drift correct) every three
slides and wait for me to confirm the accurate fixation (by pressing a button on a separate
keyboard).
After every trial (i.e., three slides: one with one picture, one with two sentences, and one
slide with three pictures) the participants looked at a fixation cross, also called a drift correct,
that ensured that the camera still accurately captured the participants’’ eye-movements. The entire
reading task took about fifteen minutes.
As a last step in the experiment, the participants completed the post-test, which was the
same as the pre-test, with the only difference being the order in which the pictures were
presented. The purpose of the pre- and posttests was to determine whether students had improved
their writing of stem-changing verbs and to see whether there is a possible correlation between
the time spent looking at the target words and scores tests. After completion of all research tasks,
the participants were paid $10.00 cash.
The German Teaching Assistants administered the vocabulary familiarity test in the
German classes on the last day of the data collection period. All students in their classes took the
test, even if they hadn’’t participated in the research. The reason behind administering the test in
the German classes as opposed to integrating it into the experimental procedures was due to
logistic reasons, mainly time limits. Completion of the vocabulary familiarity test took up to
twenty minutes. I excluded the students from the test pool who did not volunteer in the
experiment.
Analyses
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Overall, the data consisted of the gain scores from pre- to post-test, the participants’’ eyemovements and fixations as recorded during the reading task, and the vocabulary familiarity test.
As a reminder, noticing was measured through the participants’’ eye-movements and fixations,
whereas language uptake was measured by means of the pre- and post-tests.
Analysis of the Eye-tracking Data
The stem-vowel changes of the German verbs constituted the independent variable (i.e.,
target words at two conditions: regular (baseline) and irregular (critical) in this experiment. The
time spent by the participants looking at the target words (various types of fixations) was defined
as the dependent variable. Please refer to the different types of fixation measures listed in Table
3.
Table 3: Variables in the Interest Area Report
Dependent Variables
First Fixation
Duration

Description
Duration of the first fixation event that was within the interest
area.

Gaze Duration (First
Run Dwell Time)

Dwell time (i.e., summation of the durations across all fixations
in the current interest areas)

Total Time (Dwell
Time)

The summation of the duration across all fixations on the
interest area

Based on the insight provided by the literature on eye-movements during reading (e.g.,
Rayner, 1998), I mainly considered the First Fixation duration, Gaze Duration, and the Total
Time, which have been mainly used in the cognitive science of reading research (Staub &
Rayner, 2007) and are therefore likely to provide an accurate reflection of how much attention
and cognitive processes occurred upon processing the target word. First fixation duration is the
duration of the first fixation on a word, regardless of whether this word has been fixated a
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number of times. Gaze duration is the sum of all fixations on a word during the first pass, and
total time is the summation of the duration across all fixations, regardless of whether it was the
first or the second pass. To clarify the distinction of these measures, I am citing Frenck-Mestre
(2005), who explains:
We can distinguish between ‘‘first pass’’ reading time, ‘‘second pass’’ reading time and
‘‘total’’ reading time (the sum of the former two measures), for any given region of interest
(ROI). First pass reading can furthermore be broken into ‘‘first fixation’’, i.e., the first time
the eyes land in a given region of interest (ROI), and ‘‘gaze duration’’, i.e., all fixations in
the ROI from the first fixation until the eyes exit to the right or left”” (p. 176).
To answer research question 1 (Do adult beginning learners of German who are unfamiliar
with stem-changing verbs pay attention to those irregularities during reading?) I first computed
the differences in the various types of fixation times between a target word (i.e., irregular verb)
and a control word (i.e., regular verb). The calculated differences——the fixation gain——
established the dependent variables. I then conducted a mixed design factorial ANOVA as I had
two independent variables (fixed factors). According to Field (2009), a Factorial ANOVA is very
powerful because it allows us to assess the effects of each independent variable, plus the effects
of the interaction. The first independent variable (i.e., fixed factor) was established by the
regularity of the verb at three levels, or two levels, depending on the target of the analysis (i.e.,
the type of stem-change as opposed to irregularity in general).
1. The vowel change from e to ie/i, which received the value 1:
essen (du isst; er/sie isst)
geben (du gibst; er/sie gibt)
lesen (du liest; er/sie gibt)
nehmen (du nimmst; er/sie nimmt)
sehen (du siehst; er/sie sieht)
2. The vowel change from a to ä, which received the value 2 (or also 1, if I was looking at
irregularity in general):
75

fahren (du fährst; er/sie fährt)
fangen (du fängst; er/sie fängt)
schlafen (du schläfst; er/sie schläft)
tragen (du trägst; er/sie schläft)
waschen (du wäschst; er/sie wäscht)
3. No vowel change (simply regular), which received the value 3:
kochen (du kochst; er/sie kocht)
segeln (du segelst; er/sie segelt)
studieren (du studierst; er/sie studiert)
trinken (du trinkst; er/sie trinkt)
wandern (du wanderst; er/sie wandert)
zeigen (du zeigst; er/sie zeigt)
4. No vowel change (simply regular), which also received the value 3:
begrüßen (du begrüßt; er/sie begrüßt)
gehen (du gehst; er/sie geht)
kaufen (du kaufst; er/sie kauft)
lachen (du lachst; er/sie lacht)
schreiben (du schreibst; er/sie schreibt)
singen (du singst; er/sie singt)
The second independent variable (i.e., fixed factor) was the group (i.e., one of the four
counterbalancing conditions) to which each subject was assigned. The factor subject itself,
although designated as a random factor, was nested within the group. According to Sall,
Creighton and Lehman (2006), ““nested effects occur when a term is only meaningful in the
context of another term, and thus is kind of a combined main effect and interaction with term
within which it is nested”” (p. 336). All statistical tests were calculated using the IBM Statistical
Package for the Social Sciences (SPSS), version 19.
Analysis of the Test Scores
To answer research question two (Is increased attention to irregular verbs associated with
subsequent uptake of them?) I conducted correlation analyses were I investigated whether
increased first fixation times (i.e., total time gains) correlate with gain scores on the
corresponding test items. Only the target words (regular and irregular verbs) were considered for
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scoring, all other words written by the participants in the sentences were ignored. The tests were
scored twice with two different scoring procedures. With the first scoring procedure I took a
holistic approach and scored the overall improvement by the participants. With the second
scoring procedure, I only gave points for improvements on the vowel-stem change feature. The
two scoring procedures are described below, starting with the first, the holistic approach.
A participant received two points if he or she provided the correct form of the verb,
including correct inflection and the correct stem-vowel change. A verb was conjugated correctly
if all letters were present (including umlauts: ä, ö, ü) and in the correct order, I did not count as
incorrect any typographical/spelling errors, e.g., du seihst instead of du siehst. I gave one point if
a verb was conjugated correctly but the stem-vowel change was incorrect or simply disregarded
(e.g., er ‘‘sprecht’’ (he speaks) instead of er ‘‘spricht’’). To give an example, if a learner wrote ‘‘er
sprecht’’ on the pre-test and ‘‘er spricht’’ on the posttest, he or she received one point on the pretest and two points on the post-test. I gave zero points if a learner failed to conjugate a verb
correctly (e.g., ‘‘er sprechen’’ or ‘‘er ist sprechen’’). If the improvement on the posttest laid in the
correct verb inflection (i.e., a change from ‘‘er sprechen’’ to ‘‘er sprecht’’), I gave half a point,
regardless of the missing stem-vowel change. I tabulated all verbs and scores and calculated the
difference in scores (i.e., gain score) for each item (verb) between the pre-and the posttest.
The second scoring procedure disregarded any improvements on inflection or spelling; I
paid attention to vowel changes only. If a participant did not change the stem-vowel correctly, I
gave zero points. Only if the stem-vowel was changed correctly (with the exception of
typographical/spelling errors, e.g., du seihst instead of du siehst) I gave two points.
The next chapter shows the findings of the statistical analyses employed to address the two
research questions.
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CHAPTER 5 RESULTS
The results presented in this chapter are organized by the dependent variables, starting
with total time, gaze duration, and first fixation duration. Therefore, I first look at the sum of all
fixation durations on a word (i.e., total time) before looking at smaller fixation time measures
(gaze duration = the summation of the durations across all fixations in the current interest areas
during the first pass reading, and first fixation duration = only the first fixation on a word). The
main ANOVA design was nested with two crossed-fixed factors (verb type and group) and
participants nested as a random factor within group. Recall from Chapter 4 that a nested
ANOVA can be thought of as an extension of a one-way ANOVA in which each group is
divided into subgroups. Specifically, the data stemmed from 43 participants, and I assigned those
participants to groups, each of which read the sentences with a particular sentence ending and
nd
rd
subject person (2 or 3 person singular) combination. Figure 9 below illustrates the nested

design.

78

Figure 9. Nested Design
After presenting results pertaining to the three types of fixation times, the observation
data collected for this study, I present the experimental results for learning that occurred as a
result of the participants’’ attention to the stimuli. These data consist of correlations between
pretest-posttest gains and total time gains (total time of the critical condition minus the baseline
total time). This correlation analysis addresses my second research questionwhether students’’
attention to irregular verbs is associated with their subsequent uptake of them.
Results Addressing RQ1
Total Time

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My preliminary assessment of the descriptive statistics in the nested ANOVA revealed
the existence of outliers that may bias parameter estimation and, by implication, statistical
inference (Field, 2009). To avoid any bias in estimation, I followed several steps. The first step
was to remove influential outliers according to Cook’’s distance test (Cook, 1977). Cook’’s
distance is a measure of the overall influence of an observation on the model (Maindonald,
2010). However, the removal of outliers based on Cook’’s distance test did not normalize the
residuals associated with the model. As a second approach, I removed all observations whose
33

corresponding standardized residuals were larger than 3.5 or smaller than -3.5.

Both methods

(Cook’’s distance and the removal of large standardized residuals) still failed to ensure normality
of the model’’s residuals. In the fields of second language studies and bilingualism, researchers
often omit observations that fall outside of two standard deviations from the item mean (Hoover
& Dwivedi, 1998; Irena O'Brien, 2007; Lotto & De Groot, 1998; Sherman, 2007; van Heuven,
Dijkstra, & Grainger, 1998), which make up about 5% of the data points in a normally
distributed data set. In other words, researchers usually keep those values that lie within two
standard deviations from the mean. Such values make up a confidence interval, which is a range
of values that are assumed to contain, with a certain probability, usually 95% of the true
population mean (Field, 2009). In this study, I worked with a 93.4 % confidence interval, which
ensured the normality of residuals, and thereby a fundamental assumption to underlying
ANOVA models. This means, with 93.4% confidence, the general targeted population is likely to
share the characteristics of the study participants (e.g., Bird, 2004).
33

According to Meyers, Gamst and Guarino (2006) ““A traditional remedy for statistical
assumption violation is to eliminate cases with large residual values (those with absolute
standardized residual value greater than 3). This elimination can produce a better fit by the
regression equation”” (p. 203).
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The influential outliers that were removed were cases of long fixation times (usually
around 2000 milliseconds or more). Given the large number of unique data points in this study
(n=1,032), the removal of 84 records still left me with 948 records for analysis. It is important to
note that outliers are defined as observations that appear to be inconsistent with other
observations in the data set: outliers have low probability to originate from the same statistical
distribution as the other observations in the data set (Field, 2009; Sall, Creighton, & Lehman,
2005). Since 84 cases in this study revealed extremely long fixation times, they might be
fundamentally different in terms of the type and amount of processing they engage in when
reading stem changing verbs. Because I operationalized noticing as an increase in fixation time
(relative to a baseline condition), I decided to investigate the outliers in a separate analysis (see
section x). Below I present data with the 84 outliers removed.
Total time ANOVA
Since there was no main effect of group, I conducted a nested ANOVA with the fixed
factor verb type crossed with the random factor subject/group (subject nested within group) with
the trimmed data set to model the main effects of verb type (the independent variable) on total
time (the dependent variable). Recall that total time here refers to ‘‘total time gain’’, which is the
baseline-critical-item differential; in other words, for each individual, it is his or her average
fixation time on the baseline condition minus his or her average fixation time on the critical
condition. If total time gain has a negative value, irregularity (stem-vowel change) did not attract
longer fixations in comparison to the baseline ich-form (regular, no vowel change). Analogous,
if the total time gain has a positive value, then the irregularity attracted longer eye-fixations or
processing time compared to the baseline ich-form.

81

As expected, there was a significant effect of verb type, F(3, 902) = 4.278, p = 0.005,

2
η p =0.014, and a significant effect of subject F(42, 902) = 2.719, p = 0.032. The latter
accounted for a great deal of the variance in total time gain, as indicated by the effect size

2
η p ) of 0.112. Thus, 11.2% of between-subject variance in total time gain was accounted for
(
by individual differences in reading speed among the participants.
Using a Multivariate ANOVA for these data was appropriate, as judged by the F test of

2
η p = 0.152. The latter indicates
goodness of fit (p > .05): F(126, 776) = 1.102, p = 0.226 and
that 15.2% of the variation in total time gain was accounted for by this model, which is
considered to be large (Field, 2009).

34

Post Hoc Tests
The Tukey HSD post hoc test revealed that the participants’’ average total fixation time on
verbs changing their stem-vowel from a

ä was significantly longer than their average total

fixation time on regular verbs with no vowel change (p = .035). There was no significant

34

The result of the model indicated a non-significant intercept. That is, the value at which the
line crosses the y-axis does not differ significantly from zero; more precisely, the total fixation
time predicted by the model when the values of all independent variables are set to zero does not
differ significantly from zero. I kept the intercept in the model as (1) no-intercept models do not
adjust for the average of the independent variables, which leads to interpretation difficulties, and
(2) no-intercept models should be fit only when theoretical justification exists and the data
appear to fit a no-intercept framework better (Field, 2000 pp. 144 -145). The latter does not
apply to this eye-movement study, as the models preserve the same magnitude of significance for
the independent variables.
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difference between participants’’ average total fixation time on verbs changing their stem-vowel
from e

ie and regular verbs (p = .117). Figure 10 below illustrates such differentials.

Figure 10. Post-Hoc Results
Pair-wise Comparisons
Because there was a significant difference between the stem-vowel-change a

ä, I was

interested to see whether there was a significant difference in total time gain between regular
verbs on the one hand and irregular verbs on the other. The pair-wise comparison of irregular
versus regular verbs (using verbcoding2) showed that the participants’’ average total fixation time
on irregular verbs was significantly higher than their average total fixation time on regular
83

verbs. Using a Multivariate ANOVA for these data was appropriate, as judged by the test of

2
η p =0.056. The latter indicates
goodness of fit (p = .005): F (42, 862) = 1.226, p = 0.157 and
that only 5.6% of the variation in total time gain was accounted for by this model, which is
considered to be moderate (Field, 2009).
Nested ANOVA Assumptions
The assumptions for Mixed models include: (1) linearity of model, (2) independence of
observations, (3) residual normality of the independent variables’’ groups, (4) homogeneity of
residuals variances of the independent variables’’ groups, (5) lack of multicollinearity, and (6)
lack of influential outliers to produce unbiased parameter estimation, and be able to generalize
the inference from the results to the entire population of interest (Leon-Guerrero & FrankfortNachmias, 2010; Rutherford, 2001; Smith, Gratz, & Bousquet, 2008). I satisfied the first and
second assumption by my modeling and the research design. The fifth assumption was not
applicable, as I did not have continuous predictor variables. The lack of influential outliers was
ensured due to the trimmed data set (see explanation above). Therefore, I turned my attention to
examine assumptions 4 (homogeneity of residuals variances), and 3 (normality of the residuals).
Homogeneity of Residuals Variances
Homogeneity of error variance is one assumption of the fitted mixed models which
implies that the standard deviations of the error terms are constant across the levels of the
independent variables and do not depend on the x-value (Field, 2009). For the ANOVA model
for total time, the probability associated with Levene's test for equality of variances (F (171, 776)
= 1.779, p = 0.01) was less than the .05 significance level, so the assumption of equal variances
was not satisfied. It is important to note, however, that factorial analyses are not sensitive to
84

violations of the equal variance assumption when samples are moderate to large and samples are
approximately of equal size (Field, 2009), which is the case in this study.
Normality of the Residuals
Visual inspection of the histograms and QQ-Plots of the standardized residuals suggested
that the residuals for the different levels of verb types (verb coding2) were normally distributed.
Furthermore, the formal tests of normality (Kolmogorov-Smirnov and Shapiro-Wilk) indicated
that the total time data was normally distributed among the categories of the independent
variables (α = 0.001). The data appeared to be normally distributed since the histograms
followed approximately bell-shaped curves and the QQ-Plots had diagonal lines; the data did not
appear to have any nonlinear patterns. Based on these observations I concluded that residual
normality was established (Field, 2009 p. 148).
Analysis of Omitted Outliers
To analyze the characteristics of the omitted observations from the study, I conducted a
Chi-square test of independence. Specifically, by means of the Chi-square I analyzed whether
observations of extreme long fixation times occurred more often with irregular verbs than with
regular verbs than might be expected due to chance. An alpha level of α = .05 was set for all
Chi-squares. A Chi-square test measures ““the difference between a statistically generated
expected result and an actual result to see if there is a statistically significant difference between
them”” (Cohen, Manion, & Morrison, 2007, p. 562). I conducted the first Chi-square test with
irregular versus regular verbs and positive values versus negative values. It is important to
understand that positive and negative values were computed with reference to the baseline
st
sentence (1 person singular = ich, where the verb-stem never changed). Recall that I subtracted

85

the fixation time on the verb in the critical sentence from the fixation time on the verb in the
baseline sentence. Thus, negative values indicated longer fixations on the baseline condition
compared to the critical condition, which can tentatively be interpreted as ““no noticing.””
Positive values indicated longer fixations on the critical condition compared to the baseline
condition, which can tentatively be interpreted as ““noticing.”” The hypotheses in consideration of
the Chi-squares were as follows:
H0: The verb types (regular versus irregular) and fixation the times on the critical verb are
independent.
This means that the outlier values for a given verb type (regular versus irregular) are evenly
distributed among positive values (noticing) and negative values (no noticing).
H1: The verb types (regular versus irregular) and fixation the times on the critical verb are
dependent.
This means that the outlier values for a given verb type (regular versus irregular) are not evenly
distributed among positive values (noticing””) and negative values (no noticing).
The results of the Chi-square test indicated that the total fixation time (posneg) was

χ 2 = 5.098, df = 1, p =
significantly associated with verb-type (irregular versus regular),
0.015, OR = 0.309 with 95% C.I. (0.117, 0.812). As shown in Figure 11, for the majority of the
outlier observations, fixation times were significantly longer on irregular verbs as opposed to
regular verbs (both compared to the baseline condition).

86

Figure 11. Verb Type and Total Time Gain (Regular versus Irregular)
Figure 12 shows that 77% of the outlier cases revealed extremely long fixation times on vowelchanging verbs (compared to the baseline condition), whereas only 22.9% of the outlier cases
revealed longer fixation times (compared to the baseline condition) on regular verbs. This
notable difference suggests that the outlier observations demonstrate noticing (i.e., longer
fixations) of the irregularities as opposed to regular verbs.

87

To quantify the association between verb type and total time, I used the Mantel-Hänszel
estimator as a common odds ratio for the categories of sign of total time. Hosmer and Lemeshow
(2000) explained ““The odds ratio [……] approximates how much more likely or unlikely it is for
the outcome to be present among those with x = 1 than among those with x = 0”” (p. 379). For
example, in the case of this study, if the data have an odds ratio of 2, then the odds ratio
estimates that long fixation times are twice as likely to occur with irregular verbs than regular
verbs in the dataset. The results of the Mantel-Hänszel test indicated that extremely long fixation
times are 3.24 time more likely to occur with irregular verbs than regular verbs, with a 95%
confidence interval of (1.231, 8.526),

χ 2 = 4.794, df = 1, p = 0.029.

This finding is very

important in relation to RQ1, yielding that learners whose observations fell into the outlier pool
fixated significantly longer on irregular verbs with vowel changes than regular verbs with no
vowel change.
As a next step, I investigated whether I would see a similar pattern when further
separating the irregular verbs by the type of stem-vowel change. I conducted the same Chisquare analysis with verbcoding3 (1= irregular e

i/ie, 2=irregular a

ä, and 3=regular) and

posneg (1=positive total time, and 0=Negative total time). The results of the Chi-square test

revealed that Total Time (posneg) was significantly associated with verb-coding3 ,

χ

6.267, df = 2, p = 0.044, Cramer’’s V = 0.273. The results are illustrated in Figure 12.

88

2
=

Figure 12. Verb Type and Total Time Gain (by Stem-change)
Figure 13 illustrates that a negative fixation time (which can be interpreted as no
noticing) is most common with regular verbs (in nearly 50% of the cases), less common with a
vowel change a

ä, and least likely to occur with verbs that change the vowel from e

i/ie.

Positive fixation time (i.e., longer fixations on the critical sentences than baseline sentences,
which can be interpreted as noticing) occurs more commonly with a
to e

ä changes (40%), similar

i/ie changes (37%), and least common with regular verbs (only 22.9%). In summary, the
89

chart suggests that observations of no noticing occur with regular verbs almost half of the time,
while noticing is considerably more common for stem-changes a

ä and e

i/ie.

I investigated this phenomenon further by looking at the odds ratio using logistic
regression (for detailed information on logistic regression, please refer to Hosmer and Lemeshow
(2000). A logistic regression is a version of multiple regressions using dichotomous data. In my
analysis the outlier data were coded as either positive total time gain values (coded with 1) or
negative total time gain values (coded with 0). My predictor variables, verb types, were
categorical as well. The logistic regression yielded which verb types (i.e., e

i/ie; a

ä; or

regular) predicted whether a learner fixated longer on the critical sentence (where the stemchange occurred) or not. The results of the logistic regression are summarized in Table 4.
Although the difference between the stem-changes are presented in the first row of the
chart (for the purpose of completeness), I will only elaborate on the predictor variables of the a
ä change and regular verbs (second row), and the regular verbs and the e

i/ie change (third

row), Table 4 is best interpreted when looking at the odd ratios. As explained above, the odds
ratio is a measure of effect size, describing the strength of association (or non-independence if
smaller than 1) between binary data values (Field, 2009).
Looking at the second row, extreme fixation points on verbs with an a

ä change are 2.8

times more likely to have a positive value (i.e., reveal noticing) compared to fixation times on

χ 2 = 3.4785, df = 1, p = 0.0622. This finding
regular verbs with 95% C.I. (0.949, 8.262),
approached statistical significance. Looking at the third row of the chart, extreme fixation times
on verbs with an e

i/ie change are 3.9 times more likely to have a positive value (i.e., reveal

90

χ2 =
noticing) compared to fixation times on regular verb with 95% C.I. (1.236, 12.303),
5.3909, df = 1, p = 0.0202. This finding was statistically significant.
Table 4: Logistic Regression with Total Time
Fixation
Time
Positive
Value*
(=longer
fixation on
critical item
than on
baseline)

Predictor
Variable
e i/ie
a->ä

Odds ratios

Confidence Interval
Lower
Upper

1.393
1 (Reference)

0.464
4.185
1 (Reference) 1 (Reference)

0.5549

a ä
Regular*

2.800
1 (Reference)

0.949
8.262
1 (Reference) 1 (Reference)

0.0622

p-value

3.900
1.236
12.303
0.0202
e i/ie
Regular* 1 (Reference) 1 (Reference) 1 (Reference)
*To obtain odds ratios for Negative value, take the reciprocal of the odds ratios of the Positive
value
Assumptions of the Chi-Square and Logistic Regression
The use of a Chi-square test involves four assumptions: (1) the sample is random; (2) the
sample size is reasonable; (3) adequate expected cell count (larger than 5); and (4) the
observations are independent (Field, 2009). Concerning the first assumption, one could argue
that the outlier sample was not truly random as these observations shared the outlier behavior of
long fixation times. This is a minor limitation that needs to be acknowledged. My inspection
frequencies of outliers revealed that observations from 74.5% of all participants fell into the
outlier pool, and these participants contributed fairly evenly with the exception of participant 18
(11.9%) and participant 24 (10.7%). All verbs were present in the outlier pool except for the
verb ‘‘studieren’’ (= to study). Based on these inspections it was safe to assume that the first
assumption was met. The second assumption was met by outlier sample-size (n = 84).

91

The third assumption was also met; the analysis indicated no cells with expected
frequencies less than 5. The fourth assumption, the assumption of independence, was violated as
is often the case in research social science research. According to Howell (2009), ““[i]t is not
uncommon to find studies in which the assumption of independence of observations is violated,
usually by having the same participant respond more than once”” (p. 152). Yet, violating the
assumption can cause Type I errors and an overestimation of significant differences in the data
35

(Field, 2009).

Thus, the assumption of independence of data in this particular study warrants

further consideration. The unit of analysis here (i.e., fixation times) does not only originate from
the individual, but it can be thought of as co-constructed by the particular trial (out of 24), the
sentence ending (ending A or ending B) and the person used (2

nd

rd
or 3 ) in the sentence.

Therefore, even though certain individuals contributed to some of the extreme fixation times that
went into the outlier pool, they can be viewed as independent as they co-occurred with several
related factors. In fact, Saito (1999) stated: ““there is no suitable statistical procedure for
analyzing multiple frequency data. It is therefore best to admit this difficulty and accept it as a
limitation”” (p. 648).
As with Chi-square tests, one assumption of the logistic regression is that the data are
independent, which was assumed here (see explanation above). Logistic regression also works on
the assumption that the dependent variable is binary or dichotomous. This was the case in this
analysis as I only looked at either positive or negative values. I used the forced entry mode,
which means that ““all covariates are placed into the regression model on one block”” (Field, 2009,
35

It is important to note that Chi-squares do not test for interactions in the data, but it simply
compares the frequencies observed in different categories (Field, 2009). The logistic regression,
which I ran as a follow up, provided information on interactions in the data.
92

p. 226), which is an appropriate method for theory testing as opposed to finding a model that fits
my data.
First Fixation Duration
First Fixation Duration ANOVA
In this section I present the results for the ANOVA with first-fixation duration presented
in the split-plot design: two crossed fixed factors (verb-type and group) and a random subject
factor (nested within group). The effect of group was non-significant and therefore this factor
was removed from the model, yielding a two-way ANOVA with verb-type as a fixed factor and
subject (nested within group) as a random factor. Similar to my analysis with total time, the
preliminary analysis indicated lack of normality of the residuals by levels of the independent
variables verb type and group. The lack of normality of the residuals could not be fixed by (a)
removing influential outliers based on Cook’’s distance test, or by (b) removing cases whose
corresponding standardized residuals were larger than 3.5 or smaller than -3.5. Therefore,
analogous to my analysis with total time, I applied a truncation method on the dependent variable
by including observation only within the range of -270 to 270 milliseconds that make up about
93.3% of the original data. Truncation insured normality of residuals across the levels of the
independent variables and left me with 942 valid cases for the analysis. The 90 outlier-cases
were analyzed separately with a Chi-square test and a logistic regression in a manner similar to
the analyses presented in the previous section.
Because there was no main effect of group, I conducted a customized, nested ANOVA
with the trimmed data set to model the main effects of verb type on first fixation duration with
the random factor group and subject (nested within group). Similar to the modeling with total
time gain, there was a significant effect of group and the nested subject, F (39, 896) = 1.742, p =
93

0.004,

2
ηp

= 0.070, which accounted for moderate variation in first fixation duration, as

indicated by the effect size of 0.070. This means that 7.0% of between-subject variance
regarding the first fixation duration was accounted for by this interaction. Contra to my
expectations, verb type was not significantly associated with first fixation duration, F (3,896) =

0.730, p = 0.534,

2
ηp

=0.002.

Nested ANOVA Assumptions
The assumptions for the nested ANOVA with first fixation duration as the dependent
variable were the same as the ones I described for total time. I closely examined the assumption
of homogeneity of residuals variances and the assumption of normality of the residuals.
Regarding the latter, the formal test of normality (Shapiro-Wilk) indicated that first fixation
duration was normally distributed among the categories of the independent variables (α = 0.05).
Similar to my analysis with total time, the Levene’’s test of homogeneity of variances
indicated lack of constant variance across the levels of independent variables (F (171, 770) =
1.569 with p < 0.001). However, this finding should not influence the results as explained in
section x. Using a Multivariate ANOVA for these data was appropriate, as judged by the F test

of goodness of fit: F (126, 770) = 1.071, p = 0.294 and

2
ηp

= 0.149. The latter indicates that

14.9% of the variation in first fixation duration was accounted for by this model which is
considered to be large (Field, 2009).
Analysis of Outliers
94

I analyzed the omitted observations via a Chi-square test of independence (see analysis
with total time outliers for detailed information regarding the Chi-square test as well as the
relevant assumptions for its conduction). I conducted the first Chi-square test of independence (α
= .05) with irregular versus regular verbs (verbcoding2) and positive first fixation durations
(noticing) versus negative first fixation durations (no noticing). Negative values indicated much
longer fixations on the baseline condition compared to the critical condition, which can
tentatively be interpreted as no noticing), and the positive values indicated longer fixations on
the critical condition compared to the baseline condition, which can tentatively be interpreted as
noticing). The hypotheses in consideration for the Chi-square are as follows:
H0: The verb types (regular versus irregular) and first fixation time on the critical verb are
independent.
This means that the outlier values for a given verb type (regular versus irregular) are evenly
distributed among positive values (noticing) and negative values (no noticing).
H1: The verb types (regular versus irregular) and first fixation time on the critical verb are
dependent.
This means that the outlier values for a given verb type (regular versus irregular) are not evenly
distributed among positive values (noticing) and negative values (no noticing).
The results of the Chi-square test indicated that first fixation duration (Fposneg) was not

χ 2 = 2.195, df = 1, p = 0.138
significantly associated with verb-type (irregular versus regular),
OR = 0.531 with 95% C.I. (0.229, 1.231). Although the finding was not statistically significant,
Figure 13 shows that the occurrence of outliers (either positive or negative) does vary as a
95

function of verb-type. Figure 14 shows that 57.1% of the outlier cases revealed extremely long
fist fixation times on irregular verbs, whereas long first fixation times on regular verbs were less
common (42.9%). Recall that the same pattern transpired with total time gain, but the results of
total time gain were statistically significant. Yet, although the Chi-square test results of the first
fixation duration were not statistically significant, I infer that the outlier observations
demonstrate some degree of noticing (i.e., longer fixations) of irregular verbs as opposed to
regular verbs. The trend transparent with the negative values for first fixation time (i.e., no
noticing) supports my inference. No noticing occurred in 58.5% with regular verbs, and 41.5%
with irregular verbs. The Chi-square test with total time gain did not reveal a difference between
regular and irregular verbs when it came to no noticing (i.e., negative total time gain).

96

Figure 13. Verb Type and First Fixation (Regular versus Irregular)
As a next step, I investigated whether there was a significant pattern when further
separating the irregular verbs by the stem-vowel change. I conducted the same analysis with
verbcoding3 (1= irregular e

i/ie, 2=irregular a

ä, and 3=regular) and positive first fixation

duration versus negative first fixation duration). The results of the Chi-square test revealed that
First Fixation Duration Time (Fposneg) was not significantly associated with verb-coding3,

97

χ2

= 2.2230, df = 2, p = 0.328, Cramer’’s V = 0.157. The results are visualized in Figure

36

14.

Figure 14. Verb Type and First Fixation (by Stem-change)

36

The fact that the bars of regular verbs are higher is expected. There are both regular verb
groups combined together, so it is 57.2%, which is a greater count than the irregular verbs. As
such, this graph might be misleading unless the bar of the regular verb was reduced by half.
98

Figure 15 shows that negative first fixation duration (which can be interpreted as no
noticing) is most common with regular verbs, and about equally common with the two types of
vowel changing verbs. I investigated this phenomenon further by looking at the odds ratio using
a logistic regression analysis. The logistic regression yielded which verb types (i.e., e

i/ie; a

ä; or regular) predicted whether a learner fixated longer on the critical sentence or not.
Table 5 is best interpreted when looking at the odds ratios. The logistic regression
revealed that extreme fixations on verbs with an a

ä change are 1.778 times more likely to

have a positive value (reveal noticing) compared to fixation times on regular verbs with 95% C.I.
(0.640, 4.939),

χ 2 =1.2179, df = 1, p = 0.2698. Extreme fixations on verbs with e

i/ie are

2.0 times more likely to have a positive value compared to regular verb with 95% C.I. (0.702,
5.702),

χ 2 = 1.6814, df = 1, p = 0.1947.

Although the findings were not statistically

significant, they exhibit trends that are notably similar to the findings for total time gain.
Table 5: Logistic Regression with First Fixation Duration
Fixation Time

Confidence Interval
Lower
Upper

Odds ratios

1.125
0.337
1 (Reference) 1 (Reference)

3.760
1 (Reference)

0.8483

a ä
Regular

1.778
0.640
1 (Reference) 1 (Reference)

4.939
1 (Reference)

0.2698

e i/ie
Regular

Positive
Value* (=
longer fixation
on baseline
item than on
critical)

Predictor
Variable
e i/ie
a ä

p-value

2.000
0.702
1 (Reference) 1 (Reference)

5.702
1 (Reference)

0.1947

*To obtain odds ratios for negative value, take the reciprocal of the odds ratios of the positive
value.
Gaze Duration.
99

Gaze Duration ANOVA
As a last analysis to address RQ1, I conducted a nested ANOVA with the fixed factor
verb type crossed with the random factor subject/group (subject nested within group) to model
the main effects of verb type on gaze duration (the dependent variable). Recall that gaze duration
is the sum of all fixations made in an interest area before the eyes leave that area. As with total
time and first fixation duration, the preliminary analysis indicated lack of normality of the
residuals by levels of the independent variables verb type and group. The lack of normality of the
residuals could not be fixed by a) removing influential outliers based on Cook’’s distance test, or
by b) removing cases whose corresponding standardized residuals were larger than 3.5 or smaller
than -3.5. Therefore, analogous to the previous ANOVAs, I applied the truncation method on the
dependent variable by including observation only within the range of -650 milliseconds and 600
milliseconds, which make up about 93.5% of the original data. Truncation insured normality of
residuals across the levels of the independent variables. After the removal of influential outliers
(78 cases) I ran the nested ANOVA with 954 observations.
Similar to the models for total time and fist fixation duration, there was a significant

2
η p = 0.093, which
effect of group and the nested subject, F (39, 908) = 2.395, p < 0.001,

2
ηp
accounted for moderate variation in gaze duration, as indicated by the effect size

of 0.093.

Thus, 9.3% of between-subject variance in gaze duration was accounted for by individual
differences in reading speed among the readers. More important, however, verb type had no

2
η p = 0.001. This means
significant effect on the gaze duration’’ F (3,908) = 0.176, p = 0.913,
100

that the extra processing time readers allocate d to the processing of the critical sentences did not
differ as a function of verb type.
Assumptions
The formal tests of normality (Kolmogorov-Smirnov and Shapiro-Wilk) indicated that
the gaze duration was normally distributed among the categories of the independent variables (α
= 0.001).
The Levene’’s Test of homogeneity of variances indicated a lack of constant variance
across the levels of independent variables (F (171, 782) = 1.275 with p = 0.017). This finding
should not influence the results, as explained above. Using a Multivariate ANOVA for these
data was appropriate as judged by the F test of goodness of fit: F (126, 782) = 0.988, p = 0.522

and

η

2
p

= 0.137. The effect size indicates that 13.7% of the variation in gaze duration was

accounted for by this model.
Analysis of Outliers
Analogous to my outlier-analyses with total time and first fixation duration, I conducted a
Chi-square test of independence with irregular versus regular verbs and positive values of gaze
duration (noticing) versus negative values of gaze duration (no noticing). The hypotheses tested
for the Chi-square are as follows:
H0: The verb types (regular versus irregular) and gaze duration on the critical verb are
independent.
This means that the outlier values for a given verb type (regular versus irregular) are evenly
distributed among positive values (noticing) and negative values (no noticing).
101

H1: The verb types (regular versus irregular) and gaze duration on the critical verb are
dependent.
This means that the outlier values for a given verb type (regular versus irregular) are not evenly
distributed among positive values (noticing) and negative values (no noticing).
The results of the Chi-square test indicated that Gaze duration was not statistically
significantly associated with verb type (irregular versus regular),

χ 2 =1.363, df = 1, p = 0.243,

OR=0.563 with 95% C.I. (0.213, 1.485). In other words, the regularity of verbs was not
statistically significantly associated with the distributions of extreme gaze durations between
negative values (no noticing) and positive values (noticing). Figure 15 illustrates this. These
findings are different from those with total time duration where I found a significant difference.
The bar graph, however, shows the same trend found with total time and first fixation duration
with regard to negative values. No noticing occurred with regular verbs. Looking at the positive
values, I expected more positive value counts for irregular verbs based on the trend discovered
with total time and first fixation. In contrast, the bar charts of positive fixation times are
approximately the same for both, regular verbs and irregular verbs. This discontinuity of the
““noticing trend”” found with total time and fist fixation duration might be due to the fact that the
outlier pool wasn’’t exactly the same for each dependent variable. That is, for gaze duration
truncation left me with 79 outliers, with total time I identified 84 outliers, and with first fixation I
identified 90 outliers. Beyond that, it is important to keep in mind that the graphic
representations include 5% chance for error (confidence interval of 95%).

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Figure 15. Verb Type and Gaze Duration (Regular versus Irregular)
As a next step for the gaze duration outlier analysis, I investigated whether there was a
pattern when further separating the irregular verbs by the type of stem-vowel change. I
conducted the same analysis with verbcoding3 (1= irregular e

i/ie, 2=irregular a

ä, and

3=regular) and the values for positive gaze duration, and negative gaze duration. The results of
the Chi-square test revealed that gaze duration was not significantly associated with the type of

103

χ2
stem-change,

= 4.351, df = 2, p = 0.114, Cramer’’s V = 0.236. The results are presented in

Figure 16.

Figure 16. Verb Type and Gaze Duration (by Stem-change)
Figure 16 shows that extreme gaze duration values on both endsnegative and
positiveare most common with regular verbs, less common with a vowel change from a

104

ä

and least likely to occur with verbs than change the vowel from e

i/ie. I investigated this

phenomenon further by looking at the odds ratios using a logistic regression.
Table 6 is best interpreted when looking at the odds ratio. Gaze durations on verbs with a
ä vowel changes are 1.083 times more likely to have a positive value compared to gaze

durations on regular verbs with 95% C.I. (0.374, 3.138),
durations on verbs with e

χ 2 = 0.0218, df = 1, p = 0.8827. Gaze

i/ie vowel changes are 7.333 times more likely to have a positive

value compared to verbs with an e

i/ie change with 95% confidence interval (0.870, 61.840),

χ 2 =3.3542, df = 1, p = 0.0670. This finding is approaching significance. The findings are in
line with the outlier findings of total time and first fixation, although a stronger effect of a

ä

changes was expected. The results of this analysis are summarized in Table 6.
Table 6: Logistic Regression with Gaze Duration
Fixation Time
Positive
Value*
(=longer gaze
duration on
critical item
than on
baseline)

Predictor
Variable
e i/ie
a->ä

Odds ratios

Confidence Interval
Lower
Upper

6.769
1 (Reference)

0.729
1 (Reference)

62.859
1 (Reference)

0.0926

a ä
Regular

1.083
1 (Reference)

0.374
1 (Reference)

3.138
1 (Reference)

0.8827

e i/ie
Regular

7.333
1 (Reference)

0.870
1 (Reference)

61.845
1 (Reference)

0.0670

p-value

*To obtain odds ratios for Negative value, take the reciprocal of the odds ratios of the Positive
value.
Summary of Findings for Research Question 1.
Looking at the big picture of the results, the irregularity of the verbs appears to have an
effect on fixation times. Even though this finding was not statistically significant in every
105

analysis, there was a trend that learners fixated longer on verbs where the stem-vowel changed.
The nested-ANOVA with total time revealed a significant effect of verb type, and the post-hoc
test revealed that there was a significant difference of total time between a

ä vowel changes

and regular verbs (longer fixations were made on verbs with vowel changes). There also was a
notable difference of total time between e

i/ie vowel changes and regular verbs, even though

this difference was not statistically significant. My analysis of the outlier observations (cases
with very long fixation times) revealed a similar trend: Total time was significantly associated
with verb type (regular versus irregular verbs). When separating the irregular verbs by type of
vowel-change, I found that verbs with an e

i/ie change were fixated significantly longer than

regular verbs. Learners also fixated longer on a

ä changes relative to regular verbs, although

this difference was not statistically significant.
Although my analyses (regular data and outliers) with first fixation duration did not yield
statistically significant results, the findings exhibited a trend that was notably similar to my
findings for total time. Similarly, my findings with gaze durations (regular data and outliers)
were not significant, but my analysis of outliers showed that verbs with an e

i/ie change were

fixated longer than regular verbs, and this finding approached statistical significance with
moderate and occasionally large effect sizes.
Results Addressing RQ2
While the first research question asked whether beginning learners pay attention to verb
irregularities during reading, the second research question inquired whether increased attention
to irregular verbs is associated with subsequent uptake (i.e., a learning gain on post-tests) of
those irregularities (i.e., stem-vowel changes). It is important to note at the outset that I
addressed RQ2 by means of a correlation analysis. As is well known, correlation does not
106

indicate causation, and thus I will interpret any significant results as indicating a statistically
significant association rather than a causal relationship between attention and learning. Thus, I
use the Spearman (two-tailed) correlation analysis to address RQ2. It is also important to
mention at the outset, that all analyses consider the experimental trails (i.e., trials with irregular
verbs) only, as the participants were not tested on their improvements of regular verbs, which do
not contain stem changes. Figure 17 below illustrates the design of this study for clarity
purposes. The shaded part of the design is relevant for this correlation analysis.

Figure 17. Design of the Study
Based on the ‘‘eye-mind assumption’’ (Chapters 2 and 3), I expected to find a positive
correlation between eye-fixation duration (total time) and the learning gains as measured by the
difference from the pretest too posttest. Since the ANOVA models generally revealed that the
subject variable accounted for a significant variation in fixation time (i.e., participants differed
significantly in reading speed), I first ensured that the total time gain values that I used in the
correlation analysis were adjusted for the individual’’s overall reading speed by all participants.
In particular, I aimed to control for the possibility of an interaction between total time gain and
107

overall reading speed (whereby slower readers would generally show larger total time gains than
faster readers) by adjusting for participants’’ overall reading speed.
To this aim, I used to approaches: The first one was a manual calculation of a total time
gain value that was corrected, or better yet, adjusted for the differences on reading speed. To
accomplish this adjustment, I first computed the average reading time per participant. That is, for
each individual, I calculated the summed total time of all reading trials (only the sentence slides)
and I divided it by 24 (each session contained 24 trials or sentences-slides). This computation left
me with a mean reading time per trial for each participant. Next, I computed a grand mean,
which was the sum of all participants’’ mean reading time per trial divided by the number of all
included participants included (n = 43). For each participant, I then divided his or her average
reading speed by the grand sample mean sample value. This outcome provided me with the value
by which to correct each participant's total time gain value (for each verb). The adjusted total
time gains for each verb (= 1,032 observations/cases in total) were the values I used to run the
Spearman rank correlation with the learning gains.
Another way of controlling for reading speed is by running a semi-partial correlation.
According to Field (2009), when running a partial correlation between two variables, the effect
of a third variable on both variables is controlled for. When running a semi partial correlation
(which can only be done by means of a regression analysis), the effect that a third variable (e.g.,
overall reading speed) has on only one of the variables in the correlation (e.g., total time gain) is
controlled. My preliminary analysis showed a significant relation between reading speed and

108

37

holistic gain

(r = .162, p = 000), but no significant relationship between reading speed and

gains on stem changes (r = .035; p = .125). Regarding the latter, in a strict sense, a semi partial
correlation (regression analysis) would be more appropriate. However, I conducted partial
correlation analyses with both types of learning for a) ease of interpretation and b) because a
regression analysis would bring several other issues (satisfying assumptions, etc.) which would
not bring a greater value to the overall results I aim to obtain. Figures 19 and 20 below present
the concepts of a partial and semi partial correlation. I opted do conduct the partial correlation
(controlling for reading speed) to investigate the relationship between total time spent on the
irregular verb only (not total time gain) and holistic learning and the relationship between stem
change learning and total time gain. I present the findings after presenting the findings for the
analyses with total time gain adjusted (i.e., the other option of controlling for reading speed).

Figure 18. Illustration of Partial Correlation (controlling for reading speed)

37

Recall from Chapter 4 that I scored the pre-and posttest in two different ways: I scored them
(1) holistically, giving points for improvement on spelling and verb inflection, and (2) with
regard to the vowel-stem change only.
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Figure 19. Illustration of a Semi Partial Correlation (Controlling for Reading Speed)
I used the Spearman’’s correlation coefficient, r, which is a non-parametric statistic and
therefore the recommended test of correlation when the data have violated assumptions such as
38

non-normally distributed data.

The histogram of fixation time gain sowed a positive kurtosis

(a high peak in the middle) because the data had many values around zero which are indicative
of similar total times for the baseline item and the critical item. An alpha level of .05 was set for
all correlations.
Correlation of Fixation Times with Learning Gains

39

Overall, total time gain adjusted was not significantly correlated with stem-change
learning gains r = .039, p = .566. There was also no relationship between verb-type (a

ä vs. e

i/ie) and learning gains, which means that the learners’’ improvement was not related to the
type of stem-change on which they were tested. Because adjusted total time gain corresponds to

38

I also ran the Pearson’’s correlation (parametric) and Kendall’’s tau (non-parametric) and
received nearly identical results. For accuracy purposes, however, I am reporting the results of
Spearman rank correlation.
39
Recall from Chapter 4 that I am only including adjusted total time gains for the irregular
(stem-changing) verbs, not for the regular verbs. The pre-and posttest did not include the entire
regular verb set, but only a subset, which functioned as distracters.
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the difference between the baseline and the critical verb and is reflective of a participant’’s
increased attention to irregularities, this difference might not be relevant in regard to holistic
learning. Therefore, in order to investigate the relationship between fixation time and holistic
learning, I ran a correlation analysis with the total time for the critical item (du/er/sie form) and
holistic learning. The total time spent on the verb with the vowel change was significantly related
to holistic learning gains, r = .087, p = .048. This relationship was relatively weak, which means
that we need to be cautious when generalizing this finding to the overall population.
Partial Correlation of Fixation Times with Learning Gains
The partial correlation, where the effect of reading speed was controlled, revealed that
there was no relationship between the total time spent looking at the irregular verb and holistic
40

learning: r = -.056, p = .104.

There also was no relationship between the learning gains

regarding stem-changes and total time, r = -.002, p = .484.
I was then interested to see whether positive fixation times (longer fixation times on the
critical verb, which had a stem-change, than on the baseline verb, which had no stem-change)
were correlated with learning gains. In re-ran the Spearman correlation twice with negative
values excluded list-wise. I correlated positive fixation times (using the adjusted values) with the
learning of stem-changes, and I correlated positive fixation times with overall (holistic) learning
gains. The positive fixation times (i.e., noticing) were not correlated with the learning of the
stem-changes, r = .092, p = .131. However, the positive fixation times were positively correlated
with holistic learning gains, r = .187, p = .002. This means that there was a relationship between
longer fixation times on stem-changing verbs (relative to the baseline condition) and overall
40

There was a significant relationship between the two variables when I did not control for
reading time, which corroborates the effect of overall reading speed on fixation times on the
verbs.
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gains on written production of all verbs, but not on the learning of the stem-changes alone. The
relationship was not very strong, which means that 18% of students learning in all probability is
related in art (i.e., 18%) to focusing on and attending to them.
Due to the significant finding with positive fixation times, I also investigated whether
there might be a relationship between negative fixation values (i.e., longer fixation on the
baseline condition) and the test scores. In other words, I correlated negative fixation times with
learning gains on the stem changes, and I also correlated negative fixation times with overall
(holistic) learning gains. There was no significant relationship between negative fixation values
and learning gains on stem changes, r = -.055, p = .395. There was, however, a significant
negative correlation between negative fixation values and holistic learning gains, r = -.185, p =
.013, demonstrating that longer fixations on baseline verbs (as opposed to the critical verbs) was
41

related to better holistic learning.

This means that there was a relationship between longer

fixation times on baseline verbs (relative to the stem-changing verb condition) and overall gains
on the written production of irregular verbs, but not on the learning of the stem-changing verbs
alone.
This finding logically suggests the possibility of a quadratic association between adjusted
total time gain and holistic learning gains.42 For example, a participant who has a total time gain
of, for example, -10 milliseconds (i.e., fixating both, the baseline and the critical verb, about
equally long) is less likely to demonstrate learning gains on the posttest than a participant of has
a total time gain of, for example, -500 milliseconds (which is indicative for longer focus on the
baseline verb with no vowel change). In other words, there might be a parabolic relationship
41

A negative correlation coefficient indicates that as one variable increases, the other decreases,
and vice-versa.
42
A quadratic relation is represented graphically by means of a parabola.
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between the adjusted total time gain and holistic learning gains. To check whether this
assumption is correct I conducted a correlation analysis between the squared value of adjusted
total time gain (x^2) and holistic learning gains. There was a significant correlation between the
squared adjusted total time gain and holistic learning gains, r = .125, p = .004. Although the
relationship is not very strong, it indicates a strong likelihood of a linear relationship between
total time gain squared and holistic learning gains. In other words, it indicates a quadratic
association between total time gain and holistic learning gains. Even if the participants
processed the ich-form longer than the du/er/sie form (vowel change), the participants improved
in the posttest overall. Consequently, longer fixations on either form, the non-changing ich-form
or the vowel-changing du/er/sie form, was associated with holistic learning gains. Figure 20
below represents (in a simplistic manner) the quadric relationship between the adjusted total time
gain and the test scores.

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Figure 20. Simplistic Depiction of the Parabolic Relationship
My visual inspection of the testing data showed that the pre-and posttest scores for each
verb varied to some extent. That is, some irregular verbs were ““learned”” to a greater extent than
others, and the initial knowledge of the correct spelling of the verbs differed (see Figures 21 and
22).

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Figure 21. Verb and Mean Scores Stem-change

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Figure 22. Verb and Mean Scores Holistic
I expected that the participants’’ initial knowledge of stem-changes equaled zero, but very
few participants used the correct stem-vowel on the pre-test. It remains unknown whether this
was due to chance or whether the students had read ahead in their textbook. Based on these
visual inspections, I was interested whether there were significant correlations between fixation
times and learning when looking at the different verbs individually. Two verbs showed
statistically significant positive correlations between fixation times and learning gains: (1)
Fixations on ‘‘sehen’’ (to see) were positively correlated with holistic learning gains, r = .322, p =
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.036, and (2) fixations on ‘‘fahren’’ (to drive) were positively correlated with stem-change gains, r
= .472, p = .001. The remaining 10 irregular verbs did not reveal significant correlations between
their fixation times and learning gains. This finding will be discussed in the next chapter.
Summary of Findings for RQ 2
The correlations between fixation times and ““learning gains”” (measured by improvements
from the pre- to the posttest) revealed a significant relationship between positive fixation times
(i.e. longer fixation times on stem-changing verb forms than on the corresponding baseline
forms) and overall gains on the written production of those verbs. Similarly, there was a
significant negative correlation between negative fixation values and holistic learning gains.
These results gave rise to the assumption of a possible parabolic relationship, which was
confirmed. This means that longer fixation times on the ich-form verb (compared to the
du/er/sie-form verb), or longer fixation times on the du/er/sie-form verb (compared to the ichform verb) were associated with overall learning gains. This means that it was not relevant which
of the two verbs was fixated longer, but that one verb was processed for a longer time.
When looking at the verbs separately (selecting them list-wise from the data pool) and
when considering both negative and positive fixation times (total time gain adjusted) in a single
analysis, the verbs ““sehen”” and ““fahren”” stood out in that fixation times on those verbs correlated
with learning gains.

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CHAPTER 6 DISCUSSION
This dissertation was guided by two RQs:
(1) Do adult beginning learners of German who are unfamiliar with stem-changing verbs pay
attention to those irregularities during reading?
(2) Is increased attention to irregular verbs associated with subsequent learning of those verbs?
In discussing the findings pertaining to RQ1, I consider the following two outcomes that
emerged from the data in this study: (a) why, based on analyses conducted with data from the
majority of the learners,

43

I found evidence for attention to irregular verbs in regards to total

time, but not with gaze duration or first fixation duration; and (b) why there were ““outlier cases””
that exhibited extreme fixation times, and what differentiates an ““outlier case”” from an average
case. I then turn to RQ2 and discuss the following: (a) the lack of a linear association between
fixation time (i.e., total time gain) and learning; (b) the issue of non-saliency; (c) item learning
versus system learning, (d) the role of the developmental level of the learner; (e) the parabolic
relationship; and (e) why the learners in this study reacted differently when reading and when
being tested on the two verbs 'sehen' and 'fahren', compared to the other verbs.
Research Question One
As established in Chapters 2 and 3, based on the ““eye-mind assumption”” (e.g., Godfroid,
2010; Just & Carpenter, 1980; Rayner, 2009), I assumed that there is a close link between eye
position and attention during reading (Rayner, 2007). Recall that for RQ1, I predicted
participants would fixate longer on and make more regressions to stem-changing verbs, whose

43

When using the term ““majority of learners”” I am referring to the trimmed data set (i.e., the
overall data minus the outliers).
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irregular stem-vowels would constitute a novelty to them, than to regular verbs, which have no
stem-vowel changes.
Attention to Irregular Verbs
When considering the summation of all the fixations a learner makes on a word (i.e., total time),
the irregularity of the verb appeared to have effect. To give an example, when a learner fixated
on a verb for a longer time (possibly due to multiple fixations on the verb, some of them being
44

regressions), the verb was significantly more likely to be an irregular verb than a regular verb.

Moreover, when examining which type of irregular verb learners fixated on longer (either the e
i/ie type or the a

ä type), I found that the latter verb type garnered longer fixation durations

compared to non-vowel changing, regular verbs. Learners also tended to fixate longer on verbs
with an e

i/ie change, as opposed to regular verbs, but this difference did not reach statistical

significance. These joint findings lead me to assume that the learners attended to the vowel
differences in the irregular verbs. At the minimum, it might be safe to state that, overall, the
45

learners’’ eyes dwelled

significantly longer on verbs that had a vowel change, which was a new

form for them. In L1 contexts, ““[h]ow long readers fixate on a word is related to the ease or
difficulty with processing that word”” (Rayner, 1998, p. 186). It might also be the case that some
word incongruity, for example, a change in the syllable nucleus, affects the processing of that
word. The longer fixations and re-fixations on the irregular verbs are likely to be accompanied
by more processing activity, given the assumption that there is a close link between the eyes and
44

Since both conditions, regular and irregular/vowel changing consisted of a baseline verb (ichform) and a critical verb (du/er/sie-form) in both conditions, the irregular condition and the
regular condition), it is safe to use the term ““longer”” fixations. We can say that learners fixated
longer on the irregular, critical verbs than regular verb forms, as both instances were compared
to a corresponding baseline condition.
45
Recall that ‘‘dwell time’’ is the actual term for ‘‘total time’’.
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the mind, or that the learner’’s mind was the impetus guiding the eyes (Clifton, Staub, & Rayner,
2007; Just & Carpenter, 1980; Pollatsek & Rayner, 2009; Rayner, 2009; Rayner, Pollatsek, et al.,
2003). Yet, do these longer fixations indicate whether the learners actually noticed the vowelstem change? My conceptualization of noticing (see Chapter 1, pp. 33-34) is that during reading,
noticing involves focal attention to surface structures and lexical items that might create a
46

novelty for the learner.

I assumed that awareness is an involved process, even if only

peripherally. Based on this premise, I maintain that the irregularity attracted learners’’ focal
attention (i.e., noticing) even if peripherally and with——possibly——some level of awareness
involved. I will address the question of whether the learners ““noticed”” again as I take into
consideration the learning gains as well.
Based on the findings with total time, I expected to find a similar pattern with first
fixation and gaze duration, as the majority of eye-movement studies evidenced the same pattern
with all three types of fixation time measures (e.g., Calvo & Meseguer, 2002; Clifton et al.,
2007; Frazier & Rayner, 1982; Godfroid, 2010; McConkie, Kerr, & Dyre, 1994; Reichle,
Rayner, & Pollatsek, 1999; Williams & Morris, 2004). Contrary to my expectations, however,
these fixation time measures did not indicate that irregular verbs were fixated on longer than
regular verbs. This means that when the learners’’ eyes fixated on the verb during first pass (the
first time the eyes went over the verb), there was no difference depending on whether the verb
included a stem-vowel change or not. Irregularity did not have an effect on either first fixation
or gaze duration. Since the measure of total time also includes the instances when the learners
re-fixate on (i.e., go back or make a regression to) a verb, these regressions and subsequent
46

Recall also that I did not operationalize noticing at the level of understanding.
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readings appear to be triggered by the irregularities. Another possibility is that learners re-read
the verb because of contextual cues the remaining part of the sentence may have contained.
Based on these results alone I can only make tentative claims as to why learners made
regressions and therefore viewed the verb for a higher total amount of time. One possibility is
that they took time to process the vowel-change, a feature the learners had not encountered
before. Future analyses need to also look at second pass times and/or regressions separately, an
issue I will address again in the section on Future Research.
“Outlier Cases” with Extreme Fixation Times
Recall that the preliminary analysis of my data revealed outliers, which were cases of
extremely long fixation times (see Chapter 5). That is, considering the entire pool of
47

observations, there were cases

in which learners fixated much longer on certain verbs than

normally observed (about 2,000 milliseconds in total compared to an average of 200-300
milliseconds in total). These cases were, accordingly, outlier cases in that they differed
markedly from the average observations in this study; they created ““noise”” in the quantitative
statistical methods of data analysis. Dörnyei (2009) argued that ““’’Noise’’ is important”” (p. 107)
because it reflects the Dynamic Systems Theory described by Larsen-Freeman and Cameron
(2008), which attemps to account for the complexity of how humans use, learn, and teach
languages. ““[W]hat has been considered as ‘‘noise’’ in quantitative studies does matter and should
not be eliminated through the quantitative focus on the central tendency at the group level””
(Dörnyei, 2009, p. 107, highligts are mine).

47

Specifically, 75.5% of all learners displayed some outlier behavior, plus the long fixations
occurred with every verb except for ‘‘studieren’’ (to study).
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In fact, the ““noise”” in my data (i.e., outlier cases) showed a very interesting pattern: I
found that the extremely long fixations (as measured by total time) occurred with irregular verbs
(rather than regular verbs) more than 77% of the time (37.1% with e
40.0% of the time with a

i/ie vowel changes, and

vowel changes). The follow-up logistic regression analysis

confirmed this trend. I take this to mean that these learners noticed the irregularity of the verbs,
especially given the length of processing time and the eye-mind assumption. This supposition
could be interpreted as supporting the traditional Noticing Hypothesis: Learners attended to the
novel linguistic form in the input, the items were registered in working memory, and then
became candidates for further processing and, eventually, long-term storage (i.e., learning). Note
that the data in this study only lend support to the Noticing Hypothesis (the data in this study
appear to fit, if you will, the hypothesis), but the data do not (and cannot) unequivocally prove
the hypothesis is true. Also, it needs to be acknowledged that outlier (noticing) cases were rare.
In fact, this ““noticing phenomenon”” occurred in only in 84 out of 1,032 overall cases (i.e., in 8%
of all observations), which means that such processing behavior constitutes an exception rather
than the rule. At the same time, it is remarkable that 75% of all participants produced a long
fixation on some verb that fell into the outlier pool. This means that these ““outlier”” data do not
stem from a small subset of participants only. In fact, some noticing of some verb occurred with
more than three quarters of all participants.
One possible explanation for this finding is that if learners attend to some structure, they
keep it in their working-memory, which is the place where a myriad of cognitive processes take
place. According to N. Ellis (2005), working memory is ““home of explicit deduction, hypothesis
formation, analogical reasoning, prioritization, control, and decision making. It is where we

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develop, apply, and hone our metalinguistc insights into an L2. Working memory is the system
that concentrates across time, controlling attention in the face of distraction, [……]”” (p. 337).
The controlling of attentional resources in working memory is the process that Gass
(1997) coined ‘‘apperception.’’ According to this notion, apperceived words or structures are
attended to, and possibly recognized, due to one or both of the following two things: (a) the
learner’’s prior knowledge or experience, and/or (b) something salient in the word (i.e., a
peculiarity that does not match the current representation in the learner’’s mind). In this study, I
collected data from extreme beginners of German who had not yet been introduced to stemvowel-changing verbs. Thus, if apperception is occurring with these learners, it must be because
they notice that the stem-vowel-changing verbs are different——they are unexpected. However,
what learners have already established in their minds and what they expect is, of course, variable.
Yet these data do appear to demonstrate that apperception is occurring in these learners.
Alternatively, the outlier cases (as well as the total time results of all observations) might
reflect classic examples of learners engaging in attention as selection (Robinson, 2003), which
influences the perception and the selection of stimuli that enter working memory (e.g., Carroll,
1999; Gass, 1997; Leow, 1993; Tomlin & Villa, 1994). Thus, selection as attention is a very
important function of attention in general. According to Robinson (2003), attention as selection
is likely to involve awareness,48 but it is also quite possible that selective attention occurs
without the learners being aware that they are selecting information from the stimulus array or
that they are further processing of one piece of information over the other. ““Selection of
detected input happens [……], and attention is required for it to happen, but it need not (but very

48

Yet, awareness is not required for attention as selection to take place.
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often does) implicate awareness”” (Robinson, 2003, p. 636). As such, there is no clear
implication as to whether the viewing behavior of the outliers, which I tend to interpret as
““selective attention,”” involves awareness. This research did not employ any awareness
measures, so future research studies may benefit greatly from employing a multi-method
procedure that yields both data pertaining to the learners’’ (focal) attention as well as data on their
awareness, or lack thereof (Godfroid, 2010).
Research Question Two
Regarding RQ2, I hypothesized that the eye-fixation durations would not be predictive of
learning gains related to stem changes because the learning of verb morphology such as stemvowel changes is known to be difficult for learners with a language background that has limited
morphology, such as English. I predicted, however, that learners would improve their
production of verbs overall due to the exposure they had had during the treatment phase. It is
important to recall at the outset that the analyses that were conducted to address RQ2 included
the irregular verbs only.
No Association between Fixation Time and Learning of Irregular Verb Morphology
There was no association between the learners’’ total time spent processing the ich-form
and the du/er/sie-form and their learning gains. Given the fact that learners did fixate longer on
the irregular verbs compared to the regular verbs (the main finding for RQ1), it’’s possible to
assume that these increased fixation times would be reflected in the learners’’ performance on the
posttest. Yet, no relationship was found. I speculate that there are three, possibly
complimentary, ways to interpret this finding.
First, the limited saliency and redundancy of the target structure might not have lent itself
to being noticed. Or taking this assumption a step further, the target structure may not have been
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conducive for further processing following noticing——on average only noticing at the level of
apperception occurred.
Second, the low developmental level of the learners (i.e., beginning learners) may have
restricted their capacities to attend to the morphologic structure of stem-vowel changes. Below I
discuss the findings in regard to these three premises in more detail.
And third, the irregular vowel change is a rule-based structure. That is, the stem vowel of
nd

a verb will change in the 2

and 3

rd

person if it is a strong verb. On connectionist accounts,

knowledge of a rule-based structure emerges gradually (e.g., N. Ellis, 2002; Dörnyei, 2009), so it
is likely that the participants did not become aware of (albeit they displayed focused attention to)
the possible existence of an underlying ““rule”” for vowel changes.
Non-saliency of Irregular Verb Morphology
As N. Ellis and Sagarra (2010) suggest, ““early adult learning is characterized by a general
tendency to focus on lexical cues because of their psychological salience resulting from their
simplicity of form-function mapping and from learners’’ prior first language knowledge”” (p. 85,
highlights are mine). At the early stages of acquisition, the (lack of) saliency of morphological
cues affects the learners’’ attentional processes in that they attend to lexical cues rather than
morphological cues (Bell, 2006; Kintsch, 2005; Tsui & Fullilove, 1998; J. Williams, 2001;
2005). Beginning learners usually have processing difficulties in their L2, so they depend more
on contextual cues found elsewhere in the sentence or derived from other sources, such as
pictures, the situational context, or their background knowledge. As explained in Chapter 4, the
experiment in this dissertation provided pictures that contextualized the sentences presented on
the eye-tracker screens; that is, the pictures demonstrated the action verb that the participants
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encountered in the sentences that followed. It is possible that some learners in this study might
have relied on the pictures as a contextual cue——more so than linguistic cues only——when
processing the meaning of the sentences, which may, in turn, have diminished their top-down
processing of verb morphology and associated attentional foci.
Furthermore, while advanced learners and native speakers are able to perceive differences
as in ‘‘lauft vs. läuft’’, ‘‘fahrt vs. fährt’’, or ‘‘seht vs. sieht’’) both visually and aurally, beginning
learners of L2 German may not be. Put more carefully, beginning learner of L2 German (L1
English) are likely to be able to perceive phonemic contrasts between ‘‘lauft vs. läuft,’’ but they
may not do so under incidental processing conditions. That is, they may not attend to the
relevant cues in the input. The learners’’ decreased attention or failure to attend to the relevant
morphological feature (i.e., a changed-stem vowel) might be exacerbated by the fact that these
cues are redundant as the information in number and person is provided by an overt sentence
subject plus the inflectional ending of the verb. Contrary to pro-drop Japanese and Spanish, for
example, German is not a pro-drop language. In other words, the inflectional ending of verbs in
German competes with lexical cues provided by the head/subject noun of the sentence. Beyond
that, the vowel-stem change also competes with the inflectional ending of the verb, which also
carries the information regarding number and gender. From the perspective of a beginning
learner of L2 German, whether the sentence reads ‘‘Er läuft einen Marathon’’ or ‘‘*Er lauft einen
Marathon’’ (He runs a marathon) may not create any meaning difference for the learner. As the
beginning learners in this study had very limited knowledge about morphosyntactical structures
in German, they likely attended to the most salient and reliable cues to comprehend the sentence.
In fact, there is strong empirical evidence that (a) low salience, (b) low reliability, and (c)
redundancy of morphological cues affect their learnability (DeKeyser, 2009; N. Ellis, 2006a,
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2006b; 2011; N. Ellis & Sagarra, 2010; Goldschneider & DeKeyser, 2001; Jiang, 2007; Park,
2007; Tokowicz & Warren, 2010; Williams & Lovatt, 2003). Regarding the third factor,
redundancy of morphological information, DeKeyser (2005) explained that it is the lack of
transparency of the form-meaning relationship that poses the challenge to the learner, especially
when learners are ““left to their own resources instead of presented with a reasonably complete
set of rules about form-meaning relationships”” (p. 3), as it was the case in this study. What
49

DeKeyser calls ““resources”” are the L1 cue mappings

that the learners have stored in their

minds and which are changed and adapted only gradually through extensive L2 exposure
(Bordag & Pechmann, 2007; N. Ellis & Sagarra, 2010; Parodi, Schwartz, & Clahsen, 2004).
““Learners’’ sensitivity to verbal morphology will therefore depend on the degree to which their
L1 makes extensive use of it”” (Ellis & Sagarra, 2010, p. 88).
Lee (1998) investigated how native speakers of English process Spanish verb morphology
(subjunctive form) when reading. His research question was ““Are comprehension and input
processing affected by the morphological characteristics of the input?”” (p. 37). Lee’’s
experimental groups (A and B) read texts in which for Group A verb morphology was
substituted with infinitives [le ensenar los modelos tiltimos (to show you the latest models)] and
for Group B verb morphology was substituted with a nonsense morpheme; that is, the
appropriate subjunctive endings were replaced with -a -u. [le ensenu los modelos uiltimos (show
you the latest models)]. The control group read the same text with normal (unaltered) Spanish
verb morphology [le ensefie los modelos tiltimo (he shows you the latest models)]. Learners

49

In some cases, perhaps, also some L2 cue mappings from another foreign language the
participants have learned in high-school, for example L2 Spanish.
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50

comprehension of a passage was measured by a recall task,

and word identification task to

determine how many words from the passage the learners could identify after reading it. Lee’’s
hypothesis was that ““if learners noticed, detected, or otherwise cognitively registered a form,
then they would identify it in the recognition task”” (p. 38). Contrary to his expectations, the
results showed that the learners who were exposed to the correct subjunctive version recalled
significantly less than those who read the infinitive or invented -u versions. Similarly, the
comprehension scores of the passage with the correct subjunctive form were significantly lower
than the comprehension scores of the passages that had infinitives and nonsense forms. Lee
concluded that ““[w]hen naturally attending to meaning, learners comprehend better when the
forms in the input are less complex than when they are more complex”” (p. 82). In addition, the
learners’’ native language, English (which has limited morphology), might have affected their
processing, or rather, non-processing of verb endings. Based on these findings, Lee argued that:
The subjunctive forms contain more linguistically encoded information than the other
forms. VanPatten (1990) estabished that directing learners' attention to forms in the input
detrimentally affected comprehension. On their own, without orienting directions, all
participants detected the forms in the input. It may be that because the subjunctive forms
were more varied and were linguistically richer, noticing and detecting them required more
processing capacity than noticing and detecting the infinitives and invented forms. Such a
draw on capacity would indeed affect comprehension (Hulstijn, 1989; VanPatten, 1990).
(p. 41).
The learners in this dissertation study, mostly native speakers of English, might not have
been inclined to attend to changes in verb morphology in the present tense, which was then
reflected in their limited learning gains. It is likely that the learners paid attention to the verb

50

Specifically, the learners were asked to recall in English as much as they could of what they
just read. The learners were encouraged to recall not only main ideas but details, too (Lee, 1998,
p. 38).
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51

forms as a whole,

but they failed to notice the relevant dimension of the input (i.e., the vowel

change). In other words, it is likely that the learners were aware of the verb forms at a global
level, but they failed to attend to the relevant dimensions of the input. The lack of attention to
pertinent features may also be due to the rule-based nature of the target structure, which I will
discuss in more detail in the following section.
Furthermore, the results of this study point to the theoretical existence of noticing
thresholds (Sharwood-Smith &Truscott , in press). Whether focal attention leads to deeper
processing that facilitates learning depends on the level of activation of awareness, which, in
turn, appears to be dependent on the target structure (whether the feature carry important,
52

meaningful information, whether they are based on a simple or rather abstract rule).

As this

study did not measure the learners’’ awareness, stronger conclusions cannot be drawn. Yet, given
that prior research supported that learners demonstrating higher levels of awareness learn more
than learners with lower levels of awareness (e.g., Bell & Collins, 2009; Broderick, 2005; Leow
et al., 2008; Leow, 1997, 2000), I assume that learners who demonstrated some learning gains on
the posttests were likely to have become more aware of the relevant dimensions of the input
(verb forms) as their focal attention dwelled on the verb. Again, the lack of an awareness
measure precludes drawing stronger, persuasive conclusions. Nevertheless, these data appear to
suggest that, generally, adult learners of L2 German are unlikely to incidentally acquire correct

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Recall that the interest area defined on the eye-tracker screen consisted of the whole verb, not
just the critical vowel.
52
Recall that the cognitive science literature suggests that attention and awareness belong to
differential cognitive circuits.
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53

verb morphology in regards to stem-vowel changes

when reading. In fact, Ellis and Sagarra

(2010) argued that adults need to learn how to pay attention to cues that are not part of their L1 if
they are to succeed in L2 acquisition. They suggested learners’’ difficulty in attending to
morphological cues can be overcome by ““pedagogical interventions that make these cues nonredundant and more salient”” (p. 102).
Based on my data, I surmise that global attention to a language form is not sufficient for
learning, even if that language form is an exemplar of a particular target structure. While one
could argue that the learners should have some understanding of what dimension in the input to
pay attention to, it is also quite possible that the learners devoted much of their attention to the
basic facts of German verb morphology (i.e., the verb-endings, which they had just learned). I
will elaborate on this possibility in more detail below.
The majority of the learners demonstrated limited attention and limited learning in most
cases despite the input flood and the fact that the learners were exposed the two contrasting (i.e.,
contrasting in their stem-vowel) verb forms via sentence pairs. There were a few exceptions, or
outliers, however, which deviated from the general trend.
Another possible explanation of the finding is that the learners may have experienced the
effects of their attentional constraints. Usually, learners (or people in general) have a limited
capacity available for processing (e.g., Izumi, 2003; McLaughlin et al., 1983; McLaughlin &
Heredia, 1996; see also references in the introduction of my dissertation), so the new L2
structures (i.e., the irregular stem-vowel changes) might have cognitively overloaded the learner,
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Stem-vowel changes are a fairly infrequent, non-salient and redundant type of verb
morphology. Thus, it would not be justified to generalize this conclusion to other types of
German verb morphology (e.g., inflectional endings), which are more frequent and more reliable
in their form-meaning mapping.
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who had only just been taught the regular simple present. This, in turn, may have negatively
impacted L2 gain. If the developmental level of the learners had been more advanced, they
might not have experienced this type of cognitive overload.
The Developmental Level of the Learner and Attention
The premise that working memory limitations restrict learners’’ attention to multiple
features in the input has been well-established in the cognitive science literature (e.g., Baddeley,
2003; Cowan, 2005; Hasegawa, Carpenter, & Just, 2002; Just & Carpenter, 1992) and in the field
of SLA (e.g., N. Ellis & Sinclair, 1996; Elman, 1993; Gass, Svetics, & Lemelin, 2003; Goo,
2010; Sagarra, 2007; van den Noort, Bosch, & Hugdahl, 2006). Is the efficacy of attention and
working memory related to the developmental level of the learner? Ortega (2009) suggests that
““as L2 proficiency develops the lag in working memory capacity between the L2 and L1 should
become smaller.”” At the same time Ortega acknowledges that ““less is known empirically about
this widely held assumption”” (p. 91). Several SLA researchers (B. McLaughlin & Heredia,
1996; Philp, 2003; Pienemann, 1984, 1989) have suggested that advanced, or more experienced
learners may benefit from the increasing automaticity that comes with repeated practice, which
then frees up attentional resources that can be allocated to other task dimensions. A learner’’s
developmental level is then associated with their ““readiness”” to process a given aspect of the
input successfully (e.g., Spada and Lightbrown, 1999).
Farrar (1990), in a study with children, found that recasts are most effective when the
children’’s mental state is ““ready”” to acquire morphemes. Looking at L2 learners, there might
also be a ““prime time”” in which learners are ready to attend to verb morphology. Mackey (1999)
and Mackey and Philp (1998), for example, found that question formations are learned in a
predictable sequence that follows a certain pattern of their interlanguage development. It is
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possible that learners are unlikely to attend to features in the input that are beyond their current
54

interlanguage level.

Rule-based versus Item-based Learning
Robinson (1996) and N. Ellis (2002) hypothesized that noticing leads to some memory
representation of the noticed form and that different types of L2 forms may be represented
differently in the brain; that is, associatively (the learning of a system based on associations) or
symbolically (the simple memory of instances or items). Associative learning means that at the
beginning of the learning process, the learner stores instances or exemplars (i.e., ““items””) he or
she encountered in the input. As the learner processes the items more frequently, their brains
become sensitive to the properties of the input, and they ““learn”” from them. N. Ellis (2006)
explains this more specifically: Over time, the brain engages in a tally of the overall frequency of
each form and the likelihood of co-occurrence with other forms. This brain activity is assumed
to be an implicit process of which the learners are unaware. In this dissertation study, when the
learners read the forms ‘‘er wäscht’’, ‘‘er läuft’’, ‘‘du fährst’’, or ‘‘du siehst’’, ‘‘er nimmt’’, and ‘‘du
gibst’’ they might have taken a mental note of——or even temporarily stored—the instances of
stem-vowel changes. Yet, the low frequency of occurrences during the reading activity in this
study most likely did not suffice for the learners’’ mind to extract information about the
properties and the frequencies in order to generate a rule based on these stored or noted
instances. Similarly, Shanks and St. John (2005) argued that when participants demonstrate
learning but do not demonstrate awareness at the level of understanding of some underlying rule,
this is ““because their knowledge consists of instances or fragments of the training stimuli rather
54

It is important to note, however, that Robinson (2003, 2009), a pioneer of research on
attention in SLA, rejects the entire notion of developmental readiness because it does not explain
anything.
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than rules”” (p. 367). In regard to this study, I assumed that rule-based learning (i.e., the actual
explicit understanding of a an underlying rule) would not occur with the learners given that the
experiment-treatment phase created the single exposure for the learners. Recall from Chapter 1
and 2 that I conceptualized noticing a process where attention and awareness (two distinct
processes in the mind) come together (initially in working memory), whereby the attention paid
to a new linguistic structure exceeds a threshold so that this ““novelty”” is held in working memory
for long enough to increase the (initially low) level of awareness and to gain access to short-term
memory. If this noticing process (attention and awareness) occurs with sufficient frequency, the
linguistic item will eventually build a representation/become a stored exemplar in long-term
memory. I did not conceptualize or operationalized noticing at the level of understanding.
Although I did not employ a measure of awareness, I conducted this study under the premise that
the type of learning (i.e., item-based learning versus rule-based learning), which could be drawn
for the pre-and posttest results, may provide insight into the level of awareness engaged during
input processing. If the learners demonstrated correct production of the stem-vowel changes in
irregular verbs, they may have become aware of and may have learned the system of stemchanging verbs (i.e., system-based learning). However, I assumed that if I find evidence of
learning, this would be a reflection of item-based learning; that is learning without rules. Ortega
(2009) explains learning without rules as follows:
““Learning without rules leads to the formation of memories of instances that can be
accessed more easily allowing for faster performance, but without knowledge that can be
generalized to new instances. That is, without the initial provision of rules (without an
explicit learning condition), learning is bottom-up (i.e., data and memory driven), and it
does not lead to knowledge of a systematic rule of some kind”” (p. 102)
In this study, the learners’’ fixation times on a given exemplar of the vowel change might
be an indicator of attention and temporal storage of this exemplar, but the fixation time measure
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coupled with the post-test measure yields little information on the actual learning that is, whether
the learners were able to generate the vowel-change ““rule”” based on these stored exemplars, and
whether they were able to generalize across items. Future studies will have to probe into rulebased versus item-based learning, an issue I address in the section on Limitations and Directions
for Future Research. The rule-like nature of the target structure may also have some implications
the for ““validity of a retrospective awareness measure (such as stimulated recall), because later
gained awareness may be projected on earlier (unaware) cases of processing. I also address the
implementation of an ““awareness-measure”” in the section on Limitations and Directions for
Future Research.
Parabolic Relationship
Recall that in this data a quadratic association between total time gain and holistic
learning gains (i.e., learning gains that were related to the overall production of the verb, not only
to the stem-changes) was observed. Consequently, longer fixations on either form, the nonchanging ich-form or the vowel-changing du/er/sie-form, predicted learning well. This finding
suggests that the learners did attend to the sentence verbs in general, just not sufficiently to the
stem-vowel changes, possibly because of their lack of salience, absence from the L1 as a relevant
cue, and because of the learners’’ low level of automatization in general. When a learner fixated
longer on a verb form, regardless of whether it was the ich-form or the du/er/sie-form, his or her
comparatively longer fixation was associated with an overall improvement in producing these
verbs. This corroborates the eye-mind assumption.
Based on the premise that there is a close link between overt attention (i.e., the eyelocation) and covert attention, that is, a close connection between what one looks at and what one
focuses on mentally, learner’’s eye-fixations most likely coincided with their focused attention.
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The question then is not necessarily which verb form they focused on, but that the learners
showed extra processing activity (i.e., longer fixation times). Learners may not have attended to
the stem-vowel change specifically, but to the verb in its entirety and, perhaps, to the inflectional
ending of the verb. A closer look at the German 101 syllabus for the Fall semester of 2010
coupled with an examination of the learners’’ textbook revealed that the learners had learned the
verb inflections one to one and a half weeks before the data collection started. They had
practiced the correct inflectional ending of the verbs in several drills as well as in their
homework activities. Thus, the learners may have paid attention to the very feature on which
they have received prior explicit instruction. The holistic learning gains, in most cases, included
improvements in the production of the German verb endings. For example, for the verb sehen
(to see) the participants frequently wrote *““er sehe”” or *““er ist sehen”” on the pre-test, and *““er
seht,”” so their holistic performance improved despite the missing the stem-vowel change (the
correct form would be ““er sieht””). One possible interpretation is that the learners’’ prior
experience (via formal instruction) with German verb inflection might have induced the learners
to focus their selective attention toward the endings of the verbs, rather than on the stem. Put
differently, the participants may have failed to require the relevance of the stem vowel to their
processing task, thus, in effect, failing to attend (at the level of learning) to this stimulus
dimension. Given the learners’’ low developmental level, it is unlikely that they would have been
able to divide their attention to more than one linguistic stimulus or to more than one dimension
of the stimulus array (Eysenck & Keane, 2010).
It is important to note that Wicken’’s Proposed Structure of Processing Resources (2007)
(see Chapter 1) makes different predictions: Resource competition exists within, but not between
separate attention pools. That is, when language stimuli are processed via one modality only, as
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in reading (i.e., visually), there supposedly is no divide of attentional resources. As such the
findings of this dissertation study support Rosa and Neill’’s (1999) view: The multiple-resource
model does not specify where and how attention differentiates between inputs from two language
modalities and what happens if one modality requires more attentional resources than others.
Last, it is also important to note that the relationships observed in this study, while
significant, were not very strong, so caution is required when generalizing these particular
findings to larger populations. Nevertheless, although the relationships found were generally
small (e.g., r = .13), they are still informative. If learners and language instructors realize that
the beginning stages of the acquisition of verb-morphology are, in part (here, for example, a 13%
part) related to attending to these structures, they might enhance their instructional techniques
and teach their students to pay attention to morphemes in the language.
The Verbs Sehen and Fahren
While no linear relationship was found between the fixation times on the verbs and the
learning gains for those verbs, the verbs sehen (to see) and fahren (to drive) differed from the
overall picture: (1) Fixations on sehen were positively correlated with holistic learning gains for
sehen, and (2) fixations on fahren were positively correlated with stem-change gains for fahren.
There is no ready explanation to address this finding, but it is possible that the learners’’ prior
exposure to the verbs impacted their processing. Sehen and fahren had been covered at several
occasions by the German textbook up to the point of data collection, plus these verbs are also
55

used in the subsequent textbook chapter

to explain the phenomenon of vowel-stem changes in

irregular verbs. Since sehen and fahren are used as example verbs to explain the vowel change,
55

The subsequent text book chapter had not been covered by the time the data collection was
completed
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there might be something prototypical about them. The comparatively high frequency of these
verbs (sehen = 115,440 per 100 million tokens, and fahren = 35,278 per 100 million tokens,
while the average frequency of all verbs was 28,722) might be related to the fact that these verbs
stood out in regard to the linear relationship between fixation time and learning, which was not
found with the other verbs.
Limitations and Directions for Future Research
While the results from this dissertation research make an important contribution to the
field of SLA in informing SLA theory and pedagogy, there are some limitations to this research
that need to be acknowledged. In the first part of this section, I address the limitations associated
with (a) learner-internal factors, (b) the stimuli, (c) the interpretation of the results, and (d) the
design of the study. I will conclude this section by suggesting an agenda for future research
based on the questions that arise from this study.
Limitations
As explained above, noticing by the learners might have been affected by the previous
classroom instruction learners have received (e.g., Doughty & Williams, 1998; Norris & Ortega,
2000; Rosa & Leow, 2004; J. White, 1998), learners’’ processing resources and individual
differences in the amount of resources available (Dörnyei, 2005, 2009; Grigorenko, Sternberg, &
Ehrman, 2000; Miyake & Friedman, 1998; Robinson, 2002; Sawyer & Ranta, 2001; Skehan,
1998), and learners’’ (developmental) readiness to pay attention to morphological details of the
(German) language (Adams, 2003; Philp, 2003; J. Williams, 2001). While controlling for all of
these learner-internal factors is perhaps beyond the scope of this dissertation research, future
research should take these into consideration as much as possible.

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In addition to the learner-internal factors, the eye-tracking data obtained in this study
might be influenced by interfering stimuli variables; or as Godfroid et al. (2010a) phrased it:
““The reader’’s viewing behavior is determined by a complex interplay of factors”” (p. 177). For
example, the frequency and saliency of the input might have influenced participants’’ attentional
foci (Bardovi-Harlig, 1987; Carroll, 2006c; DeKeyser, 2005), and the participants’’ familiarity
with the words used in the stimuli might have influenced their eye-movements (i.e., focal
attention). In addition to familiarity, as explained in Chapter 3, the participants’’ eye-movement
might have been influenced by word length, predictability, and/or frequency (Kliegl et al., 2004;
Kliegl et al., 2006). While the counterbalancing conditions implemented in this study controlled
for these influencing factors, they might not have been eliminated entirely.
Regarding the predictability of the words or sentences, some learners might be able to
predict the content of the sentences. Recall that the stimuli were developed with help of the
German textbook used in class. Consequently, some learners might have been familiar with some
of the content of the sentences. However, at the same time, greater semantic accessibility might
enable the learners to focus more on the morphological elements targeted in this study (N. Ellis
& Schmidt, 1998).
A point worthy of special attention is the inconsistency of the fixation time findings for
the fixation time measures: total time, first fixation, and gaze duration. In particular, the results
for gaze duration did not coherently follow the patterns found with the dependent variables of
total time and first fixation duration. Recall that with total time, I found an effect of verb type
with both the regular, trimmed data set as well as the outliers. With first fixation duration my
ANOVA did not evidence any statistical differences with the regular data set, but the analyses of
outliers showed the same trend as for total time. Since gaze duration is also an ‘‘early’’ reading
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measure, I expected the results for gaze duration to mirror those found for first fixation duration.
Yet, my findings for gaze duration were not significant overall. The differing results of gaze
duration might be rooted in the design: the baseline ich-sentence was always presented first,
followed by the critical du/er/sie-sentence. Thus, the learners might have anticipated the subject
and verb of the second sentence, which is likely to have impacted their processing and eyemovements. Reichle, Rayner and Pollatsek's (1999) E-Z Reader model of Eye-movement Control
might explain the different findings of first fixation duration and gaze duration. The model
proposes that readers process a word at two levels: First, they (a) make a preliminary familiarity
check where they encode orthographic and phonological information and check whether lexical
access is immanent, and they (b) encode the word's meaning (lexical access). With this model in
mind, consider that gaze duration differs from first fixation in that the former includes all
fixations in an interest area during first pass reading (not only the first fixation) while first
fixation, as its name suggests, reflects only the first fixation on a word (so the value in
milliseconds is usually somewhat smaller). With native readers, first fixation and gaze duration
usually reveal similar results, as the two levels of processing happen (almost) simultaneously.
With beginning learners, however, the encoding of a word's meaning (level 2) as well as the
preliminary familiarity check (level 1) might take a millisecond longer as they have just learned,
to some extent, the verbs (vocabulary) one to three weeks earlier. Thus, the learners' gaze on the
verb might have lasted slightly longer when they read the first sentence as opposed to the second
du/er/sie-sentence where (according to the E-Z Reader Model) the processing on the second
level (lexical access) was accelerated. Explained in a different way, gaze duration did not
evidence an effect of verb regularity as learners had already retrieved the meaning of the verb
(i.e. lexical access had already been completed) by the time they read the second verb. Plus, a
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familiarity check (level 1) might have been speeded considerably due to learners’’ prediction. The
accelerated access might have been minimal but detectable when considering at all 1,032
observations/cases.
On the whole, the E-Z Reader Model is only one way of attempting to explain the
difference between gaze duration and first fixation duration findings, and how the difference
might be rooted in the design (i.e., that the baseline ich-sentence was always presented first).
This drawback notwithstanding, the absence of an effect of verb regularity on gaze duration,
combined with the effects for first fixation duration and total time, suggests that part of the
processing that distinguishes the noticing of irregular verbs from the ordinary reading of regular
verbs took place during second pass reading. Future analyses should consider second pass time
and/or regression measures. Likewise, future investigations of the noticing construct would be
well advised to include some later eye-movement measures such as second pass time and the
occurrence of regressions in their analyses. In addition, future research is needed because the
results stemming from these data do not suggest definitive conclusions on beginning language
learners processing during reading.
Another limitation related to the design of the study lies in its inability to measure
learning gains over time. A delayed posttest could have shown how the learning process unfolds
over time. However, I chose not to add a delayed posttest to my design for several reasons.
Firstly, with a delayed posttest, I would not have been able to discriminate between learning
caused by the treatment and learners’’ attentional processing of the treatment and learning
associated with the immediate posttest. My sense of caution is much supported by Hulstijn
(2003), who wrote:

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In evaluations of learning experiments one must bear in mind two considerations: (i) with
an immediate post-test, the researcher is able to measure the effect of cognitive processes
during the learning session - nothing more, nothing less; [……] [Research] aimed at
addressing questions concerning the effect of cognitive processing during a learning
session in which words are presented for the first time, requires only an immediate posttest. Inclusion of delayed post-tests in such research would not make sense, because it
would not be possible to differentiate the extent to which performance on delayed posttests is affected by processing during the experimental learning session or by processes
(if any) after that session. (p. 372, insertions are mine).
A last point to consider is the extent to which the research itself might affect participants’’
behavior. How learners behave in the laboratory may differ from how they behave in everyday
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life, which is likely to affect ecological validity.

Eysneck and Keane (2010), for example,

explain ““in the real worlds, people are constantly behaving so as to have an impact on the
environment [……]. Thus, the responses that people make typically change the stimuli situation.
[……] That is to say, the sequence of stimuli the experimenter presents to the participant is not
influenced by participants behavior, but rather is determined by the experimenter’’s plan”” (p. 4).
That is, learners’’ participation in the study, especially the pre-tests, may contribute to raising
their awareness to formal aspects of the German language. Consequently, the number of
noticing events may have been higher because of the sheer fact that this was a laboratory study,
thus introducing a potential limit to external and ecological validity of my findings.
““The participants focus of attention in most research is determined by the experimenter’’s
instructions. As a result, relatively little is known of the factors normally influencing the
focus of attention, relevance of stimuli to current goals, unexpectedness of stimuli,
threateningness of stimuli, intensity of stimuli, and so on. This is an important limitation
because we could not predict someone’’s cognitive processes and behavior in most
situations without detailed knowledge of the factors determining attentional focus.
(Eysenck & Keane, 2005, p. 4)

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Ecological validity refers to the extent to which the findings are applicable to everyday life.
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This note of caution notwithstanding, my instructions to the learners were very open and nonconstraining as to what participants had to pay attention to or what the goal of the experiment
was. In addition, this note of caution applies to any study that is psycholinguistic or
experimental in nature, this potential drawback is just usually not addressed.
The last limitation of this study concerns the use of a correlation analysis to address
RQ2——whether increased attention to irregular verbs is associated with subsequent learning of
those irregularities (i.e., stem-vowel changes). While the correlation analysis in this study
revealed whether there was a systematic relationship between the fixation time measure (i.e.,
total time gain) and learning (i.e., improvement on the posttest), it was unable to reveal whether
the fixation durations on the irregular verbs would predict improvement of the production of
irregular verbs. Future analyses of this relationship should be based on more sophisticated
statistical techniques, such as a GEE (Generalized Estimated Equation) analysis (Fitzmaurice,
Laird & Ware, 2004; Hardin & Hibe, 2003). A GEE can be used to predict the improvement of
the production of the irregular verbs based on the fixation times, after accounting for the
prediction of each verb individually. Furthermore, a GEE could also reveal the probability of
improvements on posttest. Godfroid (2010), for example, employed a GEE to analyze her data
and found that ““for every additional 1000 milliseconds that a participant looks at a word, the
probability that she will recognize it on the post-test increases by 8%”” (p. 172). RQ2 in this
study could have been addressed with a more detailed analysis of the data. This would have
provided a more comprehensive and intricate picture of the relationship between processing time
and learning.
Suggestions for Future Research

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This dissertation study paves the path for a challenging, yet fascinating, research agenda.
While this dissertation study provides insight into the learners’’ attentional foci on verb
morphology during reading, it does not allow for inferences about the level of awareness learners
experienced at the time they attended to verb forms in the sentences they read. The design of
this study also does not provide information of the type of learning learners might engage in (i.e.,
item-based versus system-based learning), so future experimental eye-tracking studies should
look at different grammar structures and how these might be attended to, processed, and learned
differently. Future studies should investigate whether learners are able to generalize the target
structure to new items, and how the ability to generalize is dependent on the nature of the
grammar structure. It would also be important to explore how different forms determine the
learners’’ awareness of these forms. With a target structure that has a fairly straightforward
underlying rule, the measure of awareness should happen concurrently (i.e., via think-alouds).
Regarding grammar structures that are rather abstract, it might be interesting to trace the
learners’’ awareness of such structures over time, as the level of awareness is likely to increase
with frequent exposure to the relevant form. Qualitative investigations of learner output or
stimulated recall might be appropriate research methodologies to investigate such issues.
This dissertation research opens up the crucial question as to whether the learners were
aware of the stem-changes. More specifically, it remains unknown whether the learners were
nd
rd
aware of the differences in the stem vowel between the 2 and the 3 person singular and the

ich-form or the infinitive, or whether the learners were aware of the relevance of the vowel in the
stem without being able to explain this irregularity. While, according Schmidt (1990, 1994a,
1995, 2001), attention and awareness are isomorphic, that is, two parts of the same coin or two
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concepts that are part of the noticing construct, several scholars do not support the premise that
awareness coexists with attention (e.g., Lamme, 2003). In order to tease apart the construct of
attention and awareness and to better understand the roles these cognitive mechanisms assume in
noticing, qualitative awareness measures that complement quantitative attention measures are
needed (Godfroid, 2010). In her dissertation conclusion, Godfroid (2010) suggested ways in
which attention and awareness can be measured in one research study: ““our understanding of
noticing would benefit tremendously from triangulating the eye-tracking data with some
qualitative awareness measure, such as retrospective verbal reports [……] or stimulated recall”” (p.
214). To reiterate this quote, future research should implement a carefully thought-out, mixed
methods or multi-method design that enables the researcher to distinguish between the constructs
of attention and awareness as separate components of noticing. Such an approach would
incorporate quantitative (e.g., eye-fixation counts) and qualitative (e.g., introspective) measures.
Because of the reactivity issue (Chapter 2), the possibility of using qualitative, introspective
measures on awareness to complement attention data (gathered via eye-movement measures)
warrants some elaboration. With introspective methods (e.g., stimulated recall or retrospective
reports) the data come from participants' own statements, that is, the data reflect participants’’
subjective reports about the way they perceive and understand information. As such,
introspective methods are frequently used as supplements for data gathered based on learners'
behavior (Bowles, 2010).
Simulated recall methodology is used to explore learners’’ thought processes (or
strategies) at the time of an activity or task. This is achieved by asking learners to report those
thoughts after they have completed a task or activity. In regard to an eye-tracking experiment,
where attention is measured via eye fixations on words, the stimuli that the participant has seen
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during the eye-tracking experiment could serve as the stimulus for stimulated recall.
Alternatively, a video showing the eye-movements of the relevant learner while he or she was
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reading could also serve as a strong and fine-grained stimulus for recall,

however, one

important concern is that the use of a video of the eye-movements during reading might bias the
recall of the thought processes the learners had during the treatment task, resulting in prompted
rather than stimulated recall. Whether this is the case of not is yet to be explored. A future study
should investigate the effects of using such a fine-grained stimulus (eye-movement videos) as a
support for recall.
If the eye-movement videos could be shown not to affect the validity of stimulated recall
methodology, this technique would have important advantages. The appeal of using such a finegrained stimulus relies on the eye-mind assumption (e.g., Godfroid, 2010; Just & Carpenter,
1980; Reichle, Pollatsek & Rayner, 2006). As explained above, eye fixations during reading can
be taken as a reliable index of the learner’’s focus of attention at a given time: There is a close
link between what learners look at during reading and what they are actually attending to at that
moment. On this basis, the use of the eye-movement videos as a support for stimulated recall
should truly stimulate the learner to recall their thought processes. In fact the psycholinguistic
eye-tracking literature cited in this dissertation is built on the assumption that the eye-tracking
data is a window to the mind, and that cognitive processes did take place during reading. These
arguments notwithstanding, there is a small possibility that eye-movements can be guided by
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Although video clips of learners’’ eye-movements of learners have not been implemented as a
stimulus for recall to date, traditional stimuli for retrospective recall have also consisted of some
auditory or visual recording of learners performing a given task. Examples of recall prompts
include a video of a learner writing an essay or underlining words while reading a text. The eyemovement videos would likewise be a rendering of a learner carrying out a given task (i.e.
reading) –– nothing more, nothing less. What differs is the amount of detail that is provided by an
eye-movement video stimulus as compared to, say, a video of someone writing an essay.
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58

automatic (i.e. unconscious) processes.

So, a risk is that by showing learners videos of their

eye-movements may make increase their awareness of some feature in the input. Whether this
might be the case t has yet to be explored. There is a need for future mixed methods research
that evaluates the stimulated recall protocols received from two stimuli: (1) eye-movement
videos, and (2) the eye-tracking stimulus without the eye-movement showing. If a difference in
learners’’ responses is evidenced, careful decisions need to be made as to which stimulus to use
for future eye-tracking research that also aims to implement a measure of awareness.
Another possible methodological pitfall with regard to the joint use of eye-tracking and
stimulated recall lies in the implementation of a posttest. If the stimulated recall session follows
the post-test, learners’’ responses are likely to be influenced by their testing experience given its
overt focus on the target structure. Learners’’ might report on what they believe the researcher
wants to hear rather than what they were truly thinking or were aware of at the moment when
they were performing the original language task. If the post-test was implemented after the
stimulated recall session, the post-test would also measure the learning that happened due to the
stimulated recall session. Thus, employing a stimulated recall session and a post-test in
succession is less than ideal because of potential design flaws. Godfroid (2010) suggested a
mixed within- and between subjects design for investigating the relative contributions of
attention and awareness to learning: Half the learners take the post-test and the other half
participate in the stimulated recall session. With such a design, the stimulated recall data (i.e.,
measure of awareness) could be triangulated with the eye-movement data (measure of attention),
and the post-test data (measure of learning) could also be related to the attention measure. The
58

Some aspects of eye-movements (e.g., planning, executing saccades, or making regressions)
are unconscious. However, the eye-movement records are full with information
146

administration of the stimulated recall sessions and the post-tests would not interact because the
data from each would be collected from different individuals. However, they could be linked via
a common frame of reference: the attention data.
In conclusion, I am citing Robinson (2008) who stressed the necessity for
methodologically solidified research to gain a deeper understanding of the roles of attention and
awareness:
““Future research will likely adopt increasingly sensitive measures of the contents of
awareness, and explore new methodologies for operationalizing these.
Neurophysiological measures of physical changes in brain states will also be used
increasingly to complement the behavioral and introspective methods for studying the
relationship of attention and awareness to learning.””
(p. 142)

147

CHAPTER 7 CONCLUSION
This dissertation examined what it means to attend to and ““notice”” a specific linguistic
form, a fundamental issue that has captivated many researchers over the past two decades. This
study showed that L2 learners of German do attend to novel linguistic stimuli. The amount of
attention and the accompanying level of awareness are very likely to be influenced by the
learners’’ L1, their prior knowledge, their attentional capacity, their developmental level, and the
grammar targeted. This overall conclusion is much in line with what Schmidt, in 2001, stated:
[A]ttention is a crucial concept for SLA. The allocation of attention is the pivotal point at
which learner-internal factors (including aptitude, motivation, current L2 knowledge, and
processing ability) and learner-external factors (including the complexity and
distributional characteristics of input, discoursal and interactional context, instructional
treatment, and task characteristics) come together (p. 12).
One unique finding in this study was the identification of noticing outliers; that is,
instances in which learners attend to new or unfamiliar features in the input, which I take to
reflect more extensive, deeper processing and, possibly learning. Generally, however, this study
did not evidence an association between fixation time and learning of irregular verb
morphology. Yet, there was a relationship between longer fixation times, regardless of whether
they occurred on stem-changing verb forms or not and the acquisition of regular verb
morphology, as reflected in the overall improvement in written verb production. Building on
Robinson (1995, 2003) and Godfroid (2010), I propose that increased processing times indicate
that an item is registered and processed in working memory. This cognitive activity is
accompanied by some level of awareness. This stresses the importance of attention——be it
attention to some specific morphological or syntactic form or a vocabulary item as a whole——in
learning a foreign language as an adult.
148

Pedagogical Suggestions
This conclusion lets me turn to pedagogic concerns. Teaching goals and practices should
include didactic strategies that enhance learners’’ attention and awareness via incidental focus on
form (Loewen, 2005) or forms (Long, 1991), or by implementing consciousness-raising tasks
(Fotos, 1994; Sharwood-Smith, 1997). Beginning adult learners need to learn to pay attention
and, likewise, they need to be taught how to pay attention and notice and what to pay attention to
if they are to succeed in learning a foreign language (N. Ellis & Sagarra, 2011). Skehan (1998)
reviewed several psychometric studies in which different groups were taught a specific
grammatical feature via different methodologies: (a) explicit rule giving versus highlighting of
the target grammatical feature, (b) encouraging effective input processing versus requiring much
output practice, (c) rule oriented instruction versus meaning oriented instruction, and (d)
traditional (explicit rule giving) versus consciousness-raising treatments (p. 62-66). Based on his
review, Skehan concluded that foreign language instructors should put all efforts into developing
learning tasks that are both meaningful and which encourage attention to specific forms. Merely
having to comprehend something is not sufficient for successful language learning at the adult
age (Schmidt, 1983). Ellis (2003) has described such tasks as focused in contrast to unfocused
tasks that are designed purely for communication. He describes focused activities as (a)
structure- based production tasks, (b) comprehension tasks, and (c) consciousness-raising tasks
(p. 151). Pedagogical activities that help the learners pay attention to a certain form need to be
meaningful; for example, communicative tasks that require the use of the target form help
develop the learners’’ awareness of grammar structures (e.g., R. Ellis, 2003; Robinson, 1996).
Thus, foreign language instructors need to know of the important role that attention plays in
language acquisition: Any contact their learners make with the target language has potential to
149

trigger cognitive activity that is crucial for acquisition, and much can be done to increase the
likelihood that such cognitive processing will indeed occur.
The Contributions of This Study to SLA Theory
In addition to the implications of my findings for the development of pedagogic materials
and instructional techniques, the results of this study also make an important contribution to SLA
theory. Most importantly, this study supports the notion that learners have a selective attentional
filter. The selection of stimuli that is attended to is determined by the learners’’ L1 processing
habits, the stimuli available for processing, the learners’’ prior knowledge, and contextual cues
that may determine what stimuli pass through the selective filter. Furthermore, the findings in
this study give much prominence to the following questions: (a) How much, if any, of the
learners’’ awareness is involved when they pay attention to form? (b) What is it exactly that
learners are noticing about a language form and what triggers their noticing? There are no ready
answers to these questions. It is an important task of future SLA research to tease apart the
noticing construct to determine how attention and awareness come together in noticing
(Godfroid, 2010). My suggestion is to triangulate the eye-tracking data (measure of attention)
with measures of awareness and learning (for example, stimulated recall). While the design of
such a research study poses a methodological challenge to SLA researchers, it is a very important
task to accomplish. The field of SLA needs to work with a more nuanced and empirically-based
understanding of the noticing construct. Based on this refined knowledge, we will be able to
better link acquisitioned theory to pedagogical practice.

150

APPENDICES

151

APPENDIX A: Background Questionnaire
1. Participant Number: __________ 2. Gender: M___

F___

3. Age: _______

4. Native Language (i.e., language spoken at home):
5. Are you of German heritage? (circle one)

YES

NO

If yes, do you speak German with any family members? (Please state who: parents,
grandparents, aunts, uncles, etc.)
6. How many years have you studied German in the following environments?
Elementary/Middle School________

College_________

High School_________

Private Language School/Tutoring________

7. List ALL of the college German courses that you have previously taken or are currently
taking.
8. At what age did you start to learn German?
9. Besides German, what other languages have you studied? (Please write languages, number of
years studied, and proficiency.)
10. Have you ever been to Germany? (circle one)

YES

NO

If YES, how long did you stay there?
Did you study there? Please explain.
Did you live with German people?
11. How many hours a week do you spend using German outside of the class to……
do homework: ________

listen to German (music, films, language cassettes):_____

study for tests: ___________
speak in German:________

read for pleasure in German (books, stories, etc.):_____
write in German (journals, letters)____________

12. How would you assess your proficiency in German? (Circle one for each category.)
Listening:

Beginner

Low Intermediate

Intermediate

High Int.

Advanced

Speaking:

Beginner

Low Intermediate

Intermediate

High Int.

Advanced

Reading:

Beginner

Low Intermediate

Intermediate

High Int.

Advanced

Writing:

Beginner

Low Intermediate

Intermediate

High Int.

Advanced

152

APPENDIX B
Complete List of Irregular Verbs Used in this Study
Table 7: Verbs with Stem Changes from e

i/ie

sehen
(to see)

lesen
essen
(to read) (to eat)

geben
(to give)

Nehmen
(to take)

sprechen
(to speak)

ich

sehe

lese

esse

gebe

nehme

spreche

du

siehst

liest

isst

gibst

nimmst

sprichst

er/sie* sieht

liest

isst

gibt

nimmst

spricht

wir

sehen

lesen

essen

geben

nehmen

sprechen

ihr

seht

lest

esst

gebt

nehmt

sprecht

sie

sehen

lesen

essen

geben

nehmen

sprechen

*es (it) is also part of the third person but not included here for spacing reasons.
Table 8: Verbs with Stem Changes from a

ä

fahren
(to drive)

fangen
(to catch)

schlafen
(to sleep)

waschen
(to wash)

laufen
(to run)

Tragen
(to carry)

ich

fahre

fangen

schlafe

wasche

laufe

trage

du

fährst

fängst

schläfst

wäschst

läufst

trägst

er/sie* fährt

fängst

schläft

wäscht

läuft

trägst

wir

fahren

fangen

schlafen

waschen

laufen

tragen

ihr

fahrt

fangt

schlaft

wascht

lauft

tragt

sie

fahren

fangen

schlafen

waschen

laufen

tragen

*es (it) is also part of the third person but not included here for spacing reasons.

153

APPENDIX C.
Table 9: Sentence Stimuli by Group
Group I:
baseline
Ich sehe das Meer.
Ich spreche mit dem Lehrer.
Ich esse gern Brot.
Ich nehme ein Buch.
Ich lese die Zeitung.
Ich gebe dir den Pass.
Ich fahre mit dem Zug.
Ich wasche meine Haare.
Ich schlafe auf der Couch.
Ich laufe im Sand.
Ich trage mein Gepäck.
Ich fange die Frisbee.
Ich wandere gern allein.
Ich koche um sechs Uhr
Ich studiere gern Medizin.
Ich trinke öfters Kaffee.
Ich segle mit meinem Freund.
Ich zeige auf die Tafel
Ich singe nicht so gern.
Ich kaufe das Gemüse.
Ich lache über meine Schwester.
Ich begrüße den Professor.
Ich schreibe eine Email.
Ich gehe in die Stadt.

critical
Du siehst die Natur.
Du sprichst am Telefon.
Du isst oft Müsli.
Du nimmst das Geschenk.
Du liest ein Buch.
Du gibst mir das Geschenk.
Er fährt mit dem Auto.
Er wäscht das Fenster.
Er schläft in dem Bett.
Er läuft einen 5km Lauf.
Er trägt das Fahrrad.
Er fängt einen Fisch.
Er wandert am Nachmittag.
Sie kocht das Mittagessen.
Er studiert immer Mathematik.
Sie trinkt viel Wasser.
Sie segelt manchmal allein.
Sie zeigt auf die Uhr
Du singst ein schönes Lied
Du kaufst ein Brot.
Du lachst über den Lehrer.
Du begrüßt deine Familie.
Du schreibst an die Tafel.
Du gehst zur Universität.

154

Table 10: Sentence Stimuli by Group (a)

Group II:
baseline
Ich sehe die Natur.
Ich spreche am Telefon.
Ich esse oft Müsli
Ich nehme das Geschenk.
Ich lese ein Buch
Ich gebe dir das Geschenk
Ich fahre mit dem Auto.
Ich wasche das Fenster.
Ich schlafe in dem Bett.
Ich laufe einen 5km Lauf.
Ich trage das Fahrrad.
Ich fange einen Fisch.
Ich wandere am Nachmittag.
Ich koche das Mittagessen.
Ich studiere immer Mathematik.
Ich trinke viel Wasser.
Ich segle manchmal allein.
Ich zeige auf die Uhr.
Ich singe ein schönes Lied.
Ich kaufe ein Brot.
Ich lache über den Lehrer.
Ich begrüße meine Familie.
Ich schreibe an die Tafel.
Ich gehe zur Universität.

Critical
Du siehst das Meer.
Du sprichst mit dem Lehrer.
Du isst gern Brot.
Du nimmst ein Buch.
Du liest die Zeitung.
Du gibst mir den Pass.
Sie fährt mit dem Zug.
Sie wäscht die Haare.
Sie schläft auf der Couch.
Sie läuft im Sand.
Sie trägt das Gepäck.
Sie fängt die Frisbee.
Er wandert gern allein.
Sie kocht um sechs Uhr.
Er studiert gern Medizin.
Er trinkt öfters Kaffee.
Sie segelt mit dem Freund.
Du zeigst auf die Tafel.
Du singst nicht so gern.
Du kaufst das Gemüse.
Du lachst über deine Schwester.
Du begrüßt den Professor.
Du schreibst eine Email.
Du gehst in die Stadt.

155

Table 11: Sentence Stimuli by Group (b)

Group III: baseline
Ich sehe das Meer.
Ich spreche mit dem Lehrer.
Ich esse gern Brot.
Ich nehme ein Buch.
Ich lese die Zeitung.
Ich gebe dir den Pass.
Ich fahre mit dem Auto.
Ich wasche das Fenster.
Ich schlafe in dem Bett.
Ich laufe einen 5km Lauf.
Ich trage das Fahrrad.
Ich fange einen Fisch.
Ich wandere am Nachmittag.
Ich koche das Mittagessen.
Ich studiere immer Mathematik.
Ich trinke viel Wasser.
Ich segle manchmal allein.
Ich zeige auf die Uhr.
Ich singe nicht so gern.
Ich kaufe das Gemüse
Ich lache über meine Schwester.
Ich begrüße den Professor.
Ich schreibe eine Email.
Ich gehe in die Stadt.

critical
Er sieht die Natur.
Er spricht am Telefon.
Sie isst oft Müsli.
Er nimmt das Geschenk .
Er liest ein Buch.
Er gibt mir das Geschenk.
Du fährst mit dem Zug.
Du wäschst deine Haare.
Du schläfst auf der Couch.
Du läufst im Sand.
Du trägst das Gepäck.
Du fängst die Frisbee.
Du wanderst gern allein.
Du kochst um sechs Uhr.
Du studierst gern Medizin.
Du trinkst öfters Kaffee.
Du segelst mit deinem Freund.
Du zeigst auf die Tafel.
Sie singt ein schönes Lied
Sie kauft ein Brot.
Sie lacht über den Lehrer.
Sie begrüßt die Familie.
Sie schreibt an die Tafel.
Sie geht zur Universität.

156

Table 12: Sentence Stimuli by Group (c)

Group IV:
baseline
Ich sehe die Natur.
Ich spreche am Telefon.
Ich esse oft Müsli
Ich nehme das Geschenk.
Ich lese ein Buch.
Ich gebe dir das Geschenk.
Ich fahre mit dem Zug.
Ich wasche meine Haare.
Ich schlafe auf der Couch.
Ich laufe im Sand.
Ich trage mein Gepäck.
Ich fange die Frisbee.
Ich wandere gern allein.
Ich koche um sechs Uhr
Ich studiere gern Medizin.
Ich trinke öfters Kaffee.
Ich segle mit meinem Freund.
Ich zeige auf die Tafel.
Ich singe ein schönes Lied.
Ich kaufe ein Brot.
Ich lache über den Lehrer.
Ich begrüße meine Familie.
Ich schreibe an die Tafel.
Ich gehe zur Universität.

critical
Sie sieht das Meer.
Sie spricht mit dem Lehrer.
Er isst gern Brot.
Sie nimmt ein Buch.
Sie liest die Zeitung.
Sie gibt mir den Pass.
Du fährst mit dem Auto.
Du wäschst das Fenster .
Du schläfst in dem Bett .
Du läufst einen 5km Lauf.
Du trägst das Fahrrad.
Du fängst einen Fisch.
Du wanderst am Nachmittag.
Du kochst das Mittagessen.
Du studierst immer Mathematik.
Du trinkst viel Wasser.
Du segelst manchmal allein.
Du zeigst auf die Uhr.
Sie singt nicht so gern.
Er kauft das Gemüse.
Er lacht über seine Schwester.
Er begrüßt den Professor.
Er schreibt eine Email.
Er geht in die Stadt.

157

APPENDIX D.
Table 13: Frequency of the Verbs Used in this Study
Verb
Frequency*
essen (to eat)
6559
geben (to give)
134276
lesen (to read)
14947
nehmen (to take)
66822
sehen (to see)
115440
fahren (to drive)
35278
fangen (to catch)
6842
schlafen (to sleep)
6589
tragen (to carry)
29714
waschen (to wash)
2200
kochen (to cook)
2478
segeln (to sail)
2988
studieren (to study)
4263
trinken (to drink)
6178
wandern (to hike)
2422
zeigen (to point at sth.)
42623
begrüssen (to greet)
3398
gehen (to go/walk)
95361
kaufen (to buy)
6724
lachen (to laugh)
9586
schreiben (to write)
29985
singen (to sing)
7213
*(number of hits out of 100 million tokens)
**calculated via the DWDS (Digitales Wörterbuch der deutschen Sprache = Digital Dictionary
of the German Language)
(http://www.dwds.de/)

158

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