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“WHERE WAS I?”
A PSYCHOLINGUISTIC INVESTIGATION OF
CONVERSATIONAL INTERRUPTIONS

presented by

BENJAMIN SWETS

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degree In Psychology

 

 

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“WHERE WAS .I?”: A PSYCHOLINGUISTIC INVESTIGATION OF
CONVERSATIONAL INTERRUPTIONS

By

Benjamin Swets

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Psychology
2006

ABSTRACT
“WHERE WAS l?”: A PSYCHOLINGUISTIC INVESTIGATION OF
CONVERSATIONAL INTERRUPTIONS
By

Benjamin Swets

In three experiments, speakers were interrupted in the middle of language
production in order to try to ﬁnd out 1) whether bookkeeping processes and
representations for message level planning in language production can be
demonstrated empirically, 2) how such representations are architecturally tied in
to higher— and lower-level cognitive processes such as executive planning and
memory, and 3) the applied question of whether particular interruption types are
more or less disruptive to speakers. Experiment 1 implemented a semi-natural
dialog in which a participant was interrupted at predetermined narrative points by
a confederate participant. Interruption types were manipulated, and resumption
difﬁculties were measured in order to address the above research questions.
Experiments 2 and 3 were more tightly controlled experiments that investigated
whether similar bookkeeping processes exist for syntactic planning during
language production. The study begins the investigation of two corners of a

potentially large research space on the topic of interruptions in conversation.

For Molly.

ACKNOWLEDGMENTS

The kernel for the idea behind work presented here began as a
comprehensive examinations topic that Fernanda Ferreira, my graduate advisor,
and Erik Altmann, who served on the committee, had suggested. Together, we
developed the idea to study interruptions as Dr. Altmann had, but from the
perspective of language. I thank both of them for their insightfulness, openness,
and helpfulness during this process. Dr. Ferreira has been an inspirational
advisor throughout graduate school. I hope the present work reﬂects the kind of
ingenuity that she has displayed throughout her career. I would also like to thank
the other members of my dissertation committee, John Henderson and Joyce
Chai, for their valuable input to this work.

A number of undergraduate research assistants aided some part of this
project. Emily France (pilot interrupter) and Ritik Tawari (data coding) were
notably helpful. But most of the thanks go to Twila Starosciak, the affable
interrupter in Experiment 1. I am grateful not only for her interruptions, but also
for the hours and hours of work she put into transcribing and coding those
interruptions.

Finally, I would like to acknowledge the support of my family and friends.
My parents, Patti and Tom, provided the supportive environment necessary to
reach this point. My friends, especially Mike, Dave, Steve, and Todd, have
always provided compelling interruptions to work. But no one deserves more
thanks than Molly, who is the primary inspiration for all non-psycholinguistic

aspects of my life.

TABLE OF CONTENTS

LIST OF TABLES ................................................................................. vii
LIST OF FIGURES ............................................................................... viii
KEY TO ABBREVIATIONS ...................................................................... x
INTRODUCTION ................................................................................... 1
Interruptions in a Fictional Dialog .......................................................... 1
Deﬁning Interruptions ......................................................................... 6
Interruptions and the Question of Disruptiveness .................................... 11
Non—conversational Interruptions ..................................................... 12

Are Conversational Interruptions Disruptive? .......................................... 20
Bookkeeping Processes in Language Production ............................... 25
Overview of Experiments ................................................................... 29
EXPERIMENT 1 .................................................................................. 31
Research Questions ......................................................................... 31
Pilot Studies .................................................................................... 33
Main Study ..................................................................................... 33
Method ...................................................................................... 33

Data Analysis .............................................................................. 40
Hypotheses and Predictions ........................................................... 44
Results and Discussion ................................................................. 45
EXPERIMENT 2 .................................................................................. 59
Method ...................................................................................... 60

Data Analysis .............................................................................. 67
Hypotheses and Predictions ........................................................... 72
Results and Discussion ................................................................. 74
EXPERIMENT 3 .................................................................................. 97
Norming Study A .............................................................................. 98
Method ...................................................................................... 98
Results and Discussion ................................................................. 99
Norming Study B ............................................................................ 101
Method .................................................................................... 101
Results and Discussion ............................................................... 101
Interruption Study ........................................................................... 102
Method .................................................................................... 102

Data Analysis ............................................................................ 103
Hypotheses and Predictions ......................................................... 103
Results and Discussion ............................................................... 104
GENERAL DISCUSSION ..................................................................... 120

Implications for Research on Interruptions ........................................... 122

Temporal Issues ........................................................................ 122

A Role for Interference? ............................................................... 125
Utterance Planning and Task Goals—A Useful Comparison? .............. 129
Implications for Theories of Language Production ................................. 130
Implications for Bookkeeping ........................................................ 130
Issues of Incrementality ............................................................... 133
Dialog: ls Alignment Real? ........................................................... 135
Future Directions ............................................................................ 136
Conclusions .................................................................................. 144
APPENDICES .................................................................................... 147
REFERENCES .................................................................................. 174

vi

LIST OF TABLES

Table 1. Means and standard deviations of the four measures of resumption

difﬁculty, displayed by interruption type ............................................ 47
Table 2. Latency and accuracy data, Experiment 3, Norming Study A ............ 100
Table 3. Latency and accuracy data, Experiment 3, Norming Study B ............ 102

vii

LIST OF FIGURES
Figure 1. The interruption and resumption process, involving a primary
(interrupted) and secondary (interrupting) task. Adapted from Trafton et al,
2003 ......................................................................................... 14
Figure 2. Item examples, reading span and spatial span tasks, Experiment 1....38

Figure 3. Primary (RL1) and secondary (RL2) resumption lags as function of
interruption type. Error bars are standard errors ................................. 47

Figure 4. Average number of repeated words as a function of interruption type. 50

Figure 5. Average proportion of resumptions containing disﬂuencies (uh or

um) ........................................................................................... 51
Figure 6. Average interruption length by type .............................................. 53
Figure 7. Number of repeated words as a function of clip number and interruption

type .......................................................................................... 55
Figure 8. Example Object Array, Experiment 1. .......................................... 62
Figure 9. Proportion of correctly answered interruption tasks ......................... 76

Figure 10. Interruption task judgment latencies as a function of interruption
modality and locus ....................................................................... 78

Figure 11. Resumption lag as a function of interruption modality and locus,
correct trials only ......................................................................... 81

Figure 12. Average proportion of resumptions containing repeats as a function of
interruption modality and locus ....................................................... 84

Figure 13. Average proportion of resumptions containing skips as a function of
interruption modality and locus ....................................................... 87

Figure 14. Average number of words in error (repeated and skipped) upon
resumption as a function of interruption modality and locus. ................. 89

Figure 15. Proportion of correctly answered interruption tasks ...................... 105

Figure 16. Interruption task judgment latency as a function of interruption
modality and locus. .................................................................... 107

viii

Figure 17. Resumption lag as a function of interruption modality and locus,
correct trials only. ...................................................................... 109

Figure 18. Average proportion of resumptions containing repeats as a function of
interruption modality and locus ..................................................... 112

Figure 19. Average proportion of resumptions containing skips as a function of
interruption modality and locus. .................................................... 113

Figure 20. Average number of words in error (repeated and skipped) upon
resumption as a function of interruption modality and locus. ............... 115

Figure 21. Average resumption lag as a function of interruption type, Experiment
1, Pilot Study 1 .......................................................................... 150

Figure 22. Average proportion of disﬂuent resumptions as a function of
interruption type, Experiment 1, Pilot Study 1. ................................. 151

Figure 23. Average resumption lag as a function of interruption type, Pilot Study
2. ........................................................................................... 153

Figure 24. Average number of disfluencies as a function of interruption type, Pilot
Study 2 .................................................................................... 154

ix

LIST OF ABBREVIATIONS

ANOVA: Analysis of Variance

CI: Conversational Interruption
DP: Discourse Purpose

DSP: Discourse Segment Purpose
HCI: Human-Computer Interaction
MSE: Mean Square Error

RL: Resumption Lag

RL1: Primary Resumption Lag
RL2: Secondary Resumption Lag
Tl: Task Interruption

TOT: Time on Task

INTRODUCTION
Interruptions in a Fictional Dialog

When people speak to each other in dialog, the speech stream of one
speaker is sometimes broken off, or interrupted, by the speech stream of another
speaker. When the result of the overlap between speech streams is that the
interrupting participant temporarily gains control of the conversation, or “takes the
ﬂoor", it is often difﬁcult for the interrupted participant to return to the utterance, or
topic, from before the interruption. In order to facilitate understanding of some
parameters of interruptions, I present below an excerpt from Reservoir Dogs, a
screenplay by Quentin Tarantino (1990), in which men sitting around a breakfast
table are having a conversation. According to the screenplay, “They are MR.
WHITE, MR. PINK, MR. BLUE, MR. BLONDE, MR. ORANGE, MR. BROWN,
NICE GUY EDDIE CABOT, and the big boss, JOE CABOT. Most are ﬁnished
eating and are enjoying coffee and conversation. Joe ﬂips through a small
address book. Mr. Pink is telling a long and involved story about Madonna.”

MR. PINK: “Like a Virgin” is all about a girl who digs a guy with

[deleted]1. The whole song is a metaphor for [deleted].

MR. BLUE: No it's not. It's about a girl who is very vulnerable and she's

been [deleted] over a few times. Then she meets some guy who's really

sensitive-

MR. PINK: -Whoa...whoa...time out Greenbay. Tell that [deleted] to

the tourists.

 

1 Potentially offensive and/or explicit material from the original screenplay was deleted.

JOE: (looking through his address book) Toby...who the [deleted] is Toby?
Toby... Toby... think... think... think...

MR. PINK: It’s not about a nice girl who meets a sensitive boy. Now
granted that’s what “True Blue” is about, no argument about that.
MR. ORANGE: Which one is “True Blue?”

NICE GUY EDDIE: You don’t remember “True Blue?” That was a big ass
hit for Madonna. [Deleted], I don’t even follow this Tops In Pops [deleted],
and I’ve at least heard of “True Blue.”

MR. ORANGE: Look, [deleted], I didn’t say I ain’t heard of it. All I asked
was how does it go? Excuse me for not being the world’s biggest
Madonna fan.

MR. BROWN: I hate Madonna.

MR. BLUE: I like her early stuff. You know, “Lucky Star,” “Borderline” - but
once she got into her “Papa Don’t Preach” phase, I don’t know, I tuned
out.

MR. PINK: Hey, [deleted] all that, I’m making a point here. You’re
gonna make me lose my train of thought.

JOE: Oh [deleted], Toby’s that little [deleted] girl.

MR. WHITE: What’s that?

JOE: I found this old address book in a jacket I ain’t worn in a coon’s age.
Toby what? What the [deleted] was her last name?

MR. PINK: Where was I?

MR. ORANGE: You said “True Blue” was about a nice girl who ﬁnds a

sensitive fella. But “Like a Virgin” was a metaphor for [deleted].

MR. PINK: Let me tell ya what “Like a Virgin”’s about. It’s about some

[deleted] who’s a regular [deleted]. I mean all the time, morning, day,

night, afternoon, [deleted] [deleted] [deleted] [deleted] [deleted]

[deleted] [deleted] [deleted] [deleted] [deleted] [deleted].

MR. BLUE: How many [deleted] was that?

MR. WHITE: A lot.

MR. PINK: Then one day... [proceeds to ﬁnish story while handling

several non-disruptive interjections].

In this dialog, Mr. Pink is interrupted several times, and the results of each
interruption are quite varied. Mr. Pink is ﬁrst interrupted by Mr. Blue, who
disagrees with Mr. Pink's initial thesis. Mr. Pink makes a brief return to his story,
but is then interrupted twice: ﬁrst by Joe, who seems to be ignoring Mr. Pink
entirely while consulting and muttering about his address book, and then by Mr.
Orange, who seeks clariﬁcation about the identity of the song under discussion.
Mr. Pink then explicitly asks people to please stop interrupting him, warning that
he will “lose his train of thought”. Undaunted, Joe continues to talk about his
address book, which ﬁnally draws a response from Mr. White, leading to a brief
exchange in which Joe explains the subject of his muttering.

Pink is then faced with a dilemma: he wishes to continue his story, and is
unwilling to yield the ﬂoor to anyone else; and yet, his warning seems to have

been valid, as he ﬁnds himself unable to recall where he was in his story. His

solution is to ask the group for help. His plea of “Where was I?” is graciously
answered by Mr. Orange, who happens to remember exactly where Mr. Pink had
left off. Orange provides Pink with verbal cues so that Pink can continue his
profane narrative.

lmportantly, not all of the interruptions in the example were disruptive.
After Mr. Blue and Mr. White interject jocular remarks about Mr. Pink’s
storytelling style, Mr. Pink is able to continue relaying his racy metaphor, and he
is eventually able to ﬁnish his story despite several more interjections from his
shady breakfast companions.

The goal of the present work is to examine the disruptive nature of
interruptions in the context of language production processes. First, I will explore
whether conversational interruptions are associated with observable “disruptions”
in production performance. This would seem to be a facile point, but it has no
empirical veriﬁcation in any literature. Assuming that such effects will be
observed, I will seek to discover under what conversational circumstances
interruptions cause a “disruptive” effect on a speaker. In the Reservoir Dogs
example, several differences between the “disruptive” and “non-disruptive”
interruptions are evident: the number of speakers, utterances, and words
between interruption and resumption; the amount of time elapsed between
interruption and attempted resumption; the contextual and meaningfulness
relationships (similar, different, higher-level, lower-level, meta-level, etc.)
between the interrupted message and the message of the interruption; and the

place in the narrative where the speaker gets interrupted (toward the beginning

or toward the end of the narrative). Some of these factors may have more
signiﬁcance than others, and although sorting all of these variables out will be an
important pursuit for future work, the present study only addresses some of them.
Finally, assuming I answer the whether afﬁrmatively, and can answer some of
the when experimentally, I can then ask why conversational interruptions are
disruptive (or not). Answers to these questions will offer clues about architectural
issues in language production such as bookkeeping (Levelt, 1989), message
formulation, syntactic planning, and the relationship between language
production and executive memory processes.

The rest of this document is organized as follows. First, I will operationally
deﬁne “interruption” for the purpose of the present study, borrowing from
previous deﬁnitions from linguistics and combining them with current literature on
non-conversational interruptions from the human-computer interaction (HCI)
literature. Next, I will summarize the available literature on the topic of
conversational interruptions, focusing especially on those works from
computational linguistics, linguistics, and HCI that carry implications for the
questions raised above. A discussion of the HCI work covering non-
conversational interruptions will follow. HCI investigations of interruptions lay the
groundwork for a theoretical and empirical approach to examining conversational
interruptions, and such research will be shown to link quite well in both respects
to the literature on “bookkeeping” in language production. This review of

bookkeeping representation issues in language production regarding message

planning and syntactic planning will set up the theoretical and experimental
issues that the current project seeks to address.
Deﬁning Interruptions

Interruptions have been studied empirically largely from two different
ﬁelds: linguistics and human computer interaction (HCI). However, the two ﬁelds
study different sorts of interruptions. Therefore, the ﬁrst step in deﬁning
“interruptions” is to divide them into two categories: Conversational Intenuptions
(henceforth Cls) and non-conversational interruptions, or Task Intenuptions (Tls).
The focus of this section is on deﬁning what a conversational interruption is; a
task interruption is a more general usage of the term interruption, but in the
present discussion it is meant to cover the types of interruptions that are studied
in the HCI interruptions literature (e.g., McFarlane, 2002). It is important to
distinguish the two types of interruptions at the outset because one of the
subordinate goals of the present proposal is to empirically evaluate the degree to
which Cls and Tls share cognitively relevant properties.

The categorization and deﬁnition of Cls has come down to several key
issues, including the physical overlap of speech streams between two speakers
(Sacks, Schegloff, & Jefferson, 1974); the syntactic intrusion of such physical
overlap and the resulting potential to disrupt a speaker (West & Zimmerman,
1983); and the cooperative versus competitive nature of the interruption
(Kennedy & Camden, 1982; Yang, 2001). I will presently turn to describing these

issues more fully.

Cls were ﬁrst characterized by Sacks et al (1974) as “overlap” between
speakers in a dialog that violated the “turn” of a speaker. Social conventions hold
that during a speaker’s tum-at-talk, as Sacks et al had conceived of it, each
participant in a conversation should speak only when others are not speaking, or,
if overlap is to take place, that overlap should occur quite near the anticipated
end of the current speaker’s turn, when overlap is more expected and common
(Sacks et al, 1974). These sites at which overlap ls permitted are called
transition-relevant places (TRP).

Later work by researchers interested in the socio-political relations
between men and women in society (West & Zimmerman, 1983) used Cls as
measures of attempted dominance in cross-gender interactions. West and
Zimmerman (1983) viewed interruptions as a telling symbol of domination in
human interaction, and used interruption tendencies as a dependent measure in
cross-gender studies to further their sociopolitical view that men express their
power over women through subtle, implicit means. Although the aims of their
research are rather unrelated to the present ones, there was a very immediately
useful aspect of their work: an attempt to deﬁne “interruption” in observable,
empirical terms:

In contrast to overlaps, interruptions do not appear to have a systemic

basis in the provisions of the turn-taking model. An interruption involves a

“deeper intrusion into the internal structure of a speaker's utterance” than

an overlap, and penetrates well within the syntactic boundaries of a

current speaker’s utterance (West & Zimmerman, 1977: 523). Deﬁned

operationally, candidate interruptions are incursions initiated more than
two syllables away from the initial or terminal boundary of a unit-type...We
intend the term intenuption to refer only to those deep incursions that
have the potential to disrupt a speaker’s turn, although actual disruption
(e.g., diversion of activity within a turnspace to address the intrusion,
including yielding the turnspace to the interrupter) is a product of further
interaction between the parties. (West & Zimmerman, 1983)
Interruptions, therefore, potentially take the ﬂoor away from a current speaker
(the speaker can usually raise her own speech amplitude and disallow disruption)
and signify a place where the current speaker could surrender the ﬂoor.
Conﬁrrnatory “uh-huh”-type overlaps, therefore, and other such non-disruptive
overlaps, are not interruptions. West and Zimmerman conceive of a disruption as
the successful ability of an interruption to force the speaker to address the
interruption in a cooperative manner. I will discuss shortly how this usage of the
term “disruptive” refers to something quite different from my usage of the term.
Kohonen (2004) performed an analysis that synthesized operational
deﬁnitions of interruptions from Drummond (1989), Lerner (1989), and West and
Zimmerman (1983). After excluding many “interruptions” that she found to ﬁt into
a set of predeﬁned exclusionary principles, she found that actual interruptions
(deﬁned solely in terms of what they were not) constituted less than 1% of cases
of speech overlap. However, Kohonen’s deﬁnition is too restrictive and fails to

provide an actual description of an interruption.

Dissatisﬁed with the tendency of researchers like West and Zimmerman
(1977) to deﬁne interruptions purely in negative, dysfunctional terms, Kennedy
and Camden (1982) investigated the various functions of interruptions in group-
work settings. Analyses of videotaped interruptions showed that up to half of all
interruptions served a positive purpose: to strengthen the message of the
speaker who is interrupted, whether through supporting, clarifying, or repeating
the message. This ﬁnding motivates a categorical division between cooperative
and competitive interruptions, a distinction that Yang (2001) recently upheld.
Although Yang partially deﬁnes the two types of interruptions according to
pseudo-empirical concepts related to the mindset of the interrupter, including
emotions and underlying intentions, a great contribution of his work is his ﬁnding
that cooperative interruptions generally are associated with low-pitch and low-
amplitude prosodic contours, but competitive interruptions are associated with
high-pitch and high-amplitude prosodic contours.

To sum up, Cls have been deﬁned and categorized on several levels. In
order to be an intenuption, an utterance must at least overlap with another
speaker’s utterance. From there, the categorization becomes difﬁcult. As
Kohonen demonstrated, it is possible to consider a number of points about
deﬁning interruptions in order to integrate them into a generalized deﬁnition that
ﬁts well with research in linguistics and communication, but the resulting
“deﬁnition” could be muddled and ﬁt poorly with the current research purpose. A
recent movement to categorize interruptions by their competitive versus

cooperative natures, however, seems more useful in the current context.

Esk interru_gtions. Task interruptions are generally deﬁned quite
differently than are conversational interruptions. The human-computer interaction
literature usually considers an interruption episode to be composed of three parts
(following terminology of Trafton et al, 2003): 1) a primary task that a person
must carry out, such as running a computer program or editing a manuscript; 2)
an interruption component called the “secondary task”, in which a person must
stop performing the primary task and perform the secondary task until they ﬁnish
or are instructed to stop; and 3) resumption of the primary task. This line of
research approaches interruptions primarily from an applied perspective: How do
interruptions inﬂuence performance on the primary task? Work from this
perspective will be described in more detail below. For now, it is important to note
that Tls are deﬁned very differently from Cls, and a primary reason for this is that
Cls have typically been studied and deﬁned as a means to another end (a
dependent measure of social dominance or power), and Tls have been studied to
understand their effect on primary task performance. My deﬁnition of
interruptions adopts a task interruptions approach to conversational interruptions.

Experimentally manipulating interruptions in dialog and psycholinguistic
tasks in the proposed line of research provides the ability to deﬁne interruptions
empirically. Although many other sorts of Cls could potentially be examined, I will
restrict interruptions in the present study to the sort that stop a speaker from
completing an utterance, then allow the speaker to resume the same utterance
after the interruption has been completed. In this sense, the interruptions under

investigation here all ﬁt with the West and Zimmerman (1983) deﬁnition of

10

“interruption”. This is because interruptions in the present study cause what West
and Zimmerman deﬁne as “disruption”. However, just as I am leaving the general
deﬁnition of “interruption” open in favor of a speciﬁc operational deﬁnition, I am
also going to operationally deﬁne disruption in terms of this study’s particular
dependent variables that measure difﬁculty in resuming speech production
following an interruption. Three primary measures will be used to estimate the
disruptive effects of Cls. One measure is resumption lag (RL), or the time it takes
to resume the primary task (i.e., to get back to where one was in a dialog before
being interrupted) following the completion of the secondary task (the
interruption) (Altmann & Trafton, 2002; Trafton et al, 2003). Another measure will
be ﬁller-type disﬂuencies, otherwise known as uh and um. These disﬂuencies
can be used as indirect measures of difﬁculty in preparing speech. Also, verbal
cues that a message has been forgotten, such as when Mr. Pink asks, “Where
was I?” hint at another type of measure of resumption difﬁculty: resumption error.
Interruptions and the Question of Disruptiveness

Children are taught from an early age not to interrupt another speaker.
Parents and educators generally cite rudeness to bolster their admonishments of
those who interrupt. However, research in psycholinguistics, which has
overlooked interruptions in general, has ignored a very important potential aspect
of interruptions during conversation: although interrupting a speaker may be
rude, evidence from non-conversational interruptions (see below) and current
theories of dialog (Pickering & Garrod, 2004) suggest that there may exist

circumstances under which interruptions are helpful for the cognitive processing

11

of sentences in a dialog. I will presently review evidence from TI research that
has shown both “positive” and “negative” ramiﬁcations of interruptions on task
performance. I will then discuss what Cls may have in common with Tls, and
discuss psycholinguistic theories that suggest that interruptions may be, in some
circumstances, helpful, or at least harmless, to sentence processing during
conversation (or dialog), despite what authority ﬁgures have always taught us.
Non—conversational lnterrgm

Although we have a subjective experience that Tls are disruptive to
cognitive tasks, evidence from the TI literature does not support this notion
wholesale. In fact, Tl literature has produced a wide range answers to the
question of whether Tls actually are disruptive. This state of affairs may be
disconcerting to a business manager who would like to know whether the email
interruptions that come to employees every couple of minutes hurt overall job
performance, help overall job performance, or have no effect on job performance
except to annoy their employees. One of the virtues of the TI literature is that it
has established a set of objective measures to determine what a “disruptive”
effect is, and has evaluated these measures against each other. Unfortunately,
the evidence these measures have produced is a rather mixed bag. On one
hand, some research has shown interruptions to be subjectively annoying;
another band has shown them to be objectively harmless or helpful to overall
performance; a third hand has shown that interruptions are objectively disruptive
to performance. Here, I present a brief analysis of factors that can inﬂuence the

degree to which a TI causes disruption in performance, and offer the view that

12

Tls are disruptive insofar as they can cause a delay in resuming a task because
old goals must be reactivated past the activation level of the most recent goal
(Altmann & Trafton, 2002).

There are three general measures for the possible disruptive, or helpful,
effects on task execution, but in order to deﬁne these, I should ﬁrst introduce the
set of terms to be used throughout (following terminology from Trafton, Altmann,
Brock, & Mintz, 2003; Figure 1, below, is also adopted from Trafton et al, 2003).
As shown in Figure 1, the primary task is the task that gets interrupted. The task
that interrupts the primary task is the secondary task. The period of time between
the offset of the primary task and the onset of the secondary task is called the
intenuption lag. The period of time between the offset of the secondary task and
the resumption of the primary task is called the resumption leg (or RL). The total
amount of time spent on the primary task, subtracting the time spent during the
secondary task, is called time on task or TOT. Although most experiments
reported here used TOT as the dependent measure of disruption, Trafton et al
(2003) raise a convincing point: that the measurement of time on task is not a
precise measure of disruption because, at any point in the course of the primary
task, factors such as motivation or strategy could decrease the overall time spent
on a task, but leave resumption lag times quite high. So, if researchers wish to
study the effects of interruptions on overall task performance, then TOT is a good
measure. However, if one wishes to understand the cognitive implications of an
interruption apart from motivational or strategic factors, then the time preceding

the resumption of a task (resumption lag) is likely a more valid measure (Trafton

13

et al, 2003; Altmann & Trafton, 2002). Finally, overall accuracy on a task is also a

possible measure for the disruptive effects of interruptions on performance.

Begin Alert for Begin End Resume End
Primary Secondary Secondary Secondary Primary Primary
Task Task Task Task Task Task

Time on Interruption Interruption Resumption Time on
Task (1) Lag Lag Task (2)

 

 

 

 

 

 

 

Time
Figure 1. The interruption and resumption process, involving a primary

(interrupted) and secondary (interrupting) task. Adapted from Trafton et al, 2003.

Interruptions and disruptiveness: a mixed bag. Research has shown that
interruptions annoy people. Speciﬁcally, Bailey et al (2001) and Adamczyk &
Bailey (2004) administered questionnaires to participants after experiments in
which a primary task was occasionally interrupted by a secondary task. In the
Bailey et al (2001) experiment, half of the subjects were interrupted in the middle
of primary tasks (such as arithmetic calculations), and the other half performed
all tasks to completion without any interruption. A questionnaire following the
experiment showed that participants in the interruption condition found the
primary tasks more annoying, more anxiety-inducing, and more difﬁcult than did
the non-interrupted control group, suggesting that perhaps Tls are not objectively

disruptive, but merely unpleasant.

14

In fact, other studies have failed to ﬁnd effects of Tls on empirical
measures of disruptiveness (Burmistrov & Leona, 2003; Gillie & Broadbent,
1989). During simple tasks in which subjects added paragraphs to text,
Burmistrov & Leona (2003) interrupted half of the subjects with phone calls.
There was no difference in either RL or TOT with this manipulation. Gillie &
Broadbent (1989) studied the effects of mathematics task interruptions on
subjects performing a verbal item-location matching task and similarly found no
effects of interruption on performance whether the math task was short (30 s) or
long and involved (over 2 minutes).

Even more damaging to claims that Tls are disruptive to performance is
evidence that Tls can improve performance and efﬁciency on certain simple
tasks (Speier et al, 2003; Zijlstra et al, 1999). Speier et al (2003) demonstrated
that when performing simple decision making tasks that involved the monitoring
of a few number cues, occasionally interrupting the task actually led to a faster
TOT and overall higher accuracy on the primary task. Zijlstra et al (1999) also
showed TOT improvement when a primary task was interrupted. It should be
noted, however, that this experiment also allowed participants to choose when to
perform the secondary tasks. McFarlane (2002) showed that such freedom elicits
positive strategic effects on primary task performance.

So far, this review has shown that in some circumstances, Tls do not
inhibit, and can, in fact, improve performance. The picture will soon become
murkier. Trafton et al (2003) presented some convincing data that Tls can be

disruptive to primary task performance if the proper dependent measure is used:

15

resumption lag. In their study, Trafton et al (2003) used as a primary task a game
in which participants allocated fuel to various tanks. The secondary task had
participants judge whether incoming “bogies” were friends or foes. Some
subjects received warnings that interruptions were upcoming, some did not.
When an 8-s secondary task onset warning was given to participants during the
primary fuel task, resumption lag was signiﬁcantly shorter than when interruption
by the secondary task was immediate. Not only do these data suggest that there
is a costly aspect to interruption, but the authors offered a reason for why they
are costly: memory for goals (Altmann & Trafton, 2002). During the primary tank
task, the subject is setting goals for the task and completing them. When a
secondary task begins, the primary task goal becomes less active in memory
than the secondary task goal. When the secondary task ends, the subject must
then reactivate the old, primary task goal where it was left off. This cannot be
easily done, Altmann & Trafton (2002) argue, without cues to prime the activation
of the old goal. The reason subjects in the no—warning condition are unable to
resume the primary task as quickly as the subjects in the warning condition is
that subjects in the warning condition are able to use the interruption lag (the
time between the warning of a pending interruption and the interruption itself) to
encode cues for later goal-priming.

A subsequent follow-up experiment (Altmann & Trafton, 2004)
manipulated the presence and absence of cues during the interruption lag to
verify this hypothesis. The study showed that cues were helpful in reducing the

disruptive effect of the secondary task on RL. The qualiﬁcation to these studies is

16

that the disruptive effects of interruption on RL in the warning versus no warning
conditions were limited to the ﬁrst session. In other words, it seemed that once a
participant was trained to expect the interruptions, some sort of strategic
response was learned to overcome its negative effects on RL (see also Hess &
Detweiler, 1994).

To sum up, Tls are almost always associated with negative perceptions,
but the evidence is mixed as to whether they are objectively disruptive. The
General Discussion (below) will summarize these factors and explore their
usefulness as possible future directions for the current research program. |
presently offer connections between ﬁndings from TI research and the rationale
for the design of the current experiments.

Dependent measures. As mentioned above, studies using RL as a
dependent measure of disruption may be more valid than studies using TOT as a
dependent measure. Whereas some studies (Cutrell et al, 2001) found disruptive
effects on RL for both simple and complex tasks, Speier et al (2003), taking only
TOT measures, found that simple tasks were aided by Tls. It is possible that the
TOT and RL measure completely different processes (overall task strategy and
goal retrieval, respectively). The present study is more interested in the aspects
of language production that involve goal retrieval; hence, resumption lag will be
emphasized over time-on-task in the context of speech. To measure resumption
lag in the context of a language production task, the experiments presented here
measured the amount of time that elapsed from the end of an interrupting task to

the resumption of the speech unit that had been interrupted. In the case of

17

Experiment 1, resumption lag was measured by analyzing waveform ﬁles by
hand. In Experiments 2 and 3, a software program directly encoded such
resumption lag times.

Similarity of primary and secondary tasks. The Gillie & Broadbent (1989)
study is valuable insofar as it demonstrates that simple tasks can be more
disruptive as interruptions than complex tasks as long as the simple task is more
similar to the primary task (i.e., of the same domain) than the complex task.
Findings such as this have been used to argue that interruptions will be the least
disruptive when they distribute information across many different sensory
modalities (Adamczyk and Bailey, 2004). This issue is addressed to some
degree in all of the proposed experiments. In Experiment 1, similarity is not
based on cognitive domain, but on the relation between interrupting topic to the
interrupted topic of dialog. Experiments 2 and 3 investigate the similarity issue by
asking whether interruptions to sentence production are more disruptive when
the task is of the same verbal cognitive domain (i.e., reading a sentence) or of a
non-verbal cognitive domain (i.e., performing an arithmetic computation).

Qgrse versus ﬁge break points. Adamczyk and Bailey (2004) interrupted
people performing a primary task (such as titling a video) at different points in the
task with various types of secondary tasks (such as text-titling tasks). Crucially,
some participants were interrupted at “coarse points” in the video-titling task,
while others were interrupted at “ﬁne points”. Coarse points were deﬁned as
something like the break between scenes in the movie, whereas ﬁne points

occurred in the middle of important scenes. Participants expressed more

18

frustration if they were interrupted at ﬁne break points than at coarse break
points, and they took longer to perform primary tasks (TOT). However, the
researchers failed to ﬁnd effects of ﬁne versus coarse break points on
resumption lag. They attributed this to a problem with the grain-size of their
resumption lag measure (i.e., their instruments only measures times in whole
second increments). The important implication of these ﬁndings is that
interruptions may be less disruptive at moments in task execution when a
cognitive break may be taken (McFarlane, 2002). Experiments 2 and 3
investigate this issue from the perspective of syntactic boundaries—are
interruptions less costly when they come between clauses than when they come
in the middles of clauses?

Training. Finally, both Trafton et al (2002) and Hess & Detweiler (1994)
found that the disruptive effects of interruptions can be trained away with
practice. Trafton et al (2002) showed that, by the end of their study in which
participants were interrupted somewhat frequently, the interrupting tasks were no
longer more disruptive than non-interruption control conditions. They argue that
strategies can be developed over the course of an experiment such that
appropriate cues for later recall can be encoded by interruptees to aid
resumption. It is possible that conversational interruptions share this property in
the context of a single conversation, such that the ﬁrst interruption or two disrupt
the speaker, but later become easier once they are expected. This potential

constraint must be kept in mind for the present study.

19

Are Conversational Interruptions Disruptive?

Literature on Cls has typically considered interruptions to be disruptive to
the superﬁcial extent that the speech of one person is broken off by the speech
of another (see above). This literature also considers this sort of disruption to be
a necessary condition for an interruption, not a possible result of one. However,
this deﬁnition is inadequate for two reasons: First, because it ignores cases in
which interruptions aid the overall message of a dialog, as Kennedy and Camden
(1982) and others have pointed out. To call a helpful interruption “disruptive”
simply because one person stops speaking ignores common sense and the
growing body of literature showing that conversation is a cooperative, joint
activity (for reviews, see Clark, 1996; and Pickering and Garrod, 2004). The
second problem with the typical Cl view of disruptiveness is its measurability with
respect to drawing cognitive inferences. The deﬁnition of disruptiveness used in
the TI literature reviewed above is able to account for cognitive effects of
interruptions more aptly; there, an interruption is disruptive to the extent that it
affects performance of a primary task. This view of disruptiveness allows for a
methodology to investigate what types of interruptions cause more or less
disruption to a primary task, which, in the case of dialog, would be the speaker’s
primary message or utterance.

There is little in the way of psycholinguistic research to suggest whether
interruptions do, in fact, disrupt dialog processing. The handful of researchers
who have addressed conversational interruptions actually hint that interruptions

should be helpful rather than harmful to language production. For instance,

20

Pickering and Garrod (2004), inspired to combine the study of dialog forged by
Clark with the rigorous methodological techniques of mainstream
psycholinguistics in their current model of dialog processing, have suggested that
interruptions that help conversational partners align their representations would
not be disruptive to the processing of dialog. According to this view, there should
be no disruption at all as long as interlocutors are still “aligning” on the relevant
levels of representation, most importantly the situation model. On the other hand,
if alignment between participants in a dialog is broken there is reason to believe
that the dialog will be disrupted (Pickering, personal communication with
Fernanda Ferreira). One way in which alignment could be disrupted is if the
situation model being conveyed by a speaker is suddenly altered by an
interruption that is either tangential to the speaker’s model or addresses a
different situation altogether.

Other work by Chapanis (1976) and Schober et al (2004) suggests that
the ability to interrupt may actually help the processing of dialog. The mere ability
to interrupt another conversational participant, these works suggest, may aid the
extent to which the messages expressed and agreed upon in dialog are
eventually understood. Pickering and Garrod’s (2004) contention that dialog
introduces a slew of naturalistic advantages over monologue could easily adopt
“ability to interrupt" as yet another advantage afforded by dialog.

On the other hand, intuition drawn from examples such as the Reservoir

Dogs dialog, and the work of Grosz and Sidner (1986) suggest that interruptions

21

will, in fact, disrupt speakers from completing thoughts and narratives in dialog.
Clearly, people occasionally become distracted when they are interrupted.

The computational model of Grosz and Sidner (1986) is among the most
thorough attempts to analyze and categorize conversational interruptions. In
developing their computational theory of dialog processes, Grosz and Sidner
identify three major components that must explain the process of discourse
(deﬁned as spoken or written conversation between 2 or more people): a
linguistic, discourse structure; an intentional (or goal, or task) structure; and an
attentional structure that models the focus of a discourse. The primary
motivations for their selection of this series of discourse components is that
previous accounts of discourse could not adequately explain interruptions in
discourse.

A discourse, according to Grosz and Sidner, is coherent to the extent that
the intentions of the participants are the same (or aligned). Interruptions disrupt
the coherence of a discourse by temporarily altering the overall purpose of a
discourse (discourse purpose, DP), by introducing a discourse segment with a
different purpose (discourse segment purpose, DSP). Generally, an interruption
to a discourse breaks off a discourse in some way, and is followed by a
resumption.

Grosz and Sidner divide interruptions into 3 major categories: true
interruptions, ﬂashbacks, and digressions. I will presently explain each type and

provide Grosz and Sidner’s examples of each. Interestingly, all of Grosz and

22

Sidner‘s examples of interruptions are within-speaker interruptions—that is, a
speaker interrupts herself and then resumes speaking about the primary topic.

In true interruptions, one discourse segment (D1 below) is interrupted by a
discourse segment that has “distinct, unrelated purpose(s) and convey(s)
different information about properties, objects, and relations” (DZ). So, a DP
must be altered entirely for a true interruption to take place. In the example
below, even the audience of the interruption is different than the primary
discourse audience. Note that this deﬁnition of “true” has ramiﬁcations primarily
for the intentional structure of the discourse. Grosz and Sidner point out that D1
is resumed “almost immediately” in the discourse—not after some delay, even
though the interruption did not promote alignment.

D1: John came by and left the groceries

DZ: Stop that you kids

D1: and I put them away after he left

Flashbacks are mainly different from true interruptions in that the intention,
or goal, of the interruption is related to the overall discourse purpose. There is
also generally some sort of verbal warning that an interruption of the discourse
segment is about to take place. Such a warning might call attention to something
having gone wrong that will require amendment. Unlike true interruptions, the
audience for ﬂashbacks remains the same.

D1: OK, now how do I say that Bill is...

23

02: Whoops I forgot about ABC. I need an individual concept
for the company ABC (continues with discourse segment on
ABC).

D1: Now back to Bill. How do I say that Bill is an employee of ABC?

A digression, according to Grosz and Sidner (1986), is “a strong
interruption that contains a reference to some entity that is salient in both the
interruption and the interrupted segment.” Like ﬂashbacks, digressions are
usually accompanied by a verbal warning such as “speaking of” or “that reminds
me”.

D2: Speaking of Bill, that reminds, me, he came to dinner last week...
Digressions share some properties of true interruptions (a shift in intentional
structure) and ﬂashbacks (verbal warnings of pending interruptions), and an
explicit verbal closing before resumption of the primary discourse segment such
as “getting back to ABC” or “anyway”.

A problem with Grosz and Sidner’s analysis of interruptions for present
purposes is that they focused heavily on self-initiated interruptions. The current
project will focus on the phenomenon of one person interrupting another in dialog
or conversation. One of the virtues of the Grosz and Sidner work is that it
provides a handy starting point for characterizing types of interruptions that could
be disruptive or helpful for the processing of dialog. It seems as though “true”
interruptions and digressions could sufﬁciently disrupt interlocutors’ memory for
primary conversational goals such that resuming the dialog from before one of

these interruptions could be quite difﬁcult. Flashbacks are the sorts of

24

interruptions that not only maintain the goals of a dialog, but deepen
interlocutors’ understanding of a topic. According to the framework of Pickering
and Garrod (2004), one might suppose that digressions and true interruptions
serve to break alignment between two participants in a dialog, whereas
ﬂashbacks serve to promote alignment. Experiment 1 will test this hypothesis.
Bookkeeping Processes in La_ngu_age Production

When a person attempts to converse with another in order to accomplish
some speciﬁc purpose, a speaker must develop a verbal plan for how to do so.
As an illustration, I will suppose that Bob has gone to a movie with David. In the
middle of the movie, David gets up to reﬁll his popcorn and soda. After the movie
ends, David asks Bob what happened while he was gone. Bob must develop a
plan to explain the scene to David. This plan likely has many layers to it: An
overall goal, which would be to adequately explain the scene; and subgoals,
including the event structure, as well as the subgoal to mention characters’
names. Other subgoals might also exist, such as the desire to show exasperation
with David because he always misses crucial scenes in movies to satiate his
unquenchable thirst for sugar water and his indefatigable hunger for popped
corn.

Bob must keep track of a number of different goals and intentions during
his narrative to ensure that his plan is carried out correctly. Speciﬁcally, Bob must
remember which aspects of the plan have already been executed, which aspects
of the plan have yet to be executed, and whether any part of the plan has been

missed, or forgotten. Levelt (1989) has termed this process of keeping track of

25

one’s communicative intentions during language production “bookkeeping”. In
fact, Levelt’s view of bookkeeping in dialog draws heavily from the ideas of Grosz
and Sidner (1986). Levelt views conversation as hierarchical in structure, such
that it is possible to move up and down in a conversation, i.e., back and forth
between high-level goals and low-level goals. Crucially, this structure is not just
an abstract one that bears no cognitive relevance (as it was for Grosz and
Sidner). Rather, Levelt, citing several sources from discourse and text
processing, argues that this structure of intentions is the representation from
which speakers perform bookkeeping operations during speech. Knowledge of
where one is within that represented structure is achieved via a “pointer"
mechanism that binds the current conversational content to a site in the goal
structure.

According to this framework, the conversation between Bob and David
might begin at a high level. Several researchers (Kintsch, 1974; Meyer, 1975;
Thorndyke, 1977) have argued that, in story structure, high level representations
include setting (characters, time, location), theme (event and goal), plot
(consisting of episodes), and resolution (consisting of events). Accordingly, Bob
might begin by saying which scene it was that David missed, and brieﬂy cite
which characters were involved in the scene. Bob’s narrative must soon move
down to lower levels on the hierarchy, since “(t)he lowest-level subgoals
eventually evoke individual messages and their topics; they are the smallest

ingredients in the hierarchy of discourse topics” (Levelt, 1989). In other words,

26

the low-level goals are the plans for the production of actual speech that are sent
to the next production module.

Using this hierarchical framework of Levelt, at any point during Bob’s
description, David can interrupt and do one of three things: ask for clariﬁcation of
minor points at lower levels in the narrative hierarchy; ask for information that is
considered “above” the current pointer in Bob’s narrative structure; or, in fact,
change the topic of conversation entirely (at the risk of infuriating Bob even
further). The intuition of Levelt (1989) and Grosz and Sidner (1986) is that the
further the interruption material is from the hierarchy pointer, the more disruptive
the interruption might be.

Theory from the task interruptions literature can help psycholinguists think
about the cognitive architecture that supports bookkeeping for speakers in a
dialog. Trafton et al (2003) maintain that the reason interruptions become
disruptive to performance of a primary task is that the goals of the primary task
decay from memory during engagement with the secondary task. Following
along, Cls that stray the furthest from the bookkeeping pointer may cause the
most disruption because they have the most potential to interfere with pre-
interruption dialog goals due to the memory constraints that limit bookkeeping
representations. On the other hand, Gillie and Broadbent (1989) found that
interruptions with content similar to the primary task are more disruptive than
unrelated interruptions. In that case, perhaps Cls closest to the pointer should be
the most disruptive. The types of Cls mentioned above have similar properties to

Tls: They differ in complexity, in length, and in distance from and similarity to the

27

“primary” narrative. In sum, they represent a natural testing ground for
interruptions that can be both an existence proof of narrative bookkeeping and
provide naturalistic data that address controversial issues in the task interruption
literature.

Although research on conversational interruptions has been sparse,
Glanzer and colleagues (Glanzer et al, 1981, 1984; Fischer & Glanzer, 1986)
carried out research on the representations that persist across interruptions to
the reading of text. In this work, people reading cohesive text were interrupted
with an alternative task, then asked to resume reading the text. A control group
was not interrupted. The general ﬁnding observed in these studies was that
interrupted readers took longer to read sentences after interruption than their
non-interrupted counterparts, especially when the sentence they resumed with
was thematically or referentially tied to the previous sentences. Although these
were studies of reading rather than dialog, the studies are relevant to the current
questions because they address the role of memory processes during the
interruption of verbal processing. Glanzer and colleagues conclude that short
term memory (STM) is a vital architectural necessity for the processing of text
because it maintains the meaningful dependencies between sentences for
readers. Ericsson and Kintsch (1995) have gone on to argue that this work is
even better-suited for their Long-Term Working Memory (LT-WM) model. It would
also be interesting to consider these results from the perspective of more recent
versions of WM in language comprehension (Just & Carpenter, 1992; Waters &

Caplan, 1996). Whichever model the Glanzer work best supports, it is clear that

28

memory should have a role in resumption from conversational interruptions. For
this reason, we included measures of both verbal and spatial working memory by
using a variants of reading span (Swets, Desmet, Ferreira & Hambrick, 2005)
and spatial span (Shah & Miyake, 1996).
Overview of Experiments

Three experiments were carried out in order to begin to investigate the
untapped issues of bookkeeping and interruptions from a psycholinguistic
perspective. Experiment 1 used a semi-natural dialog in which a confederate
participant interrupted at predetermined narrative junctures. Interruption types
were manipulated, and resumption difﬁculties were measured in order to
investigate what types of interruption are more (or less) disruptive to speakers’
bookkeeping abilities. Experiments 2 and 3 investigated whether similar
bookkeeping processes exist for syntactic planning during sentence production.

Theories of alignment in dialog (Pickering & Garrod, 2004) should predict
that cooperative interruption types should be more disruptive to speakers than
competitive types. In Experiment 1, speakers described movie clips and a
confederate interrupted at predictable points in the description with four different
types of interruptions. Two types were cooperative: The confederate either
attempted to make sure something was correctly understood, or to clarify some
ambiguity. The other two types were competitive: They altered the purpose of
discourse either by digressing within the framework of the topic, or by introducing
a topic that had no relevance to the task and topic at hand. To preview the

results, several measures of resumption difﬁculty, including resumption lag,

29

errors, and disﬂuencies, showed main effects of interruption type. However, the
pair-wise comparisons did not support the alignment hypothesis: Clarifying
interruptions (a cooperative type) were just as disruptive as the most irrelevant
competitive interruptions. The other two interruption types were roughly equal in
reduced disruptiveness. These results suggest that different properties of
interruptions do carry different costs to bookkeeping, but they also suggest that
other factors (such as attention) are at least as important as alignment.
Speakers in Experiments 2 and 3 were interrupted by grammaticality or
arithmetic judgment tasks during the production of two-clause sentences. Again
previewing the results, when interruption length was properly controlled, domain
had no effect. This implies that bookkeeping processes may be domain-general
memory phenomena. The location of the interruption during sentence production
was also manipulated: Interruptions could occur during the ﬁrst clause, between
clauses, or during the second clause. Analyses of resumption lag data showed
that interruptions are most disruptive at the earliest positions in sentence
production. These results support theories of strategically non-incremental
grammatical encoding (e.g., Ferreira & Swets, 2002). Resumption error data
revealed that errors were reduced when participants were interrupted at clause
boundaries rather than in the middle of clauses. We conclude that the clause is a
unit of sentence-level bookkeeping (Bock & Cutting, 1992). The two approaches
are intended to lay the groundwork for a program of research on conversational

interruptions.

30

EXPERIMENT 1
The ﬁrst experiment addresses a series of related research questions.
These issues include the disruptiveness of conversational interruptions, message
planning and bookkeeping, and the establishment of a psycholinguistic
methodology for investigating interruptions.
Research Questions
The disruptiveness of conversational interruptions. The most basic
research aim of Experiment 1 is to show that interruptions can be disruptive. Of
course, a control condition with no interruptions would be difﬁcult, if not outright
impossible to implement in this dialog study. So rather than compare interruption
to no interruption conditions, I will consider a rejection of the null hypothesis—
that different interruption types do not differ in disruptive properties—to support
the idea that interruptions are disruptive. If it is not possible to prove interruptions
disrupt speakers more than a lack of interruptions, it seems that the next best
option is to show that some types of Cls are, in fact, more disruptive than other
types of Cls. Speciﬁcally, interruptions that tend to promote the alignment of
goals and representations between the participant and the confederate should
result in less disruption than interruptions that interfere with such alignment. This
concept of alignment is inspired by the views of Pickering and Garrod (2004), but
the idea of alignment here is slightly different. Alignment in the Pickering and
Garrod model held for the speciﬁc levels of representation of phonology, syntax,
and meaning. In the present study, alignment refers to whether an interruption

helps interlocutors retain the same task goal or sub-goal as the primary task.

31

Message planning and bookkeeping. The ability of a speaker in a dialog to
keep track of what has already been said, what has yet to be said, and where he
or she currently stands in relation to those positions—a process that Levelt
(1989) termed “bookkeeping”——has very little empirical support to justify its
existence in a language production architecture. Evidence that different failure or
resumption tendencies to recover from interruptions depending on a location
within a message-level bookkeeping hierarchy would provide some empirical
evidence for such an architectural property of language production. Manipulating
Cls experimentally affords the opportunity to explore the nature of the system
that gets interrupted (see Meyer, Irwin, Osman, & Kounios, 1988).

Comparisons and analogies between the TI literature and the message
planning-as-bookkeeping theory are naturally drawn and rich with possibility. The
TI research has shown that a key component of overcoming the disruptive effects
of interruptions is keeping a memory copy of the goal structure that is robust to
the TI. Recovery from conversational interruptions similarly involves the need to
keep a goal structure in mind. I will test whether such principles apply during
language production in dialog. One way in which Experiment 1 will do this is by
testing whether individual differences in working memory are associated with
differential abilities to deal with conversational interruptions.

Establishing an interruptions methodologv for psycholinguistics. Studying
interruptions using a confederate methodology has promising implications for
research in psycholinguistics. The methodology offers a chance to study Cls

themselves by directly manipulating their properties and analyzing their effects

32

on the interruptee. The problem with inferring whether different conversational
interruptions are more or less disruptive is that such work has only been done
under naturalistic conditions in which researchers analyzed videotaped or audio-
taped dialogs (Kennedy and Camden, 1982; West & Zimmerman, 1983; etc.). An
experiment in which Cl types are manipulated experimentally could be used to
demonstrate a causal connection between interruption type and disruptiveness.
Pilot Studies

Two pilot studies were run before the main study. Full details of the
rationale and ﬁndings of these studies are given in Appendix A. Given the results
of these pilot studies and the theoretical problems with Pilot Study 2 that are
outlined in Appendix A, Experiment 1 was designed to test a rather basic idea:
that interruptions that attempt to align the interrupter’s representation with the
interruptee’s representation of a story will result in less disruption than
interruptions that do not lead to such alignment.

Main Study

EM

Participants. Forty-eight participants were tested in exchange for partial
credit in their introductory psychology classes. All were native speakers of
English and students at Michigan State University.

Materials: Dialog task. Participants viewed two ﬁlm clips. Both clips were
taken from obscure 19805 movies with no recognizable actors in order to prevent

the possibility that familiarity (or expertise) with the clips will aid some

33

participants in bookkeeping processes. The clips also have several plot points
that participants would reliably include in descriptions during pilot testing.

One clip was from the movie Basket Case, a low-budget comedy-horror
ﬁlm about a man, Duane, who is surgically separated from his deformed and
murderous conjoined twin, Belial, whom Duane now carries around in a basket.
The movie follows the travails of Duane and Belial as they search in New York
City for the doctors who separated them against their will. The clip has 4 basic
plot points targeted for inclusion in participants' descriptions: 1) a man feeding
hot dogs into a burping basket; 2) a withered, monster-like arm emerging from
the basket and accidentally breaking a television dial; 3) the man running to a
woman’s apartment; and 4) the man and the woman emerging from the Statue of
Liberty and kissing?

The second clip is taken from the movie Breakin’, a movie about two
breakdancers (Ozone and Turbo) from the streets of Los Angeles who befriend a
female ballet dancer (Kelly) from a rich family in the suburbs. The 35-minute clip
shown from this movie also contains 4 plot points targeted for inclusion within
participant descriptions: 1) Kelly and her dance school friend, Adam, drive up to a
beachside park where onlookers have gathered around some performing
breakdancers, including Ozone and Turbo; 2) Kelly and Adam then join the
dancing themselves; 3) some unsavory breakdancing characters, who apparently
belong to a rival breakdancing troupe, then dance in a threatening manner while

icily staring at Ozone and Turbo; and ﬁnally, 4) wanting to avoid a breakdancing

 

2 lmportantly, the clip has no offensive material—the violence, gore, and sexual contents that the
movie does contain are not to be found in the clip shown to participants.

34

confrontation, Kelly, Adam, Ozone, and Turbo all leave the dancing scene in
order to introduce themselves to each other more formally.

Appendix B displays the interruptions used to create a series of
interruption scripts. Each interruption script consisted of 8 interruptions—four for
each description of the two ﬁlm clips that participants watched. Each clip
description had four intended locations at which the participant was to be
interrupted, meaning that each full script contained two interruptions from each
individual interruption type. There were 24 permutations of interruption-type
orders for each description such that each interruption type occurred at each
story point an equal number of times. Forty-eight unique scripts were created.
The ﬁrst 24 scripts randomly paired a set with a particular order of interruption
types for the Basket Case script with a set of interruptions from the Breakin’ list.
The second set of 24 scripts re-shufﬂed the pairing of the Basket Case and
Breakin’ scripts and reversed the order in which the ﬁlm clips were shown to the
participant and described. The order in which the scripts were used was
determined at random before data collection began.

Before data collection, interruption types had been categorized into two
major categories: aligning interruptions that ﬁt the goals of the task to make the
confederate understand the movie clip, and non-aligning interruptions that
digressed from the goals of the task (for a description of how this deﬁnition of
alignment differs from Pickering and Garrod’s deﬁnition, see above). These
categories were themselves split into two types apiece. Aligning interruptions

consisted of repetition interruptions and clariﬁcation interruptions. For repetition

35

interruptions, the confederate stopped the participant’s speech and repeated the
last two or three points that had been made in order to verify her understanding.
For clariﬁcation interruptions, the confederate stopped the description in order to
ask a question that the participant had not addressed, such as the possible
setting of the action.

Non-aligning interruptions also comprised two types: digressions, in which
the confederate took a point the participant had just made and related it to a topic
that did not aid the task goal, and true interruptions, in which the confederate
interrupted in order to address some issue that had nothing at all to do with the
primary dialog. The properties of these two interruption types, along with their
names, are borrowed from the Grosz and Sidner (1986) taxonomy of
interruptions.

As noted previously, all scripted interruptions are listed in Appendix B by
condition and by the point at which they were used in descriptions of the two ﬁlm
clips. There were several constraints to consider in creating the interruption
scripts. The most important constraint, besides creating interruptions that ﬁt the
deﬁnitions of the interruptions types, was how natural each interruption would
sound to a participant. The ideal scenario for the creation of stimuli is one in
which each interruption type has consistent items that can be used in each
condition, but which only vary in one way across conditions. Unfortunately, this
was not possible to implement. Take clariﬁcation interruptions, for example. If a
clariﬁcation interruption at one point in a story description asks for the speaker to

describe a particular object in more detail, one would like to ﬁnd instances at

36

different story points in the two clips when an object could be described more. On
the other hand, such an approach is problematic because, now, all clariﬁcation
interruptions are being represented by only one speciﬁc sort of question. The
solution was to consider the four story points in each story: What is the most
natural interruption that could occur at that point that still ﬁts the particular type of
interruption that is being tested? Unfortunately, with such a small number of
items possible to be used per subject3, this is the best sort of counterbalancing
that could be achieved.

Materials: Working Memorv talk. Two different kinds of working memory
tasks were used: reading span to measure verbal working memory, and spatial
span to measure non-verbal, spatial working memory. Each task consisted of 36
items with 8 trials apiece. Single trials consisted of set sizes of 3, 4, 5, or 6 items.
Trials of each set size appeared twice in each working memory task. Each
participant viewed the same items in the same order.

The 36 reading span items were modeled on the Daneman and Carpenter
(1980) design and then modiﬁed in a manner similar to that described in Turner
and Engle (1989). Each item consisted of a sentence presented above a single
to-be-remembered word that was highlighted in red. Half the sentences made
sense, and the other half were semantically implausible or unlikely. A question
mark appeared after each sentence to indicate to participants that a response to

the sentence was desired (see Figure 2 for examples of each task).

 

3 Given that each description took about 1 to 5 minutes to complete, it would have been
impossible to add any more than 4 interruptions per description. The number of interruptions per
description already seemed somewhat forced at times.

37

Reading Span Spatial Span

 

After dinner the couple had a glass of tree. ?
0

 

 

Figure 2. Item examples, reading span and spatial span tasks, Experiment 1.

Apparatus. Digitized ﬁlm clips were displayed on a Dell monitor. Dialog
between the participants and the confederate was recorded onto a Marantz
digital audio recorder. Two lapel microphones fed into a sound mixer, which sent
an output signal to the Marantz recorder. PowerPoint was used to present the
working memory tasks on the same monitor. Participants recorded their
responses to these tasks in a paper answer packet.

Procedure. Participants were greeted by the experimenter upon their
arrival and brought to a waiting room where a confederate participant was
already seated. The experimenter introduced them to each other and explained,

in general terms, how the experiment would unfold: One of them will watch a ﬁlm

38

clip and describe it to the other person, who will have to take a memory test on
the description. The experimenter then asked the participant and the confederate
each to choose a number between 1 and 10. After both guesses were made, the
experimenter revealed that the number that the confederate chose was closer to
the number the experimenter had been thinking of. The experimenter then asked
the confederate which task she would rather complete: the watching/describing
task or the listening/memory test task. The confederate chose the memory test.
The memory test is merely a cover story that increases the likelihood that the
participant will include important details of the ﬁlm clip in his or her description.

The participant was brought to a room, given the consent procedure, and
then read the instructions shown in Appendix C. The participant then watched
two short clips from highly obscure ﬁlms from the early- to mid-1980’s (Basket
Case and Breakin’). The experimenter then retrieved the confederate participant
and brought her into the experiment room. When they were both seated, the
experimenter explained how to attach the microphones to their clothes and made
sure that each microphone was working properly. When the microphones were
set up, the experimenter told the participant that he or she could begin describing
the ﬁrst clip they just viewed in as much detail for the confederate as possible (for
the later memory test).

The confederate was given a script to follow during the experiment. The
confederate had been trained to interrupt according to the following guidelines.
First, she was trained to make sure to interrupt in the middle of people’s

sentences. This was done because interruptions that occur between sentences

39

may come at times when no plan for the upcoming sentence has been made,
whereas interruptions coming mid-sentence imply that there is something
deﬁnitive to return to. Second, the confederate was asked to make sure to end
the interruption herself by saying, “OK, sorry, go on.” This was done to attempt to
normalize the way in which each interruption ended. Finally, to help prevent
participants from feeling offended by interruptions, the confederate was trained to
be as courteous as possible while interrupting. Speciﬁcally, the confederate was
urged to apologize frequently and to generally act polite.

Each participant was interrupted eight times over the course of the
descriptions of the two clips. Once the participant had ﬁnished the descriptions,
the experimenter brought the confederate into another room for the supposed
memory test. Upon returning roughly 20 seconds later, the experimenter
administered reading and spatial span to the participant, as well as a
questionnaire. The questionnaire was designed to assess two factors: the
participants’ familiarity with the ﬁlm clips, and the extent to which the participant
was aware of the purpose of the experiment. Each experiment ended with a
debrieﬁng session.

Data Analysis

Four primary measures of disruptiveness were used to evaluate
disruption. Two of these were temporal measures of resumption difﬁculty
(primary and secondary resumption lags). These measures were obtained by
having a laboratory assistant listen to waveform ﬁles of the recorded

conversations, then manually measuring time lags between interruption offsets

40

and resumption onsets. Two were transcript-based measures of disruption
(repeated words and disﬂuencies). In order to obtain these measures, one
laboratory assistant transcribed segments of the dialogs containing interruptions,
and another assistant coded the transcriptions in a data ﬁle after reading the
transcriptions.

Prima_rv resumption lag. A standard measure in the task interruption
literature to evaluate the disruptive effect of secondary tasks on primary task
interruptions is the amount of time it takes the participant to resume the primary
task after completing the secondary task (Trafton et al, 2003; Altmann 8. Trafton,
2004). For this case involving naturalistic dialog, we are interested in observing
the time between the offset of the conversational interruption and the onset of the
ﬁrst meaningful word spoken after the offset. The offset is deﬁned as the end of
the last word related to or terminating the material discussed during the
interruption.

Secmd_arv resumption lag. On many occasions, the ﬁrst meaningful word
a participant utters upon resumption is not the ﬁrst word that continues the
speaker's pre-interruption conversational goal. Instead, the speaker could go
back even further in the narrative, skip ahead in the narrative, or utter words
simply to stall as she tries to recall the pre-interruption topic. A measure of
disruption designed to supplement resumption lag, secondary resumption lag,
measures the amount of time it takes after interruption offset to utter the ﬁrst

word that does continue the pre-interruption material.

41

There are two drawbacks to the secondary resumption lag measure. First,
the decision about where a speaker resumes uttering a particular message is
ultimately a subjective one. Second, the measure should be highly correlated
with primary resumption lag, since in many cases, the ﬁrst uttered word will be
the same as the ﬁrst word that resumes the pre-interruption topic. For these
reasons, the measure may not be particularly informative as a measure of
disruption. Hence, I have included two transcript-based measures of disruption:
number of repeated words and number of disﬂuencies. In order to obtain the
measures, a laboratory assistant who also served as the experiment’s
confederate transcribed the speech data from the waveform ﬁles. Then, a
different lab assistant naive to the purpose of the experiment made the relevant
judgments.

Npmber of repeated words. One way to improve the objectivity of
measures to determine whether an utterance continues from where a speaker
had intended to leave off is by examining whether he or she repeats a signiﬁcant
portion of the message that had already been expressed before being
interrupted. Repetition of a message that has already been stated could be
viewed as a fair indication of bookkeeping trouble. If a speaker knows where he
or she left off, then it should not be necessary to repeat that message unless the
speaker is having trouble remembering where the message had been cutoff. An
effective strategy to deal with this inability to correctly keep books would be to go
back in the narrative further than necessary, or else one risks skipping important

pieces of information. Doing so not only helps prevent the omission of important

42

information in the course of the narrative, but also buys the speaker time to plan
the message that was disrupted by the interruption. Granted, it could be argued
that speakers will repeat messages after interruptions solely to reestablish
common ground with the listener (Clark, 1996), but one could also argue that this
re-establishment of common ground would be necessary to use regardless of
interruption type.

Number of disﬂuencies. Speech planning difﬁculty is often accompanied
by an increase in the number of ﬁller-type disﬂuencies that a person utters (such
as uh and um). The number of disﬂuencies a person makes during the
interruption lag augments the resumption lag and word repetition data by
representing yet another indirect measure of resumption difﬁculty. The logic here
is that the more difﬁculty a speaker has recalling the place at which he or she left
off, the more disﬂuencies will be uttered.

Other measures. Two other measures were taken. First, I determined the
length of the interruptions. Although several studies of Tls (Gillie & Broadbent,
1989) have failed to reveal effects of interruption lengths on measures of
disruptiveness, it will be important to rule out this variable as a possible
explanation of effects that could be observed. Additionally, many studies of
priming in language production and comprehension (Branigan et al, 1999;
Wheeldon & Smith, 2003; Scheepers, 2003; cf. Bock & Grifﬁn, 2000) have shown
that the length of the delay between a prime sentence and a target sentence
inﬂuences the degree to which priming holds over the interim. Since the present

task is more linguistic in nature than the tasks studied in TI studies above, it is

43

possible that such length effects could be revealed. Unfortunately, it would be
impossible to regulate the lengths of the interruptions during the experiments
without sacriﬁcing ecological validity. For this reason, the interruption length data
must be examined post hoc to rule out a confound between interruption length
and speech resumption.

The ﬁnal measures to mention are reading span and spatial span. The
rationale for the inclusion of these measures of working memory span is
straightforward: to investigate the role of working memory resources during
resumption from a Cl. People with low working memory (low spans) should have
more difﬁculty resuming speech following a CI than people with high working
memory span (high spans) if working memory plays some role in bookkeeping.
Reading span scores are determined by individual items as has been done in
recent individual differences work (Swets et al, 2005) rather than by computing
the standardized reading span scores used in other studies (Waters & Caplan,
1996).

Hypotheses and Preditions

The Bookkeeping Hypothesis is the theory that speakers in a dialog must
actively maintain a representation of the past, present, and future contents of the
current dialog. This representation consists of a hierarchical dialog event
structure and a pointer that maintains one’s place in the structure. Results from
the pilot studies in concert with the literature review suggesting that bookkeeping
processes are more likely to be disrupted by interruptions that take a speaker’s

attention further away from a bookkeeping pointer (Levelt, 1989; Grosz & Sidner,

44

1986; Pickering, personal communication) suggest that the non-aligning
conditions should be the most disruptive interruption conditions. Speciﬁcally,
these conditions should elicit a) longer resumption lags, b) more disﬂuencies,
and c) more outright resumption failures. The Bookkeeping Hypothesis should
also predict differences within alignment types. Speciﬁcally, repetition
interruptions should be less disruptive than clariﬁcation interruptions because it is
likely that many clariﬁcation questions will address topics that the participant had
not intended to cover. Therefore, repetition interruptions should serve as the
baseline condition for all levels of the independent variable. Also, the true
interruptions should produce more disruption than the digressions because they
are even further from the conversational goals of the speaker.

The Bookkeeping Hypothesis also relies somewhat on the assumption
that working memory supports maintenance of bookkeeping goals during dialog.
A relationship between working memory and disruption must therefore be
observed in order to maintain this assumption.

Results and Discussion

Resumption lag measures. Average primary and secondary resumption

 

lags are shown in Table 1 as well as Figure 3. Images in this dissertation are
presented in color. A one-way ANOVA showed an overall signiﬁcant effect of
interruption type on both primary resumption lag, F(3,141) = 4.27, MSE = .97, p <
.01, and secondary resumption lag, F(3,141) = 6.56, MSE = 2.98, p < .001.
Planned comparisons among the interruption types showed that for the primary

resumption lag measure, the interruption types that resulted in the longest lags

45

were the clariﬁcation and true interruptions, whereas repetition and digression
interruptions resulted in the shortest primary resumption lags. The true
interruptions resulted in signiﬁcantly longer primary resumption lags than the
repetition interruptions, t(47) = 2.37, p < .05, and digressions, although this latter
effect was marginal, t(47) = 3.31, p = .10. However, primary resumption lags
were not signiﬁcantly longer following true interruptions than primary resumption
lags following clariﬁcation interruptions, ((47) < 1. Clariﬁcation interruptions
resulted in signiﬁcantly longer primary resumption lags than both repetition
interruptions, t(47) = 3.31, p < .01, and digressions, t(47) = 2.51, p < .05. Primary
resumption lags were not signiﬁcantly different in the repetition and digression
conditions, t(47) < 1. To sum up, in the primary resumption lag measure, the
repetition and digression interruptions seem to pattern together and the true and
clariﬁcation interruptions pattern together such that the latter types are more
disruptive than the former types, because speakers took longer to begin
speaking again after the latter types. These results are not predicted by
alignment accounts because the clariﬁcation interruptions, which are still related
to the overall discourse goal, cause more disruption than the digressions, which

are not so related.

46

Table 1. Means and standard deviations of the four measures of resumption

difﬁculty, displayed by interruption type.

 

 

 

 

 

 

 

 

 

Interruption Type
Repetition Clariﬁcation Digression True
Measure M SD M SD M SD M SD
RL1 in seconds 1.36 0.85 1.94 1.13 1.43 1.21 1.81 1.21
RL2 in seconds 2.46 2.07 2.76 1.64 1.90 1.45 3.44 1.96
Repeated words 1.14 1.63 1.90 1.86 1.06 1.29 2.19 2.09
Disﬂuencies 0.40 0.45 0.52 0.45 0.35 0.45 0.63 0.47
4 _

 

 

_ Repetition Interruptions
- Clariﬁcation Interruptions

- Digressions

- ”True" Interruptions

 

 

Resumption Lag in Seconds

 

 

 

RL1

RL2

 

 

Figure 3. Primary (RL 1) and secondary (RL2) resumption lags as function of

interruption type. Error bars are standard enors‘.

 

‘ All error bars presented in ﬁgures throughout the present document are standard errors.

47

 

The secondary resumption lag measures showed a similar pattern, with
some small differences. Mainly, the repetition interruptions were relatively more
disruptive than in the primary resumption lag measure, and the clariﬁcation
interruptions were slightly less disruptive (see Figure 3). True interruptions
resulted in the longest secondary resumption lags. They resulted in lags
signiﬁcantly greater than those following repetition interruptions, t(47) = 2.89, p <
.01, and digressions, t(47) = 4.73, p < .001; and they resulted in lags that were
marginally greater than those following clariﬁcation interruptions, t(47) = 1.89, p =
.07. Although digressions showed signiﬁcantly shorter secondary resumption
lags than clariﬁcation interruptions, t(47) = 3.20, p < .01, they were not
signiﬁcantly shorter than in the repetition condition, t(47) = 1.47, p > .10. On the
other hand, repetition and clariﬁcation interruptions did not differ from each other
signiﬁcantly, t(47) < 1. Hence, those two types patterned together as would be
predicted by alignment accounts of dialog.

The problem for the alignment account with respect to these data are the
digression results. A condition in which a completely unrelated topic is introduced
during an interruption should derail the speaker’s attention more than the two
aligning conditions. However, such a pattern of results was not observed in either
the primary or secondary resumption lag measures. Instead, what the two sets of
results share in common is rather amorphous compared to a simple alignment
account. The true interruptions, as predicted, are greatly disruptive relative to the
other interruption types in both resumption lag measures. However, in contrast to

the predictions, the clariﬁcation interruptions are quite disruptive, and the

48

digressions hardly disruptive. Because this pattern propagates through the
remainder of the measures to present, I will offer explanations for these ﬁndings
once all of the data are presented.

Number of repeated words. The dependent measure of number of words
repeated upon resumption is intended to represent the need for speakers to
backtrack upon being disrupted by an interruption. There is a need to backtrack
because of the uncertainty of where one had left off. Data from this measure are
presented in Table 1 and Figure 4. A one-way ANOVA was performed to
determine whether speakers tend to repeat words that had been uttered prior to
interruption. Results showed a signiﬁcant overall ANOVA, F(3,141) = 5.15, MSE
= 2.91, p < .01. Planned comparisons revealed a pattern very similar to that
found in the resumption lag measures. Namely, the clariﬁcation and true
interruption conditions were not signiﬁcantly different, t(47) < 1, nor were the
repetition interruptions and digressions, t(47) < 1. However, as with the
resumption lag measures, the clariﬁcation and true interruptions resulted in more
repeated words than the repetition and true interruptions (clariﬁcation vs.
repetition, t(47) = 2.14, p < .05; clariﬁcation vs. digression, t(47) = 2.66, p < .05;

true vs. repetition, t(47) = 2.59, p < .05; true vs. digression: t(47) = 3.25, p < .01).

49

 

3.0 -

- Repetition Interruptions
- Clariﬁcation Interruptions
_ Digressions

- "True" Interruptions

2.5 -

 

 

 

2.0 -

Average Number of Repeated Words
01

0.5 -

 

 

 

0.0 -
Figure 4. Average number of repeated words as a function of interruption type.

A clear pattern is beginning to emerge: the clariﬁcation and true
interruption types seem to be more disruptive than the repetition and digression
types. There is one more dependent measure of disruption to examine:
disﬂuencies.

Disﬂuencies. Table 1 and Figure 5 display the mean proportion of trials
during which speakers produced disﬂuencies upon resumption in Experiment 1,
presented by interruption condition. As Figure 5 hints, a one-way ANOVA
revealed an overall effect of interruption type, F(3,141) = 4.97, MSE = .16, p <
.01. Again, the true interruptions and the clariﬁcation interruptions seemed to be
more disruptive than the other two types of interruptions, although the difference

between clariﬁcation and repetition interruption conditions was not signiﬁcant,

50

t(47) = 1.37, p > .10. True interruptions resulted in signiﬁcantly enhanced
tendencies to produce disﬂuencies upon resumption than both repetition
interruptions, t(47) = 2.83, p < .01, and digressions, t(47) = 3.45, p < .01; the true
interruption type did not differ signiﬁcantly from clariﬁcation interruptions, t(47) =
1.49, p > .10. Finally, the difference between the clariﬁcation and digression
conditions was significant, t(47) = 2.32, p < .05, but digressions and repetitions

did not differ, t(47) < 1.

 

_O
co
4:

- Repetition Interruptions
- Clariﬁcation Interruptions
_ Digressions

- "True" Interruptions

 

 

 

p
O)
J

Average Propotion of of Disﬂuency Trials
9 o
M h

 

 

 

0.0 -

Figure 5. Average proportion of resumptions containing disﬂuencies (uh or um).
Given that the four measures of resumption difﬁculty all reveal the same

general pattern of results, it is reasonable to conclude that this pattern is robust,

and that true interruptions and clariﬁcation interruptions are more disruptive than

repetition interruptions and digressions as deﬁned in Experiment 1. However,

51

there are some alternative considerations that may explain some of the results,
such as length of interruption, practice effects, and participants’ awareness of the
purpose of the experiment. I will presently discuss these issues.

Length of interruption. It would be problematic if the Experiment 1 patterns
of disruption occurred simply because the more disruptive interruptions
(clariﬁcation and true interruptions) happened to take longer. Average
interruption lengths by condition are shown in Figure 6. It is clear that the
repetition and digression interruptions were longer on average than the
clariﬁcation and true interruptions. In fact, there was an overall signiﬁcant effect
of condition on interruption length in a one-way ANOVA, F(3,141) = 18.22, MSE
= 4.65, p < .05. Given the implausibility of an assumption that shorter interruption
lengths could lead to greater disruption, it appears that interruption length is not a

concern for the pattern of results found in Experiment 1.5

 

5 In fact, Experiments 2 and 3 test the above implausible assumption and show it to be false.

52

 

181

- Repetition Interruptions
- Clariﬁcation Interruptions
_ Digressions

_ "True" Interruptions

16-

 

 

14-

 

Time in Seconds

 

 

 

Figure 6. Average interruption length by type.

Practice effects. One could reasonably suspect that many differences that
arose between conditions were found solely in the ﬁrst half of the description
phase. That is, once a speaker has been interrupted a few times already, one
might adapt to the interlocutor and come to expect more interruptions. However,
due to the particulars of the experiment design, it is difﬁcult to examine this issue
statistically. The problem is that there were only two observations per interruption
condition per subject at most. Moreover, many participants were only subjected
to only one of the interruptions, generally because they failed to mention one of
the plot points targeted for interruption. The major goal for each participant was
to make sure that each interruption condition was represented once over the

course of the two clip descriptions, and this goal was satisﬁed. But because

53

fewer than half of the participants tested ﬁlled out each cell of the 4 (interruption
type) x 2 (clip number) design, any data presented from such an analysis would

have to be considered unstable. My hope is that future work will be able to avoid
this problem.

Having stated that, informal investigations of dependent measure means
by description number (1"t versus 2"“) hint that a large portion of the effects of
interruption type observed in the present study occurred during the ﬁrst
description. Take Figure 7, below, which displays the average number of
repeated words by condition divided into ﬁrst-clip descriptions (on the left-hand
side) and second-clip descriptions (on the right-hand side). It seems as though
the effects were much stronger on average during the ﬁrst set of descriptions
than during the second. Other measures, such as secondary resumption lag,
also showed such a trend. Again, however, it is important not to over-interpret
these informal observations, as they average across many cells of missing data.6
The experiment was not suited to measure practice effects by ensuring an

observation in each cell of the design required for such an analysis.

 

° lnforrnal observations of the data also hinted that interruptions were not any more or less costly
from the ﬁrst description to the second in terms of grand mean disruptiveness.

54

 

3 0 _ - Repetition

' _ Clariﬁcation
- Digression

2‘5 _ _ True

 

 

 

2.0 -

Number of Repeated Words
2;

0.5 -

 

 

0.0 -
Clip 1 Clip 2

Number of Clip Description
Figure 7. Number of repeated words as a function of clip number and interruption
type-

Awareness of purpose. During the course of pilot testing, it became
apparent that trying to interrupt people more than a few times within the course of
a limited time period might raise some suspicions about the purpose of the
experiment. Because of this tendency, participants were given a brief
questionnaire after the experiment in which they commented on whether they
had noticed anything “unusual” during the experiment. Participants were coded
into three categories: 1) Fully unaware of the purpose of the experiment
(comprising 29% of participants); 2) aware of the unusual way in which the other

“participant” (the confederate) kept interrupting (44% of participants); and 3)

55

aware that the confederate participant was intentionally, or artiﬁcially, interrupting
(27% of participants). When this measure was used as a covariate in the
ANOVAs of the four dependent measures presented earlier, it produced no
interactions with the effects of interruption condition, Fs < 1. Therefore, it appears
that a meta-awareness of the purpose of interruptions affords interruptees few
advantages.

Working memom Oddly, none of the dependent measures of interest
shared any type of correlation with either verbal or spatial working memory span
(all rs non-signiﬁcant). There are two possible sources of explanation: either
there was not enough power to observe the correlations, or the types of
disruption measured in the present experiment are outside the domain of working
memory. With regard to this latter possibility, what may be happening is that the
types of working memory measured by reading span and spatial span
correspond to ofﬂine processing phenomena (Waters & Caplan, 1996). That is,
perhaps the types of working memory that underlie the ability to keep track of a
bookkeeping pointer occur at a level that the memory tasks that were used do
not adequately measure (Waters & Caplan, 1996).

Summam. Although the predicted pattern of results did not emerge, the
pattern of resumption difﬁcultly that did emerge was fairly consistent. It appears
that the two interruption types most associated with relatively large costs in
speech resumption are clariﬁcation interruptions and true interruptions. This was
true in observing both primary and secondary measures of resumption lag,

unnecessarily repeated words, and disﬂuencies. Because clariﬁcation

56

interruptions were predicted to pattern along with repetition interruptions, and
digressions predicted to pattern along with true interruptions, these results are
somewhat surprising.

However, these data also allow for several conclusions to be drawn. The
ﬁrst is that different kinds of interruptions produce different amounts to disruption
to bookkeeping processes, or the memory and attention processes devoted to
keeping track of where one is in a dialog. Therefore, conversational interruptions
are, in fact, disruptive, and this is the ﬁrst study to demonstrate this phenomenon
experimentally. Furthermore, there are at least two properties of interruptions that
are likely to cause more disruption than others. The ﬁrst of these properties is the
relevance to the overall goal of a dialog, or DP according to the terminology of
Grosz and Sidner (1986). The second of these properties is the amount of
response to an interruption that is required before returning to one’s previous
discourse location. The underlying factor that unites these two properties of
interruptions is attention. If an interruption is wildly off-topic, but lacks any
noteworthy content and requires no response (as with the digression type), the
interruption will result in relatively meager disruption. On the other hand, if an
interruption is wildly off-topic, as well as interesting, unexpected, or bizarre, it
seems that disruption to bookkeeping processes can be expected because
attention has been successfully drawn away from the previous bookmark.
Conversely, if the topic of an interruption is aligned with the overall discourse

goal, but requires lengthy vocal response from the primary speaker (as with the

57

clariﬁcation interruption types), attention is similarly diverted from a dialog’s

bookkeeping pointer. I will return to these issues in the General Discussion.

58

EXPERIMENT 2

Though Experiment 1 has many positive features for the psycholinguistic
investigation of interruptions (especially its quasi-ecological nature), it does not
have a very typical psycholinguistic design or approach. Psycholinguistic
experiments are typically more tightly controlled than a naturalistic dialog
situations could ever allow (Clark, 1996; Pickering & Garrod, 2004). The area of
interest of Experiment 1 is also outside the scope of typical psycholinguistic
research—interruptions, message planning, and prospective memory are under-
represented in psycholinguistics. Experiment 2, in which individual sentences of
speakers are interrupted at predictable locations during production by tightly
controlled experimenter-induced interruption, attempts to ﬁll in these potential
deﬁciencies of Experiment 1. The objective of this experiment is to investigate
the way syntactic representations of speakers are accessed for production before
and after interruptions. I created a task to test whether syntactic planning is
analogous to ﬁnding one’s place within a bookkeeping representation in the
message representation. The task, in other words, was designed to determine
whether speakers keep “syntactic books”. The experiment also examines the
issue of incrementality in language production.

Participants in Experiment 2 were interrupted by an experimenter at
various points during the production of two-clause utterances that were elicited
with stimuli similar to those used in Smith and Wheeldon (1999). The
manipulation of interruption location (mid-ﬁrst clause, between clauses 1 and 2,

and mid-second clause) will test various hypotheses about the need to keep

59

“syntactic books” (records of syntax uttered and syntax planned) during language
production. The interruptions will be of two types: verbal (a grammaticality
judgment task) and non-verbal (an arithmetic judgment task). This manipulation
will help clarify unresolved issues from the literature on task interruptions that
were raised by Gillie & Broadbent (1989). It is important that both types of
interruptions be sufﬁciently demanding in order to properly test hypotheses
relying upon the assumption that interruptions are disruptive to primary task
performance due to such memory issues as prospective goal encoding, cue
retrieval, interference, and decay.

math—0d

Participants. Thirty-six participants were recruited in the same manner as
in Experiment 1.

Materials: Picture Description. Materials similar to those used in Smith and
Wheeldon’s (1999) picture description task were used to elicit multiple clause
sentences. The stimuli from this experiment depict arrays of several objects that
depict movement above, below, or next to each other. The individual objects that
comprised the whole picture arrays were taken from a database of rendered
color images of simple objects created by Bruno Rossion, available free of cost
at mpzllwwwcoggrown.edu/~tarrlstimuli.html. The objects are catalogued by
naming times and errors in a database available in Rossion & Pourtois (2004).
This database was chosen rather than the Snodgrass and Vanderwart (1980)
object set because Rossion and Pourtois demonstrated that the color objects in

their set were signiﬁcantly faster and more accurate to be named than the line

60

drawings used by Snodgrass and Vanderwart (1980). All objects that were
chosen for images in Experiment 2 had single-syllable names that were selected
for their high naming accuracy and low reaction time according Rossion and
Pourtois (2004). It was important that all objects be nameable within a certain
range of accuracy and time. The object names used as experimental items are
listed in Appendix D, along with their mean naming times and accuracy as
reported by Rossion and Pourtois (2004).

Objects were digitally arranged into arrays so that three objects at a time
appeared side-by-side, in a row, in each array. Twenty-four objects were chosen
to appear in 24 different object arrays which comprised the experimental items.
Hence, each object appeared in three different experimental arrays. There were
a number of constraints considered in creating these arrays. First, no object
could be shown in the same position twice. Second, no object could be paired
with another object more than once. Third, objects that were similar in visual or
phonological form, or semantically related to other objects, were placed in
separate arrays. The “experimental” objects were also placed into some ﬁller
picture arrays in order to prevent participants from expecting certain target
grammatical structures given some set of objects. The remaining objects for ﬁller
arrays were selected from a set of 30 highly nameable, single-syllable objects
that were chosen from the same database as the experimental objects.

Half of the experimental arrays depicted the left-most object moving down
by placing a downward arrow underneath it, and depicted the right-most object

moving up by placing an upward arrow above it. The other half of the arrays

61

depicted the opposite: the left-most object moving up and the right—most object
moving down. Figure 8 below shows an example object array. The numbers at
the bottom of the arrays, as shown, carried the purpose of signaling to the
experimenter when to interrupt. The middle digit of each 3-digit number at the
bottom of the screen was the key: If the middle digit was a 0 or 1, a ﬁrst-clause
interruption was signaled; If the middle digit was a 2 or 5, a between—clause
interruption was signaled; and if the middle digit was a 3 or 9, a second-clause
interruption was signaled. As revealed by a post-experiment questionnaire, all
participants were unable to learn this pattern, and most never even noticed the

numbers.

 

109

Figure 8. Example Object Anay, Experiment 1.

62

The target sentences for the experimental object arrays were two-clause
sentences such as “The spoon moves above the box and the bowl moves below
the box.” Twenty-four multi-clause sentences were elicited as experimental items
to be interrupted. Seventy-two ﬁller sentences were also elicited. Twenty-four of
the ﬁllers were multi-clause utterances similar to the experimental items;
however, no interruptions occurred during the production of these sentences in
order to minimize the extent to which participants could predict the circumstances
under which they will be interrupted. The remaining 48 ﬁller sentences were
simple, single-clause sentences in which a single movement event was
described. Half of these ﬁller descriptions were interrupted to minimize
participants’ ability to predict when interruptions would occur. All experimental
and ﬁller target descriptions are listed in Appendix E.

M_at_eﬂls: Speeded Grammaticalitv Juc_lg_ment. A list of 120 sentences that
had been used in previous grammaticality judgment experiments were pilot
tested to determine mean item latencies and accuracies. A set of 20 sentences,
half grammatical, half ungrammatical, were chosen because the eight pilot
participants answered them all correctly within a range of 3400 to 4400
milliseconds. This time range was chosen because the interruption task time
window was set for 4000 milliseconds. The goal was for the interruption task to
be as disruptive as possible (within reason). Hence, sentences that took just
under or just over 4000 ms to respond to were selected. The sentences chosen

are listed in Appendix F.

63

Materials: Speeded Arithmetic Judmnent. During the pilot testing of
grammaticality judgment problems, 26 arithmetic problems were also presented
to participants. Half of the problems showed an incorrect solution, and other half
showed a correct solution. All problems were addition problems, and they ranged
in difﬁculty from simple addition of 1-digit and 2-digit addends (easiest), to
addition to two 2-digit addends (harder), to addition of two addends with decimal
places to the tens place (hardest). Participants in the pilot test were to judge
whether the problem was solved correctly. This pilot test showed that the two 2-
digit addend problems ﬁt best within the range of the 3.5— to 4.5—second
interruption task window. Having found this, a set of 20 such problems were
taken from Ferreira and Swets (2002) and modiﬁed to include either a correct or
incorrect solution. These problems are listed in Appendix F.

Mp. Two independent variables were manipulated within-participants.
The ﬁrst, interruption domain, had two levels: verbal (same domain) and
arithmetic (different domain). The second variable, interruption site, had three
levels, chosen by their location in the sentence. Speakers were interrupted at
one of three sites: the middle of the ﬁrst clause (e.g., The spoon moves
INTERRUPT above the kite and the bowl moves below the kite), between the
ﬁrst and second clauses (e.g., The spoon moves above the kite INTERRUPT and
the bowl moves below the kite), and the middle of the second clause (The spoon
moves above the kite and the bowl moves IN TERRUPT below the kite).

Each item (deﬁned here as an object array with a particular target

description) was represented in each of the six cells of the design an equal

64

number of times. Four blocks of 6 lists apiece were created. In each block, a
particular item was counterbalanced across the two levels of the interruption type
variable and across the three levels of the interruption locus variable. This is how
the six lists were produced. Interruptions were sorted by type then assigned
randomly to the experimental and ﬁller items. In three of the lists, a particular
experimental item was associated with a particular interruption of one type—
arithmetic or grammaticality judgment. Consequently, the other three lists saw
that same item associated with a different interruption of the other type. Filler
items and ﬁller interruptions remained constant across each list in a block. Each
list was given its own pseudo-random order.

A second block of lists was created in which the interruption tokens
associated with each item were rearranged such that all of the interruptions used
for ﬁller items in Block 1 became interruptions used for experimental interruptions
in Block 2. This rearrangement was done in order to reduce possible error due to
the fact that particular interruption tokens may be disruptive than others. The lists
in Block 2 were also each given their own pseudo-random order.

Blocks 3 and 5 re-shufﬂed the random orders of the lists of Block 1, and
Blocks 4 and 6 reshufﬂed the random orders of the lists of Block 2.

Apparatus. The experiment was carried out on a Dell computer using E-
Prime software. Vocal responses were recorded as in Experiment 1 onto a
Marantz digital recorder for later transcription. An E-Prime microphone and

voicekey apparatus recorded participants’ resumption times post-interruption.

65

Procedure. Each experiment began with a practice session during which
the participant was trained to perform all three tasks required during the
experiment. First, they were trained in the speeded grammaticality judgment
task; then the speeded arithmetic judgment task; and ﬁnally, the description task.

Each trial of the experiment began in the same way: the participant looked
at a ﬁxation point in the center of a computer screen and pressed a button to
begin. An object array then appeared, and the participant began describing the
array quickly, accurately, and with well-formed, complete sentences.

On occasional predetermined experimental trials, the experimenter
pressed a button in the middle of the participant’s utterance that caused a “beep”
to sound. The beep sound was accompanied by a ﬁxation screen that lasted for
250 ms. During the practice session, the participant was told that the beep
signiﬁed that the participant was to immediately cease speech and perform the
task that came up on the screen after the 250-ms ﬁxation screen. Half of these
interruptions were speeded grammaticality judgment tasks, and half were
speeded arithmetic judgment tasks. There were 42 total interruptions during the
experiment: 24 occurred during experimental trials, and 18 occurred during ﬁller
trials. The remaining 52 ﬁller trials had no interruptions. The judgment tasks were
designed to last equal amounts of time (4 seconds), and they discontinued at the
4-second point regardless of whether the participant had ﬁnished the problem he
or she was working on.

When the 4-second window terminated, another beep sounded, and the

original image that the participant had been describing was presented again. The

66

instructions were to then resume the description of the image from precisely
where the speaker had left off. After the participant ﬁnished the description, the
experimenter pressed a button. The participant could then begin the next trial by
pressing a button. No-interruption ﬁller trials also began with a participant button
press, also contained a description, and also ended with an experimenter button
press—however, there were no beeps or judgment tasks in those trials.
Data Analysis

As in Experiment 1, several dependent measures were used to estimate
the effects of interruption type and location on disruption. These measures were
similar to those used in Experiment 1, but also had notable differences. One
measure was resumption lag, or the time lag from the offset of the interruption to
the onset of speech. This measure corresponds to the primary resumption lag
measure used in Experiment 1.7 Three separate dependent variables measured
disruption by examining resumption errors. That is, after performing an
interruption task, participants would often resume speech ahead of, but more
often, behind where they had left off. This type of error, particularly those errors
in which participants skip ahead, thus omitting information, are indicative of
bookkeeping disruption. I therefore measured the proportion of resumptions that
contained repeated words; the proportion of resumptions in which words were
skipped after an interruption; and the number of words by which speakers were

“off” upon resumption, which is like averaging the absolute values of the number

 

7 There was no measure of secondary resumption lag in Experiment 2, partially because of time
and resource limitations, but mainly because the measure would not be nearly as informative.
Speciﬁcally, there are no instances of speakers talking about anything besides the pictures in
front of them, and so there were never any points at which one could say that, yes, the participant
began speaking again here—but not about the picture.

67

of repeated words and the number of skipped words across subjects. First, I will
fully describe each of these measures below. Then I will detail some extraneous
variables that may contribute to disruption. It is important to rule out these

variables as possible causes of disruption to validly test the present hypotheses.

Resumption lag. The logic behind this measure follows that of Experiment
1: The more a particular interruption type or location disrupts bookkeeping, the
longer it should take that speaker to begin speaking again. An E-Prime voice-key
mechanism automatically collected this measure as the time from the re-onset of
the object array following the interruption to the time when the participant ﬁrst
triggered the voice key afterward.

Resumption errors. A laboratory assistant na'r've with respect to the
hypotheses of the experiment transcribed participants’ utterances and coded
them as presently described. Three types of resumption errors were coded from
transcripts of participants’ recorded utterances. The ﬁrst type of error was the
proportion of trials in which the speaker repeated words that had already been
uttered. Instructions to participants stated explicitly to resume sentences from
exactly where they had left off after the interruption task. Because of this, the
present experiment assumes that retracing words from before the interruption
must be an error. Utterance 1 provides an example of an utterance containing
such repetition.

1. “The frog moves below the cat and the cake moves (TASK) the cake

moves above the cat.

68

In this case, the speaker repeated the words the cake moves even though those
words had already been uttered, and would already have been understood by the
theoretical audience. Hence, this utterance would be (and was) coded as a
repeat. For future reference, the coder also correctly noted that three words were
repeated. The coder was instructed to include as repeats only words that had
been uttered in their entirety before performing the interruption task. In other
words, if some began saying below, but only uttered be! before being interrupted,
and then resumed by saying below in its entirety, such a scenario was not coded
as a repeat.

The second type of error to be coded was the proportion of resumptions in
which a participant skipped words germane to a grammatical sentence. Unlike
Experiment 1, in which the contents of upcoming utterances were impossible to
predict, each utterance in Experiment 2 was entirely predictable. If, as described
in Utterance 1, a participant saw a frog moving above a cat and a cake moving
below that cat, only one utterance is possible (with a few unimportant, and
likewise predictable, exceptions). Because of this predictability, it was possible to
measure how far ahead a participant “skips” into sentence production. Take
Utterance 2:

2. “The leaf moves (TASK) and the glass moves below the foot.”

The speaker of this sentence was describing a slide in which a leaf moved above
a foot and a glass moved below the foot. The target utterance is as follows: The
leaf moves above the foot and the glass moves below the foot. The underlined

portion of this target utterance, consisting of three words, is what the speaker

69

had neglected to utter, or skipped. Much like repeats, a word that was only
partially uttered before an interruption could not be counted as being skipped in
the resumption. A word had to be skipped completely to be considered a skipped
word.

The signiﬁcance of this measure of skipped words is that it is perhaps the
clearest demonstration of disrupted bookkeeping. One could reason that
repeating words is a natural component of sentence resumption, and, in fact,
participants often appeared to repeat words simply out of habit. If a speaker
repeats words, it is still possible to understand what he or she is saying, because
everything necessary is said—in some cases, twice. On the other hand, skipping
forward in a sentence often results in an utterance that is either ungrammatical or
fails to adequately capture a description. Utterance 2 is not ungrammatical, but it
does not express the entire message a hypothetical listener would need in order
to fully imagine the picture. It is important that Experiment 2 demonstrates cases
of this type of disruption, not only to measure how the independent variables
impact their occurrence, but also to justify the use of repeated words as a
measure of bookkeeping disruption. If people repeat words but never skip them
upon resumption, it could be argued that no disruption had taken place at all, but
merely some type of habit of repetition. The ﬁnding that speakers skip words in
sentences after interruptions would be excellent evidence that the interruptions
used in Experiment 2 can cause speakers to lose track of their place in sentence

production.

70

Finally, the number of repeated words and the number of skipped words
were coded into a single measure: number of words in error. For this measure,
repeating two words and skipping two words were both coded the same way.
The advantage of this measure is that it codes not only the total errors in one
measure, but does so in a way that represents the scope of the error.8

Other measures. As in Experiment 1, there are several factors that could
obscure interpretation of results. One of these factors is the accuracy and speed
with which participants performed the interruption tasks in the different
conditions. If the arithmetic problems that were used as interruption tasks were
more difﬁcult, or simply took longer, than the grammaticality judgments (or vice
versa), then it may be difﬁcult to determine whether it is the time spent doing the
task or its domain that would lead to differences in resumption measures.
Another issue is that of experimenter reaction time lag—the ability of the
experimenter to press a button to interrupt the speaker at precisely timed points
in relation to spoken clause boundaries. This could be a problem because it
recruits human timing to make the judgment call of when to hit the button to
interrupt the speaker. Since a major variable in this study is the location of the
interruption, it is important to minimize the error due to experimenter reaction
time lag.

Another precaution has to do with the HCI ﬁndings (Trafton et al, 2003;
Hess & Detweiler, 1994) that repeated exposure to interruptions during complex

primary tasks reduces the disruptive effects that can be observed in resumption

 

3 Because of the noticeable infrequency of ﬁller-type disﬂuencies in Experiment 2, such
disﬁuencies were not used as a measure of disruptiveness in Experiment 2.

71

lag measure—in other words, practice effects. This reduction of RL effects due to
practice may result in the need to include fewer rather than more interruption
trials, which could reduce the power of Experiment 2.9
Hypotheses apd Predictions

Domain effects. How is bookkeeping during language production
represented? ls bookkeeping a domain-general phenomenon that requires
general attentional and memory resources, or is it domain-speciﬁc such that only
verbal or non-verbal interruptions will sufﬁciently disrupt it? Disruptive
interruptions requiring a lot of verbal response from the speakers in Experiment 1
raised the possibility that verbal interference could play a role in resumption
difﬁculty following interruptions. Moreover, Gillie & Broadbent (1989) found that
task interruptions of the same domain as the primary task were more disruptive
than task interruptions of different domains. The hypothesis that similar effects
will be observed for interruptions to sentence production, a more natural mode
for interruptions, will be termed the Same-Cost Hypothesis. This hypothesis is
based on the assumption that within-domain interruptions produce more
interference for the prospective goal signal than extra-domain interruptions. The
hypothesis predicts that the grammaticality judgment task will be more disruptive

to speakers than the arithmetic judgment task because the interruptions taxing

 

9 It may also be of some concern that the total time spent “interrupting” in this task is almost as
great at the amount of time it takes to produce a single utterance. In that sense, this experiment
would not be a study of interruptions, but rather of task switching. However, there are answers to
this concern. First, a single trial follows the format of interruptions put forth by Trafton et al (2003):
there is a primary task (describing the picture), a secondary task (judgment task), and a
resumption of the primary task. Additionally, there are multiple trials in a row during which no
interruptions occur. In that sense, the primary task of description lasts quite a bit longer than a
single interruption because it lasts over several trials spanning up to 20 seconds, whereas
interruptions in this task last for only a few seconds.

72

the same verbal resources as primary tasks will be more disruptive than
interruptions that tax more general or otherwise-speciﬁed resources. The Same-
Beneﬁt Hypothesis, however, holds that cross-modal cognitive exertion will cause
more interference when recalling the goals of the primary task (speech) when the
recall of those goals is necessary, leading to greater resumption lag costs.
Locality effects. The other primary question to address in Experiment 2 is
one that is of both practical and theoretical concern. The question is this: At what
point during sentence production is it most costly to be interrupted? Are early,
middle, or late interruptions the most costly? The theoretical interest of this
question lies in the contrast between a purely temporal explanation for disruption
and an explanation that rests on hierarchical considerations. A temporal
explanation could be considered from two points of view—is it more costly to be
interrupted while the path ahead is a long and arduous one, or is it more costly to
be interrupted after the path has already been traveled? One point of view is that
interruptions are most costly at the ends of sentences because speakers must
have the representation of the entire already-uttered sentence stored in order to
return to the correct bookkeeping pointer. The other point of view is that it is most
costly to be interrupted at the beginning of a sentence because a great deal of
the utterance plan has yet to be executed. This view holds that speakers create a
global syntactic plan for a sentence that must be actively maintained in memory
during production of the entire sentence. The further one progresses in the
production of an utterance, the less prospective memory the speaker must

maintain to complete the sentence. This viewpoint relies on key ﬁndings in the

73

language production literature (Christianson, 2002; Ferreira & Swets, 2002,
2005; Swets & Ferreira, 2003) showing that speakers plan much more content
before speaking and during speech than allowed for by the phrase-by-phrase
view that has been previously endorsed (e.g., Smith & Wheeldon, 1999, 2001). In
other words, the hypothesis that early interruptions are more disruptive than later
interruptions is a hypothesis for non-incremental language production, and it
predicts that speakers interrupted early in a sentence show more disruption than
speakers interrupted later in a sentence.

On the other hand, perhaps the question of locality in interruptions to
sentence production has little to do with temporal issues, and much more to do
with hierarchical, or syntactic issues. It is possible that the most important
consideration in determining the relative effects of interruption at different points
in sentence production is simply the proximity to clause boundaries. It may be
either more or less disruptive to be interrupted when at or near a clause
boundary than when one is articulating the middle of a clause. If this were the
case, bookkeeping processes would be shown to involve a clear syntactic
component. Such a result would also verify that, like task interruptions,
conversational interruptions are best handled at coarse rather than ﬁne break
points (Adamczyk & Bailey, 2004).

Results and Discpssion

This section will be organized as follows. First, I discuss the accuracy and

judgment time data. These results are presented ﬁrst because understanding the

nature of these measures is crucial to interpreting the resumption lag and

74

resumption error data that are presented aftenrvard. Presentation of data relating
to possible practice effects follows. Finally, I summarize the data of Experiment 2
and discuss the larger implications of the results.

Interruption Task Accuracy and Judgment Time. Participants were quite
accurate when responding to the interruption tasks. On average, participants
were able to correctly answer the tasks within the 4-second window with 76.4%
accuracy. However, when these numbers are broken down by experimental
condition, an interesting effect occurred. The left half of Figure 9 illustrates this
effect. A 2 (interruption type) x 3 (interruption locus) repeated measures ANOVA
showed a main effect of interruption type, F1(1,35) = 21.89, MSE = .06, p < .001;
F2(1,23) = 31.28, MSE = .03, p < .001. Speciﬁcally, participants were much more
likely to judge arithmetic problems correctly, and in time (M = .84, SD = .12), than
to make correct grammaticality judgments in time (M = .69, SD = .15). There was
no main effect of interruption locus on this measure, nor did the two factors

interact, all Fs < 1.

75

 

- 1st Clause

- Between 1st and 2nd Clauses
- 2nd Clause

1.0 I

 

 

 

 

 

0.8 'I
E
t 0.6 -
O
O
C
.Q
1:
8
9 0.4 ‘
o.

0.2 —

0.0 —

Language Arithmetic Language Arithmetic
All Trials Answered In Time

Figure 9. Proportion of conectly answered intenuption tasks.

But consider the right half of Figure 9, in which accuracy rates are
depicted from trials in which participants answered in time. Clearly, if participants
gave an answer in time, they were at least as accurate for grammaticality
judgments as they were for arithmetic problems. A two-way ANOVA conﬁrmed
that there was no main effect of interruption type on accuracy when only trials
answered in time are considered, F(1,35) = 2.20, MSE = .043, p > .10.
Additionally, there was no main effect of interruption locus, nor was there an

interaction between interruption type and locus.

76

The comparison between these two accuracy measures reveals depicted
in Figure 9 suggests that participants do not ﬁnd the grammaticality judgments
any more difﬁcult than thearithmetic judgments. The grammaticality judgments
simply took longer to complete, as evidenced by the ﬁnding that accuracy rates
were so much better for language tasks if all trials were included in analyses.

More evidence that language tasks simply took longer to answer than
arithmetic judgments can be seen in the judgment time data. Figure 10, below,
shows mean judgment times broken down at each level of both independent
variables. The left half of Figure 10 shows the means by subject for each trial in
which the interruption task was answered within the available 4-second
window.10 The right half shows mean times to answer on only those trials that
were answered correctly. Since both analyses showed the same results, I will
present statistics only from the “in time” measure. A 2 x 3 repeated measures
ANOVA revealed a signiﬁcant main effect of interruption type, F1(1,35) = 102.33,
MSE = 161,200, p < .001; F2(1,23) = 147.89, MSE = 75136, p < .001, such that
grammaticality judgments (M = 3042 ms, SD = 259) took longer on average than
arithmetic judgments (M = 2490 ms, SD = 371). The main effect of interruption
locus was also signiﬁcant by participants, F1(2,70) = 3.17, MSE = 69,066, p <
.05; but not by items, F2(2,46) = 2.04, MSE = 45635, p > .10. It seems that the
locus effect was driven by the third location. When speakers were interrupted

during the second clauses of sentences, they took longer to make judgments (M

 

‘° Due to software limitations, it was not possible to obtain judgment time measures for trials in
which the participants took longer than 4 seconds to answer. If such a measure was possible, it
would probably show even larger effects of interruption type on judgment time, based on the
comparison of accuracy rates in Figure 9.

77

= 2829, SD = 355) than when interrupted during the ﬁrst clause (M = 2725, SD =
311), t(35) = 2.36, p < .05, or between the ﬁrst and second clauses (M = 2745,
SD = 270), t(35) = 1.98, p = .056. It is difﬁcult to say why such an effect occurred,
but it may suggest that interruptions are more difﬁcult to process when a

sentence has nearly been completed. The two factors did not interact.

 

 

 

 

 

 

- 1st Clause
- Between 1st and 2nd Clauses
3500 - - 2nd Clause

3000 —

2500 -
(I)
'U
C

§ 2000 -
g
S

E 1500 -
0)
.§
[—

1000 -

500 4

0 _

Language Arithmetic Language Arithmetic
Answered In Time Correct Only

Figure 10. lntenuption task judgment latencies as a function of intenuption
domain and locus.

Again, even without including trials in which participants did not answer in
time, language interruptions took about a half second longer than arithmetic
interruptions. This result is important to introduce before the rest of the results

because it could color interpretation of the remaining disruption result.

78

Speciﬁcally, the result raises the following question: If we ﬁnd that language
tasks are more disruptive than arithmetic tasks, is it the use of language
representations across the primary and secondary tasks that causes
interference, or is it simply the amount of time language interruptions take, that
causes these effects? This issue is important not only for this experiment, but for
Experiment 3.

Resumption lag. The ﬁrst measure of disruption to primary task
(description) performance under consideration is resumption lag. Two separate
ANOVA analyses were performed. The ﬁrst analysis was based on all trials in
which participants responded in time to the interruption task, regardless of
whether the answer to task was correct or not. The other analysis was performed
using only the data from correctly answered trials. The rationale for using both
analyses is that each could be informative in different ways. The “in time”
measure could be useful to show how participants are disrupted regardless of
whether they answer the interruption problem correctly or not. On the other hand,
it could be argued that using such a measure is much more noisy than a
measure in which all participants have performed the interruption task correctly.
A two-way ANOVA showed that when all correct and incorrect trials were
included in which participants answered in time, there was a marginal main effect
of interruption type by that was signiﬁcant by subjects, F1(1,35) = 3.74, MSE =
189497, p = .061, but not by items, F2(1,23) = 1.87, MSE = 265,418, p > .10. The
opposite pattern was shown for interruption locus, in which a main effect was not

found to be signiﬁcant by subjects, F1(2,70) = 1.47, MSE = 403677, p > .10, but

79

was signiﬁcant by items, F2(2,46) = 3.19, MSE = 142688, p = .05.11 The two
factors did not interact, Fs < 1.

The results analyzing only correctly answered judgment trials were more
clear, and are shown in Figure 11 below. The ANOVA revealed a main effect of
interruption type that was signiﬁcant by subjects, F1 (1 ,35) = 13.10, MSE =
133596, p = .001, and marginal by items, F2(1,23) = 2.89, MSE = 271224, p =
.10, such that grammaticality judgment interruptions (M = 1190 ms, SD = 388)
took longer to recover from than arithmetic judgments (M = 1010 ms, SD = 356).
There was also a main effect of interruption locus that was signiﬁcant by
subjects, F1(2,70) = 4.33, MSE = 184725, p < .05, and marginal by items,
F2(2,46) = 3.04, MSE = 119795, p = .06. There was no interaction, Fs < 1. The
locus main effect was driven by the difference between ﬁrst-clause interruptions
(M = 1215 ms, SD = 498) and the other two locations: between clauses (M =
1077 ms, SD = 359), t(35) = 2.08, p < .05, and during the second clause (M =
1010 ms, SD = 356), t(35) = 2.37, p < .05. The latter two interruption locations did
not differ signiﬁcantly, t(35) = 1.20, p > .20.

 

‘1 Pairwise comparisons are presented for the correct-only analysis below.

80

 

1600 — - 1st Clause
- Between 1st and 2nd Clauses
1400 _ - 2nd Clause

 

 

 

1200 -

1000 -

800 -

600 -

Time in Milliseconds

400 -

200 -

 

 

Language Arithmetic

Interruption Domain
Figure 11. Resumption lag as a function of interruption domain and locus, correct
trials only.

There are two preliminary conclusions that can be drawn: First,
interruptions that come earlier in a sentence are more disruptive than
interruptions that come toward the middle or toward the end of a sentence. In
other words, it is more costly for early-sentence planning to be disrupted than at
latter stages, at which point more material has been uttered. This conclusion can
be drawn with some conﬁdence because the accuracy and judgment time data
do not present confounds for interpretation. The second preliminary conclusion is
that language-based interruptions, because they share the same memory

resources as language production, are particularly disruptive to bookkeeping

81

processes due to interference. However, this conclusion is not as strong because
of the potential confound between interruption duration and interruption domain.

Res—umption errors. Although resumption lag offers one insight into the
disruption caused by different interruption properties, the errors speakers make
in resuming sentences after interruptions are perhaps just as telling, if not more
so. I will presently describe the effects of different interruption conditions on
repeated words, skipped words, and total words in error, in turn.

Out of every single experimental trial, including those in which no answer
was recorded for the interruption task, speakers repeated at least one word on
16.3% of trials. This is a rather high percentage considering the instructions
given to participants to resume sentences from exactly where they had left off.
This ﬁgure dropped to 12.8% when only including correctly-answered interruption
tasks. I will present analyses from both samples for this dependent measure, as
well as for skips and words in error, in order to get a complete picture of error
tendencies. It is possible that, in looking at data only from when speakers
answered interruption tasks correctly, one ignores an interesting pocket of data:
the one in which speakers were most disrupted. For this reason, both types of
analyses are included.

The analysis including all trials (see the left half of Figure 12) showed
clear main effects of both interruption type, F1(1,35) = 10.81, MSE = .023, p <
.01; F2(1,23) = 5.89, MSE = .028, p < .05, and locus, F1(2,70) = 16.58, MSE =
.036, p < .001; F2(2,46) = 18.69, MSE = .021, p < .001, as well as a signiﬁcant

interaction between type and locus, F1(2,70) = 13.73, MSE = .028, p < .001;

82

F2(2,46) = 8.29, MSE = .032, p = .001. I will begin with the main effects.
Participants were much more likely to repeat a word after resumption if the
interruption was language-based (M = .20, SD = .14) rather than arithmetic (M =
.13, SD = .12), t(35) = 2.39, p < .01. Each level of the locus effect was different
from each other level. Interruptions during the second clauses of sentences
resulted in the most repeats (M = .26, SD = .19), more than both ﬁrst clause
interruptions (M = .16, SD = .17), t(35) = 2.81, p < .01, and between-clause
interruptions (M = .08, SD = .10), t(35) = 6.01, p < .001. First-clause interruptions
resulted in more repeats than between-clause interruptions, t(35) = 2.87, p < .01.
It appears that a single difference, between second-clause language versus
arithmetic interruptions, accounted for both the main effect of interruption type

and the interaction. Speciﬁcally, during the second clause, verbal interruptions (M

.38, SD = .30) were more disruptive than arithmetic interruptions (M = .14, SD

.17), t(35) = 4.75, p < .001. When only the arithmetic data were considered,
there was no difference between ﬁrst-clause and second-clause interruption
locations, t < 1, whereas a clear difference was evident in the language-only

analysis.

83

0.5 -

 

 

 

 

 

 

 

9‘ - Ist Clause
ii - Between 1st and 2nd Clauses
a) o 4 a - 2nd Clause
m o
D
.5
.s
g
8 o 3 -
o .
to
C
_O
8
E
g 0.2 ~
0)
o:
"5
C
.9
g 0.1 -
O.
9
o.
0.0 ‘ r
Language Arithmetic Language Arithmetic
All Trials Correct Only

Figure 12. Average proportion of resumptions containing repeats as a function of
interruption domain and locus.

The analysis including only correctly-answered trials (see the right half of
Figure 12, above) showed many of the same statistical trends, but at reduced
levels of signiﬁcance. The two-way ANOVA showed no signiﬁcant main effect of
type, F1(1,35) = 2.54, MSE = .036, p = .12; F2(1,23) = 1.81, MSE = .036, p = .19,
but did show a main effect of locus, F1(2,70) = 3.31, MSE = .054, p < .05;
F2(2,46) = 6.75, MSE = .028, p < .01, as well as an interaction between type and
locus that was marginal by subjects, F1(2,70) = 2.50, MSE = .034, p = .09, and
signiﬁcant by items, F2(2,46) = 3.81, MSE = .032, p < .05. The painrvise effects
break down in much the same way as in the previous analysis, so will be omitted

for simplicity.

84

Two aspects of the repeat data bear some discussion: the odd patterns of
repeats across both samples that were analyzed, and the reduction in
signiﬁcance from the all-trials analysis to the correct-only analysis. The overall
pattern of word repetition likelihood with regard to interruption locus is quite
interesting. Why would speakers repeat words more often when interrupted
toward the ends of sentences? There are two general explanations possible. The
ﬁrst explanation appeals to bookkeeping mechanisms, and suggests that there
are two locality constraints that determine the disruptiveness of interruptions:
whether an interruption occurs mid-clause or between clauses, and whether the
mid-clause interruption is early or late in a sentence. The second possible
explanation is that speakers, when interrupted, may tend to seek out previous
clause boundaries from which to resume—and they do this here despite
instructions not to. It is difﬁcult to distinguish these two accounts, but further
looks at the error data, compared with resumption lag data, later in this section,
will help do so.

If mid-clause and late interruptions cause more bookkeeping errors than
between-clause interruptions, then speakers should not only repeat words more
often at such junctures—they should also skip words more often. In fact, given
the anecdotal evidence that it seems natural to repeat following interruptions,
skipped words are likely an even more valid measure of disruption to
bookkeeping. The likelihood that speakers would skip words was analyzed with
two different two-way ANOVAs: the ﬁrst on the set of all trials, right or wrong,

whether answered in time or not; and the second on correctly-answered

85

interruption task trials. The ANOVA performed for all trials (see the left half of
Figure 13, below) showed main effects of both interruption type, F1 (1 ,35) = 4.94,
MSE = .011, p < .05; F2(1,23) = 7.37, MSE = .006, p < .05, and locus, F1(2,70) =
8.25, MSE = .013, p =.001; F2(2,46) = 8.32, MSE = .008, p = .001, as well as a
signiﬁcant interaction between type and locus, F1(2,70) = 4.71, MSE = .008, p <
.05; F2(2,46) = 2.98, MSE = .008, p = .06. The interruption type main effect
shows, again, that language interruptions (M = .056, SD = .071) are more
disruptive than arithmetic interruptions (M = .023, SD = .058). Interestingly,
pairwise comparisons for the interruption locus effect showed that the mid-ﬁrst
clause interruptions (M = .083, SD = .12) were most disruptive, resulting in more
skipped words than the between-clause interruptions (M = .021, SD = .056), t(35)
= 2.84, p < .01, and second-clause interruptions (M = .014, SD = .050), t(35) =
3.44, p < .01. The between-clause and second-clause interruptions were not
different, t< 1. The interaction is singularly attributed to the fact that ﬁrst-clause
interruptions resulted in more skips if they were verbal (M = .125, SD = .174)

rather than arithmetic (M = .042, SD = .127), t(35) = 2.65, p < .05.

86

0.5 -

 

 

 

 

 

 

 

 

an
O.
.2
(D 0.4 - - 1st Clause
5’ - Between 1st and 2nd Clauses
g - 2nd Clause
8
0 0.3 -
m
C
O
'3
O.
E
5’,
0 0.2 "
a:
"6
C
.9
1:
g 0.1 -
9
o.
0.0 , HUL—
Language Arithmetic Language Arithmetic
All Trials Correct Only

Figure 13. Average proportion of resumptions containing skips as a function of
intenuption domain and locus.

A quick glance at right-hand side of Figure 13 hints that the effects shown
for the all-trials skips analysis disappears when computed only for trials in which
interruption tasks were correctly answered in time. In fact, all levels of the
analysis (main effects and interaction) that were signiﬁcant in the prior analysis
are not signiﬁcant in the correct-only analysis, Fs < 1. There is a logical
explanation for this result: time to prepare for resumption. When speakers
answered in time, andcorrectly, there was an average of 1239 ms between the
end of the interruption task and the re-introduction of the picture array that

signaled description resumption. It could be argued that the reason there were

87

 

any skips at all in Experiment 1 is that speakers occasionally took so long to
answer the interruption task, they had no time between the end of the task and
the resumption of the sentence to prepare to resume, and therefore had to make
a quick guess about where they were in the sentence. Since this happened more
often in the ﬁrst location, especially with language, it is possible to make the
preliminary conclusions that early, language-based interruptions are more
disruptive than later, non-verbal ones.

The ﬁnal bookkeeping error type to consider is the total number of words
spoken in error (see Figure 14). This measure is intended as an overall picture of
bookkeeping error—it captures the total number of words that participants skip
ahead or repeat, added together and averaged. The picture that emerges is quite
clear: Speakers tend to make the most and the largest bookkeeping errors when
interrupted in the middles of clauses rather than at clause boundaries, and when
interrupted by language interruptions (that take longer) rather than arithmetic
interruptions (that do not take as long). Two separate analyses were performed
as they were for repeats and skips. The two-way ANOVA in which all trials were
included yielded signiﬁcant main effects of both interruption type, F1(1,35) =
25.70, MSE = .214, p < .001; F2(1,23) = 10.53, MSE = .348, p < .01, and locus,
F1(2,70) = 9.95, MSE = .573, p < .001; F2(2,46) = 16.14, MSE = .224, p < .001,
as well as an interaction between type and locus, F1(2,70) = 10.44, MSE = .368,
p < .001; F2(2,46) = 6.64, MSE = .398, p < .01. The interruption type effect again
showed that verbal interruptions (M = .65, SD = .46) were more disruptive than

arithmetic interruptions (M = .33, SD = .34), t(35) = 5.07, p < .001. Both mid-

88

clause levels of the locus variable (ﬁrst-clause: M = .50, SD = .44; second-
clause: M: .77, SD = .81) resulted in signiﬁcantly more words in error than the
between-clause interruption type (M = .20, SD = .32), t(35) = 3.82, p = .001, t(35)
= 3.90, p < .001, respectively. The comparison between the two mid-clause
interruption locations showed a marginal difference such that second—clause

interruptions resulted in more words in error than ﬁrst-clause interruptions, t(35) =

 

1.82, p = .08.
1.6 -
_ 1st Clause
1.4 _ - Between tst and 2nd Clauses

 

 

- 2nd Clause

 

—L
N
l

.3
o
L

Average Number of Words
9 o
O) on

P
b
J

_O
N
1

 

 

   

0.0 -

 

Language Arithmetic Language Arithmetic
All Trials Correct Only

Figure 14. Average number of words in emor (repeated and skipped) upon
resumption as a function of intenuption domain and locus.
The correct-only analysis showed trends in similar directions, but failed to

produce similarly signiﬁcant effects, especially the domain effect. The main effect

89

of domain in the correct-only analysis (see the right-hand side of Figure 14) was
not signiﬁcant, Fs < 1. Also, the main effect of interruption locus was not
signiﬁcant by subjects, F1(2,70) = 2.16, MSE = .400, p = .12, but was signiﬁcant
by items, F2(2,46) = 6.76, MSE = .106, p < .01. The interaction was marginally
signiﬁcant by subjects, F1(2,70) = 2.90, MSE = .165, p = .06, and signiﬁcant by
items, F2(2,46) = 4.80, MSE = .111, p < .05. Since the interaction is the only
result that was signiﬁcant, or near signiﬁcant, for both subjects and items, I will
focus on that result for interpretation. The only difference between the language
and arithmetic simple effects is that second-clause verbal interruptions are
signiﬁcantly different in words in error (M = .52, SD = .83)_than both ﬁrst-clause
(M = .16, SD = .31), t(35) = 1,83, p = .07, and between clause verbal
interruptions (M = .16, SD = .31), t(35) = 2.48, p < .05; on the other hand,
arithmetic interruptions showed no differences in terms of locus, ts < 1. So,
again, it appears that whatever force is driving the effects of locus, it is
exacerbated by language interruptions more than mathematical interruptions.

Experimentar error. Two general approaches conﬁrm that the
experimenter who pressed the button to initiate the interruption process was able
properly instantiate the manipulation of interruption locus. The ﬁrst approach is to
show that each interruption locus showed consistent onset times by condition
across participants. The E-Prime software used to implement the experiment
registered the amount of time that passed between the initial onset of the object
array to be described and the experimenter’s button press to initiate the

interruption. This measure showed that across all trials, the time to interruption

90

onset differed signiﬁcantly across interruption locations, F(2,70) = 331.45, MSE =
97291, p < .001. Painrvise comparisons showed that, as expected, ﬁrst-clause
interruptions had the earliest onset time (M = 1568 ms, SD = 294). These

1 interruptions had onsets before those of between-clause interruptions (M = 2592,
SD = 480), t(35) = 15.24, p < .001, which in turn had onsets before second-
clause interruptions (M = 3459, SD = 454), t(35) = 11.09, p < .001. Clearly, the
temporal manipulation check reveals no problem.

The other approach that serves as a manipulation check is to sample
several participants at random to check whether they were interrupted at the
appropriate point in utterance articulation. Transcribed data from ﬁve participants
were selected at random. Each transcription showed three elements: A complete
record of what the participant said, the point at which the beep sounded to signal
the participant to stop, and the point at which the participant actually stopped
speaking to perform the interruption task. Two aspects of these transcriptions
were coded for manipulation checks: the number of words by which the
experimenter missed the intended interruption mark, and the number of words
the speaker uttered after the beep, but before completing the interruption task.
Out of the 120 trials analyzed in this way, only four of the trials contained severe
errors that violated the intention of the manipulations. One of these errors
occurred because of a combination of factors: the speaker used an abbreviated
utterance, was beeped two words too late, and continued to ﬁnish the rest of her
utterance (three additional words) before addressing the interruption task. This

trial had been excluded even before performing statistical analyses for

91

Experiment 2. The other three errors were slightly more concerning: In two
cases, for an intended between-clause interruption (before the onset of the word
“and”), the beep sounded only a single word early, as in (3) below.

3) The cake moves above the (BEEP/1' ASK) mouse and the key moves

below the mouse.
What this example shows is that between-clause interruptions may have been
particularly susceptible to experimenter error. If the experimenter anticipated this
location a word early, then the interruption occurred while the ﬁrst clause was still
being uttered. There was slightly more room for error on the other side: If the
experimenter was one word late (interrupting after and but before the), then the
interruption could still be said to occur at the clause boundary. On the other
hand, if the was articulated before the beep, then the interruption occurred during
the articulation of the second clause. The fourth error in the sampled data set
was of this latter type.

It should also be noted that one between-clause manipulation error
resulted in a “repeat”:

4) The shirt moves below the (BEEP/T ASK) below the ear and the dog

moves above the ear.
Because the interruption did not occur at the clause boundary, but was coded as
such in data analyses, it is possible that errors such as these may have biased
the data. Speciﬁcally, the data reported above (and in Experiment 3) may be
overestimating the extent to which repeats legitimately occur when speakers are

interrupted between clauses.

92

Practice effects. Data from correctly answered interruption trials were
broken into halves according to the order in which they appeared in a given
experimental session. Each half was treated as an independent variable with two
levels: ﬁrst and second half. Analyses of variance were performed to examine
whether practice effects may have reduced the extent to which the effects of
interruption domain and locus changed over the course of individual experiment
sessions. Besides one exception, none of the 4 major dependent measures
examined in Experiment 3 showed that half interacted with either independent
variable, all Fs < 1. In other words, the effects of interruption locus were present
regardless of how much practice a participant had with being interrupted. The
exception was a marginal interaction on the repeats measure that was found
between interruption domain and 1’”t versus 2"d half, F(1,35) = 2.20, MSE =
326156, p = .09. However, because the interaction was only marginal, it may not
be prudent to interpret the results.12

However, there was one main effect, of resumption lag, which showed that
performance as a whole improved from the ﬁrst half to the second half of
experiment sessions. Resumption lag measures reﬂected a drop from 1218 ms
(SD = 553) in the ﬁrst halves of sessions to 980 ms (SD = 279) during second
halves, t(35) = 2.94, p < .01. Repeats, skips, and words in error revealed no main
effects of experiment half, Fs < 1. It would seem, for this particular task, that

practice does not reduce the differences between interruption types.

 

‘2 An informal look at the means indicated that the interaction occurred not because differences
between domains were reduced throughout the session, but rather that participants became more
efﬁcient with arithmetic interruptions than verbal interruptions throughout the course of the
experiment.

93

Summary. Every major measure of resumption difﬁculty showed, to
different degrees, that the two independent variables used in Experiment 2
produced effects. The resumption lag measure revealed that it takes longer to
resume following language interruptions, as well as early interruptions. The
measure of repeats showed that people most often repeat words when
interrupted by language tasks, but showed that most repeats occur when
interrupted mid-clause, especially if the clause is later in the sentence. Speakers
most often skipped words, resulting in incomplete sentences, when faced with
language interruptions and early interruptions. The common results here are that
the within-domain interruptions were consistently more disruptive than the
different-domain interruptions, and that the mid-clause interruptions were
consistently more disruptive than the between-clause interruptions. I will ﬁrst
discuss the locality effects, since those effects have no judgment time duration
confound. I will then discuss the possible interpretations of the domain effects.

Although Experiment 3 and the General Discussion will explore these
issues in more detail, the results of Experiment 2 reveal some interesting
tendencies regarding resumption difﬁculty along temporal and location-based
dimensions. First, it was found that it simply takes longer to resume a sentence if
a speaker is interrupted early on in sentence production. I will argue that this
result supports theories of strategically incremental language production (Ferreira
& Swets, 2002; Swets & Ferreira, 2003). On the other hand, the resumption error
data revealed that most errors occurred when participants were interrupted in the

middles of clauses rather than at clause boundaries. Later, I will argue that these

94

 

results support the notion that conversational interruptions are similar to task
interruptions (Adamczyk & Bailey, 2004; McFarlane, 2002): Interruptions at
coarse break points (like clause boundaries) are more easily handled than
interruptions at ﬁne break points (mid-clause).

There is little question that the manipulations used in Experiment 2
affected speakers” ability to maintain records of utterance articulation.
Unfortunately, there are three possible sources of the effects that took place:
First, there are the two variables that I intended to manipulate, interruption
domain and interruption locus. But there was also a third variable that may have
inﬂuenced disruption: time, both spent in judgment (allowing decay to act longer
in the language interruptions), and available after judgment to prepare to resume
(giving arithmetic interruptions a longer resumption preparation window).
Because the grammaticality judgment interruptions simply took longer to answer
than the arithmetic judgment interruptions, an unfortunate confound arises.
Namely, since they did not take as long, and because there was a constant 4-
second interruption window, participants gained extra resumption lag time when
answering arithmetic as opposed to language interruptions.

But why did verbal interruptions take longer, especially given the attempt
to equate verbal and arithmetic interruption on such a dimension before testing?
Two general explanations are possible. The ﬁrst explanation is the more
theoretically interesting one. It is possible that having to maintain information
about sentence production is a process that interferes with a verbal task more so

than it does with an arithmetic task—the particular memory resources overlap to

95

 

such a degree that interruption task performance is degraded, which in turn leads
to the longer times to answer the task, which in turn reduces the amount of time
and verbal resources necessary to resume sentence production (Reitman, 1971,
1974). The other possibility is less interesting: because the interruption domains
were not particularly well controlled for temporal factors before testing, the time
required to perform each task may have been different a priori, independently of
a possible interference factor. A third experiment was performed to help
determine whether the interference or temporal account of the domain effect in

Experiment 2 is more valid.13

 

'3 The main effects of locus found in Experiment 2 are uncontaminated by any interruption length
confound.

96

EXPERIMENT 3

Experiment 2 showed that a speaker interrupted by a grammaticality
judgment task is more likely to be disrupted upon resumption than when
interrupted by a non-verbal, arithmetic judgment task. However, it was also found
that the grammaticality judgments in Experiment 2 simply took longer to answer
than the arithmetic judgments. This difference in length between interruption
types could have been due to interference from primary and secondary tasks that
share the same representational resources (Gillie & Broadbent, 1989).
Otherwise, it could also be the case that if interruptions types are carefully
equated in latency dimensions before testing their effects on resumption, the
domain effects disappear, thus supporting a notion of temporal effects on
disruption that Gillie and Broadbent (1989) had not found. Experiment 3
calibrated a set of grammaticality judgments and a set of arithmetic judgments to
be equal in mean latencies. These tasks were then used as interruptions in a
task exactly like Experiment 2. If temporal factors account for the domain effects
of Experiment 2, then there should be no effect of interruption type on the various
measures of resumption difﬁculty. On the other hand, two outcomes could
support the interference explanation: First, longer judgment times for the verbal
interruptions despite careful pilot testing would be somewhat compelling. Such a
result might suggest that there is some aspect of bookkeeping that is disruptive
to secondary task performance (Reitman, 1971, 1974). But more importantly,

verbal interruptions must result in greater primary task resumption difﬁculty. The

97

former result would be evidence for interference. The latter result would be
evidence for interference that causes disruption to bookkeeping.
Norming Study A

The results of Experiment 2 showed a confound in resumption lag results.
On one hand, the verbal interruption domain could have produced greater effects
on resumption lag and other measures of disruption because of interference
related to domain-speciﬁc processing resources. On the other hand, because
verbal interruptions lasted longer than arithmetic interruptions, it is possible that
participants used the extra time available in the arithmetic condition as a helpful
boost to resumption lag before the beep signaled them to resume speaking.
Then again, it could be that the verbal interruptions took longer because of
domain-dependent interference. In order to sort out this issue, a norming study
was run to determine whether the interruption sets used in Experiment 2 would
show domain differences in a simple judgment task in which representations or
processes from a primary task could not interfere with the secondary task
judgments. The sentences and arithmetic problems used as interruptions in
Experiment 2 were presented in isolation to a group of participants to establish
baselines of judgment latency and accuracy.
Math—0d

Participants. Twenty-three participants were recruited in the same manner
as in Experiments 1 and 2.

Materials. The same 21 sentences and 21 arithmetic problems that were

presented as interruptions in Experiment 2 were used as the stimuli.

98

Daaiggu All 42 items were presented in random orders to each participant.

Apparatus. An E-Prime software program was used to present the stimuli
and collect the dependent measures of judgment latency and accuracy.

Procedure. After being administered informed consent, participants read
instructions that asked them to respond to the grammaticality of sentences and
the truth of solutions to the arithmetic problems by pressing yes or no on a button
box. The instructions stated that they were to respond to the sentences and
problems as quickly and as accurately as possible, and the experimenter
reinforced this particular instruction verbally.

Each trial began with participants ﬁxating a cross in the center of the
computer monitor. Pressing either the yes or no button allowed participants to
view the following sentence or arithmetic problem to judge. No limit was placed
on the amount of time participant could use to judge the stimuli. After the
participants judged the 42 stimuli, they were debriefed about the purpose of the
experiment.

Results and_Discussion

Table 2 shows the means and standard deviations of the accuracy and
latency data. The most salient result was that grammaticality judgments took
about a whole second longer to answer than arithmetic judgments whether
analyzing all trials, t(22) = 7.64, p < .001, or only the correct trials, t(22) = 7.84, p

< .001. However, accuracy rates remained the same across type, t< 1.

99

Table 2. Latency and accuracy data, Experiment 3, Norming Study A.

 

 

Language Arith_metic
Measure Mean SD Mean SD
Latency in ms (all trials) 3881 721 2879 536
Latency in ms (correct only) 3838 690 2881 556
Accuracy (percent) 94.0 7.52 95.0 4.65

Establishing this baseline of interruption latencies is revealing for the
results of Experiment 2. It is quite clear now that interference is not a viable
explanation for the longer latencies for secondary task response. Instead, the set
of verbal interruptions simply took longer for participants to judge than the set of
arithmetic interruptions. However, domain-dependent interference could still
explain the disruption to primary task performance seen across domain types in
Experiment 2. In order to test this, a series of iterative norming studies was ﬁrst
carried out. The objective of these studies was to obtain a set of grammaticality
judgments that yielded the same latencies as a set of arithmetic judgments so
that the interference assumption could ﬁnally be laid to rest.

Three norming studies followed the ﬁrst one, which established the
baseline. There were two aspects of equating the two interruption sets. First,
arithmetic problems were made more difﬁcult by using only problems that added
up to two-digit sums with both the ones place and the tens place having integers
greater than or equal to 5. The more challenging step was to reduce the amount
of time that participants spent reading sentences during grammaticality

judgments. This was done by reducing the number of content words in the

100

sentences to about 5 per sentence. Rather than give results for the two norming
studies in which equal latencies were not achieved across domain, I will
presently proceed straight to the fourth norming study (which I will call Norming
Study B).
Norming Study B

m

Participants. Twenty participants were recruited in the same manner is in
Experiments 1 and 2.

Materials. A set of 21 sentences and 21 arithmetic problems was created
by modifying the materials of Norming Study A over a series of iterations. The
ﬁnal materials list for the present study is presented in Appendix G.

Design Apparatus and Procedure. All details of design, apparatus, and

 

procedure were identical to the details of Norming Study A, above.
Results and Discussion

Table 3 shows the means and standard deviations of the accuracy and
latency data. T-tests showed that, although a slight trend seemed to exist for
grammaticality tasks to take longer and be more accurate than arithmetic tasks,
these trends did not approach signiﬁcance, ts(19) < 1. Because the differences
do not approach signiﬁcance, the set of 21 items per domain were used in the

Experiment 3 interruption task.

101

Table 3. Latency and accuracy data, Experiment 3, Norming Study B.

 

 

Language Arith_metic
Maaaure Mean SD Mean SD
Latency in ms (all trials) 2732 841 2658 547
Latency in ms (correct only) 2710 824 2658 522
Accuracy (percent) 92.6 7.33 89.7 10.9
Interruption Study

mam

Participants. Forty-eight participants were recruited in the same manner
as in Experiments 1 and 2.

Materials: Picture Description. The same set of 24 experimental object
arrays and 72 ﬁller arrays that were used to elicit descriptions in Experiment 2
were used in Experiment 3.

Materials: Speeded Grammaticality Judgment. The list of 21 sentences in
Appendix G, which were shown to be judged with equal latencies and accuracy
rates as the arithmetic tasks, were used as interruptions to participants’
descriptions in Experiment 3.

Materials: Speeded Arithmetic Judgment. The list of 21 arithmetic
problems in Appendix G, which were shown to be judged with equal latencies
and accuracy rates as the sentences, were used as interruptions to participants’
descriptions in Experiment 3.

m. The design details of Experiment 3 are the same as those in

Experiment 2, with a few minor exceptions. First, 48 rather than 36 unique lists

102

were created to accommodate the 12 extra participants to be run. Second, block
creation was slightly different, in that interruptions were randomly re-assigned to
different description items after each block of 6 lists. This was done to reduce
even more the chances that particular interruption-description pairings could bias
the data.

Apparatus. Apparatus details match those of Experiment 2.

Procedure. Details of procedure are identical to those of Experiment 2,
with a single exception: the time given to participants to judge interruption tasks
was reduced from 4 seconds to 3.25 seconds. This was done because the
average judgment time for each task domain, as pilot tested above, was just
under 3 seconds. About 75% of the responses took about 3.25 seconds or less.
Because it was important for the interruptions to take as much of the interruption
time as possible to ensure some disruption upon resumption, the 3.25 second
ﬁgure seemed like a reasonable ﬁgure to use.

Data Analysis

The same dependent measures used in Experiment 2 were used for
Experiment 3.

Hypotheses and Pregictions

The purpose of Experiment 3 was to test two different accounts of the
domain effect found in Experiment 2. According to one hypothesis, the domain
effects occurred because verbal interruptions caused domain-speciﬁc
interference for recall of primary tasks (descriptions). This hypothesis predicts

that such effects will again be found in the present experiment. The other

103

hypothesis is that the different interruption lengths between the interruption
domains accounted for the effects. This hypothesis predicts a null result of
domain in the present experiment.
Results and Discussion

Interruption task accuracy and iudg_ment time. As in Experiment 1,
participants were quite accurate overall in being able to accurately complete the
interruption tasks—despite the smaller time window. On average, participants
were able to correctly answer the tasks within the 3.25-second window with
74.0% accuracy. Recall that in Experiment 2, there was an overall accuracy
difference between language and arithmetic interruptions when all trials,
including those in which no response was given, were considered (arithmetic was
more accurate). The left half of Figure 15 (below) shows that this effect did not

occur in Experiment 3, nor was there an effect of locus, or an interaction, Fs < 1.

104

 

- 1st Clause
- Between 1st and 2nd Clauses

 

 

 

 

 

1'0 _ - 2nd Clause
0.8 -
E
t 0.6 d
O
0
C
.9
1:
8
9 0.4 -
o.
0.2 -
0.0 -
Language Arithmetic Language Arithmetic
All Trials Answered In Time

Figure 15. Proportion of correctly answered intenuption tasks.

The right half of Figure 15 depicts accuracy rates from trials in which
participants answered in time. A two-way ANOVA showed a main effect of
interruption type on accuracy when only such trials were considered, F(1,47) =
26.15, MSE = .027, p < .001, F2(1, 23) = 30.25, MSE = .013, p < .001. This effect
occurred because when participants gave an answer in time, they were more
accurate for grammaticality judgments (M = .94, SD = .10) than they were for
arithmetic problems (M = .84, SD = .13). There was no main effect of interruption

locus, nor was there an interaction between interruption type and locus (Fs < 1).

105

 

The comparison between the two sides of Figure 15 suggests a speed-
accuracy trade-off was taking place for the two types of interruption tasks. When
participants failed to answer in time, it usually occurred during a language task.
However, if they did answer in time, they were correct on language tasks more
often. However, it is the judgment time data that matters most.

Figure 16, below, shows mean judgment times in Experiment 3. The left
half of Figure 16 shows the means by subject for each trial in which the
interruption task was answered, correctly or not, within the available 3.25-second
window. The right half shows mean times to answer on only those trials that were
answered correctly. Both analyses showed similar results, and suggested a
speed-accuracy tradeoff indeed existed in Experiment 3 based on interruption
type. However, the degree of the tradeoff in terms of speed was not nearly as
severe. Again the results were quite similar when examining results from the two
sets of data, so only the correct-only analysis will be used. When only correct
trials were considered, a 2 x 3 repeated measures ANOVA revealed a main
effect of interruption domain that was marginal by subjects, F1(1,47) = 3.13, MSE
= 119,300, p = .08, and non-signiﬁcant by items, F2(1,23) = 1.64, MSE = 48,171,
p = .21. On average, grammaticality judgments (M = 2471 ms, SD = 269) took 72
milliseconds longer than arithmetic judgments (M = 2399 ms, SD = 321). There
was no main effect of locus, Fs < 1. However, there was a marginally signiﬁcant
interaction between type and locus, F1(2,94) = 2.29, MSE = 52,677, p =.11;

F2(2,46) = 2.94, MSE = 33245, p = .06. However, this interaction was non-

106

signiﬁcant, and contributes little to interpretation of upcoming resumption

 

 

 

 

 

 

measures.
- 1st Clause
3000 1 - Between 1st and 2nd Clauses
- 2nd Clause

2500 ~

.3 2000 ~
C
O
0
o
a

5 1500 a
.E
(D
.E

r— 1000 —

500 -

0 _

Language Arithmetic Language Arithmetic
In Time Correct Only

Figure 16. Interruption task judgment latency as a function of interruption domain
and locus.

The primary concern of Experiment 3 was to create interruption tasks that
took the same amount of time to answer across domain. The marginally
signiﬁcant difference, by subjects, between language and arithmetic interruptions
could be considered a problem if not for three reasons: First, the two interruption
types showed no difference in the above normative test results. This might
suggest that, once the primary task is involved, it is within-domain interference

that forces the difference in judgment times across domain. I should note,

107

however, that such a conclusion would be weak in the present circumstance.
Second, the effect did not approach signiﬁcance by items, and the difference
itself was on average 72 ms (recall that the differences in Experiment 2 and
Norming Study A reported above were much higher, on the order of 500-1000
ms). And ﬁnally, as I will show presently, interruption type had little if any effect
on any measures of resumption difﬁculty. Hence, the endeavor to equate
interruption types on the basis of judgment time was a modest success.
Resumption lag. The ﬁrst measure of disruption to primary task
(description) performance under consideration is resumption lag. A single 2 x 3
ANOVA was performed using only the data from correctly answered trials. The
rationale for using only the correct trials was that the two interruption types were
closer in judgment times than when all correct and incorrect trials were included.
Because the major goal of the present study was to analyze the effects of
interruption type and locus without judgment time confounding interpretation, the
other analysis was discarded. Results are presented in Figure 17 (below). The
ANOVA revealed that interruption type had no effect on resumption lag, Fs < 1.
However, as in Experiment 2, there was a main effect of interruption locus that
was signiﬁcant by subjects, F1(2,94) = 13.74, MSE = 107007, p < .001; F2(2,46)

= 15.26, MSE = 38228, p < .001. There was no interaction, Fs < 1.

108

 

 

- 1st Clause
- Between 1st and 2nd Clauses
1400 _ - 2nd Clause

 

 

 

1200 -

1000 -

800 -

600 -

Time in Milliseconds

400 -

200 -

 

 

Language Arithmetic

Interruption Domain
Figure 17. Resumption lag as a function of intenuption domain and locus, correct
trials only.

There are two important results that should be addressed: The non-
signiﬁcant effect of domain, which does not replicate Experiment 2 results, and
the replication of the Experiment 2 locus effect (with a small twist). The lack of a
domain effect here makes one point very clear: the only reason there was an
effect of interruption domain on resumption lag in Experiment 2 was because the
two interruption sets tested took different amounts of time to perform a priori. In
other words, the interpretation of Experiment 2 that relied on effects of
interference is untenable. However, as I will discuss later on, this difference

between the two experiments does have theoretical and practical import:

109

Namely, that a temporal factor plays into resumption difﬁculty, which has rarely
been shown in HCI interruptions literature (Gillie & Broadbent, 1989).

Recall in Experiment 2 that the locus main effect was attributed to ﬁrst-
clause interruptions resulting in longer resumption lags than both between-clause
and second-clause interruptions. The main effect of locus in Experiment 3 was
statistically more robust than in Experiment 2, and the pairwise comparisons also
displayed one key difference from Experiment 2. Although the ﬁrst-clause
interruptions still resulted in longer resumption lags (M = 1138 ms, SD = 350)
than second—clause interruptions (M = 906 ms, SD = 242), t(47) = 5.28, p < .001,
ﬁrst-clause interruptions were not signiﬁcantly more disruptive than between-
clause interruptions (M = 1096 ms, SD = 433), t < 1. Also, the between-clause
interruption resulted in longer resumption lags than the second-clause
interruptions, t(35) = 3.50, p = .001, whereas Experiment 2 did not show a
difference between those locations. However, these differences are not
problematic for the conclusions that were drawn on the basis of the results of
Experiment 2. That conclusion was that interruptions coming earlier in a
sentence production are more disruptive than interruptions that come later in a
sentence. It seems that the locality effects on resumption lag are explained by
the fact that the earlier a speaker is within the course of utterance production, the
weightier the plan for the rest of the utterance is. As one reaches the middle of
the ﬁnal clause of an utterance, there is less of a plan to re-load once an
interruption has been adequately handled. The next question to address is

whether resumption errors show patterns similar to resumption lag results.

110

R_es_iﬂption errors. Resumption errors will be presented as both all-trial
and correct-only analyses in the ﬁgures below, but statistical analyses will only
be presented for the correct-only analyses. Too many variables could be
inﬂuencing resumption errors when more than just correct trials are included. It
was useful to present these analyses in Experiment 2, when the gathering of
information of many sorts was important. However, the present scope of interest
is more narrow, so analyses will focus on cases in which interruptions were
judged correctly and within the 3250 ms window.

I will ﬁrst consider repeats. The two-way analysis including only correctly-
answered trials (see the right half of Figure 18, below) showed no signiﬁcant
main effect of domain, nor did it show an interaction between domain and type,
Fs < 1. However, the ANOVA did show a main effect of locus, F1(2,94) = 5.74,
MSE = .078, p < .01 ; F2(2,46) = 6.49, MSE = .027, p < .01. Pairwise
comparisons again showed that second-clause interruptions resulted in more
repeats (M = .23, SD = .30) than both ﬁrst-clause (M = .13, SD = .21) and
between-clause (M = .10, SD = .22) interruptions, t(47) = 2.60, p < .05, t(47) =

2.74, p < .01, respectively.“

 

1‘ The all-trials analysis showed evidence for a domain effect, but again, this effect is difﬁcult to
interpret with all the noise from accuracy and temporal confounds.

111

 

- 1st Clause
0,35 - - Between 1st and 2nd Clauses
_ 2nd Clause

 

 

 

0.30 -

0.25 -

0.20 -

0.15 -

0.10 -

0.05 -

Proportion of Resumptions Containing Repeats

 

 

 

0. 00 " r
Language Arithmetic Language Arithmetic
All Trials Correct Only

Figure 18. Average proportion of resumptions containing repeats as a function of
interruption domain and locus.

In Experiment 2, the effect of interruption locus on skips was only
signiﬁcant when all trials were included for analysis. This weakened the claim
that any aspect of Experiment 2 measured actual bookkeeping disruption, since
the cleanest way to analyze the present data is to use only those trials in which
the interruption task is answered correctly. Experiment 3, in which the average
time lag between pressing the button to answer the interruption task and hearing
the signaling sentence resumption was greatly reduced, found effects of

interruption locus on skipping tendencies in the correct-only analyses (see Figure

112

19, below). The two-way ANOVA yielded a signiﬁcant effect of locus, F 1 (2,94) =
12.05, MSE = .009, p <.001; F2(2,46) = 13.67, MSE = .063, p < .001. Just as in
Experiment 1, the ﬁrst-clause interruptions resulted in skips (M = .070, SD =
.116) more often than both between-clause interruptions (M = .015, SD = .044)
and second-clause interruptions (M = .010, SD = .038), t(47) = 3.50, p = .001,
t(47) = 3.95, p < .001, respectively. Between-clause and second-clause

interruptions did not differ, t< 1.

 

 

 

 

 

 

 

 

0.5 ~
(I)
.9-
x
g, 0.4 -
g - lst Clause
II; - Between 1st and 2nd Clauses
5 - 2nd Clause
0 0.3 -
to
C
O
23
O.
E
a
m 0.2 "
o:
”5
C
.9
t
g 0.1 -
2
o.

0.0 - r

Language Arithmetic Language Arithmetic
All Trials Correct Only

Figure 19. Average proportion of resumptions containing skips as a function of
intenuption domain and locus.
The ﬁnal bookkeeping error type to consider is the total number of words

spoken in error (see Figure 20, below). The correct-only analysis found no main

113

effect of domain, Fs < 1. There was a main effect of interruption locus, F1(2,94) =
4.16, MSE = .767, p < .05, F2(2,46) = 7.21, MSE = .255, p < .01. Domain and
Locus did not interact, Fs < 1. Planned comparisons among levels of the Locus
variable revealed that speakers produced the greatest magnitude of resumption
errors when interrupted during the second clause (M = .68 words, SD = .90): The
difference between second-clause and between-clause interruptions (M = .38
words, SD = .70) was signiﬁcant, t(47) = 2.18, and the difference between
second-clause and ﬁrst-clause interruptions was marginally signiﬁcant, t(47) =
1.89, p = .07. Although the means suggested such a trend, ﬁrst-clause
interruptions did not show any greater error magnitude than did between-clause
interruptions, t < 1. However, the analysis in which all trials were included (see
the left half of Figure 20) indeed showed that this difference was significant.
Hence, the overall resumption error magnitude data contribute to the patterns of
results showing that resumption errors are reduced when interruptions come

between clauses.

114

 

 

 

 

 

 

 

1.4 - - 1st Clause
- Between 1st and 2nd Clauses
1 2 - 2nd Clause
0)
g 1.0 -
E
3 0.8 -
o
.0
E
3
2 0.6 —
c
U)
S!
3% 0.4 ~
0.2 ~
0.0 ~ 1
Language Arithmetic Language Arithmetic
All Trials Correct Only

Figure 20. Average number of words in error (repeated and skipped) upon
resumption as a function of intenuption domain and locus.

Practice effects. Data from correctly answered interruption trials were
broken into halves according to the order in which they appeared in a given
experimental session. Each half was treated as an independent variable with two
levels: ﬁrst and second half. Analyses of variance were performed to examine
whether practice effects may have reduced the extent to which the effects of
interruption domain and locus changed over the course of individual experiment
sessions. None of the 4 major dependent measures examined in Experiment 3
showed that half interacted with either independent variable, all Fs < 1. In other

words, the effects of interruption locus were present regardless of how much

115

practice a participant had with being interrupted. This result differs from previous
research on task interruptions (Hess & Detweiler, 1994; Trafton et al, 2003)
which showed that training could eliminate the disruptive effects of interruptions.

However, there were main effects of experiment half showing that
performance as a whole improved from the ﬁrst half to the second half of
experiment sessions. For example, resumption lag measures reﬂected a drop
from 1128 ms (SD = 357) in the ﬁrst halves of sessions to 979 ms (SD = 348)
during second halves, t(47) = 3.21, p < .01. First-half repeats (M = .18, SD = .23)
were marginally more common that second-half repeats (M = .13, SD = .19),
t(47) = .08, and although ﬁrst-half skips occurred on 4% of correct trials versus
1.9% in the second half, the difference was not signiﬁcant, t(47) = 1.21, p = .13.

Experimenter error. Just as in Experiment 2, each interruption locus
showed consistent interruption onset times by condition across participants.
Across all trials, the time to interruption onset differed signiﬁcantly across
interruption locations, F(2,70) = 324.53, MSE = 132772, p < .001. Pairwise
comparisons again showed that ﬁrst-clause interruptions had the earliest onset
time (M = 1674 ms, SD = 292). These interruptions had onsets before those of by
between-clause interruptions (M = 2591, SD = 446), t(47) = 16.61, p < .001,
which in turn had onsets before second-clause interruptions (M = 3569, SD =
763), t(35) = 11.95, p < .001. So, again, the temporal manipulation check reveals
no problem.

The other manipulation check sampled several participants at random to

observe whether they were interrupted at the appropriate point in utterance

116

 

articulation. Transcribed data from six participants were selected at random. Out
of the 144 trials analyzed in this way, only seven of the trials (4.8%) contained
severe errors that violated the intention of the manipulations. One error occurred
because the experimenter never initiated the interruption task for an intended
ﬁrst-clause interruption. This was the only intended ﬁrst-clause interruption that
did not occur during ﬁrst-clause articulation. For the other 6 errors, 3 occurred for
intended between-clause interruptions, and 3 occurred for intended second-
clause interruptions. Although the three between-clause errors were similar in
nature to those described in Experiment 2, the second-clause errors were
different in kind because they did not occur due to experimenter error. Rather, in
these cases, the experimenter initiated the interruption task at the correct locus in
articulation, but rather than immediately stop speaking, as instructed, speakers
ﬁnished the present sentence before completing the interruption task. These
trials had been excluded from analyses, and so trials such as these do not
present a problem for any of the above analyses.

Summam. Unlike Experiment 2, in which all measures of resumption
difﬁculty showed that verbal interruptions were more costly to speakers than non-
verbal interruptions, not a single measure of resumption difﬁculty showed such
an effect in Experiment 3. Resumption lag, repeats, and skips were affected
equally by interruption domain in Experiment 3. Clearly, the Experiment 2 domain
effects were due to inadequately-controlled task sets. As such, the possibility that
domain-speciﬁc interference was driving the domain results of Experiment 2

(Reitman, 1971, 1974) is dead. On the other hand, this contrast in results is

117

interesting for another reason: it shows that the amount of time someone has to
perform an interruption task and the amount of time a person has to recover from
that task are important factors in recovery from interruption (Gillie & Broadbent,
1989). The General Discussion will explore this issue further.

Although the domain effects in Experiment 2 failed to replicate with
properly controlled stimuli, the locality effects were replicated with some small
exceptions. Experiment 2 had shown that interruptions coming during the middle
of the ﬁrst clause of a two-clause picture description were resulted in higher
resumption lag and more skips than interruptions coming at a major clause
boundary or during the second clause. Experiment 3 also showed that
resumption lag increased during the ﬁrst clause, but also showed an elevated
resumption lag at the clause boundary. Repeats came more often in the middle
of clause 2 in both experiments, and both experiments revealed that mid-clause
interruptions are more costly in terms of overall words spoken in error than
between-clause interruptions.

There are two conclusions to be drawn from this set of results, which will
be expanded upon in the General Discussion. The ﬁrst conclusion is that the
resumption lag measure in this paradigm is sensitive to production planning
load—the more burdensome the utterance plan is that must be reloaded upon
resumption, the longer it will take to reload it. The second conclusion is that the
resumption error measures are sensitive to bookkeeping load. Because errors
are less likely to occur at clause boundaries than in when a speaker is mid-

clause, it must be easier for speakers to maintain a bookkeeping pointer when

118

interrupted at a clause boundary (a coarse break point, Adamczyk & Bailey,
2004) than when interrupted in the middle of a clause (a ﬁne break point,

Adamczyk & Bailey, 2004).

119

GENERAL DISCUSSION

The present project could be viewed as taking a rather asymmetrical
approach to a scientiﬁc issue. Whereas Experiment 1 is rather open-ended,
exploratory, and (psycholinguistically speaking) uncontrolled, the project ends
with Experiment 3, a highly controlled experiment with speciﬁc questions about
one or two issues dealing with interruptions. Although this asymmetry could be
viewed as being scientiﬁcally unaesthetic, there are legitimate reasons for the
disparate approaches. Experiment 1 approaches the problem of conversational
interruptions from a naturalistic, applied perspective, and answers questions of
bookkeeping at the message level during interactive conversation. The
experiment tests whether there are types of conversational interruptions that, due
to their differences in relatedness to the goals of the primary dialog, are more
disruptive than other interruptions.

But besides being about interruptions, what truly ties all of the experiments
together is that they all approach the same question from very different angles.
When we are interrupted, what is it about those interruptions that makes us lose
track of where we were? Although Experiment 1 begins to answer this question
in a broad sense, it is clear that the full answer to this question will require the
careful decomposition of many disparate factors. For this reason, studies that are
more like Experiment 1 will never be able to fully embrace the challenge of
explaining conversational interruption cost until studies like Experiments 2 and 3
break large problems down into smaller pieces. For example, the result in

Experiment 1 showing that clariﬁcation interruptions, which required a verbal

120

response from the speaker before resuming the primary dialog, were more
disruptive than repetition interruptions, raised the possibility that verbal
interference played some role in resumption difﬁculty. Experiments 2 and 3 set
out to test such an interference hypothesis, a process that would be difﬁcult to
accomplish using the techniques of Experiment 1.

The General Discussion will be organized as follows. First, I will address
the ways in which the present results contribute to ongoing research on
interruptions in ﬁeld of human-computer interaction. The present research will
contribute to that ﬁeld’s work on temporal issues of resumption from
interruptions, the inﬂuence of interruption domain on primary task performance,
and the role of practice effects in recovering from interruptions. A thorough
examination of the ramiﬁcations of the present study for theories of language
production and dialog will follow. First, I will address how the ﬁndings of
Experiment 1 reﬂect on the model of dialog put forth by Pickering and Garrod
(2004). Then, I will discuss how the results of Experiment 1, 2, and 3 begin to lay
the groundwork for a more well-speciﬁed theory of bookkeeping at the levels of
both dialog and sentence-level language production. Finally, I will explain how
the results of Experiments 2 and 3 provide further support for the notion that
sentence planning is a somewhat non-incremental process. The General
Discussion will conclude with a series of proposals for a future program of

research on interruptions.

121

Implications for Research on Interruptions

The Introduction laid out a number of issues that have been investigated
by researchers in human-computer interaction. The temporal issues of coarse
versus ﬁne break points (Adamczyk & Bailey, 2004), task length (Gillie &
Broadbent, 1989), and practice effects (Hess & Detweiler, 1994; Trafton et al,
2002) are all common issues for research on interruptions, and the present work
sheds light on all of them. The present work also deals with such issues as
domain-speciﬁc interference, the role of attention in recovery from interruptions,
and, importantly, the memory for task goals. I will address each of these issues in
turn.
Temporal Issues

Experiments 2 and 3 were highly-controlled examinations of speech
resumption tendencies following interruptions. Each experiment manipulated the
timing of the interruption in two ways: early versus late, and mid-clause versus
between clause. Both of these issues pose Important practical implications for
human-computer interaction—namely, that the timing of interruptions can have a
measurable effect on resumption (Adamczyk & Bailey, 2004). The present
experiments were also relevant to such issues as interruption length and practice
effects.

Sentence Location as st Progression. Is it more costly to be interrupted
early or late in task performance? Little work has been done related to this
question in the HCI literature, and the present study makes it clear that perhaps

this issue should be addressed. Experiments 2 and 3 both found that the further

122

one reaches in sentence production, the faster one is able to resume utterance
production when the interrupting task ends. The explanation that was offered
relates to the size of the plan that becomes disrupted. Early in sentence
production, assuming a non-incremental view (Christianson, 2002; Ferreira &
Swets, 2002), there is a plan for sentence production that extends far ahead.
Later in sentence production, that plan has largely been executed. I concluded
that there is a memory cost to have to re-Ioad a weightier plan early in sentence
production. However, there is some question as to whether these ﬁndings would
extend to realms beyond sentence production.

Coarse vs. ﬁne break points. Recall the Adamczyk and Bailey (2004)
study, in which participants expressed more frustration if they were interrupted at
ﬁne break points than at coarse break points, took longer to perform primary
tasks after ﬁne break point interruptions, but showed no effect of coarse versus
ﬁne break point interruption on resumption lag. The authors noted that the
apparatus that measured resumption lag was only able to record time in full-
second intervals, and hence, they were unable to rule out effects of coarse
versus ﬁne break points on the local measure of resumption lag.

An examination of Experiments 2 and 3 afﬁrms the general pattern of
results shown in the Adamczyk and Bailey (2004) study. I submit that being
interrupted in the middle of a clause is analogous to a ﬁne break point, and that
being interrupted at a clause boundary is the analog of a coarse break point.
Given this analysis, Experiments 2 and 3 showed that resumption errors are

more likely to occur when interruptions occur at coarse break points in sentence

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production, i.e., at clause boundaries. In sum, present results represent a
potentially informative contribution to the notion that coarse and ﬁne break points
in human representation of tasks play a large role in dealing appropriately with
interruptions (Zacks & Tversky, 2001).

Issues of Resumption Lag and Task Length. In one of the foundational
studies of what types of interruptions cause more disruption to primary task
performance, Gillie and Broadbent (1989) failed to ﬁnd any effects of the duration
of the task. When Gillie & Broadbent (1989) studied the effects of mathematics
task interruptions on participants performing a verbal item-location matching task,
they found that interrupting had no effect on performance whether the math task
was short (30 s) or long and involved (over 2 minutes). Insofar as the
comparisons between Experiments 2 and 3 of the present study showed that the
length of an interruption does, in fact, inﬂuence resumption difﬁculty, the present
study represents evidence to assert that interruption length can, under some
circumstances, affect primary task performance. The upshot of this ﬁnding is that
the results may be considered rather artiﬁcial. It is questionable whether
resumption tendencies would have been affected if speakers had been shown
the picture target and been allowed to resume speaking as soon as they pressed
a button to answer the interruption tasks. The present results cannot decide this
matter. However, perhaps future research can answer this question with more
precision.

On the other hand, Experiment 1 showed that the length of an interruption

had little to do with disruption that interruption caused for speech resumption

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during dialog. It would be interesting to dissect the issue of task length further,
because it clearly has ramiﬁcations for theories of how memory supports the
storage of prospective goals over interruptions. Speciﬁcally, what role, if any,
does decay play in explaining the disruptiveness of interruptions?

gramme effects (and the lack thereof). Studies by Trafton et al (2003) and
Hess and Detweiler (1994) found that the disruptive effects of interruptions can
be trained away with practice. Although several dependent measures of
disruption in Experiments 2 and 3 showed main effects of practice such that
performance in general improved from the ﬁrst to the second halves of
experimental sessions, the order variable rarely if ever interacted with the
manipulated variables of interruption domain or locus. In other words, if a ﬁrst-
clause interruption caused a higher resumption lag than a second-clause
interruption for a particular speaker during the ﬁrst half of the Experiment 2/3
paradigm, such a relationship would likely remain intact during the second half of
the experiment as well. Such results imply that, at least in the context of the tasks
employed here, there are cases in which interruptions do not lose their ability to
disrupt primary task performance.
A Role for Interference?

A sizable portion of the preceding document has been devoted to trying to
understand whether domain-speciﬁc interference could play a role in how
different sorts of interruptions might disrupt performance of a primary task.

Experiments 2 and 3 reveal that domain-speciﬁc interference could be a non-

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issue for the interruptions literature, but Experiment 1 raises the possibility of a
different sort of interference.

ﬂpport for the Similarity vaothesﬁ As noted in the Introduction, Gillie &
Broadbent (1989) had shown that the total time spent on a primary task (TOT) in
a verbal item-allocation task was unaffected by an arithmetic task interruption.
However, when a verbal task interrupted the item-allocation task, TOT increased,
regardless of whether an interruption warning was given. The authors concluded
that similarity between primary and secondary tasks, in particular along the
dimension of domain, seems to affect the degree to which an interrupting task is
disruptive. To be precise, arithmetic tasks should disrupt arithmetic tasks to a
greater degree than verbal tasks should, and verbal tasks should disrupt verbal
tasks to a greater degree than arithmetic tasks should. The notion at work here is
that interruptions that occupy the same mental resources as primary tasks should
be more disruptive than interruptions that recruit other mental resources
(Adamczyk & Bailey, 2004). In other words, the particular domain or modality that
an interruption takes is vital for determining whether that interruption will cause
disruption via interference to domain-speciﬁc memory mechanisms.

Experiment 3 demonstrates that the particular domain of an interruption
has no effect on the resumption of sentence production following interruptions if
other factors are properly controlled. Before proceeding, I should note that the
dependent measures used in Experiment 3 and the dependent measures used in
Gillie and Broadbent (1989) differ. Whereas Gillie and Broadbent (1989) used a

global measure of time on task as their dependent measure of disruption,

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Experiment 3 used several local measures, including resumption lag and three
types of resumption error. Perhaps the original study would have made similar
observations if the present measures had been used—it is impossible to say.
What can be said is this: Experiment 3 of the present study contributes to a
growing body of evidence supporting the notion that domain-general short-term
and working memory factors underlie a signiﬁcant proportion of memory
phenomena (Kane et al, 2004; Swets, Desmet, Hambrick, & Ferreira, 2005) that
have typically been regarded as phenomena of domain-speciﬁc memory factors
(Shah & Miyake, 1996). In the present case, it seems as though both arithmetic-
and language-based tasks disrupt memory for utterance plans and bookkeeping
pointers equally. That is, if interference from a secondary task is responsible for
disruption to primary task performance, the null results of the Experiment 3 might
suggest that this interference is domain-general. However, this conclusion must
be considered tentative for two reasons. First, conclusions based on null results
are simply not convincing. Second, it is possible that the reason domain effects
were not observed in Experiments 2 and 3 is that the task that had been used to
introduce non-verbal interference, the arithmetic judgment task, actually may
have a verbal component. That is, participants may have used verbal strategies
to support their arithmetic calculations, thereby producing the same verbal
interference that grammaticality judgments did. Future research will test whether
tasks providing little opportunity for verbal coding will elicit domain effects.

T_ask involvement. Experiment 1 revealed a surprising ﬁnding: One of the

interruption types that was hypothesized to produce relatively little disruption, the

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clariﬁcation interruption type, was one of the most disruptive types tested. The
property of this interruption type that seems most saliently responsible for this
unexpected effect is the level of involvement required by the interruptee to deal
with the interruption. Namely, it was the only interruption type that necessitated a
meaningful and lengthy verbal response from the interruptee before resuming the
primary dialog. Although, perhaps, a more carefully controlled experiment may
have ﬁt the hypotheses better, it is a happy accident such as this that could help
drive the science of interruption further by looking at how the level of involvement
required by interruptions inﬂuences their effects on primary task performance and
resumption.

Consider the following scenario in a dialog. Bob is again telling David
about a scene in a movie that he missed. When David casually interrupts to
mention how unusual an aspect of the scene sounds, Bob is able to resume
without incident. However, when David asks what a particular character looked
like so he can know which character Bob is talking about, Bob answers, and then
stammers, repeats, and is disﬂuent upon his resumption of the scene description.
lmportantly, both interruptions take the same amount of time. The difference is
the level of attention Bob needed to pay to each interruption type. For the ﬁrst
interruption, there was actually no need at all for Bob to consider David’s
interruption beyond mere superﬁcial encoding. For the second interruption,
however, there was a requirement to encode, comprehend, and answer the
question David posed. The issue at stake here is akin to the idea of good-enough

processing in language comprehension (Ferreira, Bailey, 8. Ferraro, 2002;

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Sanford & Sturt, 2002). Perhaps interruptions are disruptive (in part) to the extent
that they require a deep level of processing rather than shallow processing: The
more attention an interruption demands, the greater its cost to the storage of
primary task goals.

Utterance Planningﬂl Task Goals—A usefulﬁcomparison?

This document makes frequent and important analogies between
interruptions to language production and task interruptions. These connections
are made at the levels of theory, design, terminology, and dependent measures.
A valid question could be posed to undermine this approach: What if language is,
indeed, special? Why should the theories, processes, measures, and research
approaches used in studying task interruptions translate to the study of
interruptions to language production? I respond to this hypothetical and slightly
opinion-based question with a slightly opinion-based answer: First, there are
those of us in psycholinguistics who study language as though it is an untouched
island apart from the otherwise crowded land masses of the mind. Then, there
are others who view language as just another facet of mind that is subject to the
same mental limitations of memory and attention that all mental processes face. I
consider myself to be of the latter camp, and it seems to me that it is just as
useful to understand what language has in common with other mental faculties
as it is to know how it differs. But more importantly, the analogy has, for the sake
of the present project, been effective for the sake of learning from a domain in

which interruptions are somewhat understood, obtaining objective measures of

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disruption to language production from this domain, and examining what these
measures mean for language production. I will presently turn to these issues.
Implications for Theories of Language Production

Although the implications of the present research for the study of task
interruptions is interesting and rich, the potential implications for theories of
language production are just as broad. | presently review how the current work
helps not only begin to sketch out a model of bookkeeping during language
production, but adds a new approach to the incrementality debate, and also
poses an important question for the dialog model of Pickering and Garrod (2004).
Mions for Bookkﬁmg

The clause as a unit of sentence-level bgkkeeping. When speakers are
interrupted in the middle of articulating a sentence, there is an apparent
preference to return to a clause boundary. Two pieces of resumption error
evidence from Experiments 2 and 3 support this notion. The ﬁrst is that errors are
far less common at clause boundaries than they are when speakers are
interrupted mid-clause. The second is that speakers interrupted early in a
sentence will often skip ahead to a clause boundary, and speakers interrupted
late in utterance production will backtrack to a previous clause boundary. It is
almost as though the sentence-level bookkeeping system has two tiers. At the
ﬁrst tier, the speaker is able to recall with exact detail the locus where he or she
had left off. Resumption in this case is error-free, and this error-free resumption
comprises the majority of trials in the experimental paradigm used here. At the

second tier, the bookkeeping pointer has been lost, but the overall representation

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of the utterance plan is still intact. When the pointer is lost, the system employs a
strategy borne either of past success, convenience (as it conforms to the
architecture of the system), or both: go back, or go forward, to the nearest major
syntactic boundary. For these reasons, it makes some sense to argue that
maintaining a bookkeeping pointer at the sentence level uses the clause as the
major unit of information storage.

So, where was I? A third tier. Although such cases never arose in
Experiments 2 and 3, and arose only in the Pilot Studies for Experiment 1, there
certainly are also cases in which interrupted speakers lose track not only of the
place in a particular sentence where they had left off, but the entire utterance
itself. Cases like this are often illustrated in discourse by the question, “Where
was I?”, as in the opening scripted conversation. It would be interesting to try to
ﬁnd circumstances under which these types of errors can be elicited. Knowing
when these error occur could be very informative in future studies of dialog-level
bookkeeping.

Although participants in Experiment 1 did not explicitly lose track of their
place in sentences, indirect measures of resumption difﬁculty revealed that
certain types of interruptions disrupted bookkeeping with more frequency than
other types. As noted previously, it is likely that the amount of attention paid to
conversational interruptions plays a large role in how disruptive they become to
dialog-level bookkeeping. Immersive interruptions that demand attention by
requiring thoughtful responses or by simply being outlandish seem the most likely

interruptions to produce disruptions to maintaining track of a dialog. Although this

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analysis does not mesh especially well with the idea that cooperative, or aligning,
interruptions will aid bookkeeping, but competitive, or non-aligning, interruptions
will disrupt it, the analysis ﬁts marvelously with the model of dialog that inspired
much of this work (Grosz and Sidner, 1986). The Grosz and Sidner model uses a
goal stack in which discourse goals get pushed and popped depending on how
interruptions are able to capture attention. This model creates a passable bridge
for the two research domains of language production and task interruptions. By
combining the study of conversational interruptions with the notion that
interruptions require the maintenance of primary task goals (e.g., Altmann &
Trafton, 2004; Trafton et al, 2003), the present study can be viewed as empirical
support for the model of dialog proposed by Grosz and Sidner.

Let us try to unpack this argument by using Experiment 1 as an example.
In Experiment 1, the primary goal of the speaker was to convey enough
information about the ﬁlm clips for the listener to be able to take a memory test
based on the description. When they were interrupted at hypothetical Point X, the
subgoal that was interrupted was to inform the listener about the plot details of
Point X. Describe PointX is the goal that requires attention, according to Grosz
and Sidner, and memory maintenance, according to Altmann & Trafton (2002).
Interruptions that push this goal down into the attentional/memory stack are
either the ones that veer wildly from Describe Point X, or require a higher degree
of attention than usual if the interruption is related to Describe Point X. The
further attention is diverted from the primary goal, the more the attended

interruption will interfere with memory for Describe Point X (Altmann & Trafton,

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2002). This analysis of interruptions thus synthesizes principles from
computational linguistics and task interruptions literature to lay the groundwork
for a cognitive model of conversational interruptions.
Isires of lncrementaﬁt

An ongoing debate in the language production literature centers on the
amount of planning that speakers do before and during utterance production.
According to inﬂuential models (Kempen & Hoenkamp, 1987; Levelt, 1989) and
empirical studies (Schriefers, Teruel & Meinshausen, 1998; Smith & Wheeldon,
1999, 2001) of grammatical encoding in language production, syntactic planning
proceeds incrementally, phrase by phrase, through an utterance. These models
(Kempen & Hoenkamp, 1987; Levelt, 1989) also assume that grammatical
encoding, unlike conceptual/semantic planning, operates in a reﬂexive, cost-free
fashion. In one study (Smith & Wheeldon, 2001) it was argued that grammatical
encoding is costly before articulation, but comes for free once speech has begun.
Research supporting incremental, phrase-by-phrase planning of syntax have
typically focused on studies showing that latencies to begin speaking are not
affected by information beyond the earliest parts of an utterance (Smith &
Wheeldon, 1999, 2001). In recent years, new artillery has been recruited to
demonstrate the narrow scope of syntactic planning: eye-tracking (e.g., Grifﬁn &
Bock, 2000). The eye-tracking methodology has shown that speakers tend to
follow rigid looking-and-speaking schedules such that ﬁxation on an object is
shortly (within 500 ms) followed by the naming of that object in the context of

simple sentences.

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Other researchers claim that the scope of planning in language production
is not rigidly incremental, but rather, is under the strategic control of the speaker
(Costa & Caramazza, 2002; Ferreira & Swets, 2002, 2005; Schriefers & Teruel,
1999; Swets & Ferreira, 2003). This research has shown that different task
circumstances, especially time pressure, elicit different syntactic planning ranges.
In one such study (Ferreira & Swets, 2005), speakers under time pressure were
affected during sentence production by syntactic information that would not be
uttered for two full clauses. Such research shows not that speakers always plan
ahead by a clause or two in advance, but merely that, if desired, they are capable
of doing so.

Experiments 2 and 3 represent a new approach to the question of
planning scope in sentence production. By interrupting participants at various
points in two-clause sentence production (early, middle, and late), it is possible to
measure how much planning has been done before interruption (Meyer et al,
1986). Because interruptions caused the most disruption when a full clause or
more had yet to be uttered, and the least amount of disruption (in terms of
resumption lag) when only a portion of a single clause remained, one must
conclude that the participants in Experiments 2 and 3 were planning their syntax
at least a clause at a time. Consider this: if speakers only planned one phrase at
a time, why would they bother taking more time to resume speech following an
interruption when more of the sentence remained to be planned? If syntactic
planning is truly phrasal in scope, then planning the next phrase in the middle of

a sentence should surely be just as easy as when only one phrase remains to be

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planned. For this reason, the data presented in Experiments 2 and 3 should be
considered as more evidence against the radically incremental view of syntactic
planning, and for the strategically incremental view.15

Dialog: ls Alignment Real?

In the model of dialog espoused by Pickering and Garrod (2004), the
alignment of representations between interlocutors in a dialog is regarded as the
essential component in their theory of why dialog seems so easy compared to
monolog. This notion was paramount when designing the interruptions for
Experiment 1, and testing whether interruptions that helped promote
representational alignment between interlocutors resulted in easier dialog (and
hence, more efﬁcient speech resumption). But a funny thing happened on the
way to supporting the alignment model: One of the interruption types
(clariﬁcation) designed to promote alignment caused just as much disruption to
speakers as the interruption designed to produce the most disruption (true
interruptions). Conversely, one of the interruptions designed to produce a good
deal of disruption by breaking alignment (digression) was just as un-disruptive as
the least disruptive, and most aligning type (repetition). I will not argue that this
result does any notable damage to the alignment model of dialog. I would like to
note, however, that if the results obtained in Experiment 1 are replicable such
that an interruption that promotes alignment does more damage to the primary

dialog than an interruption that does not promote alignment, then clearly, there

 

‘5 I would be curious to see what Experiments 2 and 3 would show if an eyetracking component
were added. It seems as though this could be a case in which the eyes trick experimenters by
appearing to betray an incremental following of items to be named, while the syntactic planner is,
in actuality, moving beyond the scope of the eye movements.

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M‘ L
l ... I

are factors that are just as important as, if not more than, alignment. Earlier in
this General Discussion, I argued that the degree to which it is important to pay
attention to an interruption is quite likely to be such a factor.
Future Directions

Establishing a baseline. The HCI literature on task interruptions uses a
tool for establishing whether interruptions are disruptive: performing a task with
interruptions in one condition, and performing a task without interruptions in
another condition. In which condition does task performance show negative
effects? Because of the nature of the designs of the two paradigms used in the
present study, this type of baseline could not be researched. Future research
could address this in two ways: by changing the dependent measures used to
evaluate primary speech production difﬁculty (for example, switching from RL to
TOT), or by modifying the paradigms employed herein. For example, a study in
which grammaticality judgments interrupt speech compared to a condition in
which speech is interrupted by 3.25 seconds of empty silence could easily
establish that ﬁlled interruptions are more disruptive than mere pauses. In fact,
Experiment 2 begins to demonstrate that fact by showing that time spent waiting
to resume after answering the arithmetic problems essentially functions as extra
resumption lag time.

Testing the Good-Enough Theogy. Are interruptions really more disruptive
when speakers are required to pay attention to them? Future work could test this
idea by interrupting speakers with various bits of information to read. In one

condition, speakers are told that they will later be tested on this information. In

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another condition, speakers are told that there will be some meaningless
sentences that appear on the screen, and that they will not need to know that
information later on. A result showing that the former condition results in greater
disruption could represent evidence for a kind of good-enough theory of
interruption cost (Ferreira et al, 2002).

The role of storv grammars. Appendix A refers to story structure as a
possible inﬂuence on the ability to recover from conversational interruptions.
Levelt (1989) hints at a link between storytelling and the structure of intentions
behind the production of stories by citing research in which primary goals of
storytellers are treated differently in sentence structure than secondary goals. For
instance, Brown and Dell (1987) showed that storytellers use different
grammatical structures depending on whether an instrument of an action (e.g.,
stabbing) is a typical instrument (e.g., a knife) or an atypical instrument (e.g., an
ice pick). Two ﬁndings emerged from this study. First, typical instruments were
explicitly mentioned less often than atypical instruments. Second, when typical
instruments were mentioned, they tended to be grammatically encoded outside
of the action clause (e.g., The robber grabbed a knife and stabbed the man),
whereas atypical instruments were mentioned in the same clause as the verb
(e.g., The robber stabbed the man with an ice pick). Typical and atypical
instruments are encoded differently because the intentions behind mentioning
each are different. Atypical instruments are mentioned more often than typical
ones because, for example, although it can be assumed that a knife was used to

stab someone, the same cannot be assumed for an ice pick. It is imperative for

137

the overall intention of telling a story to know which instrument was used stab
someone. On the other hand, if one can assume that a knife was used, then
explicitly mentioning the knife must be motivated by a “side intention” such as
embellishing the story. Unfortunately, Levelt does not draw the connection
directly between goal hierarchies and story structures. Therefore, it would seem
to be best to reserve investigation into this area for future work.

Although Experiment 1 did not go in this direction, it is possible that future
work could address this issue. However, it will be necessary to ﬁnd some
research that has adequately explained how a question about, say, a character’s
hair color, resides at a fundamentally different “level” than asking about the
setting of an action.

One way to carry the ﬁndings of Experiments 2 and 3 into the domain of
dialog would be to interrupt people at coarse versus ﬁne break points in
storytelling (Zacks & Tversky, 2001). The major challenge in such research
would be untangling the ﬁne and coarse break points in storytelling from mid-
sentence versus between-sentence interruptions.

Familiarity as expertise. The video clips that participants had to memorize
for Experiment 1 were most likely more difﬁcult to remember than clips of movies
that participants had already seen. There are two possible reasons for this: First,
the clips are presented out of context, so the only story hierarchy that participants
can develop as they watch these clips is the local hierarchy. They do not get an
overall sense of the themes of the ﬁlms from which the clips are taken. As a

result, participants lack the sort of meaningfulness context that has been shown

138

to aid story recall in classic experiments in cognitive psychology (Bransford &
Johnson, 1972). Second, having never seen the ﬁlms before, it may be more
difﬁcult for participants to chunk a great deal of information by using a well-worn
hierarchical representation of the events they are to remember. This point is also
inspired by a classic study in which chess players that have developed expertise
in chess are able to chunk more chess-piece conﬁguration information than
novices, thus allowing them to recall chess board positions better (Chase &
Simon, 1973; see also Ericsson & Kintsch, 1995). Essentially, participants in
Experiment 1 are watching the movies as novices (no one has ever seen Basket
Case) without any context (for example, they do not know that the creature in the
basket is Duane’s formerly-conjoined twin brother, Belial—such a context could
make it easier to remember other details of the ﬁlm). For these reasons, it is
possible that one might observe that participants have more trouble recovering
from interruptions while describing these sorts of ﬁlms as opposed to describing
well-known movies. Future studies could manipulate familiarity and context as
independent variables in order to test such a prediction.

Warnings of uacominainterruptions—adapting to interruptiona, A human-
computer interaction study performed by Zijlstra et al ( 1999) on task-interruptions
provided support for a comprehensive taxonomy of interruption coordination put
forth by McFarlane (2002). McFarlane proposed that there are four ways of
coordinating interruptions: 1) immediate, 2) negotiated, 3) mediated, and 4)
scheduled. Immediate interruptions are those that come as in the no-warning

condition of Trafton et al (2003), and they are supposed to be best for efﬁciency

139

on the secondary task. Negotiated interruptions are considered to be the most
user-friendly, and primary task performance on these types of tasks, in which the
subject chooses when to begin performing any number of built-up interruption
tasks, is the best of the four. This is because people tend to delay the interruption
task until they reach a coarse break point in the primary task. On the other hand,
mediated interruption (a computer decides which moments are prime for
interruption performance), and scheduled interruption (a rigid schedule decides
that the user should be interrupted every n units of time) are associated with
generally low user-friendliness as well as performance. It seems apparent that
most conversational interruptions are of the ﬁrst type (immediate). However,
perhaps there are signs interrupters give off that could signal an upcoming
interruption (Susan Brennan, personal communication). Such signals could
include gestures (a raised ﬁnger) or a verbal signal that a rebuttal is forthcoming
("But...”).

Trafton et al (2003) and Altmann & Trafton (2004) demonstrated that the
extent to which a subject can rehearse cues to goal retrieval during the
interruption lag can affect the duration of the RL. A potentially fruitful direction for
research in this area to take would be to experimentally manipulate the presence
or absence of warnings that an interruption is coming up. This could be done
using gestures or verbal signals as described above for follow-ups to Experiment
1, or by using signals such as ﬂashes of light for follow-ups to the more controlled
Experiment 2. Such studies could provide evidence that interruptions are, in fact,

a negotiated process between interrupter and interruptee. It would reveal that

140

there are strategies people use in dialog in order to adapt to the potential for
interruption.

Effects of interruptions on production strategies. The aim of Experiment 1
was to test whether interruptions to speakers cause disruptions in memory of a
bookkeeping structure at a local level. However, it could be interesting to
investigate what effect interruptions have on a more global level of production
strategies. Earlier, I mentioned that some studies have indicated that the
possibility of interruption from a conversational participant may result in more
concise (Chapanis, 1976) and higher quality production (Schober et al, 2004).
However, these issues have not been directly examined. The protocol developed
in Experiment 1 allows this issue to be tested directly. Speciﬁcally, participants
could describe scenes to confederates under one of two conditions: Either the
participant is told beforehand that the confederate must remain silent until he or
she is ﬁnished, or the participant will be told that the confederate will interrupt in
order to clarify, or expand on, the participant’s description. Independent ratings
showing that the interruption condition results in better descriptions could
demonstrate what Pickering & Garrod (2004) propose: that monolog is an
inherently inferior method of communication than dialog. It would also show that
the ability to interrupt someone comprises a signiﬁcant portion of this difference.

Task interruptions to conveﬁtjons. During a pilot testing session for
Experiment 1, a participant’s cell phone rang during her description. The resulting
disruption was as strong as the most effective true interruption from any of the

pilot testing (she actually asked, “Where was l?”). It may be interesting to perform

141

an experiment in which participants are interrupted not just by typical verbal
interruptions, but by interruptions that more closely resemble task interruptions.
Is “power outage” more disruptive than another lab member “accidentally"
intruding on an interview between the experimenter and the participant? Are
these sorts of interruptions actually more disruptive than conversational
interruptions? This latter question is the familiar domain issue, but this study
could be a straightfonrvard way to test it in a dialog.

Effects of leagth aad complexuy' of interruptions on recoyerv from syntactic
disruptions. An appealing aspect of the design of Experiments 2 and 3 is that
their tightly controlled nature allows for more freedom to play with various
properties of the interruptions that are tested. One area that is ripe for follow-up
work is how the manipulation of length and complexity of interruptions could
affect the ability to resume sentence production. Once the general technique is
mastered, it would be relatively effortless to implement changes in how long the
judgment tasks are to last, and how difﬁcult the tasks are. Previous research
(Gillie & Broadbent, 1989) on Tls has found little evidence that such
manipulations ever work, but in sentence production, the currently “hot” syntactic
priming literature could offer a reason to try these manipulations out in the dialog
domain.

Priminﬂ amogel of mg. The recent work of Picking and Garrod (in
press, 2004) appears to be a promising enterprise that will bridge the disciplines
of language production, language comprehension, dialogue, and social

psychology. The model they propose is a mechanistic account of dialog, in which

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they account for the ease with which people converse by assuming that
representations between the participants of a dialog become aligned at many
different levels, from phonological, to syntactic, to semantic, to the overall
situation model. The common goals of the interlocutors contribute to the drive to
align representations, but it is another process—priming (Bock, 1986)—that does
the most work for Pickering and Garrod’s (2004) model.

The potential pitfall for setting syntactic priming as the motivation for this
ambitious theory is that researchers are still having a difﬁcult time sorting out
what exactly syntactic priming is. Is it a long-lasting effect that reﬂects the
learning of certain abstract concept-to-structure mapping procedures (Bock &
Grifﬁn, 2000; Chang et al, 2000), or is it a strong, temporary activation of lexical
and syntactic representations (Pickering and Branigan, 1998; Branigan et al,
2000b)? The two views of priming differ primarily along one empirical dimension:
Whereas the implicit learning view requires that priming be evident over many
intervening trials (up to 10, Bock & Grifﬁn, 2000) and over large amounts of time
(20 minutes), it seems as though a theory in which priming is used to help
conversation-speciﬁc representations align between two people would require
that priming not last so long.

The evidence for long durations of priming effects primarily comes from a
study by Bock and Grifﬁn (2000) in which priming effects were elicited after
intervening trials numbering 1, 2, 3, and 10. However, there was no effect of
priming after 4 intervening trials. After failing to ﬁnd structural priming after even

a single intervening trial using written completion priming (Branigan et al, 1999),

143

Branigan et al (2000a) tried again using an oral sentence completion task and
found that priming occurs after a single intervening ﬁller trial. Other studies of the
durations of priming effects have generally found rapid decay of structural
representations (Wheeldon & Smith, 2003).

The methodology developed in Experiments 2 and 3 could help address
the robustness of syntactic representations over time and intervening tasks.
Although it does not address syntactic priming directly, the syntactic priming
literature addresses the same issue: syntactic persistence, which, in fact, is what
syntactic priming is still sometimes called. Are syntactic representations long-
Iasting learning phenomena, or are they strong, but transient, activations? By
manipulating the duration and complexity of interruptions to utterances, the
methodology developed in Experiments 2 and 3 might be able to sort out the
some of the happy mess that has been cultured by researchers in syntactic
priming.

Conclusions

At the outset of this document, a ﬁctional conversation demonstrated the
possibility that interruptions can disrupt a speaker’s train of thought. A series of
questions were then raised: First, are interruptions, in fact, disruptive to the ability
of speakers to remember what they were talking about? If so, what interruption
properties are the most disruptive? And ﬁnally, what do these properties suggest
about the nature of the language production system—how it keeps its books and

plans its utterances?

144

The ﬁrst question was answered without resorting to ﬁctional scripts.
Experiments 1, 2 and 3 showed that different properties of interruptions elicited
different degrees of disruption to speakers’ bookkeeping. Using the reasoning
that if interruptions are disruptive, then some interruption types must be more
costly than other types, the results of Experiments 1-3 all reveal that interruptions
are, in fact, disruptive.

I then addressed the question of what interruption properties are the most
disruptive. Experiment 1 revealed that interruptions that require more attention
are more disruptive than interruptions that require less attention. Speciﬁcally, an
interruption that is wildly off topic and diverts attention is more disruptive than a
mild digression. Similarly, an interruption that requires verbal response is more
disruptive than a digression, regardless of whether it helps align the interlocutors
on the primary goal of the dialog. Experiments 2 and 3 together showed that, at
the sentence level, interruptions coming earlier in sentence production take the
most time to recover from, but that resumption errors are more tied to the
location of interruptions relative to clause boundaries. Also, longer interruptions
are more costly than shorter interruptions, but verbal interruptions may be just as
disruptive as non-verbal interruptions when interruption length is controlled.

The ﬁnal question asked was this: What do these disruptive properties tell
us about the nature of bookkeeping processes and language production
architecture? First, as Grosz and Sidner (1986) alluded to early on, attention paid
to various levels of a goal hierarchy seems to play a role in the ability to handle

interruptions. The further an interrupting goal brings the speaker from the primary

145

goal of the dialog, the more difﬁcult it will be to recall that goal later. Second, at
the sentence level, bookkeeping is much easier when interruptions occur at
natural break points in the representation of sentence information, such as
clause boundaries. In that sense, sentence-level bookkeeping occurs at the
interface of verbal and non-verbal processes such as attention and memory.
Finally, the result that early interruptions take longer to recover from than later
interruptions is also further evidence that language production is a strategically
non-incremental process.

I conclude with a suggestion. It seems that the process of bookkeeping in
language production is not so different from bookkeeping in any complex task. In
both cases, the process of interruption can be subjected to the same conceptual
analyses (as in Figure 1), manipulations (e.g., length, domain, similarity, etc.),
and measurements (e.g., resumption lag). Likewise, a conversational interruption
is subject to the same limitations in attention and memory that dictate resumption
from a task interruption. An interruption at a coarse break point is less costly than
an interruption at a ﬁne break point in the speciﬁc contexts of each domain. So
although it may be interesting to see how conversational interruptions differ from
task interruptions, it could be more scientiﬁcally productive to unite several
disciplines by investigating how interruptions among different domains can be

viewed as the same.

146

APPENDICES

147

Appendix A

Pilot Studies 1 and 2, Experiment 1
Pilot Studv 1

An experiment with 6 participants was run in order to determine the
viability of a procedure in which a confederate interrupts a participant in such a
way as to measure the disruptiveness of various Cls.

Participants in Pilot Study 1 were instructed to watch a three minute video
clip from a Lord of the Rings ﬁlm. I instructed participants that the purpose of
watching the clip was to memorize the events, dialog, and characters’ names as
accurately as possible because another participant (a confederate) was going to
listen to the clip description for a later memory test. During the ensuing
descriptions, the confederate participant interrupted the experimental participant
at pre-established story junctures in the participants’ descriptions.

Interruption type was manipulated as a between participants independent
variable. Interruption types were selected to correspond to Grosz and Sidner’s
taxonomy of interruptions. Here are descriptions of each type presented in order
of predicted disruptiveness:

Flashbacks. The confederate asks for clariﬁcation of something the
participant had just said. For example, the ﬁrst time the participant said the word
“hobbit”, the confederate would immediately interrupt the participant mid-

sentence and ask, “What’s a hobbit? Are those the short characters?”

148

Digressions. The confederate follows up a piece of the participants’
description with a connected thought, but a thought that diverts from the goal of
the dialog. For example, if the participant mentioned that a tree came to life, the
confederate might say, “I hate it when stuff happens like that in movies—trees
coming alive. Don’t you?”

True interruptions. The confederate interrupts the description by bringing
up a completely unrelated topic and addressing a different audience (the
experimenter). For example, after the participant mentions that a monster was
threatening the hobbits, the confederate turns to the experimenter and asks,
“How long is this experiment supposed to last? I was just wondering because I
have a class in 20 minutes.”

Two dependent measures were used to determine the disruptiveness of
the types of interruptions. The ﬁrst measure, resumption lag (RL), was taken to
be the number of milliseconds between the offset of the interruption and the
onset of the ﬁrst word uttered by the participant that carries meaningful
connections to what they had been saying before being interrupted. The second
measure was the number of disﬂuencies uttered by the participant during the
measured RL. Only ﬁller-type disﬂuencies were counted (uh and um). Pauses
and restarts were not counted, but the effect of such behavior would be reﬂected
in larger RL times. As with RL, disﬂuency measures are assumed to reﬂect the
difﬁculty a participant is having planning speech.

Resumption lag results and disﬂuency results are shown in Figures 21 and

22, respectively. With the small number of participants tested (N = 6), no

149

signiﬁcant differences were found between the conditions in either measure.
However, a clear trend emerged such that true interruptions and digressions
resulted in longer mean RLs and mean number of disﬂuencies than ﬂashbacks.
Also, task-related conversational interruptions were less disruptive than unrelated

interruptions.

5000 -

4000‘

3000 -

Time in ms

2000 -

1000 -

 

 

   

"Flashback" "Digression" "True interruption"

Interruption Type

Figure 21. Average resumption lag as a function of interruption type, Pilot Study

1.

150

0.4 -

 

 

 

Proportion of Resumption containing Disfluency

"Flashback" "Digression" "True lntenuption"
Interruption Type

Figure 22. Average proportion of disﬂuent resumptions as a function of
intenuption type, Pilot Study 1.
Pilot Study 2

A second pilot study modiﬁed the Pilot Study 1 method in two ways. First, I
changed the types of interruptions. Rather than following the Grosz and Sidner
taxonomy, l categorized interruptions according to their hierarchical relationship
to the “story grammar” of participants’ narratives. Types and examples of the
interruptions, in the order of predicted disruptiveness (from least to most
disruptive) are given below:

Low-level interruptions: The confederate interrupts at a speciﬁc story point
and asks that a lower-level story element be explained further. For example,
when the participant mentions that a tree came alive, the confederate interrupts

by asking what the tree looked like.

151

ﬂgh-Ievel interruptions: As the participant comes a certain story point, the
confederate interrupts by asking that a higher-level story element be clariﬁed. For
example, if the participant has just explained that the tree came to life, the
confederate interrupts by asking about the overall setting of the story—when and
where it took place.

Off the hierarchy: The third type of interruption is like the “true” interruption
or digression—the interruption has no relation to the story hierarchy whatsoever.
I reused true interruptions from Pilot Study 1 as the items for this type of
interruption.

The second major difference from Pilot Study 1 is that l manipulated
interruption type as a within-participants variable. This difference could make
cause either smaller or greater differences to emerge: either the effects found in
Pilot Study 1 will be more robust because between-subjects variation is reduced,
or the effects could diminish because such variation explained the differences
observed between conditions.

Preliminary results for resumption lag and disﬂuencies are presented in
Figures 23 and 24 (below), respectively. It appears that interruptions that probe
information from “further down” in the narrative hierarchy are more disruptive
than interruptions that probe for information at a higher story level than the
present one. In addition, “true” interruptions that require attention to be given to
elements outside the narrative hierarchy seem to cause the same amount of
disruption as “downward” interruptions. These results were not predicted. It is

possible that the downward interruptions presented information to the participant

152

that had never been considered as a part of the narrative. In that case, the
interruption actually is more like an “off” interruption than a “downward" one, and
requires just as much attention. On the other hand, perhaps it is more difﬁcult to
resume a narrative after downward interruption. This would imply that it is more
resource-demanding to pay attention to lower-level discourse planning elements,
and subsequently return to a higher-level element, than it is to go from high back

to low.

5000-

4000 -

3000 -

Time in ms

2000 ~

1000 *

 

 

   

"Downward" "Upward" "Off"

Interruption Type
Figure 23. Average resumption lag as a function of intenuption type, Pilot Study

2.

153

1.4 -

1.2 -

1.0 -

0.8 a

0.6 -

Time In ms

0.4 -

0.2 -

 

 

 

0.0 -

IlDownll "Up" lloffll
Interruption Type

Figure 24. Average number of disﬂuencies as a function of intenuption type, Pilot
Study 2.
Pilot Studies: Lessons Learned

Pilot Studies 1 and 2 highlighted several methodological issues that were
addressed in the proposed experiments. First, it was important to run the
experiment using a within-participants design. Second, it was important to
account for the length of the interruptions by making sure that interruption length
is not a confound with predicted disruptiveness.

Participants offered some helpful suggestions for how to make the
confederate’s “performance” more credible. One of these suggestions was to
initially bring both participant and confederate in a room before the experiment
begins and to rig a game of chance in favor of the confederate. The winner of the

game of chance would win the right to choose between “describing" or “listening”

154

to the description of the movie clip. This suggestion was adopted in Pilot Study 2,
and was used in Experiment 1. Another suggestion offered by a pilot participant
was that the confederate should not seem as excited and eager as she appeared
to be. Such personality traits, the participant advised, are not expected from a
Michigan State University undergraduate.

Finally, the results of Pilot Studies 1 and 2 made it a difﬁcult task to
choose between the two interruption manipulation strategies. The advantages of
the “up, down, off the hierarchy” manipulation was that it straightforwardly tested
various levels of representation of message level planning and bookkeeping. The
Grosz and Sidner (1986) classiﬁcation was slightly more nebulous in that regard.
However, the results do not appear as clean for the Pilot Study 2 manipulation,
and the idea that some story details are “low-level” versus “high-level” is difﬁcult
to verify, even though this is precisely what a story grammar is designed to do.
The fact is, it is difﬁcult to determine a priori what is and is not included in
participants’ narrative plans. If results of Pilot Study 2 had been signiﬁcant such
that “low-level” interruptions required longer RLs than “high-level” interruptions, it
would be impossible to determine whether these results were obtained because
participants have difﬁculty going from lower-level to higher-level aspects of their
story representation; it could just as easily be the case that participants did not
intend to produce lower-level information, or worse yet, had simply never
encoded the information at the lower level. The other problem with the design of
Pilot Study 2 is that the story structure literature does not make it completely

clear that asking about the location in which an event takes place is a “higher"

155

level than asking about the color of a character’s hair. According to Thorndyke
(1977), there are 4 high-level components to a story: setting, theme, plot, and
resolution. Each of these levels has lower levels achieved through re-write rules.
For the examples above, both location and hair color are lower levels under the
“setting” component. In that sense, one does “dominate” the other, but is more
like a syntactic sister of the other.

Given the results of the pilot studies and the theoretical problems with
Pilot Study 2 that were just outlined, Experiment 1 was designed to test a rather
basic idea: that interruptions that attempt to align the interrupter's representation
with the interruptee’s representation of the story will result in less disruption than
interruptions that do not lead to such alignment. In that sense, Experiment 1
follows the design of Pilot Study 1 more closely than Pilot Study 2, although the
issues addressed in Pilot Study 2 were listed as a possible future direction of this

work in the General Discussion.

156

Appendix B

Interruptions Used in Experiment 1

Repetition interruptions for Breakin’

Approach: “Sorry, let me see if I got this right—so a guy and girl got out of a car
to watch some people dancing?”

Dance: “Sorry, let me see if I got this right—so the guy and the girl that had
gotten out of a car went out on the dance ﬂoor and started dancing?”

Other guys: “Sorry, let me see if I got this right—so while the guy and the girl
were dancing, some other guys came up and starting dancing in front of them?”
Leave/introductions: “Sorry, let me see if I got this right—so after they left the
dance ﬂoor, they all started talking to each other and introduced Turbo under

some sort of “street name”?

Repetition interruptions for Basket Case

Hot dogs: “Sorry, let me see if I got this right—so a guy walks into the hotel room,
starts talking to a basket, and drops a bunch of hot dogs into it?”

Arm: “Sorry, let me see if I got this right—so after the guy the leaves, some sort
of alien or monster arm or something comes out of the basket and breaks the

television dial?”

157

Girl: “Sorry, let me see if I got this right—so after the guy leaves the apartment
and the arm breaks the TV, the guys runs out and meets some girl somewhere?”
State of Liberty/ Grass: “Sorry, let me see if I got this right—so after the guy
leaves, some arm breaks a TV dial, and the guy and the girl go the statue of

libertyl talk on some lawn?”
Clariﬁcation interruptions for Breakin’

Approach: “Wait, sorry—so what time period is this taking place in? Is it like,
current times, because people don’t just dance in public much anymore.”

Dance: “Wait, sorry—so what time period is this taking place in? Is it like, current
times, because people don’t just dance in public much anymore.”

Other guys: “Wait, sorry—so, what did these guys look like?”
Leave/introductions: “Wait, sorry—so were those other guys, like, dancing in a

threatening way or something? So that’s why the main people left?”
Clariﬁcation interruptions for Basket Case

Hot dogs: “Wait, sorry—so the hot dogs weren’t like cooked hot dogs, they were
just raw hot dogs? They didn’t have ketchup or mustard or anything?”

Arm: “Wait, sorry—so what kind of basket was it? Was it like a ﬂower basket or a,
like, sizable basket?”

Girl: “Wait, sorry—so is this all taking place, like, in the city or something?”

158

Statue of Liberty/grass: “Wait, sorry—so is this all taking place, like, in the city or

something?”
Digressions for Breakin’

Approach: “Oh no, is this one of those movies about dancing? Like, where
people just dance in public and people watch? Those movies are so annoying.
Anyways, sorry, go on.”

Dance: “Oh, man, I hate movies with dancing scenes, don’t you? I just think it’s
so boring to watch dancing, I don’t understand why people watch these movies.”
Other guys: “Oh no, is this one of those movies where people, like, dance in front
of each other, like that You Got Served movie? Oh, I hate those movies.”
Leavefrntroductions: “Oh no, is this one of those movies where people, like,
dance in front of each other, like that You Got Served movie? Oh, I hate those

movies.”

Digressions for Basket Case
Hot dogs: “Oh, man, I can’t stand hot dogs, I think they’re pretty much the
grossest food imaginable. Don’t you think so? Anyways, sorry, go on.”

Arm: “Ugh, is this like a horror movie or something? I hate movies about aliens

and monsters. They just seem so pointless. Anyways, sorry, go on.”

159

Running/Girl: “Wow, this sounds like a really bad movie. It sort of reminds me of
this movie I watched this weekend, it was just terrible. Anyways, sorry, go on.”
Statue of Liberty/grass: “Wow, this sounds like a really bad movie. It sort of
reminds me of this movie I watched this weekend, it was just terrible. Anyways,

sorry, go on.”

“True” interruptions for Breakin’

Approach: (After looking brieﬂy to the side) “I’m sorry, do you guys hear that
noise in the hall? It’s like a high-pitched buzzing or something? No, ok, sorry, go
on.”

Dance: (After looking brieﬂy to the side, then to experimenter and the subject)
“I’m sorry, do you guys hear that noise in the hall? It’s like a high-pitched buzzing
or something? No, ok, sorry, go on.”

Other guys: (After looking brieﬂy to the side, then to experimenter and the
subject) “I’m sorry, do you guys hear that noise in the hall? It’s like a high-pitched
buzzing or something? No, ok, sorry, go on.”

Leave/introductions: (After looking brieﬂy to the side, then to experimenter and

the subject) “I’m sorry, do you guys hear that noise in the hall? It’s like a high-

pitched buzzing or something? No, ok, sorry, go on.”

“True” interruptions for Basket Case

160

Hot dogs: (To experimenter) “I’m sorry, do you know about how much longer this
experiment is supposed to last, because I have class in like a half hour.”

Arm: (To experimenter) “I’m sony, do you know about how much longer this
experiment is supposed to last, because I have class in like a half hour.”
Running/Girl: (To experimenter) “I’m sorry, do you know about how much longer
this experiment is supposed to last, because I have class in like a half hour.”
Statue of Liberty/grass: (To experimenter) “I’m sorry, do you know about how
much longer this experiment is supposed to last, because I have class in like a

half hour.”

161

Appendix C

Experiment 1 Instructions

You are about to perform a memory task. In this task, you will be shown
clips from movies. Please watch the clips carefully. After watching the ﬁlm clips,
you will be asked to describe from memory the events and dialog contents from
the clips as accurately as possible. In your description of the scene, please
include as many details about the events and characters as you can remember,
whether this knowledge was acquired from simply watching the clip or from
previous experience watching the ﬁlm. Also, please offer event descriptions in
the order in which they occurred.

There will be another participant in this study whose job is to listen to and
understand your description. This other participant will be tested for their memory
of your description. Therefore, he or she will need to have a good understanding
of your description, and may ask some questions about the clips in addition to
what you describe.

The entire experiment should take about 30-45 minutes.

162

Appendix D

Experimental Objects, Naming Accuracies, and Times, Experiments 2 and 3

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Name Naming Accuracy Naming Time
shoe 100 730
foot 100 730
ear 100 716
glass 95 684
key 100 640
chair 100 672
spoon 100 651
bell 100 644
lamp 100 655
truck 100 732
eye 100 683
dog 100 670
fork 1 00 61 5
pants 100 668
cake 100 762
cat 100 736
bed 100 672
tree 100 683

 

 

 

 

163

 

 

 

 

 

 

 

coat 100 727
mouse 100 604
house 95 662

frog 100 719
book 100 702

leaf 100 789

 

 

 

164

 

Appendix E

Target Sentences, Experimental Items, Experiment 2.
The ear moves above the book and the shoe moves below the book.
The leaf moves above the foot and the glass moves below the foot.
The key moves above the chair and the spoon moves below the chair.
The bell moves above the lamp and the truck moves below the lamp.
The eye moves below the dog and the fork moves above the dog.
The pants move below the cake and the mouse moves above the cake.
The bed moves below the tree and the coat moves above the tree.
The cat moves below the house and the frog moves above the house.
The shoe moves above the leaf and the lamp moves below the leaf.
The glass moves above the key and the chair moves below the key.
The spoon moves above the bell and the foot moves below the bell.
The truck moves above the eye and the book moves below the eye.
The fork moves below the pants and the house moves above the pants.
The mouse moves below the bed and the tree moves above the bed.
The frog moves below the cat and the cake moves above the cat.
The coat moves below the ear and the dog moves above the ear.
The lamp moves above the frog and the ear moves below the frog.
The dog moves above the coat and the leaf moves below the coat.
The cake moves above the mouse and the key moves below the mouse.

The tree moves above the fork and the bell moves below the fork.

165

The house moves below the truck and the eye moves above the truck.
The book moves below the spoon and the pants move above the spoon.
The foot moves below the glass and the bed moves above the glass.

The chair moves below the shoe and the cat moves above the shoe.

Target Sentences, Filler Items, Experiment 2.
The toe moves above the pen and the axe moves below the pen.
The broom moves above the drum and the skirt moves below the drum.
The bus moves above the bear and the fun moves below the bear.
The gloves move above the doll and the wheel moves below the doll.
The lips move above the leg and the shirt moves below the leg.
The snail moves above the frog and the bow moves below the frog.
The cow moves above the snake and the arm moves below the snake.
The swan moves above the wheel and the ﬁsh moves below the wheel.
The harp moves above the boot and the train moves below the boot.
The chain moves above the box and the desk moves below the box.
The pen moves below the wheel and the ﬁsh moves above the wheel.
The cow moves below the bow and the box moves above the bow.
The broom moves below the harp and the train moves above the harp.
The frog moves below the chain and the gun moves above the chain.
The toe moves below the bus and the boot moves above the bus.
The leg moves below the skirt and the snake moves above the skirt.

The snail moves above the lips and the axe moves above the lips.

166

The cap moves below the desk and the swan moves above the desk.
The arm moves below the doll and the pen moves above the doll.

The drum moves below the shirt and the bear moves above the shirt.
The desk moves next to the gun and the cow moves above the gun.
The snake moves next to the swan and the leg moves below the swan.
The doll moves above the toe and the pen moves next to the toe.

The chain moves below the gloves and the bow moves next to the gloves.
The axe and the train move next to the frog.

The arm moves next to the cap and the skirt moves above the cap.
The snail moves next to the bus and the boot moves below the bus.
The bear moves above the shirt and the lips move next to the shirt.
The broom moves below the box and the harp moves next to the box.
The drum moves next to the ﬁsh and the wheel moves next to the ﬁsh.
The swan moves next to the toe.

The skirt moves next to the chain.

The box moves next to the frog.

The snake moves next to the cow.

The boot moves next to the desk.

The drum moves next to the train.

The wheel moves next to the broom.

The gloves move next to the harp.

The pen moves next to the gun.

The snail moves next to the cap.

167

 

The lips move above the book and the snail.

The broom moves below the leaf and the gun.

The shoe moves above the bow and the toe.

The foot moves below the gloves and the frog.

The drum moves above the snake and the ear.

The shirt moves below the glass and the bus.

The ﬁsh and the key move above the doll.

The chain and the chair move below the box.

The spoon and the skirt move above the cap.

The bell and the axe move below the wheel.

The desk and the arm move above the lamp

The bear and the pen move below the truck.

The cow moves next to the eye and the leg moves below the eye.
The boot moves next to the dog and the harp moves above the dog.
The swan moves next to the fork.

The chain moves below the frog and the cap moves next to the frog.
The gloves move above the house and the bus moves next to the house.
The arm moves next to the mouse.
The coat moves next to the skirt.
The tree moves next to the boot.
The harp moves next to the ﬁsh.
The cat moves next to the gun.

The cake moves next to the drum.

168

The pants move next to the pen.

The desk and the bear move next to the fork.
The train and the frog move next to the dog.
The snake and the leg move next to the eye.
The toe and the broom move above the truck.
The axe and the doll move below the lamp.

The shirt and the swan move above the bell.

169

Appendix F

Grammaticality Judgment Task Items, Experiment 2.

Whenever the main pipe was closed because of the terrible ﬂooding.
It was expected Janis to win the race.

The whole team lost the coach’s money himself.

Bring Carrie the backpack in the corner to Troy.

Jill will use Weight Watchers and thirty pounds were lost.

Visiting the museum is a good experience for most children.

The county commissioner was forced to resign after the scandal.
The mayor announced that the recycling program would be reinstated.
Joan decided that she would never attend a concert again.

To be energy the battery was created by the scientist.

The jelly jar that my grandma ﬁlled and sent to me.

Jill gave the man and he enjoyed the book.

The policewoman who held at hostage radioed for backup.

What did you think the fact that Peter was explained.

Spending time outdoors made Andy's allergies worse.

Running over herself was the worst that Joan seems done.

The memories of spending a week in Mexico would last a lifetime.
Vicky ﬁnished her homework and was able to watch some television.

Vince used to trade baseball cards with all of his friends.

170

Arithmetic Judgment Task Items, Experiment 2.
27 + 21 = 49
35 + 21 = 66
37 + 21 = 52
35 + 22 = 47
37 + 22 = 58
35 +23=48
23+26=49
23 + 36 = 59
71 +13 = 84
65 + 32 = 97
45 +14 = 59
12 + 25 = 37
35 + 24 = 60
72 +11 = 93
62 +15 = 73
42 + 34=67
13 +24= 39
21 +26=47

21+28=49

171

Appendix G

Interruption Items, Norming Study B and Experiment 3.

Visiting the museum is a good experience for children.
The senator was forced to resign after the scandal.

The book had been stored for many years.

The mayor reinstated the recycling program.

Joan decided that she would never drink again.

Going outdoors made Andy's allergies worse.

The memories of a week in Mexico would last a lifetime.
Vicky ﬁnished her homework and watched some television.
Vince always trades baseball cards with his friends.
The lion killed the gazelle and ate it immediately.
Whenever the main pipe was closed because.

It was expected Janis for win the race.

The whole team lost the money himself.

Kyle threw the frisbee for the dog brought it back.

Jill will diet and thirty pounds were lost.

To be energy the battery was by scientists.

The jelly jar that my grandma sent to me.

Jill gave the man and he enjoyed book.

The policewoman who radioed for backup.

172

What did the fact that Peter was explained.
That was the worst that Joan seems done.
45 + 52 = 97
23 + 45 = 68
35 + 34 = 69
53 + 24 = 77
32 + 46 = 78
25 + 54 = 79
62 + 24 = 86
32 + 65 = 97
32 + 47 = 79
32 + 45 = 77
32 + 45 = 76
42 + 35 = 87
35 + 54 = 79
62 + 34 = 86
75 + 34 = 99
73 + 24 = 98
42 + 54 = 98
42 + 34 = 67
37 + 32 = 79
24 + 73 = 98

23+76=89

173

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174

 

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