HELPING UNDERGRADUATES IMPROVE THEIR GRADES:
A COMPARISON OF VERBAL AND IMAGERY REHEARSAL FOR ENHANCING
TRAINING TRANSFER
By
Abigail Q. Billington

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Psychology
2012

ABSTRACT
HELPING UNDERGRADUATES IMPROVE THEIR GRADES: A COMPARISON OF VERBAL AND
IMAGERY REHEARSAL FOR ENHANCING TRAINING TRANSFER
By
Abigail Q. Billington

The purpose of this study was twofold: to explore an imagery-based alternative to
verbally based training transfer interventions and to examine whether individual differences in
preferred processing style impact transfer.In sport and therapeutic settings, imagery-based
rehearsal has been found to be very effective at changing behavior.Despite the widespread use of
techniques related to future-oriented mental rehearsal in other fields, there has been very little
exploration into the possibility of its use in workplace or classroom training.
For this study, undergraduate students were presented with training on strategies to
improve their scores on multiple-choice exams followed by a verbally based intervention, an
imagery based intervention, or no intervention. Within the month following training, all of the
students completed at least one multiple-choice exam in a psychology course. After the exam,
the students reported whether they had used the strategies they learned in training and whether
their exam grade had improved.
The evidence showed that the training had a positive effect on transfer, but that the
interventions were no more effective than the control. It was expected that the participants in
both the verbal and imagery based intervention groups would use more of the trained skills and
show more improvements in their exam scores than the participants in the control group.
However, there were no differences between the participants in the three groups, indicating that
the interventions were not effective at promoting transfer.Although the interventions were not

effective, the training was. The use of trained skills was positively related to improvements in
exam scores.
Although the original experimental hypotheses could not be addressed, the study
contributed to the literature by providing evidence that processing style may be an important
characteristic to consider for training design. Participants who naturally process information
visually (rather than verbally) had more test anxiety and felt less efficacious about performing
the trained skills. In addition, the study provided more evidence that trainees’ motivation to
transfer the trained material is a key predictor of whether they use the trained skills.

TABLE OF CONTENTS
LIST OF TABLES ......................................................................................................................... vi
LIST OF FIGURES ...................................................................................................................... vii
Introduction ..................................................................................................................................... 1
Imagery Rehearsal as an Intervention ....................................................................................... 15
Individual Differences in Preference for Verbal versus Imagery-Based Interventions ............ 28
Model and Hypotheses .............................................................................................................. 30
Method .......................................................................................................................................... 33
Participants ................................................................................................................................ 33
Procedure ................................................................................................................................... 34
Measures.................................................................................................................................... 35
Results ........................................................................................................................................... 38
Descriptive Statistics ................................................................................................................. 38
Evaluation of Hypotheses.......................................................................................................... 39
Follow-up Analyses................................................................................................................... 42
Discussion ..................................................................................................................................... 43
Limitations ................................................................................................................................ 48
Conclusion and Future Research ............................................................................................... 49
Appendices .................................................................................................................................... 50
Appendix A: Consent Form ...................................................................................................... 56
Appendix B: Instructions .......................................................................................................... 58
Appendix C: Westside Test Anxiety Scale ............................................................................... 59
Appendix D: Multiple-Choice Test-Taking Training Script (Time Point 1) ............................ 60
Appendix E: Baseline Measure of Use ..................................................................................... 65
Appendix F: Style of Processing Scale ..................................................................................... 66
Appendix G: Interventions ........................................................................................................ 68
Appendix H: Manipulation Check ............................................................................................ 71
Appendix I: Process Variables .................................................................................................. 72
Appendix J: Demographics ....................................................................................................... 74
Appendix K: Use of Trained Content ....................................................................................... 75
Appendix L: Test Scores ........................................................................................................... 76

iv

References ..................................................................................................................................... 77

v

LIST OF TABLES
Table 1. Demographic Characteristics of Participant Sample…………………………………...52
Table 2. Paired Comparisons of Number of Strategies Used Before and After Interventions…..53
Table 3. Descriptive Statistics and Correlations of All Variables……………………………….54

vi

LIST OF FIGURES
Figure 1. Hypothesized Model…………………………………………………………………...51
Figure 2. Model Based on Follow-up Analyses………………………………………………….55

vii

Introduction
Training transfer has been defined as the extent to which the learning that results from a
training experience transfers to the job and leads to meaningful changes in work performance
(Baldwin, Ford, &Blume, 2009; Goldstein & Ford, 2002). Baldwin and Ford (1988) introduced a
model of training transfer which included three broad categories of factors influencing transfer:
learner characteristics, intervention design and delivery, and work environment influences
(Burke & Hutchins, 2007).Since 1988, research on factors impacting training transfer has largely
been conducted within this framework.Recent research about training transfer has focused on
how factors prior to and after training affect transfer. Reviews of training transfer have pointed
out substantial knowledge gains about how individual traits of trainees and how the climate of
the workplace impact transfer (Blume, Ford, Baldwin, & Huang, 2010; Burke & Hutchins, 2007;
Cheng &Hampson, 2008).
Although such information is valuable for understanding the processes associated with
changing behavior on the job, trainers have little control over who participates in a training
session and what their workplace is like. Trainers do have a high degree of control over the
experience of the trainee during the training event. They are responsible for designing the
content of the training as well as to prepare trainees to transfer that content on the job. For that
reason, it is especially important to know what types of training design interventions impact
subsequent transfer and why.That is the main goal if this study.
Transfer Interventions
The training design interventions that have been linked to positive transfer can be divided
into two broad categories: practice and self-management. There are a variety of interventions that
have to do with different ways of having trainees engage with and practice the material presented

1

in training that have been linked to transfer (Driskell, Willis, & Copper, 1992; Frese et al., 1991;
Goodman & Wood, 2009; Keith &Frese, 2008; Latham &Saari, 1979; Rohrer et al., 2005;
Schmidt & Bjork, 1992; Taylor, Russ-Eft, & Chan, 2005). In addition, researchers have proposed
strategies or tools for teaching trainees to self-manage the transfer process during or soonafter
the training event (Brown, 2003; Brown & McCracken, 2010; Brown & Morrissey, 2004;
Gollwitzer&Sheeran, 2006; Hutchins & Burke, 2006; Marx, 1982; Millman& Latham, 2001;
Orbell, Hodgkins, &Sheeran, 1997; Wexley& Baldwin, 1986). Whereas practice interventions
are designed to enhance learning of the training content, self-management interventions are
specifically intended to enhance transfer.
Practice.Practice interventions are designed to help the trainee acquire and retain the
material to meet training objectives. The practice interventions typically occur during the
training event and are primarily intended to develop knowledge and skills. Learning is an
important precursor to transfer (Baldwin & Ford, 1988). Therefore, the interventions
areprimarily designed to enhance learning, which is expected to prepare the trainee to generalize
and maintain the trained knowledge and skills once on the job.
Making learning more difficult.In 1992, Schmidt and Bjork pointed out a series of
paradoxes they found in the experimental learning literature. First, they showed that whereas
blocked practice (sequential practice of a single element of a task at a time) resulted in higher
performance during a training event, spaced practice resulted in higher transfer after the event.
Second, they showed that whereas providing feedback to trainees at frequent intervals produced
higher performance during training, providing feedback at less frequent intervals produced
higher transfer. Third, they showed that whereas constant practice on each dimension of a task at
a time produced higher performance during training, variable practice on the different

2

dimensions of a task produced higher transfer. They summarized all three of their findings by
pointing out that requiring learners to participate in additional information-processing activities
during a training event may degrade performance during the event but is likely to increase
transfer. Their explanation for why this phenomenon occurs is that requiring the seemingly
detrimental informational processing during learning prepares learners for the processes that will
be required of them when they perform the learned tasks outside of the training environment. In
general, Schmidt and Bjork’s synthesis and summary have been supported by more recent
experiments in more organizationally relevant contexts (i.e., Holladay & Quinones, 2003).
Overlearning.Overlearning refers to the immediate continuance of practice beyond the
criterion of one instance of perfect performance (Rohrer et al., 2005). Overlearning is believed to
be an effective training intervention because it creates automatic responses that conserve a
trainee’s cognitive resources so that cognitive ability may be dedicated to solving novel or more
complex tasks (Burke & Hutchins, 2007; Shiffrin& Schneider, 1977). For many years,
overlearning has been considered to be a very effective intervention for improving retention of
material. Driskelland colleagues(1992) conducted a meta-analysis of 11 overlearning studies and
found a moderate effect on retention. What they were unable to thoroughly evaluate, however,
was whether the effect was lasting. They noticed that, as time increased between the training
event and the retention test, there were fewer differences between participants in overlearning
conditions and control conditions, indicating that the effects of the overlearning intervention may
not last beyond a few weeks. Rohrer and colleagues (2005) conducted two experiments in which
they evaluated the effect of overlearning on the long-term retention of paired words. In both
experiments, they found that participants in the overlearning condition outperformed those in the
control condition for several weeks, but that the effects disappeared by four weeks. They

3

concluded that overlearning might be an appropriate strategy for students who are studying for a
test coming up soon and whose main goal is short-term retention, but that overlearning
wouldlikely be an ineffective strategy for trainers who wish to provide lasting effects unless
supplemented by other techniques.
Providing feedback.Feedback is information provided to the trainee about his or her
performance on the trained task (Kluger&DeNisi, 1996). The rationale behind providing
feedback to trainees as they learn is that the trainee may be able to adjust his or her performance
based on the feedback. As discussed above, though, there is a paradox associated with providing
feedback to trainees as they learn. Providing specific, immediate, and frequent feedback during
training has been associated with improved performance during training (i.e., quicker learning),
but is also associated with lower levels of transfer (Goodman & Wood, 2009; Schmidt & Bjork,
1992).
One method for reaping both the short-term benefits of providing frequent feedback and
the long-term benefits of spacing or withholding feedback is called fading. Using this method,
the trainer provides high levels of feedback during initial skill acquisition and then, as the trainee
begins to master the skill, gradually reduces (or fades) the amount of feedback. Goodman and
Wood (2009) conducted a study to explore the efficacy of fading feedback,using a computer
simulation task in which participants were given responsibility for managing a group of workers
in a factory. They found that fading feedback led to less exploratory behavior on the part of
trainees even after feedback was reduced. In other words, participants in the group that initially
received high levels of feedback which was later faded made less of an effort to experiment with
the different decision choices allowed by the simulation and, therefore, were exposed to fewer
opportunities to score highly. Given that exploratory behavior tends to be associated with

4

increased transfer (see the discussion below about error-based training interventions), Goodman
and Wood’s findings suggest that fading feedback may not be effective for increasing transfer.
Their findings, however, are not conclusive about the optimal amount and timing of feedback to
use as an intervention for enhancing learning and transfer.
Using behavioral models.Goldstein and Sorcher (1974) presented behavioral modeling as
an alternative training method (especially for supervisory training) to the prevalent strategy of
training for attitude change. They argued that the attitude change strategy focuses on convincing
trainees that they should change their behavior (an attitude shift), but that behavioral modeling
trainingteaches trainees how to change their behavior. Knowledge about how to act differently
should presumably lead to a more direct behavioral shift. Behavior modeling training generally
consists of four components: a) describing to trainees a specific set of behaviors to be learned, b)
modeling the behavior for trainees, c) having the trainees practice the behaviors, and d)
providing trainees with feedback about their performance of the behaviors.
Latham and Saari (1979) conducted an early study of the effectiveness of behavioral
modeling training with a group of male supervisors. Participants who received the training
participated in a 9-week course with a 2-hour session per week. At each weekly session,
participants were trained in a different set of supervisory skills (for example, giving recognition
and motivating a poor performer). Each session opened with introductory remarks about the
topic by two trainers, which was followed by a film of a supervisor model effectively handling a
situation by following specific learning points. After the film, the trainers led a group discussion
of the effectiveness of the model, the participants practiced role playing related situations, and
then the class gave one another feedback about their behavior during the role play exercise. After
each session, participants were required to practice the new skills with at least one employee and

5

report back to the class about their experience. Participants in the control group did not receive
any training because they were told that they would be receiving the training at a later date.
Three months after the training, both the training and the control groups participated in taperecorded role play exercises designed to evaluate the skills presented in the training. The trained
group outperformed the control group. Six months after the training, both the training and the
control groups took a situational test designed to evaluate interpersonal supervisory skills. The
trained group outperformed the control group, despite the fact that the test included situations not
trained. One year after training, the participants were compared on job performance using two
measures, one specially designed for the study and another used regularly in the organization.
The trained participants outperformed the control group on both measures of job performance.
In a meta-analysis of the effects of behavior modeling training on training outcomes,
Taylor, Russ-Eft, and Chan (2005) reported that whereas effects on learning outcomes were
substantial and consistent across studies, effects on transfer were smaller and less consistent.
They found that transfer was greatest when both positive and negative models were presented to
trainees (as opposed to positive models alone). This finding is consistent with Baldwin’s (1992)
finding that trainees who were presented with both positive and negative models performed
better in a generalization task than trainees who were presented with only positive models.
Baldwin suggested that one reason that mixed models lead to better transfer is that the contrast
between effective and ineffective behaviors provides greater distinctiveness to the stimuli. Taylor
et al. also suggest that trainees may be more motivated to perform the desired behaviors when
they see the positive model reinforced for effective performance whereas the negative model
does not achieve the desired outcome. Taylor et al. also found that transfer was greater when
trainees practiced work-related scenarios that they developed themselves (as was done in the

6

Latham and Saari study), as opposed to practicing scenarios provided by trainers. One possible
explanation for this finding is that having trainees generate their own work-related practice
scenarios leads to greater retention because of the personal relevance (Decker & Nathan, 1985).
This explanation is supported by the principle of identical elements and the theories of encoding
specificity and situated cognition (Anderson, Reder, & Simon, 1996; Greeno, 1998; Tulving&
Thomson, 1971, 1973)
Encouraging errors. In error-based training, participants are explicitly encouraged to
make errors during the learning process. The rationale for error-based training is that errors can
have an informative function for the learner as they identify areas where improvement is needed
(Ivancic&Hesketh, 1995/1996). In other words, errors made during training serve an important
feedback function by helping the individual to form a more complete mental model. Trainers
using error-based methods encourage trainees to explore the training material and to view errors
as opportunities to learn rather than as negative feedback. Frese et al. (1991) conducted an early
study comparing the outcomes of error-based versus error-avoidant training for a word
processing system. Participants in the error-avoidant condition (the control) received step-bystep instructions for each tutorial whereas the participants in the error training condition were left
to figure out the steps through exploration and were encouraged to learn from errors. The
participants in the error-based training condition outperformed those in the error-avoidant
condition on several performance tests immediately after the training. The differences in
performance were most pronounced for the most difficult tasks.
Keith and Frese (2008) conducted a meta-analysis comparing error-based training with
alternative training methods, such as error-avoidance instructions or immediate correction by a
trainer. Consistent with the paradox introduced by Schmidt & Bjork (1992), they found that error

7

management training was more effective than the alternative methods for post-training
performance, but not for performance measured during training. They also found that error
management training was most effective for adaptive transfer (or generalization) of the learned
skills to tasks not presented in the initial training. One limitation in the existing literature on error
based training is that few, if any, researchers have evaluated on-the-job performance as a
measure of transfer. All of the studies included in the meta-analysis measured post-training
performance via tests of performance rather than by measuring behavior on the job.
Self-management strategies.In contrast to practice interventions, which were primarily
designed to enhance learning and retention, self-management strategies were developed
specifically to enhance the generalization and maintenance of trained knowledge and skills to the
job. Self-management is an umbrella term that is used to describe strategies that are taught to
trainees (often at the end of training) for use in managing their own behavior after the training
event. Although trainees are ultimately responsible for using the self management techniques on
their own, the choice to include self management strategies in the training content is that of the
trainer.
Goal setting. Participants in training interventions that use goal setting strategies are
asked to set goals about their use of the training material back on the job. In an early study of
goal setting as a transfer intervention, Wexley and Baldwin (1986) examined the effects of
assigned goals versus self-developed goals on changes in behavior related to time management.
All of the undergraduate participants received training in time management followed two days
later by a transfer intervention. One treatment group was assigned behavioral goals and a second
treatment group was led through the process of developing their own behavioral goals. Both
treatment groups received feedback about their goal achievement at the end of four weeks. A

8

third group served as the control and received no intervention or feedback. Self-reports of
behavioral change were collected eight weeks after the initial training. Both treatment groups
reported greater levels of behavioral change than the control group, indicating that both goalsetting interventions successfully led to increased transfer.
Brown and McCracken (2010) reviewed the literature on goal setting and summarized the
findings about how different types of goals contribute to training transfer. They found that distal
outcome goals, which focus on longer term performance, may be suited for simple,
straightforward tasks for which trainees have the prerequisite skills, but that distal outcome goals
alone are generally not well suited for transfer of complex skills. Learning goals, in which
trainees are encouraged to focus on learning the material as thoroughly as possible, are slightly
more effective than distal outcome goals. Breaking distal goals into short term goals is a more
effective intervention. Setting short-term goals designed to help achieve a longer-term goal may
be effective because each “check in” point provides the individual with feedback about how
effective the current path is toward reaching the longer term goal. Interventions in which
participants are asked to set short term goals are most in line with Locke and Latham’s (1990)
finding that specific, challenging goals lead to better performance than easy or non-specific
goals. Similarly, behavior outcome goals, or goals for specific performance, also seem to be
effective in promoting transfer. One limitation of the existing research on goal-setting
interventions highlighted by Brown and McCracken is that the majority of goal-setting studies
use simulated and/or short-term tasks. There have been fewer naturalistic studies of people
setting goals to implement trained behaviors in the long-term, so it is unclear how powerful the
intervention can be.

9

Implementation intentions.The formation of implementation intentions is a type of goalsetting intervention that capitalizes on the literature suggesting that more specific and behavioral
goals are more likely to be achieved. Whereas goal intentions specify what an individual wants
to achieve (“I will achieve X”), implementation intentions specify the behavior required to
achieve the goal and the situational context in which the individual will perform the required
behavior (“If situation Y occurs, then I will initiate goal-directed behavior Z;”
Gollwitzer&Sheeran, 2006). Implementation intentions are hypothesized to be more effective
than goal intentions for facilitating goal achievement because the process of developing if-then
plans leads to increased accessibility of the critical cues associated with performance (or
inhibition) of the desired behavior. The performance of behavior specified in an implementation
intention exhibits features of automaticity such that individuals do not have to be consciously
aware of recognizing and responding to the critical cue in the planned way to do so.
In an early study of the efficacy of implementation intentions, Gollwitzer (1993) asked
university students to write a report within two days of Christmas about how they spent
Christmas Eve. Half of the participants were asked to specify, in writing, where and when they
planned to write the report. The other half were not asked to do so. Seventy-one percent of the
participants in the implementation intentions group completed the report within the two days
after Christmas whereas only 32% of the control group did so.
Gollwitzer and Sheeran (2006) conducted a meta-analysis of 94 tests of the impact of
implementation intentions on goal achievement. They found an effect size of d = .65 with a 95%
confidence interval from .60 to .70, indicating that forming implementation intentions has a
relatively stable moderate effect on goal achievement. Although the authors tested a variety of
moderators, they found that the effect was fairly stable across different samples and tasks. As

10

discussed above in relation to goal-setting, one limitation of the literature on implementation
intentions is that it has largely been limited to tasks in a laboratory setting so more study is
needed to evaluate whether implementation intentions are effective interventions in a less
structured and self-managed context.
Relapse prevention. Relapse prevention is based on a program of interventions developed
for use by clinicians in treating addictions (Marlatt& Gordon, 1980; 1985). The original purpose
of relapse prevention was to help addicts anticipate and cope with setbacks in efforts to curb
addictive behavior. Relapse prevention is based on social cognitive theory, which suggests that
people can control their own behavior if they understand their motives as well as how to
reinforce desirable behavior (Bandura, 1982). In relapse prevention training, individuals are
trained to become aware of situational threats that jeopardize the maintenance of their behavior
change and are given strategies for dealing with those threats (Hutchins & Burke, 2006).
Marx (1982) adapted relapse prevention strategies to the training environment. For
trainees, relapse prevention involves learning how to identify and recover from setbacks in
transfer behavior. Relapse prevention is hypothesized to work because individuals who feel
capable of managing their reactions to slip-ups and situational threats experience increased selfefficacy, which in turn motivates them to keep working toward successful transfer (Hutchins &
Burke, 2006; Larimer, Palmer, &Marlatt, 1999). Although relapse prevention is recognized as a
potentially effective transfer intervention, empirical studies have demonstrated inconsistent
findings with regards to its efficacy.
Hutchins and Burke (2006) reviewed all published and in-press studies using relapse
prevention strategies to increase training transfer in order to explore the reasons for inconsistent
findings. They found eight studies spanning two decades that met their criteria for inclusion in

11

their review, which indicates that there has not been a large amount of empirical research in the
area. From their review of the studies, Hutchins and Burke came up with three main reasons for
inconsistent findings: insufficient samples, inconsistent operationalization of relapse prevention,
and poor design. First, they pointed out that five of the eight studies reported extremely small
sample sizes (under 40), which limited the statistical power of analyses. Second, the eight studies
included inconsistent definitions of relapse prevention and, thus, did not consistently provide the
same intervention nor did they include the full range of characteristics of a relapse prevention
program as outlined by Marx. Third, most studies failed to measure whether the relapse
prevention strategies trained were utilized after training and, in all but one study, the effects of
the intervention were measured as a main effect, making it virtually impossible to examine the
efficacy of individual components of the relapse prevention program. Therefore, despite some
encouraging studies of relapse prevention and its intuitive appeal, the jury is still out on relapse
prevention as a unique intervention. However, it is likely that some of the components, like goalsetting and coping strategies are promising interventions for transfer.
Self talk. Another self management intervention involves training individuals to covertly
talk themselves through a task. One intervention involving self talk is called verbal self guidance.
(Meichenbaum,1975; 1977).Verbal self guidance includes teaching individuals to talk
themselves through a task as well as teaching individuals to monitor their self-statements and
change negative statements to positive statements. Like relapse prevention, verbal self guidance
interventions were originally developed for clinical settings and have been adapted by
organizational researchers. Although there is a small body of evidence that suggests that verbal
self guidance is an effective intervention in organizational settings, only a few studies have
examined verbal self guidance on its own without combining it with other interventions.

12

Millmanand Latham (2001) trained unemployed managers using the verbal self guidance
technique. Nine months after training, a greater number of trained participants were employed
than control group participants. Trained participants also had higher levels of self-efficacy.
Brown and Morrissey (2004) found that participants trained in verbal self guidance had higher
self efficacy about class presentation skills than participants in the control group. Higher self
efficacy was associated with better presentation performance. Similarly, Brown (2003) presented
evidence that verbal self guidance training was related to improvements in both individual and
team performance and efficacy for student teams in a business school class.
Summary.Two types of training interventions have been linked to positive transfer.
Practice interventions are those that were designed to enhance learning, but also have
implications for transfer. Paradoxically, many practice interventions that enhance immediate
learning (i.e., overlearning and frequent feedback) inhibit transfer whereas interventions that
appear to inhibit initial learning (i.e., spaced practice and error-based training) enhance transfer.
Schmidt and Bjork (1992) suggested that interventions during learning that are distracting or
detrimental to initial learning better prepare learners for the processes that will be required of
them outside the training environment. Another practice intervention, behavioral modeling
appears to be most successful when it is personally relevant and increases self-efficacy, which
increases motivation to transfer.Self-management interventions were developed specifically to
increase transfer. Setting specific goals, such as implementation intentions, is effective at
increasing transfer because it increases accessibility to critical cues associated with the desired
behavior and allows responding to the cues with the desired behavior to become automatic. Like
behavioral modeling, self talk is related to increased self-efficacy, which is related to increased
performance.

13

The most effective transfer interventions seem to share two main characteristics,
similarity to the transfer environment and the enhancement of an individual’s feelings of
competence. Training interventions that are similar in some way to the context of the transfer
environment are effective probably because they provide realistic practice, increase self-efficacy
for performance of the desired behavior in the transfer context, and increase motivation to
transfer. Training interventions that increase participants’ self efficacy for the desired behavior
are very effective becauseself-efficacy is related to motivation to act and to effective
performance.
There is a lot of evidence about what makes for effective practice during training.
Effective practice is difficult and may hinder performance at first, it can involve viewing and
mimicking a model performing the desired (and undesired) behavior, and it incorporates errors or
allows the trainee to make mistakes and to learn from them. In addition, there is some evidence
that self management strategies can add to effective practice by helping people stay focused on
the goal to apply what they learn in training to the job.
Although there is evidence that self-management strategies can be very effective transfer
interventions, one limitation of the existing literature is that all of the strategies involve verbal
rehearsal. Goal-setting, implementation intentions, and self talk all involve the use of verbal
statements. Marlatt’s model of relapse prevention (1988) included visual imagery rehearsal as
part of a set of tools that patients are taught to use to help manage their own behavior. Although
Marlatt’s model of relapse prevention has been widely accepted and used, Marlatt never
elaborated on the original suggestion about using visual imagery techniques as a component. In
sport and clinical settings, however, imagery-based rehearsal has been found to be very effective
at changing behavior. Imagery-based interventions share many of the most effective elements of

14

the transfer interventions described above, such as the personal relevance associated with
modeling and goal-setting and the planning and rehearsal involved in forming implementation
intentions. There is also some evidence that certain individuals may respond better to imagerybased rehearsal than to verbal statements.
Imagery Rehearsal as an Intervention
We spend a significant portion of our time thinking, daydreaming, and fantasizing about
the future (Singer, 1966). A recent study suggests that we think about the future approximately
once every sixteen minutes during waking hours (D’Argembeau, Renaud, & Van der Linden,
2011). Despite the fact that most people spend a considerable amount of time and energy focused
on what will occur in the future, it is unclear how much of that time and energy is productive –
applied towards planning, strategizing, or other effective ways of improving future experiences.
Clinicians and athletes have both utilized future-oriented thinking to their advantage. Two
examples of how clinical psychologists and practitioners in related fields have utilized what they
call mental rehearsal and covert modeling include helping people with addictions resist
temptations and helping patients with phobias to reduce their anxiety. Elite athletes use mental
imagery techniques to rehearse physical performance as well as to strategize and to manage
anxiety.Despite the widespread use of techniques related to future-oriented mental rehearsal in
several fields, there has been very little exploration into the possibility of its use in workplace
training.
There are good reasons to believe that interventions using mental imagery could be very
effective in increasing training transfer. First, the interventions used by clinicians and athletes
adhere to effective learning principles, including practice and self-generation. Second, behavioral
modeling, practice and rehearsal have proven extremely effective as interventions in training.

15

Mental imagery interventions are a less expensive and less time-consuming (at least for the
trainer) alternative or supplement to active practice. Training individuals to use mental imagery
on their own is practical because it allows those who wish to do so to practice on their own time
wherever they can.
Relatively recently, cognitive psychologists and neuroscientists have begun to conduct
basic research on the phenomenon of future-oriented thought. Although this area of research is
still in its infancy, it is already apparent that future-oriented thoughts are both prevalent and
natural. This new and emerging literature is summarized below, followed by a summary of
applications of future-oriented imageryto changing behavior in sport and clinical settings.
Episodic future thought.Even thoughfuture-oriented techniques have existed in the
literature for many years, it is only recently that cognitive psychologists and neuroscientists have
begun to explore the processes related to future thought. The most relevant research is on the
phenomenon called episodic future thought. Episodic future thought involves the simulation or
pre-experience of personal events that may happen in the future (Atance& O’Neill, 2001). The
term is drawn from the emerging relationship between future simulation and episodic memory
(Szpunar, 2010). Episodic memory is memory of one-time events from one’s past. It is typically
contrasted with semantic memory, which is memory about the past, but without a feeling of reexperiencing it. For example, remembering the events from your wedding would come from
episodic memory, but remembering the fact that you were married in East Lansing, Michigan
would come from semantic memory.
The connection between simulation of the future and episodic memory emerged from
studies of patients with brain damage who demonstrated deficits with both episodic memory and
future simulation (Szpunar, 2010). It is not completely clear how the two phenomena are related,

16

but one hypothesis is that future simulations may be formed from fragments pulled from episodic
memory. In other words, the ability to think about future events may rely on the ability to pull
ideas from events experienced in the past (Addis, Wang, &Schacter, 2007).
Despite the fact that the study of episodic future thought is relatively new, Szpunar
(2010) suggests that there are already several consistent findings that can be drawn from the
literature. First, it appears that the neural regions related to the retrieval of personal memories are
engaged during episodic future thought. Second, damage to those neural regions is associated
with impairments in both episodic memory and episodic future thought. Third, patient
populations that demonstrate poor episodic memory also tend to have difficulty with imagining
the future. Fourth, verbal protocols of episodic future thought are characterized by content that is
highly familiar to participants. In other words, when people imagine the future, they tend to stay
within the context of their own lives. Fifth, episodic future thoughts make up a considerable
portion of daily thought. In addition, episodic future thoughts are rated as less vivid than
memories. Mental representations of events occurring in the near future are more vivid than
events in the distant future. Finally, mental representations of the future have more positive
valence than memories, indicating that people are generally optimistic in their simulations of the
future.
The study of episodic future thought has mainly been limited to the laboratory using the
word-cuing technique. In the word-cuing technique, participants are presented with a word cue
and asked to mentally generate a future scenario (e.g., Szpunar, Watson, & McDermott, 2007).
For example, participants might be given the word cue “birthday” and then describe specific
details about a future scenario related to whatever first comes to mind when they hear that word
(Szpunar, 2010). A participant might describe a detailed mental representation of what her son’s

17

first birthday party will be like. Recently, researchers have also begun to utilize thoughtsampling procedures outside of the laboratory. Thought-sampling procedures require participants
to either estimate or systematically monitor the frequency and content of their thoughts
throughout the day (Berntsen& Jacobsen, 2008; D’Argembau, Renaud, & Van der Linden,
2011).
There areseveral implications that can be drawn about the functional role of episodic
future thought. First, given the relationship between episodic future thought and episodic
memory, mental simulations will probably need to remain within the realm of the experience of
the individual in order to be effective. In other words, asking people to simulate an event that is
unlike any they can imagine is unlikely to be effective. Second, episodic future thought is natural
for most people, so asking people to simulate the future should be familiar to them and should
not require an overwhelming amount of training.
Mental imagery in sport psychology.Mental imagery is used widely by athletes in
competitive sports hoping to improve performance. This focus on mental imagery suggests that
practice of motor skills is not enough for success. Athletes of all types report using imagery in
many different ways and for a variety of reasons. Researchers have gathered data about typical
use of imagery by athletes. Athletes tend to use imagery more for performance enhancement
(i.e., planning strategy for competition) than for learning skills (Barr and Hall, 1992; Hall, 2001).
The most common time that athletes use imagery is immediately prior to competing (Barr &
Hall, 1992; Hall, Rodgers, & Barr, 1990; Munroe et al., 1998), although many athletes use
imagery during breaks in their daily activities and just before going to sleep at night (Hall et al.,
1990; Rodgers, Hall, &Buckholz, 1991). Both elite and nonelite athletes report extensive use of
visual imagery. Athletes are more likely to imagine themselves performing their sport skills

18

accurately than inaccurately and are more likely to imagine winning than losing (Barr & Hall,
1992; Hall et al., 1990). One of the most consistent findings is that the higher the skilllevel of the
athletes, the greater their use of imagery (Hall, 2001).
The existing literature related to the application of mental simulations of the future for
athletes mainly focuses on the practical details of using the interventions and less on why or how
the interventions are effective. Although sports psychologists present models of the processes
believed to be involved in the imagery process, those models have generally not been tested or
are not testable. However,there is evidence to suggest that athletes successfully apply mental
simulation to improve performance and impact behavior.
There is a small body of literature examining the efficacy of using imagery to improve
performance of motor skills in sports. In studies directly pitting physical practice against mental
practice using imagery, physical practice tends to be more effective for learning motor skills.
However, mental practice using imagery appears to facilitate learning and performance of motor
skills in comparison to a control condition of no practice (Driskell, Copper, & Moran, 1994; Hall
et al., 1994). In studies in which a combination of physical and mental practice was compared
with physical practice alone, the combination condition appears to be comparable to physical
practice alone (Durand, Hall, &Haslam, 1997). Therefore, it appears that is often possible to
substitute some imagery practice for physical practice without detracting from learning and
performance of motor skills. This finding has important implications for athletes in situations in
which they cannot physically practice as much as they would wish (for example, while traveling
or while injured) (Hall, 2001). In addition to the practical implications of this finding for athletes
who cannot conduct physical practice, the finding that mental imagery can be substituted for
some physical practice without detracting from the performance of motor skills is powerful

19

because it suggests an important psychological component to motor skill performance that is
often overlooked with a focus solely on physical practice.
In general, researchers recommend that athletes supplement physical practice with
imagery practice. Research examining the visual imagery perspective of athletes suggests that
both an external perspective (imagining oneself performing as if watching a video) and an
internal perspective (imagining what one would see through one’s own eyes during performance)
are effective in different contexts (Cumming &Ste-Marie, 2000; Hardy & Callow, 1999). Task
differences probably impact the efficacy of the use of each perspective (Hardy, 1997; White &
Hardy, 1995). For example, the external perspective probably is better for the acquisition and
performance of skills that depend heavily on form (so “seeing” oneself is beneficial for the
correction of mistakes in form) whereas the internal perspective is probably better for skills that
depend heavily on perception and anticipation (where “seeing” from one’s own perspective is
beneficial).
In addition to the literature on using imagery to improve motor performance, researchers
have also examined what types of athletes are more likely to use imagery and to use it
effectively. Athletes with greater knowledge about imagery techniquesare better able to use those
techniques more effectively (Calmels et al., 2003). Athletes who view imagery as more relevant
use it more frequently and athletes at higher competitive levels find imagery to be more relevant
(Cumming & Hall, 2002; Cumming et al., 2006). Athletes who are more motivated and athletes
who are encouraged by their coaches to use imageryhave been found to use imagery techniques
more frequently (Cumming et al., 2002; Harwood, Cumming, & Fletcher, 2004; Harwood,
Cumming, & Hall, 2003; Hall & Rodgers, 1989; White & Hardy, 1998). Evidence suggests that

20

imagery is a skill and, like any skill, can be improved through regular, deliberate practice
(Rodgers et al, 1991).
In sport psychology, there has been relatively little exploration of the effectiveness of
mental imagery for purposes other than motor skill improvement. Weinberg (2008) suggests that
this is probably due to the overlap between imagery and mental practice. Whereas mental
practice is a broader term that involves other types of mental preparation such as self-talk or
relaxation, imagery is generally limited to picturing or seeing oneself performing. However, two
meta-analyses of mental practice and performance combined effects from studies that used
various forms of mental practice (Feltz& Landers, 1983; Hinshaw, 1991). Both meta-analyses
found evidence that mental practice is more effective than no practice, but did not evaluate the
effectiveness of imagery alone. Weinberg (2008) synthesized the evidence for whether imagery
alone is an effective intervention for athletes. He evaluated multiple case studies and determined
that the overwhelming evidence was that imagery enhances performance. One case study that
Weinberg includes in his analysis was conducted by Savoy and Beitel (1996). They followed 10
players on an NCAA women’s basketball team. The researchers used an ABABAB time series
design in which the participants practiced both physical and mental practice during the
intervention period (A) followed by only physical practiceduring the control period (B). The
intervention and control periods were repeated twice. The participants increased their free-throw
percentages significantly between the control and intervention periods. Unfortunately, Weinberg
found that although there has been a fair amount of research into the use of imagery by athletes,
the studies share few aspects in common, so he was unable to provide meta-analytic estimates of
effect size. For example, the studies vary in terms of the control or comparison condition for
mental imagery practice. Some studies used no intervention for the control group, so that mental

21

imagery is compared to no practice, whereas others compared mental imagery practice in
combination with physical practice as compared to physical practice alone. In addition, the
criterion for some studies was performance, but other studies examined outcomes presumably
related to performance, such as self-efficacy, anxiety (reduction), and motivation.Weinberg’s
conclusion based on his synthesis of diverse studies of mental imagery use by athletes is that it
appears that mental imagery use positively impacts performance and related factors, but that
more systematic research is needed.
In the sport psychology literature, there are two main models used to explain how to use
mental imagery to improve performance. The first model is based on the theory of functional
equivalence, that imagery draws on the same neural network that is used in actual perception and
motor control that activate neural circuits used in memory and emotion (Murphy, Nordin, &
Cumming, 2008). Therefore, the theory suggests that the more similar the imagery is to reality,
the more effective it will be for improving performance. The model is called PETTLEP, which is
an acronym derived from the seven areas that the creators suggest are important for mimicking in
developing effective imagery (Holmes & Collins, 2001). In this way, the model is essentially a
seven-point checklist including the following elements: physical (active involvement in the
imagery process, by holding implements or actually moving), environment (as accurate as
possible in imagination or in perceptual experiences), task (focus on same thoughts, feelings, and
actions as when physically performing the skill), timing (corresponds to actual movement time,
especially when timing is important for the task), learning (imagery content should evolve to
accommodate learning that has taken place), emotion, and perspective (internal or external).
According to the model, effective images contain important stimulus, response, and meaning
propositions from as many of the seven elements as possible. Stimulus propositions provide

22

information about the environment where the behavior will take place, response propositions
provide information about what is felt in response to the behavior, and meaning propositions
include information about the perceived importance of the behavior to the individual.Although it
is useful as a practical guide for developing detailed imagery, one major limitation of the
PETTLEP model is that it focuses almost solely on explaining imagery as part of motor skill
development and improvement. It does not address imagery use for other purposes, such as
motivation, confidence, and arousal (Murphy, Nordin, & Cumming, 2008).
Whereas the PETTLEP model emphasizes the content of imagery, the applied model of
imagery use focuses on the function of imagery. Martin, Moritz, and Hall (1999) propose that
different types of imagery are used for different athletic goals. They outlined five types of
imagery associated with five different objectives. Cognitive Specific (CS) imagery, the most
commonly studied type of imagery, involves rehearsal of specific athletic skills, such as penalty
shots or balance beam dismounts. Cognitive General (CG) imagery focuses on strategies related
to a competitive event, such as imaging using full-court pressure in basketball. Motivational
Specific (MS) imagery involves imaging specific goals and goal-oriented behaviors such as
imagining oneself winning an event or standing on a podium receiving a medal. Motivational
General-Mastery (MG-M) imagery focuses on effective coping and mastery of challenging
situations, such as imagining being mentally tough and focused curing competition. Motivational
General-Arousal (MG-A) imagery represents emotional and somatic experiences in sport, such
as feelings of relaxation, stress, anxiety in conjunction with competition.
One limitation of the applied model is that it is extremely difficult to test. The model
predicts that athletes would be more effective if they use the type of imagery that will be most
useful in achieving their desired outcomes. Research evidence has been inconsistent, which is

23

probably due to the fact that any given image can serve multiple functions, depending on the
meaning it holds for the person. Although people can be told to imagine specific content, that
same content may be related to different functional goals for different people (Short, Monsma, &
Short, 2004). Most likely, because individuals have complex sets of motivations, most imagery
falls into more than one of the five categories at any one time, making it even more difficult to
cleanly compare the different types of imagery.
The examination of the application of mental imagery to the improvement of
performance in sport has in some ways demonstrated that mental imagery can be an effective
intervention. It is used frequently by elite athletes who tend to believe strongly in its
effectiveness. What the literature lacks, however, are testable models of the processes by which
mental imagery techniques lead to improved performance. The literature on covert modeling in
clinical psychology provides one avenue for generating more testable models of process.
Covert modeling in clinical psychology.Covert modeling is a therapeutic intervention in
which an individual imagines a model successfully performing desirable behavior. Depending on
the specific intervention, the individual either imagines performing the behavior or imagines a
model performing the behavior. Covert modeling has been used as a successful intervention to
help treat anxiety and phobias, among other issues.
In experiments comparing covert modeling with other therapeutic interventions,
researchers have demonstrated that covert modeling is at least as effective as similar
interventions such as behavior rehearsal and cognitive restructuring. Zielinski and Williams
(1979) compared the relative efficacy of a covert modeling intervention in which the participants
imagined various models behaving and behavior rehearsal intervention in which participants
practiced responding to scenarios for training assertive behaviors. They argued that covert

24

modeling was a more cost-effective intervention because behavior rehearsal required the
presence of a therapist or the use of an audio-tape whereas patients could practice covert
modeling on their own. They found evidence that the two interventions were equally effective.
Bistline, Jaremko, and Sobleman (1980) found that a covert modeling intervention in which
participants imagined themselves coping effectively was more effective for reducing test anxiety
than a cognitive restructuring intervention in which the participants imagined themselves
replacing negative thoughts with positive ones.
There is consistent evidence that covert modeling is an effective therapeutic intervention
andresearchers have posited a number of reasons why covert modeling might be effective.Kazdin
(1973, 1974a, 1974b, 1975, 1976) found that the factors that make live modeling effective are
also important for covert modeling. For example, the similarity of the age and sex of the model
with that of the patient, the number of models, the coping skills of the model, and whether the
model is reinforced for desirable behavior enhance covert modeling in the same manner
demonstrated in research on live modeling. The benefits of live modeling are often explained in
terms of Bandura’s (1991) social cognitive theory. Uhlemann and Koehn (1989) pointed out that
Bandura’s argument for the benefits of live modeling referred to the cognitive and
representational processes that guide behavior rather than to the mode through which the
behavior is observed. Therefore, they argued that covert modeling should be as effective as overt
modeling if the important processes are as Bandura described. Uhlemann and Koehn sought to
determine whether the benefits of covert modeling were due to the imagery component. They
suggested that in many studies of covert modeling, the imagery component (i.e., imagining
oneself or a model performing the desirable behavior) has been confounded with an overt
modeling intervention because participants in the covert modeling condition are told exactly

25

what to imagine the model doing and saying. Uhlemann and Koehn told participants in all
experimental conditions what the proper behavior should be, but only asked participants in the
covert modeling condition to imagine themselves and a model performing the behavior. They
didnot find any differences between the experimental conditions, which they argued indicated
that the imagery component (or rehearsal) is not what makes covert modeling effective. There
were limitations to their design which preclude the interpretation that the imagery or rehearsal
component of covert modeling is irrelevant. However, the study does suggest that any
advantages of covert modeling over overt modeling may not be due to the imagery or rehearsal
component alone.
Vallis and Bucher (1986) evaluated whether individual differences in natural coping
styles impacted the effectiveness of covert modeling versus a verbal coping strategy for reducing
fear in phobic individuals. They randomly assigned participants to participate in the two
interventions, but measured the natural coping styles of all of the participants. They found that
both interventions were equally effective in the reduction of fear and that participants in both
groups significantly increased their approach of the phobic object. However, they also found that
participants who had been assigned to the intervention that aligned with their natural coping style
had even better outcomes than other participants. In other words, participants who naturally used
visual imagery to cope with their phobia and anxiety had even more positive effects from the
covert modeling intervention than participants who naturally used verbal strategies to talk
themselves through their anxiety. Vallis and Bucher’s finding suggests that, although covert
modeling may be an effective intervention for most individuals, it may be especially effective for
people who naturally use imagery or mental rehearsal as a coping mechanism.

26

The effects of covert modeling interventions in therapeutic contexts have been
demonstrated to be maintained over time and to generalize beyond the trained behaviors. Kazdin
(1979) found that gains in assertive skills obtained aftercovert modelingtreatment transferred to
novel role-playing situations and were maintained at a 6 month follow-up. Hersen et al. (1979)
also found evidence that treatment effects from both covert and live modeling of social behaviors
generalized to novel stimuli in a clinical population. Harris and Johnson (1980) compared covert
modeling and desensitization in the treatment of test anxiety. They also gave study skills training
to both experimental groups and the control group. Although all three treatment groups reported
less test anxiety, the covert modeling group was the only one that demonstrated significant
improvements in academic performance in the subsequent academic quarter. Kazdin (1982)
found that covert modeling, overt rehearsal, and a combination of the two interventions were
equally effective in improving assertive skills. In addition, the gains generalized to novel roleplaying exercises and were maintained 8 months after the intervention.
Summary.As noted previously, the most effective transfer interventions shared two
characteristics; the training environment was as similaras possible to the transfer environment
and the individual’s sense of competence for performing the desired behavior was enhanced.
Based on the study of imagery use by athletes and of covert modeling used for clinical purposes,
it appears that imagery rehearsal shares both characteristics with the effective transfer
interventions.The evidence from the study of athletes suggests that it is possible to substitute
some physical practice with imagery practice without detracting from the learning and
performance of motor skills. The theory of functional equivalence suggests that imagery is more
effective the more similar it is to the transfer environment. Covert modeling is essentially
equivalent to behavioral modeling except that all of the models are self-generated. Given that

27

behavioral modeling is more effective when the participants generate their own scenarios, it is
likely that effective covert modeling operates similarly to effective behavioral modeling by
increasing self efficacy for the desired behavior.
Although imagery-based interventions are uncommon in organizational settings, they
share many characteristics with effective self-management transfer interventions and probably
operate in similar ways. The purpose of this study is to examine whether the processes involved
in imagery-based rehearsal are similar to those involved in more commonly used verbal rehearsal
and whether either technique is more effective for certain populations.
Individual Differences in Preference for Verbal versus Imagery-Based Interventions
Most of the research described so far compares the effectiveness of verbal or imagerybased interventions to no intervention or to one another. The evidence suggests that both verbal
and imagery-based interventions can be effective at changing behavior and that neither is
necessarily better or worse than the other. One possible moderator in the relationship between
the type of intervention and its effectiveness is the preference or natural inclination of the
individual. Vallis and Bucher (1986) found that the effectiveness of both a covert modeling
strategy and a verbal coping strategy for dealing with phobia were enhanced when they were
aligned with the participants’ natural coping styles. Just as individuals have different natural
coping styles, they may have different natural ways of rehearsing behavior change. Whereas one
person who learns a new skill may, without prompting, imagine himselfperforming it correctly or
rehearse it using imagery, another person may practice talking herself through performing it and
may rely on words rather than images in the process. There is evidence that there are individual
differences in the way people process and remember information with some people being more
oriented towards imagery and others towards words.

28

Riding and colleagues have proposed and found evidence to support individual
differences in cognitive style. One dimension they have identified is the verbal-imagery
dimension (Sadler-Smith & Smith, 2004). Cognitive style refers to an individual’s general
approach to organizing and processing information (Riding & Sadler-Smith, 1997). Verbalizers
think in terms of words and word associations whereas imagers think in terms of pictorial mental
pictures. Cognitive style is not related to intelligence, but instead is related to preferences for
learning. Riding and colleagues have mainly studied the verbal-imagery dimension in terms of its
relation to preferences in mode of presentation in instructional settings. Not surprisingly,
verbalizers prefer textual presentation and imagers prefer pictorial or diagrammatic
presentation.Kirschoff and Buckner (2006) found support for the idea that there are individual
differences in tendencies for verbal versus imageryprocessing using neuro-imaging. They found
that self-reported tendencies to process information visually were correlated with fMRI indices
of activity in the occipital temporal lobes of the brain whereas tendencies to process information
verbally were associated with activation of prefrontal regions.
Although both verbal transfer interventions and imagery-based techniques for behavior
change have been shown to be effective in general, Riding and colleagues’ work demonstrating a
relationship between cognitive style and preference for mode of presentation and Vallis and
Bucher’s (1986) work demonstrating a relationship between natural tendency and the
effectiveness of verbal and imagery-based interventions suggest that it may be beneficial to
match transfer interventions to individuals’ natural tendencies and preferences. Therefore, the
purpose of this study is to examine the effects of verbal and imagery-based transfer interventions
in combination with participants’ inclinations.

29

Model and Hypotheses
The overall model to be tested in this study can be found in Figure 1. In the model, use
of trained strategies is seen as the immediate precursor to a change in test performance. Use of
trained strategies is seen as a function of self-efficacy for the trained skills, perceptions of the
personal relevance of the trained skills, and motivation to use the trained skills. Transfer
interventions are seen as a method for increasing self-efficacy, perceptions of relevance, and
motivation to transfer. However, transfer interventions aligned with an individual’s natural
processing style (verbal versus visually-based) are seen as more effective at increasing these
factors.
A number of hypotheses are generated from this model that will be tested in this study.
Training transfer has been defined as the extent to which the learning that results from a training
experience transfers to the job and leads to meaningful changes in work performance (Baldwin,
Ford, &Blume, 2009; Goldstein & Ford, 2002). Self-management strategies are training
interventions that occur during or soon after training and are specifically designed to enhance
transfer. Verbal self-management strategies, such as goal-setting and self talk, have been linked
to self-reported changes in behavior (Wexley& Baldwin, 1986) as well as to ratings of job
performance (Brown, 2003). In addition, athletes and clinicians have successfully utilized
imagery-based interventions to change behavior (Kazdin, 1979, 1982; Vallis& Bucher, 1986)
and improve performance (Harris & Johnson; 1980; Savoy &Beitel, 1996). The test of the first
hypothesis is consistent with findings in these literatures.
Hypothesis 1a: Individuals who receive the verbal self management intervention of goal
setting will use more trained skills and will demonstrate greater
improvement in performance than individuals who do not receive a
transfer intervention.
Hypothesis 1b: Individuals who receive the imagery based intervention will use more of
the trained skills than those who do not receive a transfer interventionand
30

will demonstrate greater improvement in performance than individuals
who do not receive a transfer intervention.
Both verbal and imagery-based interventions have been linked to increased use of trained
skills and improvements in performance in previous studies, but the relationship between use and
performance is generally not tested empirically. The second hypothesis seeks to examine this
link directly.
Hypothesis 2a: The relationship between the verbal self managementintervention and
improvements in performance subsequent to training will be mediated by
use of trained skills.
Hypothesis 2b: The relationship between the imagery based intervention and
improvements in performance subsequent to training will be mediated by
the use of trained skills.
The evidence discussed above suggests that both verbal and imagery-based interventions
can be effective at changing behavior and that neither is necessarily better or worse than the
other. One possible moderator in the relationship between the type of intervention and its
effectiveness is the preference or natural inclination of the individual. The work demonstrating a
relationship between cognitive style and preference for mode of presentation (Riding & SadlerSmith, 1997; Sadler-Smith & Smith, 2004) and Vallis and Bucher’s (1986) work demonstrating a
relationship between natural tendency and the effectiveness of verbal and imagery-based
interventions suggest that it may be beneficial to match transfer interventions to individuals’
natural tendencies and preferences. This hypothesis is that there is an aptitude-treatment
interaction between intervention-type and processing style (Cronbach, 1957).
Hypothesis 3: The relationship between the type of intervention (verbal or imagerybased) and use of trained skills is moderated by preference for processing
style (verbal or visual) such that individuals who receive an intervention
aligned with their preferred processing style will use more trained skills
than individuals who receive an intervention not aligned with their
preferred processing style.

31

There are two main explanations proposed for why both verbal and imagery-based
interventions are effective at enhancing transfer. The explanations are the same for both types of
interventions. The first explanation is that the interventions enhance individuals’ perceptions of
the relevance of the trained skills to their personal situation. One type of verbal intervention,
implementation intentions, leads to increased accessibility of critical cues associated with the
desired behavior (Gollwitzer&Sheeran, 2006). In addition, goal-setting is most effective when
the goals are short-term and specific. The theory of functional equivalence (Murphy et al., 2008)
argues that imagery-based interventions are more effective the more closely the imagery mimics
the future performance event. The second explanation is that the interventions enhance
individuals’ feelings of competence and motivation for using the trained skills. Goal-setting has
been linked to increases in self-efficacy (Bandura, 1986; 1997). Uhlemann and Koehn (1989)
suggested that, based on Bandura’s theory, covert modeling, an imagery intervention, should
operate in much the same way that behavioral modeling does. In their meta-analysis of studies of
behavioral modeling, Taylor et al. (2005) found that transfer was greater when trainees practiced
work-related scenarios that they developed themselves due to the personal relevance of the
scenarios. In addition, they also suggested that modeling increases motivation to transfer when
the models are reinforced for effective performance.
Hypothesis 4: The relationship between the intervention and use of trained skills
subsequent to training will be explained by perceptions of the personal
relevance of the trained skills, self-efficacy for using the skills, and
motivation to use the skills.
One proposed moderator in the relationship between the type of intervention and its
effectiveness is the individual’s preference for processing style. Given the work demonstrating a
relationship between cognitive style and the effectiveness of verbal and imagery-based
interventions (Riding & Sadler-Smith, 1997; Sadler-Smith & Smith, 2004; Vallis and Bucher,

32

1986), it is likely that matching transfer interventions to individuals’ natural tendencies enhances
the mechanisms by which transfer is enhanced by intervention.
Hypothesis 5:The relationship between the type of intervention (verbal or imagerybased) and the proposed process variables is moderated by preference for
processing style (verbal or visual) such that individuals who receive an
intervention aligned with their preferred processing style will have
stronger perceptions of the personal relevance of the trained skills, higher
self-efficacy for using the skills, and higher motivation to use the skills
than individuals who receive an intervention not aligned with their
preferred processing style.
Method
Participants
The participants were undergraduate students at Michigan State University who
registered with the Psychology Human Subject Pool and were also enrolled in at least one of six
courses that each included at least three multiple-choice tests during the Spring 2011 semester.
Three hundred students participated in the Time 1 data collection. One hundred students were
assigned to each of the three groups in the experiment (verbal intervention, imagery-based
intervention, and no intervention). Assignment was based on the online session chosen by the
student. All three sessions were posted simultaneously and contained identical descriptions of the
study. Each was capped at 100 participants. The participants received course credit for
participating in both the initial training and the follow-up session. As an incentive for
participating in the follow-up approximately one month after the initial session, participants were
entered into a raffle to win one of five $20 gift cards in addition to receiving course credit. Two
hundred and twenty-two students (74%) participated in the Time 2 session. The demographic
composition of the participants who participated in both sessions is shown in Table 1.

33

Procedure
All of the participants self-selected to participate in the study via the online portal for the
Human Subject Pool.After completing informed consent (see Appendix A) and reading through
instructions (see Appendix B), participants responded to a measure about test anxiety and then
received online training about effective strategies for improving scores on multiple choice exams
(see Appendix D for script). The training was a video in which ten strategies for improving test
scores were presented with rationales for why each strategy is effective. For example, one of the
strategies is to circle or underline key trigger words in a question that may change the meaning
of the question, such as “not” or “always.” The subject matter for the training was selected
because it is relevant to the participants, all of whom are undergraduate students, and because all
participants had an opportunity to use the trained skills on at least one multiple-choice exam
within the month following training. Following the online training, all participants were assessed
on how often they currently use the trained strategies as well as their inclination for verbal or
visual processing.
Following the assessment, participants participated in one of three conditions to which
they were randomly assigned. One group of participants was trained to develop implementation
intentions for using the test-taking strategies (see Appendix G). As part of this intervention,
participants were introduced to the main components of an implementation intention (or if-then
statement). They were then walked through the steps of writing an implementation intention or
specific goal for using five of the ten trained strategies. A second group of participants was
trained to develop personal imagery-based scenarios in which they used the test-taking strategies
(see Appendix G). In the imagery-based intervention, participants were prompted to describe
different aspects of the physical and emotional experience of taking an exam in their specific

34

class. They then imagined themselves using five of the trained strategies while taking their next
exam. Both experimental groups were counseled that the likelihood that they would succeed in
using the desired techniques would be improved if they practiced the rehearsal techniques. A
third group served as a control. Theydid not receive any guidance about how to improve the
likelihood of transfer.
Following the intervention, participants were assessed abouthow seriously they took the
intervention, how personally relevant they found the test-taking strategies, their self-efficacy for
using the strategies, and their motivation to use the strategies.
Within several weeks of initial participation (after participants had taken their next class
exam and had received their scores), participants were contacted via email and asked to
participate in a follow-up online survey. Participants were asked to self-report transfer (their use
of the trained test-taking skills during an in-class exam) and to report their scores on the
multiple-choice exams taken before and after the training.
Measures
Test anxiety.Test anxiety was assessed using the 10-item Westside Test Anxiety Scale
(Driscoll, 2007; see Appendix C), which has a 5-point response scale ranging from “always true”
to “never true.”The test is designed to measure whether anxiety impairs the individual during a
test; for example, “I finally remember the answer to exam questions after the exam is already
over.” Cronbach’s alpha for the scale was .84.
Processing style.Processing style was assessed prior to the intervention. First,
participants completed the Style of Processing Scale (Childers, Houston, & Heckler, 1985; see
Appendix F), which consists of 22 items and has a 4-point response scale ranging from “always

35

true” to “always false”. Higher scores indicate a verbal processing style whereas lower scores
indicate a preference for imagery-based processing. Coefficient alpha reliability was .72.
Use. Use of the trained material was assessed twice, once as a baseline immediately after
training and again as an outcome measure on the follow-up survey. At Time 1, participants
responded to 18 items about the extent to which they used each of the trained test-taking
strategies when they took multiple-choice tests in the past. The measure was scored on a 5-point
scale ranging from “Never” to “Always” (see Appendix E). Coefficient alpha reliability was .75.
In the follow-up survey, use was assessed using the same items, but with a 5-point scale ranging
from “Strongly Disagree” to “Strongly Agree” (see Appendix K). At Time 2, participants were
instructed to respond to the items in reference to how they behaved at their most recent multiple
choice test. Coefficient alpha was .84.
Manipulation checks.Immediately after the intervention, participants in the experimental
groups responded to four items asking about how much effort they put forth in completing the
intervention (see Appendix H). For example, “I concentrated fully on the goal-writing exercise.”
The items had a 5-point response scale ranging from “strongly disagree” to “strongly agree.”
Coefficient alpha reliability was .87 for both conditions. Eleven participants (5%) provided an
average response of 3 or below and were eliminated from further analyses.
An alternative manipulation check was conducted to determine whether participants in
each of the conditions used more trained skills after training than they used prior to training. For
this manipulation check, the baseline and follow-up use scales were recoded to compare use
before and after training. Responses of 4 (“often” or “agree”) and 5 (“always” or “strongly
agree”) were assigned a value of “1” and all other responses were assigned a value of “0.” Each
participant received a baseline score of how many skills they used prior to training and a follow-

36

up score of how many trained skills they used after training by summing the converted scores.
Paired sample t-tests were conducted to determine whether participants in each condition utilized
more trained skills after training than before. There was a significant increase in the number of
strategies used by participants in all three conditions after training (see Table 2), indicating that
the interventions were no more effective at increasing strategy use than the control condition.
Personal relevance.After the manipulation check, participants rated each of the ten
trained strategies as to how helpful it would be for improving their score on future multiplechoice tests (see Appendix I). The scale was modeled after Lim and Morris’s (2006)
Applicability scale and was scored on a 4-point scale ranging from “Not helpful at all” to “Very
helpful”. Coefficient alpha reliability was .77.
Self-efficacy.After rating personal relevance, participants ratedtheir confidence in their
capability to employ each of the ten trained strategies. The scale was modeled after Stevens and
Gist’s (1997) Self-Efficacy scale and wasscored on a 4-point scale ranging from “Not at all
confident” to “Very confident” (see Appendix I). Coefficient alpha reliability was .83.
Motivation to Transfer.After rating self-efficacy, participants rated how motivated they
were to use each of the ten trained strategies. The scale was modeled after Stevens and Gist’s
(1997) Self-Efficacy scale and was scored on a 4-point scale ranging from “Not at all motivated”
to ‘Very motivated” (see Appendix I). Coefficient alpha reliability was .81.
Demographics.Participants were asked to report in which of six specific psychology
courses they were enrolled (see Appendix B) as well as their sex, age, and race (see Appendix J).
The participants were asked about course enrollment so that they could be re-contacted for the
follow-up survey within a week after their next multiple-choice exam. The purpose of asking for
sex, age, and race was to provide a general idea of the demographic composition of the sample.

37

Test scores.Participants reported the scores they received on multiple-choice tests in one
of the six designated Psychology coursesduring the current semester both prior to and after the
training (see Appendix L).The professors for the courses provided the class means and standard
deviations for each course exam. The class data was used to transform each score into a z-score
to control for differences in exam difficulty both within and between classes. The standardized
scores each participant received on exams prior to participating in the study were averaged to
create a “pre-training” score to compare to the score received on the exam following the training.

Results
Descriptive Statistics
Descriptive statistics and correlations of all of the measures can be found in Table 3. The
correlations indicate that participants who naturally tend to be visual processors had more test
anxiety (r = -0.34) and had lower levels of self-efficacy after training (r = 0.17) than participants
who tend to be verbal processors. Participants with higher levels of test anxiety performed lower
on multiple-choice exams both prior to the intervention (r = -.35) and after the intervention (r = .28). Participants who received the verbal intervention were motivated to transfer (r = 0.22). In
addition, participants who had used more of the test-taking strategies prior to the training saw the
trained strategies as more relevant (r = 0.31), felt more efficacious of performing the strategies (r
= 0.36), and felt more motivated to use the strategies (r = .35) after training. They were also
more likely to use the strategies on their next exam (r = .44).Use of the strategies at Time 2 was
related to higher ratings of personal relevance (r = .28), self-efficacy (r = .29), and motivation to
transfer (r = .37) at Time 1.

38

Evaluation of Hypotheses
Hypotheses 1a and 1b. Hypotheses 1a and 1b suggested that individuals who received
the verbal self-management or imagery based interventions would use more trained skills and
demonstrate greater improvement in performance than individuals who did not receive an
intervention. Hypotheses 1a and 1b were assessed using analysis of covariance.
An ANCOVA [between-subjects factor: condition (verbal, imagery, control); covariate:
use of the trained skills prior to training] revealed no main effect of condition, F(2, 205) = 1.22,
2

p = .30, ηp = .01, on use of the trained skills after the intervention. There was a significant main
2

effect of prior use, F(1, 205) = 49.74, p< .001, ηp = .20. An ANCOVA [between-subjects
factor: condition (verbal, imagery, control); covariate: grades prior to intervention] revealed no
2

main effect of condition, F(2, 192) = .12, p = .89, ηp = .00, on post-intervention grades. There
2

was a significant main effect of prior grade, F(1, 192) = 160.97, p< .001, ηp = .46.
Therefore, Hypotheses 1a and 1b were not supported. There was no effect of condition on
either use of trained skills or on grades at Time 2 after taking into account use of trained skills
prior to training. Neither the verbal nor the imagery-based intervention was more effective than
the control condition at increasing use of the trained skills or improving exam scores.
Hypotheses 2a and 2b.Hypothesis 2 suggested that the relationship between the
interventions and improvements in performance would be explained by use of the trained skills.
Because there was no effect of condition on either use of trained skills or on grades at Time 2,
the preconditions necessary to find the hypothesized mediation were not present.
Despite this finding, planned analyses were conducted for exploratory purposes. An
ANCOVA [between subjects factor: condition (verbal, imagery, control); covariates: grades prior

39

to intervention and use of the trained skills after training] revealed no main effect of condition,
2

F(2, 191) = .09, p = .91, ηp = .00, on post-intervention grades. There was a significant main
2

effect of grades prior to intervention, F(1, 191) = 168.28, p< .001, ηp = .47, as well as a
2

significant main effect of the use of trained skills after training, F(1, 191) = 5.22, p = .02, ηp =
.03.
Hypothesis 2 was not supported. Although there was no significant relationship between
the interventions and improvements in exam scores, use of the trained skills was related to
improvements in exam scores.
Hypothesis 3.Hypothesis 3 suggested that matching the transfer intervention to the
individuals’ preferential processing style would lead individuals to use more trained skills. In an
assessment of hypothesis 3, an ANCOVA [between subjects factor: condition (verbal, imagery,
control); covariates: use of trained skills prior to training and style of processing] revealed no
2

main effect of condition, F(2, 201) = 1.00, p = .37, ηp = .01, on use the of the trained skills after
the intervention. There was a significant main effect of use of the trained skills prior to training,
2

F(1, 201) = 51.32, p< .001, ηp = .20, but there was no main effect of style of processing, F(1,
2

201) = 1.26, p = .26, ηp = .01.
Hypothesis 3 was not supported. Neither the type of intervention nor the individual’s
preferred style of processing was related to the use of trained skills after the intervention after
taking into account the covariates.
Hypothesis 4.Hypothesis 4 suggested three process variables (personal relevance
of the trained skills, self-efficacy for using the trained skills, and motivation to transfer the

40

skills)to explain why the interventions might lead to transfer.Because there was no effect of
condition on use of trained skills at Time 2, the preconditions necessary to find the hypothesized
mediation were not present.
Despite this finding, planned analyses were conducted to explore whether the process
variables impacted use over and above use at Time 1. An ANCOVA [between-subjects factor:
condition (verbal, imagery, control); covariates: use of the trained skills prior to training,
relevance, self-efficacy, and motivation] revealed no main effect of condition, F(2, 199) = 1.41,
2

p = .25, ηp = .01, on use of the trained skills after the intervention. There were significant main
2

effects of use of the skills prior to training, F(1, 199) = 26.52, p< .001, ηp = .12, and motivation
2

to transfer, F(1, 199) = 7.09, p = .01, ηp = .03, but no main effects of relevance, F(1, 199) = .01,
2

2

p = .94, ηp = .001, or self-efficacy, F(1, 199) = .20, p = .66, ηp = .00.
Hypothesis 4 was not supported. Although there was no relationship between the
interventions and transfer, the process variable motivation to transfer was a predictor of transfer
even when use prior to training was taken into account. The more motivated they were to use the
trained skills, the more skills the participants used on their next exam.
Hypothesis 5.Hypothesis 5 suggested that processing style would impact the relationship
between the interventions and the process variables predicted to influence transfer. An
ANCOVA [between subjects factor: condition (verbal, imagery, control); covariates: relevance,
self-efficacy, style of processing] revealed no main effect of condition, F(2, 197) = .36, p = .70,
2

ηp = .00, on motivation to transfer. There were significant main effects of relevance, F(1, 197)
2

2

= 79.14, p< .001, ηp = .29, and self-efficacy, F(1, 197) = 32.67, p< .001, ηp = .14, but no main

41

2

effect for style of processing, F(1, 197) = .23, p = .63, ηp = .00. The interaction between
2

condition and style of processing was also not significant, F(2, 197) = .59, p = .56, ηp = .01.
Hypothesis 5 was not supported. Processing style had no moderating effect on the
relationship between condition and motivation to transfer.
Follow-up Analyses
Despite the fact that the main variables of interest (condition and style of processing) did
not demonstrate meaningful relationships with the main outcomes of interest (increased use of
the trained strategies and improvement in grades), there were other variables measured that did
demonstrate predictive value in relation to the outcomes. In order to explore these relationships
further, additional analyses were conducted.
An ANCOVA [between subjects factor: condition (verbal, imagery, control); covariates:
sex, test anxiety, grades at Time 1, and use at Time 2] revealed no main effect of condition, F(2,
2

188) = .10, p = .91, ηp = .00, on grades at Time 2. There were significant main effects of sex,
2

2

F(1, 188) = 6.90, p= .01, ηp = .04, grades at Time 1, F(1, 188) = 140.86, p< .001, ηp = .43,
2

and use at Time 2, F(1, 188) = 4.80, p= .03, ηp = .03, but no main effect for test anxiety, F(1,
2

188) = 2.38, p = .13, ηp = .01. An ANCOVA [between subjects factor: condition (verbal,
imagery, control); covariates: baseline use and motivation to transfer] revealed no main effect of
2

condition, F(2, 202) = 1.50, p = .23, ηp = .02, on use at Time 2. There were significant main
2

effects of baseline use, F(1, 202) = 29.35, p< .001, ηp = .13, and motivation to transfer, F(1,
2

202) = 15.49, p< .001, ηp = .07.

42

A model demonstrating the relationships described above can be found in Figure 2.
Female students who received higher grades prior to training and used more of the trained skills
after training had the highest grades after training. Although test anxiety was significantly related
to grades at Time 2, the effect disappeared after the other variables were included in the analysis.
The best predictors of use of the trained skills at Time 2 were use prior to training and motivation
to transfer.

Discussion
The main goal of this study was to further explore what types of training design
interventions impact subsequent transfer and why. Because one of the only training variables that
trainers can directly control is design, arming trainers with knowledge about how to maximize
transfer should be an effective way to empower them to provide a lasting effect on the behavior
of trainees. The purpose of the study was twofold: to explore an imagery-based alternative to
verbally based transfer interventions and to examine whether individual differences in preferred
processing style impact transfer.
In reviewing the literature on self-management transfer interventions, it became apparent
that the effective strategies in the literature all involve verbal rehearsal. For example, Gollwitzer
and Sheeran (2006) meta-analyzed the effect of the use of implementation intentions on goal
achievement and found a fairly stable moderate positive effect. In addition, a small group of
studies has found positive evidence to suggest that self talk is related to improvements in
performance following training (Brown, 2003; Brown & Morrissey, 2004; Millman& Latham,
2001). As an alternative to the verbal techniques, Marlatt (1988) proposed the use of visual
imagery rehearsal in his model of relapse prevention, but never elaborated on it. In sport and
therapeutic settings, however, imagery-based rehearsal has been found to be very effective at
43

changing behavior. There is evidence to suggest that mental practice is a sufficient substitute for
some physical practice of athletic motor skills (Durand, Hall, &Haslam, 1997) and the effects of
covert modeling interventions in clinical settings have been found to be as effective as other
interventions (Bistline, Jaremko, &Sobleman, 1980; Zielinski & Williams, 1979) and to have
lasting effects (Harris & Johnson, 1980; Hersen et al,, 1979; Kazdin, 1979, 1982). Despite the
widespread use of techniques related to future-oriented mental rehearsal in other fields, there has
been very little exploration into the possibility of its use in workplace or classroom training.
For this study, a verbally based transfer intervention (the development of implementation
intentions) was compared to an imagery-based intervention (based on a combination of covert
modeling and the PETTLEP model). Undergraduate students were presented with training on
strategies to improve their scores on multiple-choice exams followed by a verbally based
intervention, an imagery based intervention, or no intervention. Within the month following
training, all of the students completed at least one multiple-choice exam in a psychology course.
After the exam, the students reported whether they had used the strategies they learned in
training and whether their exam grade had improved.
The evidence showed that the training had a positive effect on transfer, but that the
interventions were no more effective than the control. It was expected that the participants in
both the verbal and imagery based intervention groups would use more of the trained skills and
show more improvements in their exam scores than the participants in the control group.
However, there were no differences between the participants in the three groups, indicating that
the interventions were not effective at promoting transfer. Although the interventions were not
effective, the training was. The use of trained skills was positively related to improvements in
exam scores.

44

There are several plausible explanations for why the interventions were not more
effective than the control. Both interventions were conducted immediately following the training
video and a brief questionnaire. It is possible that participants had lost interest in the study by the
time they reached the intervention or that they did not take the procedures seriously. Because the
study was conducted online, the interventions were conducted at the participants’ own pace and
under their own control. There were several features included in the design of the study to allow
for the evaluation of how seriously the participants took the interventions. First, participants
were asked to complete open-ended responses at each step in the intervention process, but they
were not required to provide responses in order to advance the survey. The vast majority of
students provided responses relevant to the questions, indicating that they were reading and
considering each item as they completed it. Second, each participant completed a brief survey
following the intervention that asked about how much effort he or she had put into completing
the intervention. The mean score for that survey was 4.08 out of 5 points, indicating that the
average participant reported that they had put forth a positive effort as they completed the
intervention. Previous studies that reported positive effects related to transfer interventions have
allowed participants a similar amount of autonomy in completing the intervention. For example,
in his 1993 study using implementation intentions, Gollwitzer asked students to specify in
writing when they planned to write a report and then waited to follow up with them until after
their Christmas vacation. The evidence available suggests that the participants in the study were
fairly engaged during the interventions.
An additional explanation for why the interventions were not more effective than the
control is that the participants in all three groups were sufficiently motivated to utilize the trained
skills prior to the intervention. This explanation is essentially that there was a “ceiling effect”

45

such that participants were so motivated to use the skills prior to the interventions that the
interventions could not add any additional meaningful amount of motivation.Because motivation
was only measured after the interventions, it is not possible to evaluate whether such an effect
may have occurred.
In this study, there were in effect two interventions, the training and the transfer
intervention. All participants received the training and two of the three groups of participants
received a transfer intervention. In previous studies of transfer interventions such as goal-setting
and implementation intentions, this design is somewhat unusual. For example, in Gollwitzer’s
1993 implementation intentions study, the only intervention was the development of
implementation intentions. Participants were not also presented with training on new skills prior
to developing the intentions. One notable exception is Wexley and Baldwin’s 1986 goal-setting
study. Their design included extensive training in new skills followed by goal-setting
interventions. Both their training and transfer interventions were much more intensive in time
and resources than the scope of this study, however. In their review of the goal-setting literature,
Brown and McCracken (2010) pointed out that most studies in this area have involved simulated
tasks with short-term measures of transfer, which makes it difficult to estimate how powerful a
goal-setting intervention must be to have an effect on behavior in an naturalistic setting, like in
this study where the desired behavior occurred weeks after training and while taking a real
classroom exam.
In addition to comparing the outcomes of the two interventions, three process variables
were also included in the study to examine whether the processes involved in imagery-based
rehearsal are similar to those involved in verbal rehearsal. The three process variables,
perceptions of the personal relevance of the trained material, self-efficacy for use of the material,

46

and motivation to transfer the material, were related to the use of the trained skills. The effects of
relevance and self-efficacy, however, disappeared when the other predictor variables were
included in the analyses. There were no differences found between participants in the different
conditions for perceptions of the personal relevance of the trained material and for self-efficacy
for using the trained skills. However, participants who received the verbal intervention reported
higher levels of motivation to use the trained skills than did participants in the other two
conditions. Again, the relationship disappeared when the other predictor variables were included
in the analyses.
Natural processing style was proposed as a moderator in the relationship between the
type of intervention and its effectiveness. Because the interventions were not differentially
effective, it was not possible to evaluate this hypothesis directly. Instead, the relationships
between processing style and the outcomes and process variables were evaluated. Individuals
who preferred verbal processing had less test anxiety than individuals who preferred visual
processing, most likely because the tests that the participants take are generally in verbal or
written form. Processing style was not related to the use of the trained skills after training, but
individuals who preferred verbal processing used more of the skills prior to training. Perhaps
because the skills are all related to taking written tests, verbal processors are at an advantage for
figuring out strategies for success on those tests. Verbal processors also had higher levels of selfefficacy for performing the trained skills than individuals who prefer visual processing, perhaps
because they already used more of the strategies to begin with and because the strategies were
related to their strengths to begin with. In the predictive models that included the other variables,
all of the advantages for verbal processors disappeared.

47

Limitations
There were several limitations to the methodology of the study; causality was not
definitive, the interventions may not have been powerful enough, the transfer measured a
snapshot of behavior rather than change in behavior, and the Style of Processing scale may not
be valid.Participants reported how many skills they used on their exam after they had already
received their exam grades. Therefore, it is possible that their grades may have impacted their
responses, such that if their exam scores improved, they may have attributed the improvement to
the use of the trained skills. It is also possible that that the positive effect of the transfer on
improvements in grades may have been a misleading finding since students’ grades may have
improved throughout the semester regardless of their participation in the study or that students
who participated in the study improved their grades because they believed they would (a
Hawthorne effect; Landsberger, 1958).
One method for strengthening the effect of the interventions would be to provide more
time between the initial training and the subsequent intervention. It is possible that participants
were overwhelmed by the amount of information they absorbed from the training and had not
had time to absorb it and make realistic training goals or mental practice sessions. Perhaps with
more time to absorb the material, they would have benefitted more from the interventions.
Unfortunately, however, it may not be possible for trainers to expect to have access to their
students at more than one point in time.
The initial intent was to measure transfer as participants’ self-reported change in testtaking behavior subsequent to training. For this reason, directly after training, participants were
asked to report how often they utilized each of the trained strategies. However, the survey asked
participants to report how often they had used each strategy in the past whereas it should have

48

asked them whether they had used each strategy on the previous exams in the specific targeted
class. In addition, the response scale for the baseline and time 2 surveys was different. For this
reason, the two surveys could not be combined to create a measure of behavior change.
The Style of Processing Scale (Childers et al., 1985) was used to evaluate each
participant’s natural preference for verbal or visual processing style. Although the scale has been
widely cited and used, there are concerns that the scale may not assess valid distinctions between
preferences in processing style (Bagozzi, 2008). Future research should examine the validity of
the scale.
Conclusion and Future Research
Although the original experimental hypotheses could not be addressed, the study
contributed to the literature by providing evidence that processing style may be an important
characteristic to consider for training design. Participants who naturally process information
visually had more test anxiety and felt less efficacious about performing the trained skills. In
addition, the study provided more evidence that trainees’ motivation to transfer the trained
material is a key predictor of whether they use the trained skills. Future research should examine
better methods for measuring natural preferences in processing style. In addition, it would be
worthwhile to examine whether there are other contexts in which processing style is a
moderating effect for the effect of training interventions as well as whether there are other
individual difference characteristics that may moderate the effectiveness of training design
interventions.

49

Appendices

50

Figure 1
Hypothesized Model

Individual difference
in processing style

Type of selfmanagement transfer
Intervention

Personal Relevance
Use of trained
skills

Self-efficacy
Motivation to transfer

51

Performance

Table 1
Demographic Characteristics of Participant Sample
Characteristic

N (% of total N)

Condition
Verbal

76 (34.23%)

Imagery

75 (33.78%)

Control

71 (31.98%)

Sex
Female

156 (70.27%)

Male

62 (27.93%)

Age
18

61 (27.48%)

19

68 (30.63%)

20

39 (17.57%)

21 or older

49 (22.07%)

Ethnicity
Hispanic

7 (3.15%)

American Indian or Alaska Native

1 (0.45%)

Asian

11 (4.95%)

Black or African American

12 (5.41%)

Native Hawaiian or Other Pacific Islander

1 (0.45%)

White

199 (89.64%)

Note. Total N was 222. Participants were asked to mark all ethnicities with which they identified.

52

Table 2
Paired Comparisons of Number of Strategies Used Before and After Interventions
Intervention
Verbal

Use Before Training Use After Interventions
M
SD
M
SD
9.87
3.03
12.44
3.41

t
-5.50

t-test
df
69

p
< .001

Imagery

9.93

2.50

12.73

3.01

-9.18

69

< .001

Control

10.11

2.95

12.07

3.04

-5.18

69

< .001

53

Table 3
Descriptive Statistics and Correlations of All Variables

M

SD

1

2

3

4

5

6

7

8

9

1. Sex
2. Age
3.46 1.37
-.31
3. Verbal Intervention
.00
.02
4. Imagery Intervention
.00
.01
-.50
5. Test Anxiety
-.11
-.05
-.02 0.84
2.79 0.62
.22
6. Baseline Use
3.56 0.42
-.04
-.01
-.02
-.02 0.75
.16
7. Style of Processing
3.00 0.27
-.05
.01
-.07
.14
-.34
.16 0.72
8. Personal Relevance
3.38 0.40
-.10
.10
.00
.05
-.02 0.77
.29
.31
9. Self Efficacy
.11
.02
.09
-.02
-.07
3.51 0.41
.36
.17
.47 0.83
10. Motivation to Transfer
3.36 0.46
.01
-.06
-.02
.07
.17
.22
.35
.66
.57
11. Time 2 Use
3.75 0.54
.03
.02
-.03
.08
.02
-.01
.44
.28
.29
12. Grade Pre-Intervention
-.08
-.11
-.01
.01
.01
.10
-.06
0.24 0.86
-.35
-.15
13. Grade Post-Intervention
.06
-.00
.01
.10
.04
-.05
-.05
0.31 0.79
-.16
-.28
Note. Bolded correlations are significant at .05 level. Sex is a dummy coded variable. 1 = Female and 0 = Male.
Verbal and Imagery Intervention are also dummy coded variables. 1 = Intervention, 0 = No intervention.

54

10

11

0.81
.37
-.13
-.03

0.84
-.08
.06

12

.68

Figure 2
Model Based on Follow-up Analyses
Grades prior to Training
Sex

Use of Trained Skills
prior to Training
Use of Trained Skills
after Training
Motivation to transfer

55

Grades after Training

Appendix A: Consent Form
Multiple-Choice Test Taking Strategies Training Study
The purpose of this research study is to explore what variables affect whether and why
individuals use what they learn in a training session.
What does participation in this research study involve?
In this research study, you will be asked to complete a training session about strategies
for scoring well on a multiple-choice exam. You will also be asked to complete a questionnaire
about your reactions to the training and about your cognitive style. Your participation today may
take up to an hour. In several weeks, after you’ve taken another class exam, you will receive an
email invitation to participate in a second questionnaire about whether or not you used what you
learned in the training session. The second questionnaire may take up to 30 minutes to complete.
Your Rights and Benefits as a Participant in this Research study
You will receive up to 3 research credits for participating in this study. You will receive 2
credits for your participation today and an additional 1 credit when you complete the second
questionnaire in a few weeks. As an incentive to participate in the second questionnaire, all
participants will also be entered into a lottery to win a $50 gift certificate to Amazon.com.
Your participation in this research study is voluntary. You are free to refuse to participate
in this project or any part of the project. You may refuse to answer some of the questions and
may discontinue your participation at any time without penalty. Your confidentiality will be
protected to the maximum extent allowable by law. Your responses will be stored in a locked
filing cabinet for four years. Your responses will be stored separately from your identifying
information and it will not be possible for them to be linked, except by the principal
investigators. Only the principal investigators will have access to your responses. Your
participation in this research study may contribute to the understanding of how to make training
in the workplace more effective and may also help you to perform better on future multiplechoice exams.
If you have concerns or questions about this study, such as scientific issues or how to do
any part of it, please contact the researcher, Abigail Billington: (813) 777-9812 or email:
billinga@msu.edu. You may also contact Dr. Kevin Ford, Professor, Department of Psychology
at Michigan State University: (517) 353-5006, email: fordjk@msu.edu, or regular mail: 315
Psychology Building, East Lansing, MI 48824.
If you have any questions or concerns about your role and rights as a research participant,
or would like to register a complaint about this study, you may contact, anonymously if you
wish, the Director of MSU’s Human Research Protection Program at 517-355-2180, Fax 517432-4503, or e-mail irb@msu.edu or regular mail at 202 Olds Hall, MSU, East Lansing, MI
48824. You indicate your voluntary agreement to participate in this research and have your
answers included in the data set by completing and returning the attached survey.
By continuing to the following page of the survey, you indicate your voluntary agreement
to participate in the research study.

56

Thank you for your participation!

57

Appendix B: Instructions
The following training session and related questionnaires may take you about an hour to
complete. You will need to complete the entire session in one sitting to receive the 2 credits for
your participation. If something unexpected occurs that interrupts your session and you would
like to continue, you may contact me by email at billinga@msu.edu so that I can instruct you
about how to finish your session. Do NOT press the BACK or RELOAD buttons on your
browser if there is a problem. Your data may be lost and I may not be able to give you credit for
your participation.
The point of this study is to examine how a variety of factors influence whether individuals use
what they learn in a training session. For that reason, it is really important to find out whether or
not you use what you learn today the next time you take a multiple-choice exam. I will contact
you by email within the next month to find out whether you used you learned today. Although I
hope you’ll find the training valuable, I want you to be honest about whether or not you used the
material so I can evaluate the effectiveness of the training. I need to collect your email address so
that I can contact you again, but I will store your email address separately from the data you
provide to protect your privacy. If you participate in the 2nd questionnaire, you will receive 1
more research credit and will be entered into a lottery to win a $50 Amazon.com gift certificate.
Please provide your email address below so that I can contact you in the next month: ____
Please verify your email address: ____
This study is limited to students enrolled in specific courses because of the schedule of multiplechoice exams in those courses. Which of the following courses are you enrolled in? Select all
that apply:
a.
b.
c.
d.
e.
f.

PSY 101 with Professor Jackson
PSY 101 with Professor Sakai
PSY 101 with Professor Lucas
PSY 209 with Professor Nunez
PSY 236 with Professor Donnellan
PSY 255 with Professor Ryan

58

Appendix C: Westside Test Anxiety Scale

Rate how true each of the following is of you.
Scoring: 1 = Never true; 2 = Seldom true; 3 = Sometimes true; 4 = Usually true, 5 = Always true
1.
2.
3.
4.

The closer I am to a major exam, the harder it is for me to concentrate on the material.
When I study for my exams, I worry that I will not remember the material on the exam.
During important exams, I think that I am doing awful or that I may fail.
I lose focus on important exams, and I cannot remember material that I knew before the
exam.
5. I finally remember the answer to exam questions after the exam is already over.
6. I worry so much before a major exam that I am too worn out to do my best on the exam.
7. I feel out of sorts or not really myself when I take important exams.
8. I find that my mind sometimes wanders when I am taking important exams.
9. After an exam, I worry about whether I did well enough.
10. I struggle with written assignments, or avoid doing them, because I feel that whatever I
do will not be good enough. I want it to be perfect.

59

Appendix D: Multiple-Choice Test-Taking Training Script (Time Point 1)
The following is a script of the narration for the PowerPoint presentation. Where [CLICK]
appears, the arrow key or mouse should be used to advance the presentation. The narration will
be recorded and added to the presentation. The entire presentation will be uploaded as a video
on YouTube for the participants to watch online.
Slide 1:
I’m going to present you with 10 strategies to help you improve your score on multiple-choice
exams. If you’d like to take notes, please feel free to pause the video and grab a pen.
Many students report that even though they study hard and feel like they really know the
material, when they sit down to take the exam, they aren’t able to demonstrate that knowledge.
Although multiple choice exams are intended to test your knowledge of the course material,
there are certain tricks to scoring well that have less to do with your knowledge of the material
and more to do with your competence with the multiple choice test format. [CLICK]
Slide 2:
In this presentation, all of the strategies have to do with success on the day of the test. [CLICK]
I want to emphasize that using these strategies is NOT a substitute for studying well for the
exam. These strategies will be the most helpful if you really know the material on the test.
[CLICK]
If you need or want help improving your study skills, I recommend contacting the Learning
Resource Center at MSU. You can also find links to websites with good study tips on their
website. [CLICK]
Slide 3:
The first strategy is to arrive early for the exam. You should decide how early makes the most
sense for you, but I would recommend arriving at least 10-15 minutes before the exam is
scheduled to begin. [CLICK]
Bring all of the materials you’ll need for your exam, such as [CLICK]
Number 2 pencils [CLICK]
A calculator [CLICK]
A watch [CLICK]
Or your ID. Whatever makes the most sense for your specific class. [CLICK]

60

Pick a good seat where you’ll feel comfortable during the exam. Maybe you like to sit in the
front during exams so you won’t be distracted by others or maybe you like to sit by the window
where there’s natural light. Whatever will help you feel comfortable and stay relaxed is best.
[CLICK]
While you’re waiting for class to start, don’t talk about the exam with your classmates. First of
all, anxiety is contagious and other students can get you riled up. Second of all, even if they
mean well, they might tell you wrong information about the test material. [CLICK]
Don’t cram. You won’t be able to learn enough in the last few minutes to help you on the exam.
If you studied well, you already know the information. Put your notes away and think about
something other than the exam. Staying relaxed and calm is your main goal at this point.
[CLICK]
Do keep your ears open in case other students ask the professor questions. By answering their
questions, the professor may give out useful information or clarification that can help you, too.
[CLICK]
Slide 4:
Once the exam begins, do a brain dump in the margins of the test or on scrap paper. [CLICK]
Write down any key information you’re holding in your memory [CLICK]
Such as formulas or mnemonic devices that you might need to remember later on during the test.
Mnemonic devices are memory aids such as acronyms. You’re essentially creating a cheat sheet
for yourself, except it’s not cheating because the test has begun. [CLICK]
For example, if you knew that the test might include a question about the colors of the rainbow,
you could write down your mnemonic device: [CLICK]
ROY G BIV and later, if you saw a question about the colors of the rainbow, you could flip back
to your brain dump and remember that the colors are [CLICK]
Red, Orange, Yellow, Green, Blue, Indigo, Violet. [CLICK]
Slide 5:
The third strategy is to preview the test before you start filling out the answers. Take a few
minutes or so to [CLICK]
Carefully read all of the instructions. It’s easy to overlook an important direction if you quickly
jump into answering the questions. [CLICK]
As you look over the test, note the number of questions and the number of points each question
is worth so you can [CLICK]
61

Budget your time and give every question the amount of attention it deserves. [CLICK]
Slide 6:
The fourth strategy is to anticipate the answer to each question before reading the answer
choices. For example, if you had the following question, you would first [CLICK]
Cover the answers with your hand or a piece of paper [CLICK]
Then, answer the question in your own words [CLICK]
Look for the answer choice like yours. And you’re done! Remember that some of the answer
choices were designed to distract you from the correct answer. If you answer in your own words
first, you’re less likely to fall for the distracters. [CLICK]
Slide 7:
The fifth strategy is to watch for trigger words. [CLICK]
Trigger words are negative or absolute words like [CLICK]
All, except, most, least, not, never, always, or only. Trigger words change the meaning of the
question or answer choice. [CLICK]
Underline or circle them so you won’t forget to take them into account when you select your
answer. For the following example, the word NOT [CLICK]
Signifies that only one of the following answer choices is wrong. First, I would underline it
before reading eh answer choices. [CLICK]
And then select my answer accordingly [CLICK]
Slide 8:
The sixth strategy is to read all the answer choices before selecting one. [CLICK]
As you read each answer choice, mark it with a plus sign if it is correct, a minus sign if it is
definitely not correct, and a question mark if you’re not sure. [CLICK]
Doing this will help you avoid selecting an answer choice too soon. [CLICK]
Like in the following example. Which of the following states borders Michigan? I know
Pennsylvania is wrong. [CLICK]
But Ohio is correct. [CLICK]
So is Indiana [CLICK]

62

It’s a good thing that I read all of the choices because choice D is the correct answer. [CLICK]
If I had chosen b as soon as I saw it was correct, I would have missed the real correct answer.
[CLICK]
Slide 9:
The seventh strategy is to answer the easy questions first. [CLICK]
Easy questions are the ones you know the answer to right away. [CLICK]
Answering them first will help you build confidence [CLICK]
And warm up. Plus, it may help jog your memory for the more difficult material. [CLICK]
Slide 10:
The eighth strategy is to save the hard questions. [CLICK]
Mark the questions you can’t answer right away and [CLICK]
Come back to them at the end. [CLICK]
You might find that questions later in the test will provide cues to help you answer the earlier
questions that stumped you. [CLICK]
Slide 11:
The ninth strategy is to answer ALL the questions on the test. Even if you’re not completely sure
that your answer is correct, [CLICK]
Use the process of elimination to [CLICK]
Make your best guess [CLICK]
It is very unusual to lose points more points for wrong answers than for blanks. Usually, a wrong
answer is worth the same as a blank answer – nothing. If you guess, you at least have a chance of
getting the points. If you leave it blank, you’ll definitely lose the points. [CLICK]
Slide 12:
The final strategy is to use all of the time allotted for the test. [CLICK]
If you finish early, [CLICK]
Check your answers. [CLICK]
Re-read all of the instructions and make sure that you followed them correctly [CLICK]

63

And make sure you caught all of the trigger words. [CLICK]
Slide 13:
Okay, let’s review. The 10 strategies that can help you improve your score on multiple choice
tests are: [CLICK]
Arrive early to get a good seat and settle in. [CLICK]
Do a brain dump of all the material you’re holding in short-term memory. [CLICK]
Preview the test and read all the instructions before you start. [CLICK]
Anticipate the answer by covering the answer choices and answering in your own words first.
[CLICK]
Watch for and circle or underline the trigger words. [CLICK]
Read all the answer choices before selecting one. [CLICK]
Answer all the easy questions you can first. [CLICK]
Mark the hard questions and go back to them later. [CLICK]
Answer all the questions. A guess is better than a blank. [CLICK]
Use all the time available. [CLICK]
Take a minute to think about which strategies might help you on your next exam.
Thanks for watching the presentation. I hope that these strategies help you!

64

Appendix E: Baseline Measure of Use
Please rate how often you usually use the following strategies when you take multiple choice
tests.
Scoring: 1 = Never; 2 = Rarely; 3 = Sometimes; 4 = Often; 5 = Always
1.
2.
3.
4.
5.

I arrive early for the exam.
I bring all of the materials I need for the exam.
I pick a comfortable seat for the exam.
I don’t talk with other students about the exam before it starts.
Once the test begins, I do a “brain dump” in which I write down important formulas or
key terms I was holding in my memory.
6. I look over the entire test before I start to respond.
7. I carefully read all of the instructions on the test.
8. When I read each question, I try to come up with the correct answer in my own words
before I read the response choices.
9. I watch for and mark trigger words like “never” or “always.”
10. I read all of the answer choices for each question.
11. I mark each answer choice with a symbol like +, -, or ?to help me with the process of
elimination.
12. I answer all of the questions I know first.
13. I go back at the end to respond to questions I skipped because they were too hard.
14. I answer all of the questions on the test.
15. I use all of the time provided for the test.
16. I go back and check my answers when I finish the test.
17. After I finish, I go back and re-read the instructions to make sure I followed them
correctly.
18. After I finish, I go back to make sure I caught all of the trigger words in the test.

65

Appendix F: Style of Processing Scale
Instructions: The aim of this exercise is to determine the style or manner you use when carrying
out different mental tasks. Your answers to the questions should reflect the manner in which you
typically engage in each of the tasks mentioned. There are no right or wrong answers, I only ask
that you provide honest and accurate answers. Please answer each question by selecting one of
the four possible responses. For example, if I provided the statement, “I seldom read books,” and
this was your typical behavior, even though you might read say one book a year, you would
select the “ALWAYS TRUE” response.
Answer choices: 1 = ALWAYS TRUE; 2 = USUALLY TRUE; 3 = USUALLY FALSE; 4 =
ALWAYS FALSE
1. I enjoy doing work that requires the use of words
2. There are some special times in my life that I like to relive by mentally “picturing” just
how everything worked (Reverse-scored)
3. I can never seem to find the right word when I need it (Reverse-scored)
4. I do a lot of reading
5. When I’m trying to learn something new, I’d rather watch a demonstration than read how
to do it (Reverse-scored)
6. I think I often use words in the wrong way (Reverse-scored)
7. I enjoy learning new words
8. I like to picture how I could fix up my apartment or a room if I could buy anything I
wanted (Reverse-scored)
9. I often make written notes to myself
10. I like to daydream (Reverse-scored)
11. I generally prefer to use a diagram rather than a written set of instructions (Reversescored)
12. I like to “doodle” (Reverse-scored)
13. I find it helps to think in terms of mental pictures when doing many things (Reversescored)
14. After I meet someone for the first time, I usually remember what they look like, but not
much about them (Reverse-scored)
15. I like to think of synonyms for words
16. When I have forgotten something I frequently try to form a mental “picture” to remember
it (Reverse-scored)
17. I like learning new words
18. I prefer to read instructions about how to do something rather than have someone show
me
19. I prefer activities that don’t require a lot of reading (Reverse-scored)
20. I seldom day-dream

66

21. I spend very little time attempting to increase my vocabulary (Reverse-scored)
22. My thinking often consists of mental “pictures” or images (Reverse scored)

67

Appendix G: Interventions
Verbal Intervention
You are more likely to use what you learned today if you make a decision about where and when
you will do so. People are more likely to achieve their goals the more specific the goals are.
Coming up with a specific goal is a three-step process.
1. First, think of your general goal. For this exercise, your general goal is to use one of the
10 strategies from the training video. Which of the ten strategies do you think would help
you the most when you take multiple choice tests?
__________________________________ (if you want to refer back to the video, here’s
the link/ also provide the list of ten strategies)
2. Second, think about when you will achieve your goal. For this exercise, you will want to
achieve your goal when you take your next multiple-choice exam in your psych class.
Here is a list of the class dates from the syllabi for the courses. What’s the date of your
next exam? ______
3. Third, think about how you will know it is time to achieve your goal (use the strategy).
We call this a cue. For example, will you use the strategy right after the test starts? Will
you use it when you see a hard item? Write your cue: __________________
Now, put the three pieces of information into the following form (like MadLibs):
When I take my next exam in (class) on (date), I will (strategy) when (cue). Here are some
examples:
When I take my next exam in PSY 101 on April 4, I will brain dump (write down the key
mnemonic devices) as soon as the exam begins.
When I take my next exam in PSY 236 on April 8, I will watch for and circle trigger words
when I read each of the questions.
When I take my next exam in PSY 101 on March 31, I will make sure to read all of the
answer choices when I get to each question on the test.
Write your full goal below using this format:
When I take my next exam in PSY ____ on _______, I will
_________________________________ when
______________________________________________.
Great! Now, go back and write a goal for how you plan to use 5 of the 10 strategies using this
same format. Choose the 5 strategies that would be most useful to you:(there will be five
blanks for them to fill out)

68

Take a few minutes to commit the goals to memory or write them down. If you take them
seriously and remember them, you are more likely to use these strategies at your next exam,
which may help improve your score!
Visual Intervention
Many famous athletes and performers use visualization exercises to help improve their skills. To
successfully use imagery, an individual imagines themselves performing successfully.
For example, in his mind, a baseball player might see the ball being released from the pitcher,
feel his muscles in his upper arm as he gets ready to swing, and then hear the “crack of the bat”
when he makes contact with the ball.
Here’s a quote from the famous retired baseball player, Hank Aaron, about how he used imagery
before a game:
“. . .I would start visualizing—like I’m standing at the plate with, say, runners at first and
second, or second and third —how he’s (the pitcher) going to pitch me in that given situation.
Then, I would start visualizing, for example, if the bases were loaded, how he would try to get me
out…I would put myself in all these different positions and put him in the same positions and try
to figure out what is best for him and what I am going to be looking for.”
You can use the same type of imagery exercise to help yourself improve your score on your next
multiple choice exam. I’ll walk you through the steps.
•
•

•

Here is a list of the class dates from the syllabi for the courses. When is your next exam?
_________
Now, pick five strategies from the training video that you want to use next time you take
an exam. (give link to video/ also provide the list of ten strategies)
___________________________
Based on your experiences with other exams in this class, imagine what it will be like
when you are taking the test.
o What does your classroom look like? ___________________
o Is the room warm or cold? ____________________
o Where will you sit when you take the test? ___________________
o What will you be able to see from where you’re sitting? _______________
o What type of desk or table will you have? ________________
o What will the test booklet look like? ___________________
o How will you be feeling when you arrive for the test? __________________
o How will you feel during the test? _________________________
o What will other students be doing around you before and during the test?
_____________

69

•

o Can you think of any other details about your experience that will make your
mental picture more detailed? ___________
Now, imagine yourself taking the test in those conditions and using the five strategies as
you complete your exam. Walk through it in your mind step by step and imagine what it
will be like to successfully use the strategy. Think about what you will see and hear, think
about what you will do, think about how you will feel.
o Picture yourself arriving in the classroom. What will you do first?
o Now imagine the start of the exam. How will you feel? What will you do?
Imagine yourself using each of the strategies, one by one.

Great! You have successfully used a visualization technique to help yourself prepare for the
exam. If you take this exercise seriously and carefully imagine yourself using the strategies
as you take your exam, you may improve your score on your next exam!
Control Intervention
Just as a reminder, these were the 10 strategies presented in the video (provide list and link). Feel
free to refer back to the video to remind yourself of the strategies if you would like to.

70

Appendix H: Manipulation Check
Please answer the following questions as honestly as possible.
Scoring: 1 = Strongly disagree; 2 = Disagree; 3 = Neither agree nor disagree; 4 = Agree; 5 =
Strongly agree
Verbal Intervention
1. As I went through the goal-setting exercise, I thought carefully about setting goals to help
myself succeed on my next exam.
2. I really thought through the goals I was writing.
3. I concentrated fully on the goals I was writing.
4. I made an effort to complete the goal-writing exercise carefully.
Imagery Intervention
1. As I went through the visualization exercise, I clearly imagined the strategies taught.
2. As I imagined myself using the strategies on the next exam, the scene was vivid in my
mind.
3. I concentrated fully on the visualization exercise.
4. I made an effort to complete the visualization exercise carefully.

71

Appendix I: Process Variables
Personal Relevance
Please rate how helpful you think each of the strategies would be for improving your score if you
used it on future multiple choice tests.
Scoring: 1 = Not helpful at all; 2 = A little bit helpful; 3 = Fairly helpful; 4 = Very helpful
1.
2.
3.
4.

Arrive early to get a good seat and settle in.
Do a brain dump of all the material you’re holding in short-term memory.
Preview the test and read all the instructions before you start.
Anticipate the answer by covering the answer choices and answering in your own words
first.
5. Watch for and circle or underline the trigger words.
6. Read all the answer choices before selecting one.
7. Answer all the easy questions you can first.
8. Mark the hard questions and go back to them later.
9. Answer all of the questions.
10. Use all of the available time.
Self Efficacy
Please rate how confident you feel in your capability to use each of the following strategies to
improve your score on future multiple choice tests.
Scoring: 1 = Not at all confident; 2 = A little bit confident; 3 = Fairly confident; 4 = Very
confident
1.
2.
3.
4.

Arrive early to get a good seat and settle in.
Do a brain dump of all the material you’re holding in short-term memory.
Preview the test and read all the instructions before you start.
Anticipate the answer by covering the answer choices and answering in your own words
first.
5. Watch for and circle or underline the trigger words.
6. Read all the answer choices before selecting one.
7. Answer all the easy questions you can first.
8. Mark the hard questions and go back to them later.
9. Answer all of the questions.
10. Use all of the available time.
Motivation to Transfer

72

Please rate how motivated you are to use each of the following strategies on future multiple
choice tests.
Scoring: 1 = Not at all motivated; 2 = A little bit motivated; 3 = Fairly motivated; 4 = Very
motivated
1.
2.
3.
4.

Arrive early to get a good seat and settle in.
Do a brain dump of all the material you’re holding in short-term memory.
Preview the test and read all the instructions before you start.
Anticipate the answer by covering the answer choices and answering in your own words
first.
5. Watch for and circle or underline the trigger words.
6. Read all the answer choices before selecting one.
7. Answer all the easy questions you can first.
8. Mark the hard questions and go back to them later.
9. Answer all of the questions.
10. Use all of the available time.

73

Appendix J: Demographics
1. What is your sex?
a. Female
b. Male
2. How old are you?
a. Under 18
b. 18
c. 19
d. 20
e. 21
f. 22
g. Over 22
3. Are you Hispanic/Latino?
a. Yes
b. No
4. What is your race/ethnicity? (Select all that apply)
a. American Indian or Alaska Native
b. Asian
c. Black or African American
d. Native Hawaiian or Other Pacific Islander
e. White

74

Appendix K: Use of Trained Content
Scale:1 = strongly disagree; 2 = disagree; 3 = neither agree nor disagree; 4 = agree; 5 = strongly
agree
Please answer the following questions about how you behaved during your multiple choice exam
in PSY ____ on (date):
1.
2.
3.
4.
5.

I arrived early for the exam.
I brought all of the materials I needed for the exam.
I picked a comfortable seat for the exam.
I didn’t talk with other students about the exam before it started.
Once the test began, I did a “brain dump” in which I wrote down important formulas or
key terms I was holding in my memory.
6. I looked over the entire test before I started to respond.
7. I carefully read all of the instructions on the test.
8. When I read each question, I tried to come up with the correct answer in my own words
before I read the response choices.
9. I watched for and marked trigger words like “never” or “always.”
10. I read all of the answer choices for each question.
11. I marked each answer choice with a symbol like +, -, or ?to help me with the process of
elimination.
12. I answered all of the questions I knew first.
13. I went back at the end to respond to questions I skipped because they were too hard.
14. I answered all of the questions on the test.
15. I used all of the time provided for the test.
16. I went back and checked my answers when I finished the test.
17. After I finished, I went back and re-read the instructions to make sure I had followed
them correctly.
18. After I finished, I went back to make sure I caught all of the trigger words in the test.

75

Appendix L: Test Scores
In PSY _______, you have had ___ exams so far this semester. Please provide your scores
below. Please provide how many points you earned and how many points the test was
worth.Please be honest so that my data will be as accurate is possible. I promise to keep your
scores confidential.
1. Exam 1 on ____ (date): _______ points out of _________ points
2. Exam 2 on ____ (date): _______ points out of _________ points
3. Exam 3 on ____(date): _______ points out of _________ points

76

REFERENCES

77

REFERENCES

Addis, D. R., Wong, A. T., &Schacter, D. L. (2007). Remembering the past and imagining the
future: Common and distinct neural substrates during event construction and elaboration.
Neuropsychologia, 45, 1363-1377.
Anderson, J. R., Reder, L. M., & Simon, H. A. (1996). Situated learning and
education.Educational Researcher, 25, 5-11.
Atance, C. M.,& O’Neill, D. K. (2001). Episodic future thinking.Trends in Cognitive Sciences, 5,
533-539.
Baldwin, T. T. (1992).Effects of Alternative modeling strategies on outcomes of interpersonalskills training.Journal of Applied Psychology, 77, 147-154.
Baldwin, T. T., & Ford, J. K. (1988). Transfer of training: A review and directions for future
research. Personnel Psychology, 41, 63-105.
Baldwin, T. T., Ford, J. K., &Blume, B. D. (2009). Transfer of training 1988-2008: An updated
review and agenda for future research. International review of industrial and
organizational psychology, 24, 41-70.
Bandura, A. (1982). Self-efficacy mechanism in human agency.American Psychologist, 37, 122147.
Bandura, A. (1991). Social cognitive theory of self-regulation.Organizational Behavior and
Human Decision Processes, 50, 248-287.
Barr, K., & Hall, C. (1992). The use of imagery by rowers.International Journal of Sport
Psychology, 23, 243-261.
Berntsen, D., Jacobsen, A. S. (2008). Involuntary (spontaneous) mental time travel into the past
and future.Consciousness and Cognition, 17, 1093-1104.
Bistline, J. L., Jaremko, M. E., &Sobleman, S. (1980). The relative contributions of covert
reinforcement and cognitive restructuring to test anxiety reduction.Journal of Clinical
Psychology, 36, 723-728.
Blume, B. D., Ford, J. K., Baldwin, T. T., & Huang, J. L. (2010). Transfer of training: A metaanalytic review. Journal of Management, 36, 1065-1105.
Brown, T. C. (2003). The effect of verbal self-guidance training on collective efficacy and team
performance.Personnel Psychology, 56, 935-964.

78

Brown, T. C., & McCracken M. (2010). Which goals should participants set to enhance the
transfer of learning from management development programmes?Journal of General
Management, 35, 27-44.
Brown, T., & Morrissey, L. (2004). The effectiveness of verbal self guidance as a transfer of
training intervention: Its impact on presentation performance, self efficacy and anxiety.
Innovations in Education and Teaching International, 41, 255-271.
Burke, L. A., & Hutchins, H. M. (2007). Training transfer: An integrative literature review.
Human Resource Development Review, 6, 263-296.
Calmels, C. d’Arripe-Longueville, F., Fournier, J. F., Soulard, A. (2003). Competitive strategies
among elite female gymnasts: An exploration of the relative influence of psychological
skills training and natural learning experiences. International Journal of Sport and
Exercise Psychology, 1, 327-352.
Cheng, E. W. L., &Hampson, I. (2008). Transfer of training: A review and new insights.
International Journal of Management Reviews, 10, 327-341.
Childers, T. L., Houston, M. J., & Heckler, S. E. (1985).Measurement of individual differences
in visual versus verbal information processing.Journal of Consumer Research, 12, 125134.
Cronbach, L. (1957). The two disciplines of scientific psychology.American Psychologist, 12,
671-684.
Cumming, J.,& Hall, C. (2002). Deliberate imagery practice: The development of imagery skills
in competitive athletes. Journal of Sports Sciences, 20, 137-145.
Cumming, J., Hall, C., Harwood, C., &Gammage, K. (2002). Motivational orientations and
imagery use: A goal profiling analysis. Journal of Sports Sciences, 20, 127-136.
Cumming, J. L., Nordin, S. M., Horton, R., & Reynolds, S. (2006). Examining the direction of
imagery and self-talk on dart-throwing performance and self-efficacy. The Sport
Psychologist, 20, 257-274.
Cumming, J. L., &Ste-Marie, D. M. (2000). The cognitive and motivational effects of imagery
training: A matter of perspective. The Sport Psychologist, 15, 276-287.
D’Argembeau, A. D., Renaud, O., & Van der Linden, M. (2011).Frequency, characteristics, and
functions of future-oriented thoughts in daily life.Applied Cognitive Psychology, 25, 96103.
Decker, P. J., & Nathan, B. R. (1985).Behavior modeling training: Principles and applications.
New York: Praeger.

79

Driscoll, R. (2007). Westside test anxiety scale validation.Education Resources Information
Center.
Driskell, J. E., Copper, C., & Moran, A. (1994). Does mental practice enhance performance?
Journal of Applied Psychology, 79, 481-491.
Driskell, J. E., Willis, R. P., & Copper, C. (1992). Effect of overlearning on retention.Journal of
Applied Psychology, 77, 615-622.
Durand, M., Hall, C., &Haslam, I. R. (1997). The effects of combining mental and physical
practice on motor skill acquisition: A review of the literature and some practical
implications. The Hong Kong Journal of Sports Medicine and Sports Science, 4, 36-41.
Feltz, D. L., & Landers, D. M. (1983). The effects of mental practice on motor skill learning and
performance: A meta-analysis. Journal of Sport Psychology, 5, 25-57.
Frese, M., Brodbeck, F., Heinbokel, T., Mooser, C., Schleiffenbaum, E., & Thiemann, P. (1991).
Errors in training computer skills: On the positive function of errors. Human-Computer
Interaction, 6, 77-93.
Goldstein, A.P., &Sorcher, M. (1974). Changing supervisor behavior. New York: Pergamon
Press.
Goldstein, I.,& Ford, J. K. (2002). Training in Organizations. Belmont, CA: Wadsworth.
Gollwitzer, P. M. (1993). Goal achievement: The role of intentions. European Review of Social
Psychology, 4, 141-185.
Gollwitzer, P. M., &Sheeran, P. (2006). Implementation intentions and goal achievement: A
meta-analysis of effects and processes. Advances in Experimental Social Psychology, 38,
69-119.
Goodman, J. S., & Wood, R. E. (2009). Faded versus increasing feedback, task variability
trajectories, and transfer of training. Human Performance, 22, 64-85.
Greeno, J. G. (1998). The situativity of knowing, learning, and research.American Psychologist,
53,5-26.
Hall, C. (2001). Imagery in sport and exercise. In R. N. Singer, H. Hausenblaus, & C. M. Janelle
(Eds.), Handbook of Sport Psychology (2nd ed., pp. 529-549). New York: Wiley.
Hall, C.,& Rodgers, W. (1989). Enhancing coaching effectiveness in figure skating through a
mental skills training program. The Sport Psychologist, 2, 142-154.

80

Hall, C., Rodgers, W., & Barr, K. (1990). The use of imagery by athletes in selected sports. The
Sport Psychologist, 4, 1-10.
Hall, C. R., Schmidt, D., Durands, M., &Buckolz, E. (1994).Imagery and motor skills
acquisition. In A. A. Sheikh & E. R. Korn (Eds.). Imagery in sports and physical
performance (pp. 121-134). Amityville, NY: Baywood.
Hardy, L. (1997). The Coleman Roberts Griffith address: Three myths about applied consultancy
work. Journal of Applied Sport Psychology, 9, 277-294.
Hardy, L.,& Callow, N. (1999). Efficacy of external and internal visual imagery perspectives for
the enhancement of performance of tasks in which form is important.Journal of Sport and
Exercise Psychology, 21, 95-112.
Harris, G., & Johnson, S. B. (1980). Comparison of individualized covert modeling, self-control
desensitization, and study skills training for alleviation of test anxiety.Journal of
Consulting and Clinical Psychology, 48, 186-194.
Harwood, C., Cumming, J., & Fletcher, D. (2004). Motivational profiles and psychological skills
use within elite youth sport. Journal of Applied Sport Psychology, 16, 318-332.
Harwood, C., Cumming, J., & Hall, C. (2003). Imagery use in elite youth sport participants:
Reinforcing the applied significance of achievement goal theory. Research Quarterly for
Exercise and Sport, 74, 292-300.
Hersen, M., Kazdin, A. E., Bellack, A. S., & Turner, S. M. (1979). Effects of live modeling
covert modeling, and rehearsal on assertiveness in psychiatric patients. Behavior
Research and Therapy, 17, 369-377.
Hinshaw, K. E. (1991). The effects of mental practice on motor skill performance: Critical
evaluation and meta-analysis. Imagination, Cognition, and Personality, 11, 3-35.
Holladay, C. L., & Quinones, M. A. (2003). Practice variability and transfer of training: The role
of self-efficacy generality. Journal of Applied Psychology, 88, 1094-1103.
Hutchins, H. M., & Burke, L. A. (2006). Has relapse prevention received a fair shake? A review
and implications for future transfer research. Human Resource Development Review, 15,
8-24.
Holmes, P. S.,& Collins, D. J. (2001). The PETTLEP approach to motor imagery: A functional
equivalence model for sport psychologists. Journal of Applied Sport Psychology, 13, 6083.
Ivancic, K., &Hesketh, B. (1995/1996). Making the best of errors during training. Training
Research Journal, 1, 103-125.

81

Kazdin, A. E. (1973). Covert modeling and the reduction of avoidance behavior.Journal of
Abnormal Psychology, 81, 87-95.
Kazdin, A. E. (1974a). Covert modeling, model similarity, and reduction of avoidance
behaviors.Behavior Therapy, 5, 325-340.
Kazdin, A. E. (1974b). Effects of covert modeling and model reinforcement on assertive
behavior.Journal of Abnormal Psychology, 83, 240-252.
Kazdin, A. E. (1975). Covert modeling, imagery assessment, and assertive behavior.Journal of
Consulting and Clinical Psychology, 43, 716-724.
Kazdin, A. E. (1976). Effects of coert modeling, multiple models, and model reinforcement on
assertive behavior.Behavior Therapy, 7, 211-222.
Kazdin, A. E. (1979). Effects of covert modeling and coding of modeled stimuli on assertive
behavior. Behavior Research and Therapy, 17, 53-61.
Kazdin, A. E. (1982). The separate and combined effects of covert and overt rehearsal in
developing assertive behavior.Behavior Research and Therapy, 20, 17-25.
Keith, N., &Frese, M. (2008). Effectiveness of error management training: A meta-analysis.
Journal of Applied Psychology, 93, 59-69.
Kirchhoff, B. A., & Buckner, R. L. (2006). Functional-anatomic correlates of individual
differences in memory.Neuron, 51, 263-274.
Kluger, A. N., &DeNisi, A. (1996). The effects of feedback interventions on performance: A
historical review, a meta-analysis, and a preliminary feedback intervention theory.
Psychological Bulletin, 119, 254-284.
Landsberger, H. A. (1958). Hawthorne Revisited. Ithaca, NY: Cornell University.
Larimer, M. E., Palmer, R. S., &Marlatt, G. A. (1999). Relapse prevention: An overview of
Marlatt’s Cognitive-Behavioral Model. Alcohol Research and Health, 23, 151-160.
Latham, G. P., &Saari, L. M. (1979). Application of social-learning theory to training
supervisors through behavioral modeling.Journal of Applied Psychology, 64, 239-246.
Lim, D. H., & Morris, M. L. (2006). Influence of trainee characteristics, instructional
satisfaction, and organizational climate on perceived learning and training transfer.
Human Resource Development Quarterly, 17, 85-115.
Locke, E. A., & Latham, G. P. (1990). A Theory of Goal Setting and Task Performance.
Edgewood Cliffs, NJ: Prentice Hall.

82

Marlatt, G. A., & Gordon, J. R. (1980). Determinants of relapse: Implications for the
maintenance of behavior change. In P. O. Davidson & S. M. Davidson (Eds.), Behavioral
Medicine: Changing Health Lifestyles (pp. 410-452). New York: Brunner/Mazel.
Marlatt, G. A., & Gordon, J. R. (1985). (Eds.) Relapse Prevention: maintenance Strategies in the
Treatment of Addictive Behaviors. New York: Guilford.
Marlatt, G. A. (1988). Research on behavioral strategies for the prevention of alcohol
problems.Contemporary Drug Problems, 15, 31-45.
Martin, K. A., Moritz, S. E., & Hall, C. (1999). Imagery use in sport: A literature review and
applied model. The Sport Psychologist, 13, 245-268.
Marx, R. D. (1982). Relapse prevention for managerial training: A model for maintenance of
behavior change. Academy of Management Review, 7, 433-441.
Meichenbaum, D. (1975). Enhancing creativity by modifying what subjects say to themselves.
American Educational Research Journal, 12, 129-145.
Meichenbaum, D. H. (1977).Cognitive behavior modification: An integrative approach. New
York: Plenum Press.
Millman, Z., & Latham, G. P. (2001). Increasing reemployment through training in verbal self
guidance. In M. Erez, U. Kleinbeck, & J. Thierry (Eds.), Work Motivation in the Context
of a Globalizing Economy (pp. 87-97). Mahwah, NJ: Erlbaum.
Munroe, K., Hall, C., Simms, S., & Weinberg, R. (1998).The influence of type of sport and time
of season on athletes’ use of imagery.The Sport Psychologist, 12, 440-449.
Murphy, D., Nordin, S., & Cumming, J. (2008). Imagery in sport, exercise, and dance. In T. S.
Horn (Ed.), Advances in Sport Psychology (pp. 297-324). Champaign, IL: Human
Kinetics.
Orbell, S., Hodgkins, S., &Sheeran, P. (1997). Implementation intentions and the theory of
planned behavior. Personality and Social Psychology Bulletin, 23, 945-954.
Riding, R. J., & Sadler-Smith, E. (1997). Cognitive style and learning strategies: Some
implications for training design. International Journal of Training and Development, 1,
199-208.
Rodgers, W., Hall, C., &Buckholz, E. (1991). The effect of an imagery training program on
imagery ability, imagery use, and figure skating performance. Journal of Applied Sport
psychology, 3, 109-125.
Rohrer, D., Taylor, K., Pashler, H., Wixted, J. T., &Cepeda, N. J. (2005). The effect of
overlearning on long-term retention.Applied Cognitive Psychology, 19, 361-374.

83

Sadler-Smith, E., & Smith, P. J. (2004).Strategies for accommodating individuals’styles and
preferences in flexible learning programmes.British Journal of Educational Technology,
35, 395-412.
Savoy, C., &Beitel, P. (1996). Mental imagery for basketball.International Journal of Sport
Psychology, 27, 454-462.
Schmidt, R. A., & Bjork, R. A. (1992). New conceptualizations of practice: Common principles
in three paradigms suggest new concepts for training. Psychological Science, 3, 207-217.
Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information
processing: II. Perceptual learning, automatic attending, and a general
theory.Psychological Review, 84, 127-190.
Short, S. E., Monsma, E. V., & Short, M. (2004). Is what you see really what you get? Athletes’
perceptions of imagery functions.The Sport Psychologist, 18, 341-349.
Singer, J. L. (1966). Daydreaming. New York: Random House.
Stevens, C. K., & Gist, M. E. (1997). Effects of self-efficacy and goal orientation training on
negotiation skill maintenance: What are the mechanisms? Personnel Psychology, 50,
955-978.
Szpunar, K. K. (2010). Episodic future thought. Perspectives on Psychological Science, 5, 142162.
Szpunar, K. K., Watson, J. M., & McDermott, K. B. (2007). Neural substrates of envisioning the
future.Proceedings of the National Academy of Sciences, USA, 104, 642-647.
Taylor, P. J., Russ-Eft, D. F., & Chan, D. W. L. (2005). A meta-analytic review of behavior
modeling training.Journal of Applied Psychology, 90, 692-709.
Tulving, E., & Thompson, D. M., (1971). Retrieval processes in recognition memory: Effects of
associative content. Journal of Experimental Psychology, 87, 116-124.
Tulving, E., & Thomson, D. M. (1973). Encoding specificity and retrieval processes in episodic
memory. Psychological Review, 80, 352-373.
Uhlemann, M. R., & Koehn, C. V. (1989). Effects of covert and overt modeling on the
communication of empathy.Canadian Journal of Counseling, 23, 372-381.
Vallis, T. M., & Bucher, B. (1986). Individual difference factors in the efficacy of covert
modeling and self-instructional training for fear reduction. Canadian Journal of
Behavioral Science, 18, 146-158.

84

Weinberg, R. (2008). Does imagery work? Effects on performance and mental skills.Journal of
Imagery Research in Sport and Physical Activity, 3, 1-21.
Wexley, K. N., & Baldwin, T. T. (1986). Posttraining strategies for facilitating positive transfer:
An empirical exploration. Academy of Management Journal, 29, 503-520.
White, A.,& Hardy, L. (1995). Use of different imagery perspectives on the learning and
performance of different motor skills. British Journal of Psychology, 86, 169-180.
White, A.,& Hardy, L. (1998). An in-depth analysis of the uses of imagery by high-level slalom
canoeists and artistic gymnasts.The Sport Psychologist, 12, 387-403.
Zielinski, J. J., & Williams, L. J. (1979). Covert modeling vs. behavioral rehearsal in the training
and generalization of assertive behaviors: A crossover design. Journal of Clinical
Psychology, 35, 855-863.

85