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Q (ab/V

This is to certify that the

thesis entitled

REGULATORY FOCUS AND EVALUATIVE FEEDBACK:
EFFECTS ON FEEDBACK SEEKING,
LEARNING, AND PERFORMANCE

presented by

Heather W. Dobbins

has been accepted towards fulfillment
of the requirements for

M.A. Psychology

degree in

 

 

jor prof so

V W
Date December 10. 2001

0-7639 MS U is an Affirmative Action/Equal Opportunity Institution

 

 

LIBRARY
Michigan State
University

 

 

 

PLACE IN REFURN Box to remove this checkout from your record.
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DATE DUE DATE DUE ' DATE DUE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6/01 c-JCIRC/Dataouepes-sz

 

REGULATORY FOCUS AND EVALUATIVE FEEDBACK: EFFECTS ON
FEEDBACK SEEKING, LEARNING, AND PERFORMANCE

By

Heather W. Dobbins

A THESIS

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

MASTER OF ARTS
Department of Psychology

2002

ABSTRACT

REGULATORY FOCUS AND EVALUATIVE FEEDBACK: EFFECTS ON
FEEDBACK SEEKING, LEARNING, AND PERFORMANCE

By

Heather W. Dobbins

Feedback improves learning, motivation, and performance (Ammons, 1956; Mento,
Steel, & Karren, 1987). Therefore, understanding what factors lead to feedback seeking
can suggest ways to leverage performance. The purpose of this study was to determine
the effects of regulatory focus and evaluative feedback on the type and amount of
feedback trainees seek, and the consequences of this feedback-seeking behavior for
learning, performance, and transfer. It was predicted that a promotion focus combined
with positive feedback, or a prevention focus combined with negative feedback would
lead to a more adaptive pattern of feedback seeking than any other combination of
regulatory focus and evaluative feedback. Although the hypotheses regarding the
interactive effects of regulatory focus and evaluative feedback on different types of
feedback seeking were not supported, regulatory focus had a direct effect on the overall
amount of feedback seeking, such that trainees with a promotion focus sought more
feedback than those with a prevention focus. The overall amount of feedback seeking had
a positive relationship with performance. A post-hoe model predicting overall feedback
seeking and performance from regulatory focus, evaluative feedback, and goal orientation

fit the data well. Implications and future directions for research are discussed.

ACKNOWLEDGMENTS

I would like to thank the chair of my committee, Dr. Steve Kozlowski, for the
many hours he spent helping me to clarify and organize my ideas. I would also like to
thank Dr. Rick Deshon and Dr. Kevin Ford for their helpful comments, and Brad Bell for

showing me the intricacies of TANDEM.

iii

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................ vii
LIST OF FIGURES ......................................................................................................... viii
INTRODUCTION ............................................................................................................... 1
The Changing Workplace ........................................................................................ 1
Regulatory Focus ..................................................................................................... 3
Effects of Regulatory Focus on Feedback Seeking ................................................. 3
Purpose .................................................................................................................... 4
Literature Review ............................................................................................................ 4
Feedback Seeking ........................................................................................................ 5
Definition ................................................................................................................. 5
Effects of Feedback Seeking on Learning and PerformanCe ................................... 6
Regulatory Focus ......................................................................................................... 9
Ideals and Oughts as Motivationally Distinct End States ........................................ 9
Effects of Regulatory Focus on Cognitive, Motivational and Affective Outcomes
............................................................................................................................... 14
Effects of Regulatory Focus on Feedback Seeking ............................................... 25
Interaction of Regulatory Focus and Feedback Sign ............................................. 26
Effects of Feedback Seeking on Learning and Performance ................................. 33
Hypotheses ..................................................................................................................... 33
Main Effects of Regulatory Focus on Feedback Seeking ...................................... 33
Main Effects of Regulatory Focus and Feedback Sign on Affect ......................... 34
Interactive Effects of Regulatory Focus and Feedback Sign on Feedback Seeking
............................................................................................................................... 36
Effects of Feedback on Affect, Cognitive Effort, Attention to Basic and Strategic
Elements and Self-Efficacy ................................................................................... 40
Effects of Affect, Cognitive Effort, Attention to Basic and Strategic Elements, and
Self-Efficacy on Learning and Performance During the Practice Trials ............... 43
Effects of Learning During the Practice Trials on Performance During the
Practice Trials ........................................................................................................ 45
Effects of Learning and Performance on the Practice Trials on Learning and
Performance on the Generalization Trial ............................................................... 46
METHOD .......................................................................................................................... 47
Design ............................................................................................................................ 47
Overview ................................................................................................................ 47
Simulation. ............................................................................................................. 47
Skill Components ................................................................................................... 48
Procedure ....................................................................................................................... 49
Participants ............................................................................................................ 49
Prior to the Experiment .......................................................................................... 50
Informed Consent .................................................................................................. 50

iv

Prior to Familiarization Trial ................................................................................. 50

Practice .................................................................................................................. 51
Generalization ........................................................................................................ 51
Manipulations ................................................................................................................ 52
Regulatory Focus ............................................ ' ....................................................... 52
Feedback Sign ........................................................................................................ 53
Measures ........................................................................................................................ 54
Demographics ........................................................................................................ 54
Ability .................................................................................................................... 54
Goal Orientation Measures .................................................................................... 54
Trait Regulatory Focus Measures .......................................................................... 55
Affect Measure ...................................................................................................... 56
Feedback Seeking .................................................................................................. 58
Self-Efficacy .......................................................................................................... 6O
Perceived Cognitive Effort .................................................................................... 61
Basic and Strategic Task Knowledge .................................................................... 62
Study Sequence ...................................................................................................... 62
Basic and Strategic Performance ........................................................................... 63
RESULTS .......................................................................................................................... 64
Analysis Plan ................................................................................................................. 64
Correlations .................................................................................................................... 64
Correlations Between IV’s and Feedback Seeking ............................................... 64
Correlations Between IV’s and Affect .................................................................. 65
Correlations Between the IV’s and Outcomes ....................................................... 65
Correlations Between Feedback Seeking and the Mediators ................................ 65
Correlations Between the Mediators and Outcomes ............................................. 66
RM-MANCOVA ........................................................................................................... 67
Hypothesis Tests ............................................................................................................ 68
Main Effects of Regulatory Focus on Feedback Seeking ...................................... 68
Main Effects of Regulatory Focus on Affect ......................................................... 69
Interactive Effects of Feedback Sign and Time on Affect .................................... 69
Interactive Effects of Regulatory Focus and Feedback Sign on Feedback Seeking
............................................................................................................................... 70
Interactive Effects of Regulatory Focus, Feedback Sign, and Time on Feedback
Seeking .................................................................................................................. 71
, Effects of Feedback Seeking on Affect, Self-Efficacy, Studying, and Cognitive
Effort ...................................................................................................................... 72
Effects of Affect, Cognitive Effort, Studying, and Self-Efficacy on Knowledge
and Performance During the Practice Trials .......................................................... 75
Effects of Learning During the Practice Trials on Performance During the
Practice Trials ........................................................................................................ 77
Effects of Learning and Performance on the Practice Trials on Learning and
Performance on the Generalization Trial ............................................................... 78
Exploratory Analyses ..................................................................................................... 79
Explanation for Lack of Support of Hypotheses ................................................... 79

V

Reduced Model ...................................................................................................... 81

DISCUSSION .................................................................................................................... 85
Summary of Hypothesis Tests ....................................................................................... 85
F inal Model .................................................................................................................... 86
Effects of Regulatory Focus ...................................................................................... 86
Effects of Evaluative Feedback ................................................................................. 87
Self-Efficacy .......................................................................................................... 87
Dejection ................................................................................................................ 88
Effects of Goal Orientation ........................................................................................ 88
Self-Efficacy .......................................................................................................... 88
Affect ..................................................................................................................... 90
Relationship Between Self-Efficacy and Dejection .................................................. 91
Effects of Self-Efficacy ............................................................................................. 92
Effects of Dejection ................................................................................................... 92
Implications for Embedded Training Systems .......................................................... 93
Future Research ............................................................................................................. 95
Methodological Issues ............................................................................................... 95
Order Effects .......................................................................................................... 95
Measures ................................................................................................................ 96
Manipulating Evaluative Feedback ..................................................................... 101
Exploring the Similarities Between Regulatory Focus and Goal Orientation ......... 102
Approach vs. Avoidance ...................................................................................... 102
Antecedents .......................................................................................................... 103
Consequences ...................................................................................................... 105
Propositions About the Relationship Between Regulatory Focus and Goal
Orientation ........................................................................................................... 106
Conclusions .................................................................................................................. 107
APPENDD( A .................................................................................................................. 117
APPENDIX B .................................................................................................................. 121
APPENDIX C .................................................................................................................. 122
APPENDIX D .................................................................................................................. 123
APPENDIX E .................................................................................................................. 131
APPENDIX F .................................................................................................................. 141
APPENDIX G .................................................................................................................. 161

vi

LIST OF TABLES

Table 1: Examples of Feedback Types ...................................................... 141
Table 2: Reduced Correlation Matrix ........................................................ 142
Table 3: Full Correlation Matrix .............................................................. 145
Table 4: Analysis Summary ................................................................... 161

vii

LIST OF FIGURES
Figure l: Heuristic Model ....................................................................... 167
Figure 2: Confirmatory Factor Analysis of Basic and Strategic Knowledge Scales... .168
Figure 3: Path Diagram for Reduced Model .................................................. 169

Figure 4: Path Diagram for Final Model ...................................................... 170

viii

INTRODUCTION

The Changing Workplace

The cognitive demands on employees are increasing (Wall & Jackson, 1995;
Smith, Ford, & Kozlowski, 1997). Rapidly changing technology (Hage, 1995;
Kozlowski, Toney, Mullins, Bell, & Weissbein, 1998) and the introduction of new
manufacturing initiatives such as JIT and TQM (Tailby & Turnbull, 1987; Gryna, 1988)
require workers to be continuously adaptive and to engage in the solution of complex
problems. A consequence of this increasing demand for adaptability is that organizations
are searching for ways to incorporate training opportunities into the workplace in order to
make training available just in time (Kozlowski et al., 2001). An efficient way to
incorporate training into the workplace is to embed training in the technology that
employees use to perform their jobs. The increasing use of embedded training in
organizations raises the question of how trainees interact with the feedback that is
available in a technological system.
Importance of Feedback Seem

It is important to understand how trainees interact with feedback in embedded
training systems, because according to control theory (e. g. Carver & Scheier, 1998),
feedback is central to self-regulation, learning, and performance. Feedback allows people
to compare their current status with their standards. If this comparison process yields a
sufficiently large negative discrepancy, it captures people’s attention (Lord & Levy,
1994) and motivates them to increase their effort, alter their strategy, or (less commonly)
decrease their standards (Carver & Scheier, 1998). If people are unable to compare their

current status with their standards, they have no basis to alter their effort, strategy, or

standards. The positive effects of feedback on learning and performance proposed by
control theory are generally supported in the literature (Ammons, 1956; Mento, Steel, &
Karren, 1987). Although Kluger and DeNisi (1996) argue that feedback does not always
lead to increased performance, their meta-analysis indicates that feedback interventions
improve performance on average ((1 = .41).

The importance of feedback for self-regulation and learning raises the question of
how much and what type of feedback trainees using embedded training systems seek. The
learner control literature holds that learners often make sub-optimal decisions regarding
their own learning strategies, such as quitting training too early (Tennyson, Tennyson, &
Rothen, 1980), or practicing skills that they have already mastered (Tennyson, 1980;
1981). The decisions that trainees make regarding how many practice episodes to engage
in and how much time to spend practicing have a large impact on knowledge acquisition
(Brown, 2001). Given that trainees using embedded training systems will have control
over how much and what type of feedback they seek, the decisions that trainees make
will have significant consequences for their learning outcomes.

Self-Regulatory Perspective on Feedback Seeking

Very little research has examined trainees’ feedback-seeking choices in embedded
training systems. Instead, the majority of the feedback-seeking literature focuses on the
perceived self-presentational costs and benefits of seeking feedback in a social
environment (e.g. Ashford & Northcrafi, 1992). This literature is of limited relevance to
developing an understanding of the choices people make with regards to seeking different
types of computer-based feedback. This study adopts a self-regulatory perspective rather

than the typical expected utility perspective in order to investigate feedback seeking,

because the self-regulatory perspective enables predictions regarding how people’s
regulatory focus influences what types of feedback they seek.
Re ato Focus

An important construct related to learning and adaptability is regulatory focus, a
motivational variable relating to the way people approach pleasure and avoid pain
(Higgins, 1997). Individuals with a promotion focus self-regulate around ideal self-
guides, whereas those with a prevention focus self-regulate around ought self-guides. An
ideal self-guide is a mental representation of the way a person would like to be, or the
way others would like him/her to be (Higgins, as cited in Higgins, Klein, & Strauman,
1985). An ought self-guide is a mental representation of the way a person should be,
according to oneself or others. Individuals with a promotion focus are motivated to
reduce the discrepancy between their actual self-guides and their ideal self-guides,
whereas individuals with a prevention focus are motivated to reduce the discrepancy
between their actual self-guides and their ought self-guides (Higgins, 1997). Kluger and
Van Dijk (2001) have argued that regulatory focus is equivalent to goal orientation, a
motivational variable that predicts behavior in achievement situations. In order to explore
the similarities between regulatory focus and goal orientation, their antecedents and
consequences will be compared in the discussion section.
Effects of Reguirtorv Focu_s on Feedbag Seefing

Regulatory focus is expected to affect the way individuals approach learning
situations through its impact on their motivational and self-regulatory processes.
However, the literature has not directly addressed the impact of regulatory focus in

learning situations. Particularly, the literature has not addressed the impact of regulatory

focus on feedback seeking, and the effect of regulatory focus through feedback seeking
on learning and performance.
EMS;

Therefore, the purpose of this study is to determine the effects of regulatory focus
on the type and amount of feedback trainees seek, and the consequences of this feedback-
seeking behavior for learning, performance, transfer, and adaptability. The following
sections review the literature on adaptability, feedback and regulatory focus, and then
specify this study’s hypotheses.

Literature Review

As discussed in the introduction, adaptability is becoming a more important skill
in the workplace. This section will define adaptability and discuss its antecedents,
including regulatory focus and feedback seeking.

Adaptability is evidenced when an individual responds successfully to changes in
the nature of the trained task (Smith, Ford, & Kozlowski, 1997). Adaptability is an
essential skill for generalization, which is one of the two types of training transfer
identified by Baldwin and Ford (1988). Generalization is the adaptation of trained
knowledge and skills to a more difficult and complex task situation. The other type of
training transfer identified by Baldwin and Ford (1988) is maintenance, which is the
reproduction of trained skills in a new setting. Maintenance is a necessary but not
sufficient condition for generalization, since a trainee must be able to reproduce the
trained skills in a new setting before adapting them to a more difficult task situation

(Kozlowski et al., 2001).

Since adaptability is an important training outcome, it is useful to consider what
the antecedents of adaptability are. Declarative knowledge and past training performance
influence adaptive performance, because they are necessary preconditions for
maintenance, which is a necessary precondition for generalization (Kozlowski et al.,
2001). In addition, Smith et a1. indicate that adaptability is facilitated by training design
elements that engage the learner’s self-regulatory processes. Regulatory focus influences
the learner’s self-regulatory processes by influencing feedback seeking and type of
feedback sought. Therefore, regulatory focus has the potential to leverage adaptability.
Since feedback seeking influences training outcomes such as learning, performance, and

adaptability, the next section will review the literature on feedback seeking.

FeedbaLk Seem

Definition

For the purposes of this study, feedback seeking is defined as actively requesting
information regarding one’s performance. This definition is broad enough to include
seeking feedback from a computer, but does not include feedback seeking using the
monitoring strategy, which is defined by Ashford and Cummings (1983) as observing the
situation and the behaviors of other actors for cues useful as feedback. The monitoring
strategy was excluded from this study’s definition of feedback seeking because using the
monitoring strategy yields much more ambiguous information than direct inquiry
(Ashford & Cummings, 1983), and therefore is of limited usefulness to employees who

are trying to adapt to a rapidly changing environment.

Effects of Feedback Seeking on Learning_and Performance

Feedback has a positive effect on learning, motivation, and performance
(Ammons, 1956; Mento, Steel, & Karren, 1987). Although Kluger and DeNisi disputed
Ammons’ claim that feedback almost universally enhances learning, citing examples of
when feedback can be detrimental to learning, they acknowledge that in certain
situations, feedback can yield large performance increases, possibly greater than one SD.
Given the positive effects of feedback on motivation, learning, and performance, it is not
surprising that employees’ feedback seeking behaviors result in learning and performance
improvements, for several reasons. First, employees who seek feedback are likely to
receive feedback more often, and more frequent feedback leads to greater interest in and
satisfaction with the task, better performance, and higher levels of aspiration (Cook,

196 8).

Second, employees can seek the types of feedback that they find most useful, as
opposed to passively receiving whatever feedback others choose to give them. Gioia and
Sims (1986) found that face-to-face conversations between managers and subordinates
are characterized by reciprocal task-related information exchange, which implies that
subordinates have enough influence over their conversations with their managers to get
the kind of feedback they want. For example, employees can seek feedback on certain
aspects of their jobs that they perceive as ambiguous, or they can seek negative feedback,
or specific feedback.

Supervisors are often reluctant to give negative feedback (Larson, 1986), even
though negative feedback highlights discrepancies between current performance and

reference values, which leads to behavior changes designed to reduce these discrepancies

(Carver & Schier, 1982). Larson (1986) speculates that reluctance to give negative
feedback may be due to concern with subordinates’ feelings, or because supervisors want
to avoid an unpleasant interaction. If supervisors do not perceive organizational pressure
to give negative feedback to their subordinates, they may simply give less frequent
feedback to their subordinates who are performing poorly. Since frequent feedback is
associated with learning and performance increases, this reluctance to give negative
feedback may result in a downward spiral of poor performance for employees whose
initial performance is below average. Alternatively, if supervisors do experience
organizational pressures to give feedback, they may procrastinate giving the feedback or
distort the feedback so that it seems more positive. Since feedback given immediately
after a performance episode is more effective than delayed feedback (Gibson, 2000), and
false feedback is unlikely to be helpful, supervisors’ reluctance to give negative feedback
is likely to be harmful to their poorly performing subordinates’ learning and performance.
However, if subordinates actively seek negative feedback, this may alleviate supervisors’
reluctance to give negative feedback by reassuring them that their subordinates’ feelings
will not be hurt by the negative feedback, and that the interaction will not be unpleasant.
Supervisors may then give more honest, timely negative feedback, which would enable
their subordinates to pinpoint the areas of their performance where they most need
improvement. Seeking negative feedback increases people’s understanding of how their
work is evaluated by feedback sources (Ashford & Tsui, 1991), and negative feedback is
necessary for corrective action (Ilgen & Davis, 2000)

While poorly performing employees may be able to improve the frequency and

accuracy of the feedback they receive by actively seeking negative feedback, employees

with any level of performance may improve the utility of the feedback they receive by
seeking specific feedback. People prefer specific feedback, whether specificity is
operationalized as more detailed information related to the task itself (Ilgen, Mitchell, &
Fredrickson, 1981), or information from which causal inferences can be made, such as
information about the feedback recipient’s past performance or the performance of peers
(Liden & Mitchell, 1985). People’s preference for specific feedback is functional,
because feedback specificity improves motivation (Bandura & Cevone, 1983; 1986) and
performance (Kopehnan, 1986; Earley, 1988). The relationship between feedback
specificity and performance is mediated by planning (Earley, 1988). Kopelrnan
speculated that the reason performance improvements result from specific feedback
might be because the additional information causes people to re-evaluate their task
strategies. Consistent with this idea, Earley (1988) found that individuals who receive
more specific feedback engage in more planning, resulting in performance improvements.
Employees may also benefit from seeking feedback because they may be more
likely to attend to feedback (and implement changes based on the feedback) when they
seek it themselves. The employee participation literature is relevant to this issue. Wagner
(1994) conducted a meta-analysis, which found that the sharing of influence between
superiors and subordinates has a positive effect on performance and job attitudes.
Although Wagner concluded that the average effect size is too small to have utility, other
authors have argued that employee participation does have utility. For example, Cotton
(1995) argued that Wagner’s small effect sizes were due to low power and a narrow
definition of employee participation, which led him to analyze predominantly short-term

lab studies of participation. The positive effect of employee participation on performance

and job attitudes suggests that employees may be more willing to implement changes
based on feedback when they seek the feedback themselves.

So, employees who seek feedback are likely to increase the overall amount of
feedback that they receive, to increase certain kinds of feedback that they feel will benefit
them the most, and to increase their acceptance of the feedback. Therefore, they are
expected to learn more and perform better than employees who do not seek feedback.
The next section discusses regulatory focus, which is expected to affect learning and
performance through its impact on feedback seeking.

Regulatog Focus

13315 and Ough;tsa;s Motivationally Distinct End States

 

Since individuals with a promotion focus self-regulate around ideal self-guides,
and those with a prevention focus self-regulate around ought self-guides, the first step in
demonstrating that regulatory focus has a critical impact on feedback seeking is to
demonstrate that ideal self-guides and ought self-guides are motivationally distinct. This
section will briefly explain self-discrepancy theory, then review evidence that ideal and
ought self-guides are motivationally distinct.

Higgins (1987) proposed self-discrepancy theory, which holds that everyone has
both ideal and ought self-guides, but that people differ in the extent to which their actual
selves differ from their ideal and ought selves (ideal/ought discrepancies). Situations also
differ in the extent to which they tend to activate these ideal/ought discrepancies.
Gain/non-gain situations and situations that prime nurturance needs tend to activate ideal
discrepancies, whereas non-loss/loss situations and situations that prime security needs

tend to activate ought discrepancies.

Higgins proposed that ideal and ought self-guides are motivationally distinct.
Specifically, he proposed that these self-guides would be differentially related to
sensitivity to positive vs. negative events, tendencies to use approach vs. avoidance
strategies, and tendencies to experience certain emotions.

SLsitiviQ/ to events. Higgins and Tykocinski (1992) hypothesized that
individuals with predominant ideal self-discrepancies tend to focus on positive events,
while individuals with predominant ought self-discrepancies tend to focus on negative
events. This is because individuals with predominant ideal self-discrepancies focus on
approaching positive outcomes and avoiding the absence of positive outcomes, while
those with predominant ought self-discrepancies focus on avoiding negative outcomes
and approaching the absence of negative outcomes. Higgins and Tykocinski tested this
prediction by first measuring participants’ chronic ideal and ought self-discrepancies
using the Selves Questionnaire. This questionnaire asks participants to list a number of
their ideal self-guide attributes and ought self-guide attributes, and to rate to what extent
they possess each of those self-guide attributes. Then, participants who had either high
ideal and low ought self-discrepancies, or vice versa were selected. These participants
read a story, then wrote down as much of the story as they could remember. As predicted,
individuals with high ideal and low ought self-discrepancies remembered more positive
events in the story, while those with high ought and low ideal self-discrepancies
remembered more negative events.

Apple—ash vs. avoidance strategie_s. Just as ideal vs. ought self-guides lead to
sensitivity to positive vs. negative events, these self-guides also lead to tendencies to use

approach vs. avoidance strategies. Although self-regulation around either ideals or oughts

10

 

leads to approaching desired end states at the system level, individuals still have approach
or avoidance inclinations at the strategic level. Higgins, Roney, Crowe, and Hymes
(1994) hypothesized that individuals with predominant ideal self-guides would prefer
approach strategies, while individuals with predominant ought self-guides would prefer
avoidance strategies. They tested this hypothesis empirically in two studies. In study 1,
participants’ ideal or ought self-guides were primed by asking them to either describe
how their hopes have changed over time, or how their obligations have changed over
time. Then, participants read scenarios, and later recalled as many of the scenarios as they
could. As predicted, individuals whose ideal self-guides had been primed recalled more
scenarios involving approaching matches to desired end states, while those whose ought
self-guides had been primed recalled more scenarios involving avoiding mismatches to
desired end states.

In a second study, Higgins et al. (1994) tested whether the relationship between
self-guides and preferences for approach vs. avoidance strategies would hold for chronic
self-guide discrepancies. Participants’ chronic actual-ideal discrepancies and actual-ought
discrepancies were measured using the Selves Questionnaire, and then participants
completed a questionnaire assessing their strategies for friendship. Consistent with
predictions, individuals with predominant ideal self-guides were more likely to endorse
friendship strategies involving approaching matches to desired end states, whereas
individuals with predominant ought self-guides were more likely to endorse friendship
strategies involving avoiding mismatches to desired end states.

Emotions. In addition to influencing their use of approach vs. avoidance

strategies, individuals’ self-guides can influence their emotions. Higgins et al. (1986)

ll

proposed that individuals with predominant actual-ideal discrepancies would tend to feel
dejection-related emotions such as sadness and disappointment, whereas individuals with
predominant actual-ought discrepancies would tend to feel agitation-related emotions
such as anxiety and uneasiness. Higgins et al. tested this hypothesis by first using the
Selves Questionnaire to select individuals with either actual-ideal and actual-ought
discrepancies, or neither type of discrepancy. Participants received either ideal or ought
priming. In the ideal priming condition, participants were asked to describe their hopes
and ideals, and how they had changed over time. In the ought priming condition,
participants were asked to describe their duties and obligations, and how they had
changed over time. Participants’ mood was measured before and after they received
either ideal or ought priming. Individuals with both types of discrepancy experienced an
increase in dejection-related emotions with ideal priming, and an increase in agitation-
related emotions with ought priming. Those with neither type of discrepancy did not
experience a change in emotions as a result of the priming.

Straurnan and Higgins (1987) replicated and extended the above study. They
proposed that priming a desirable characteristic in an individual’s own self-guide
activates that self-guide and evokes the emotions associated with discrepancies to that
self-guide. They first measured participants’ ideal and ought discrepancies using the
Selves Questionnaire, then they primed a characteristic in either an ideal or ought self
guide by having participants complete sentences in the form of “An X person __,”
where X was a trait, such as “friendly” or “intelligent.” There were three priming

conditions. In the nonrnatching priming condition, X was a characteristic in the

individual’s self-guide, but not actual self. In the mismatching condition, X was a

12

characteristic in the individual’s self guide, and the actual self was discrepant from it. In
the “yoked mismatching” priming, X was not a characteristic in the individual’s self
guide or actual self, but was a characteristic in another subject’s self-guide. As predicted,
only in the mismatching condition were differences found between the emotions
experienced by individuals with idea] or ought self-discrepancies. In the mismatching
condition, individuals with ideal self-discrepancies experienced a dej ection-related
syndrome (i.e. increased dejected mood, lowered standardized skin conductance
amplitude, and decreased total verbalization time), whereas individuals with ought self-
discrepancies experienced an agitation-related syndrome (i.e. increased agitated mood,
raised standardized skin conductance amplitude, increased total verbalization time).
Strauman (1990) extended the above research by exploring whether priming
characteristics of individuals’ own self-guides activates memories with certain emotional
content. Specifically, he hypothesized that priming characteristics of individuals’ own
ideal self-guides activates memories with dejection content, while priming characteristics
of individuals’ own ought self-guides activates memories related to anxiety. To test this
hypothesis, Strauman measured participants’ ideal and ought discrepancies using the
Selves Questionnaire, then gave participants either mismatching or yoked mismatching
priming. Then, participants were asked to report childhood memories. Participants were
more likely to report memories with dej ection-related content when they were primed
with discrepancies to their own ideal self-guides than when they were primed with
discrepancies to their own ought self-guides. They were more likely to report memories
with agitation-related content when they were primed with discrepancies to their own

ought self-guides than when they were primed with discrepancies to their own ideal self-

13

guides. Participants who were primed with discrepancies to other subjects’ self-guides
reported few memories with either dejection or agitation content.

The above studies represent considerable evidence that self-regulation around
ideal self-guides is motivationally distinct from self-regulation around ought self-guides.
Actual-ideal discrepancies lead to a focus on positive outcomes, approach strategies, and
dejection-related emotions. In contrast, actual-ought discrepancies lead to a focus on
negative outcomes, avoidance strategies, and agitation-related outcomes. Given that self-
regulation around ideal and ought self-guides is motivationally distinct, the next section
explores the effects of regulatory focus on cognition, motivation, and emotion, and
explains how regulatory focus affects feedback seeking, learning, and performance.
Effects of Regulatory Focus on Cogrritive. Motivational and Affective Outcomes

This discussion of regulatory focus is closely tied to the previous discussion of
ideal and ought self-guides, because promotion focus is self-regulation around ideal self-
guides, whereas prevention focus is self-regulation around ought self-guides (Higgins,
1997). The effects of regulatory focus are categorized in the literature according to three
latent constructs: cognition, emotion, and motivation. Each of these categories will be
discussed with respect to how regulatory focus influences the learning process in general,
and feedback seeking in particular.

Cogm'tion. Regulatory focus affects the learning process by influencing what
aspects of the situation individuals attend to, and the strategies they use to accomplish
goals. Stepper, Strack, and Higgins (as cited in Higgins, 1998) hypothesized that
individuals with a promotion focus are sensitive to the presence or absence of positive

outcomes, while those with a prevention focus are sensitive to the presence or absence of

14

negative outcomes. They gave participants the cover story that they were measuring the
effects of physical and mental exercise on saliva. They first had participants ride a
stationary bike with either a sweet or bitter cotton ball in their mouths. Then, they had
participants read a story with a cotton ball in their mouths that was either the same flavor
as the cotton ball they had before, or that had no flavor. Participants who were given the
sweet cotton ball both times were assumed to experience promotion-working (the feeling
of having succeeded in attaining their ideals). Those who were given the bitter cotton ball
both times were assumed to experience prevention-not working (the feeling of having
failed to reach one’s oughts). Those who were given the sweet cotton ball, then the
neutral cotton ball were assumed to experience promotion-not working (the feeling of
having failed to reach one’s ideals). Those who were given the bitter cotton ball, then the
neutral cotton ball were assumed to experience prevention-working (the feeling of having
succeeded in meeting one’s obligations). The rationale for this manipulation was that a
sweet cotton ball is a positive outcome, a bitter cotton ball is a negative outcome, and an
unflavored cotton ball is a neutral outcome, so individuals who are focused on positive
outcomes (the first sweet cotton ball) and continue to experience positive outcomes (the
second sweet cotton ball) experience promotion-working.

The story that participants read while they had the second cotton ball in their
mouths contained vignettes representing the presence of positive outcomes, the presence
of negative outcomes, the absence of positive outcomes, and the absence of negative
outcomes. As predicted, participants in the promotion-working condition remembered
more of the vignettes representing the presence of positive outcomes, those in the

promotion-not working condition remembered more of the vignettes representing the

15

absence of positive outcomes, those in the prevention-working condition remembered
more of the vignettes representing the presence of negative outcomes, and those in the
prevention-not working condition remembered more of the vignettes representing the
absence of negative outcomes. So, regulatory focus influences what aspects of the
situation individuals attend to. Since promotion-focused individuals attend to the
presence and absence of positive outcomes, it is likely that they would seek feedback
about positive outcomes. Similarly, since prevention-focused individuals attend to the
presence and absence of negative outcomes, they would likely seek feedback about
negative outcomes.

In addition to influencing which aspects of the situation people focus on,
regulatory focus also affects what strategies people use to accomplish goals. In order to
explain Crowe and Higgins’ (1997) predictions regarding how regulatory focus
influences strategies, it is first necessary to define signal detection terminology. A “hit” is
when an individual correctly identifies a stimulus as belonging to a certain category. A
“correct rejection” is when an individual correctly identifies a stimulus as not belonging
to a certain category. An “error of omission” is when an individual incorrectly identifies a
stimulus as not belonging to a certain category, and an “error of commission” is when an
individual incorrectly identifies a stimulus as belonging to a certain category.

Crowe and Higgins (1997) hypothesized that individuals with a promotion focus
would make decisions that would maximize their hits and minimize their errors of
omission. They predicted that individuals with a prevention focus would make decisions
that would maximize their correct rejections and minimize their errors of commission.

This is because promotion-focused individuals are eager to attain their ideals, and are

16

willing to take risky actions in order to maximize their accomplishments, while
promotion-focused individuals are vigilant to meet their obligations, and are less willing
to take risky actions for fear of failing to meet the minimal requirements of their
obligations. Although prevention-focused individuals are less willing to take risky actions
(relative to promotion-focused individuals) they are more willing to accept the risks of
inaction.

In keeping with the risk preferences of individuals with different regulatory foci,
Crowe and Higgins (1997) predicted that promotion-focused individuals would persist
longer and perform better than prevention-focused individuals on difficult tasks, or after
experiencing failure. In addition, they predicted that individuals with a prevention focus
would be more repetitive, and generate fewer alternatives than those with a promotion
focus. Persistence and generating more alternatives are consistent with a promotion focus
because the more alternatives that are generated, the greater the possible number of hits
and the smaller the possibility that correct responses would be omitted. Withdrawal and
generating fewer alternatives are consistent with a prevention focus because the fewer
alternatives are generated, the smaller the possibility of committing an error, and the
greater the opportunity to self-censor any incorrect responses.

In their first study, participants filled out two questionnaires weeks before the
experiment. One of the questionnaires asked participants to rate how much they liked 16
tasks. The tasks that each participant liked most and least were used in the experimental
framing, which is described later. The other questionnaire was the Selves Questionnaire,
which was used to control for participants’ ideal and ought self-discrepancies.

Participants were given one of five framing manipulations before the tasks began. In four

17

of these five flaming manipulations, participants were told that they would do either their
most desired task or their least desired task at the end of the study, depending on their
performance on the experimental tasks. In the fifth flaming condition, participants were
told that they would be randomly assigned to either their most preferred or their least
preferred task. The four contingent framing conditions were promotion working,
promotion not working, prevention working, and prevention not working. In the
promotion working condition, participants were told that if performed well on the
exercises, they would get to do their desired task instead of the other task. In the
promotion not working condition, they were told that if they performed poorly on the
exercises, they would not get to do their desired task, but would have to do the other task
instead. In the prevention working condition, participants were told that as long as they
did not perform poorly, they would not have to do the undesired task, but would do the
other task instead. In the prevention not working condition, participants were told that if
they performed poorly, they would have to do the disliked task instead of the other task.
There were six experimental tasks. For the first task, participants listed
characteristics of eight objects for 1.5 minutes per object. The second task was an easy
trial and a difficult trial of counting backwards. The third task was sorting objects into
categories. Participants could use as many categories as they wanted, as long as they
categorized objects consistently. The fourth task was an embedded figures task, in which
the fifth figure was especially difficult. Participants had the option of quitting any
embedded figure and moving on to the next one. The fifth task was an anagrams task, in
which the third anagram was unsolvable. Participants were given as much time as they

wanted to work on each anagram.

18

Findings were consistent with the hypothesis that promotion-focused individuals
would be more concerned with gaining hits and avoiding missing opportunities to gain
hits, while prevention-focused individuals would be more concerned with errors of
commission and avoiding the mistake of producing them. Promotion-focused individuals
persisted longer and performed better than prevention-focused individuals on difficult
tasks, such as the difficult version of the counting backwards task and the difficult
embedded figure, or after experiencing failure, as in the anagrams task. In addition,
individuals with a prevention focus were more repetitive, and generated fewer
alternatives than those with a promotion focus in the characteristic listing task and the
sorting task.

In a second study, Crowe and Higgins (1997) used a signal detection task to test
the hypothesis that promotion-focused individuals would try to maximize hits and
minimize errors of omission, and prevention-focused individuals would try to maximize
correct rejections and minimize errors of omission. Once again, participants filled out the
task rating questionnaire and the selves questionnaire before the experimental session.
During the experimental session, participants filled out a mood questionnaire, then were
given one of the same five flaming manipulations used in the study above. Then
participants performed a recognition memory task, where they were shown a list
nonsense words, then were shown another list of nonsense words, and had to answer
“yes” or “no” to whether or not they had seen the words previously. Participants
subsequently filled out another mood questionnaire.

Consistent with predictions, individuals with a promotion focus were more likely

to answer “yes” to the question of whether they had seen the stimulus previously, while

19

individuals with a prevention focus were more likely to answer “no.” This is because
maximizing hits and minimizing errors of omission requires answering “yes,” while
maximizing correct rejections and minimizing errors of commission requires answering
“no.” In addition, individuals with a prevention focus had longer response latencies than
those with a promotion focus, which is consistent with the idea that promotion-focused
individuals are vigilant against making mistakes.

The results of the above studies support the idea that regulatory focus influences
the strategies people use to accomplish goals. These findings are relevant to feedback
seeking because individuals with a promotion focus attend to the presence/absence of
positive outcomes, which means that they would probably seek feedback about aspects of
the task they have performed correctly rather than aspects of the task they have
performed incorrectly. In contrast, individuals with a prevention focus attend to the
presence/absence of negative outcomes, which means that they would probably seek
feedback about aspects of the task they have performed incorrectly rather than aspects of
the task they have performed correctly. In addition, the above studies found that
individuals with a promotion focus try to maximize their hits and minimize their errors of
omission, while those with a prevention focus try to maximize their correct rejections and
minimize their errors of commission. Therefore, individuals with a promotion focus
would likely seek feedback about hits and errors of omission, while those with a
prevention focus would likely seek feedback about correct rejections and errors of
commission. The next section reviews the effects of regulatory focus on motivation, and

the implications of this for feedback seeking.

20

Motivation. One of the studies reviewed above has implications for the impact of
regulatory focus on motivation. Crowe and Higgins (1997) found that individuals with a
promotion focus performed better than those with a prevention focus on solvable
anagrams, and persisted longer on unsolvable anagrams. This is because individuals with
a promotion focus are eager to reach their hopes and ideals, and those with a prevention
focus are vigilant against failing to meet their obligations. Hopes and ideals function as
maximal goals, meaning that individuals try to reach them to the greatest extent possible,
and duties and obligations function as minimal goals, meaning that once individuals have
attained them to a minimal degree, they no longer exert additional effort (Brendl &
Higgins, 1996). Individuals with a prevention focus may be more likely to quit early on
unsolvable anagrams for fear of making mistakes (errors of commission), while those
with a promotion focus are likely to persevere in order to improve their chances of
getting correct answers (hits).

Roney, Higgins, and Shah (1995) also provide evidence that individuals with a
promotion focus persevere for longer on a challenging task than those with a prevention
focus. In two studies, they manipulated regulatory focus by flaming the situation in gain
vs. non-gain terms or loss vs. non-loss terms. Then, they had participants do an anagrams
task that included some unsolvable anagrams. Individuals with a prevention focus
persisted for longer on the unsolvable anagrams than those with a prevention focus.

The effects of regulatory focus on motivation are relevant to the effects of
regulatory focus on feedback seeking because individuals who are more motivated are
more likely to seek feedback about hits and errors of omission than they are to seek

feedback about correct rejections and errors of omission. This is because individuals who

21

are highly motivated have maximal goals, and those with maximal goals are interested in
bits and errors of omission. Another reason why the effects of regulatory focus on
motivation are relevant for feedback seeking is that highly motivated individuals are
more likely to seek process feedback than outcome feedback. This is because process
feedback is the most helpful type of feedback offered in this experiment for learning how
to do the task.

2.19.0399- Higgins, Shah, and Friedman (1997) hypothesized that the strength of
trait regulatory focus moderates the relationship between chronic ideal/ought self-
discrepancies and emotional experiences. Specifically, they proposed that individuals
with a strong promotion focus experience emotions on the elation-dej ection continuum
that are stronger and more frequent than the emotions experienced by individuals with a
weaker promotion focus. In contrast, those with a strong prevention focus experience
emotions on the agitation-quiescence continuum that are stronger and more flequent than
those with a weaker prevention focus. The strength of trait regulatory focus was
conceptualized as the accessibility of people’s ideal and ought self-guides in memory,
and was operationalized as participants’ response latencies for questions about their ideal
and ought self-guides. Ideal and ought self-discrepancies were measured using the Selves
Questionnaire, and affect was measured using a mood questionnaire. Higgins, Shah, and
Friedman conducted three correlational studies testing the relationships among self-guide
strength, self-discrepancies, and affect. Two of the studies measured the flequency of
different kinds of emotions experienced during the previous week, and one measured the
intensity of emotions experienced before beginning a task. In all three studies, they found

an interaction of ideal (ought) self-guide strength and actual/ideal (actual/ought)

22

discrepancy, such that the correlation between actual/ ideal (actual/ought) discrepancy and
feeling dejected (agitated) increased as ideal (ought) self-guide strength increased.

Higgins, Shah, and Friedman (1997) did a fourth study to determine whether the
manipulated regulatory focus can moderate the relationship between momentary
ideal/ought self-discrepancies and emotional experiences. They manipulated regulatory
focus by flaming the situation in gain/non-gain or loss/non-loss terms. Participants
performed a memorization task, then received false feedback saying either that they had
succeeded in reaching the goal, or that they had failed to reach the goal. Participants in
the promotion-focus condition (as compared to those in the prevention-focus condition)
were more likely to report elation if they were given positive feedback, and dejection if
they were given negative feedback. In contrast, participants in the prevention-focus
condition (as compared to those in the promotion-focus condition) were more likely to
report anxiety if they were given negative feedback, and quiescence if they were given
positive feedback.

Roney, Higgins, and Shah (1995) also found evidence that manipulating
regulatory focus influences affect. They manipulated regulatory focus by flaming the
situation in gain vs. non-gain terms or loss vs. non-loss terms. Then, they had participants
do an anagrams task that was easy enough that all participants succeeded. Consistent with
hypotheses, individuals with a promotion focus felt more cheerful after performing well
on the task, while those with a prevention focus felt more quiescent. In a second study,
Roney, Higgins, and Shah manipulated regulatory focus using feedback that directed
participants’ attention to the presence or absence of a positive outcome, or the presence

or absence of a negative outcome. Participants completed an anagrams task with enough

23

unsolvable anagrams that all participants would fail to reach the assigned goal.
Participants with a promotion focus felt more dejected, whereas those with a prevention
focus felt more anxious.

In summary, individuals with a promotion focus tend to feel emotions on the
elation-dej ection continuum, while those with a prevention focus tend to feel emotions on
the anxiety-quiescence continuum. This study allows us to examine the effects of
regulatory focus on learning and performance through affect. It is expected that
individuals who experience dejection or anxiety will learn less and perform worse on the
task than individuals who experience pleasant affect. Dejected and anxious individuals
are expected to have low levels of learning and performance because unpleasant affect
has been shown to inhibit cognitive flexibility, which decreases performance quality on a
complex task (Murray, Sujan, Hirt, & Sujan, 1990). In addition, anxious individuals are
expected to have low levels of learning and performance because they tend to divide their
attention between task-relevant and self-relevant variables (Wine, 1971). Also, according
to Easterbrook’s (1959) cue-utilization hypothesis, arousal increases attention to focal
cues and decreases attention to peripheral cues. On complex tasks that require alternating
attention between focal and peripheral cues, arousal may have a negative impact on
learning and performance.

This review of the regulatory focus literature suggests that regulatory focus is
likely to be related to seeking certain types of feedback. These feedback types are

discussed in the next section.

24

Effects of Regalatorv F ocu_s_ on Feedback Seeking

This study will extend Higgins’ research by examining the effects of regulatory
focus on another aspect of self-regulation (feedback seeking), and exploring the
implications for learning and performance. Stepper et al. (as cited in Higgins, 1998)
proposed that individuals with a promotion focus are sensitive to the presence and
absence of positive outcomes, whereas those with a prevention focus are sensitive to the
absence and presence of negative outcomes. This suggests that individuals with a
promotion focus may be more likely to seek feedback about aspects of the task that they
performed correctly, whereas those with a prevention focus may be more likely to seek
feedback about aspects of the task that they performed incorrectly. The relationship
between regulatory focus and seeking feedback about aspects of the task performed
correctly or incorrectly is not expected to be moderated by feedback sign, unlike the
relationship between regulatory focus and the other two feedback-seeking types.

According to Crowe and Higgins, (1997) individuals with a promotion focus are
more concerned with hits and errors of omission, whereas those with a prevention focus
are more concerned with correct rejections and errors of commission. This implies that
individuals with a promotion focus may seek feedback about hits and errors of omission,
while those with a prevention focus may seek feedback about correct rejections and
errors of commission. Crowe and Higgins (1997) and Roney, Higgins, and Shah (1995)
found that individuals with a promotion focus are highly motivated to strive towards their
hopes and ideals, while those with a prevention focus are less motivated. These findings
are consistent with Brendl and Higgins’s (1996) findings that hopes and ideals flmction

as maximal goals, and duties and obligations function as minimal goals. Because process

25

feedback is potentially more useful, but requires more effort to read and implement,
individuals with a promotion focus may seek more process feedback, while those with a
prevention focus may seek more outcome feedback. The different patterns of feedback
seeking predicted for individuals with different regulatory foci are expected to lead to
different learning and performance outcomes.

Interaction of Regfltorv Focus and Feedback 8ng

Predictions were made above for the impact of regulatory focus on feedback
seeking. These predictions are expected to hold when individuals receive positive
feedback. However, if participants receive negative feedback, their patterns of seeking
process vs. outcome feedback, and feedback about hits and errors of omission vs. correct
rejections and errors of commission are expected to be different.

Effects on seiing process vs. outcome feedbaak. For individuals who receive
positive feedback, those with a promotion focus are expected to seek feedback about
process, while those with a prevention focus are expected to seek feedback about
outcome. However, when individuals receive negative feedback, the opposite pattern is
expected to hold. The reason that those with a promotion focus who receive positive
feedback are expected to seek feedback about process is because they have maximal
goals. They will expend a great deal of effort striving to reach their hopes and ideals.
Consider what happens when individuals with a promotion focus receive negative
feedback. They are failing to meet their hopes and ideals, so they lower their goals to
make them easier to achieve. Individuals with minimal goals are expected to seek
feedback about outcome rather than process, because it is more effortful to read the

process feedback and implement the strategies that it advises.

26

Those with a prevention focus who receive positive feedback are expected to seek
feedback about outcomes, because they have minimal goals. They will expend only the
minimum amount of effort required to meet their obligations. When individuals with a
prevention focus receive negative feedback, they perceive that they are failing to meet
their obligations, which is unacceptable to them. They realize that a great deal of effort
will be required to meet their obligations, so their minimal goals fimction more like
maximal goals. Individuals with maximal goals are expected to seek feedback about
process rather than outcome, because process feedback is more useful than outcome
feedback. Process feedback gives sequenced, adaptive advice that will help participants
succeed on the task.

Although the proposition that regulatory focus leads to certain types of feedback
seeking has not been directly tested in the published empirical literature, there is some
evidence to suggest that the interaction of regulatory focus and feedback sign affects
motivation. This is relevant to the current study, because more motivated individuals
would tend to have maximal goals, and seek process feedback. Less motivated
individuals would tend to have minimal goals, and seek outcome feedback. Kluger, Van-
Dijk, Kass, Stein, and Lustig (2000) predicted that regulatory focus would interact with
feedback sign such that those with a promotion focus who received positive feedback,
and those with a prevention focus who received negative feedback would be more
motivated than those with a promotion focus who received negative feedback, and those
with a prevention focus who received positive feedback. The basis for his hypothesis was
that feedback can be viewed as an incentive, and incentives that are consistent with

individuals’ goals are more effective than those that are inconsistent with individuals’

27

goals. Positive feedback is consistent with a promotion focus, because individuals with a
promotion focus are interested in the presence or absence of positive outcomes. In
contrast, negative feedback is consistent with a prevention focus, because individuals
with a prevention focus are interested in the absence and presence of negative outcomes.
To test this hypothesis, Kluger et al. (2000) conducted five experiments. In all the
experiments, participants were asked to imagine certain scenarios. Feedback sign was
manipulated immediately afier the regulatory focus manipulation by asking respondents
to imagine receiving information about success or failure. In experiments one and two,
regulatory focus was manipulated by contrasting a job one really liked (promotion) with a
job one had to keep (prevention). Post-feedback motivation was assessed using a one-
item questionnaire asking the participants how much effort they would put forth in the
future in relation to the effort they had put forth in the first part of the scenario. The
hypothesis was supported. In experiment three, regulatory focus was manipulated by
contrasting an elective course (promotion) with a required course (prevention). Post-
feedback motivation was assessed using a one-item questionnaire asking the participants
how much effort they would put forth in the future in relation to the effort they had put
forth in the first part of the scenario. The results for participants in both the elective
course and required course conditions were consistent with a prevention focus, indicating
that maybe students have difficulty thinking of an elective course as something that they
want to take rather than something they have to take. To test this interpretation,
experiment four measured regulatory focus using a proxy variable. Undergraduate
students who were required to take a Hebrew class were assumed to have a prevention

focus, while graduate students who chose to take the same class, although it was not

28

required for them, were assumed to have a promotion focus. Post-feedback motivation
was assessed using a one-item questionnaire asking the participants how much effort they
would put forth in the future in relation to the effort they had put forth in the first part of
the scenario. Experiment four yielded results that were consistent with a prevention
focus. Students were interviewed to determine whether those who voluntarily chose to
take a Hebrew course could be accurately classified as having a promotion focus. The
interviews indicated that the students viewed the course as something that was required to
get by in Israel, even though it was not required by the university. Therefore, another set
of interviews was conducted to identify tasks that the overseas students perceived as
purely ideal. Based on these interviews, the promotion focus manipulation in experiment
five was to ask participants to imagine that they were taking an elective internship related
to Judaism. In the prevention focus manipulation, participants were asked to imagine that
they were taking a required course. Motivation was measured by a one-item
questionnaire before and after feedback. The hypothesis was supported.

Therefore, Kluger et al.’s (2000) findings support the idea that regulatory focus
and feedback sign interact to affect motivation. This is consistent with my hypothesis that
individuals with a promotion focus who receive positive feedback and individuals with a
prevention focus who receive negative feedback will have maximal goals, and will seek
process feedback. It is also consistent with my hypothesis that individuals with a
prevention focus who receive positive feedback and individuals with a promotion focus
who receive negative feedback will have minimal goals, and will seek outcome feedback.

Effects on seeking feedback about hits, errors of omission. correct reiectiong,an_d

errors of commission. Just as the interaction of regulatory focus and feedback sign is

29

expected to affect feedback seeking about process vs. outcome, this interaction is also
expected to affect feedback seeking about hits, errors of omission, correct rejections, and
errors of commission. When individuals receive positive feedback, those with a
promotion focus are expected to seek feedback about hits and errors of omission, because
their maximal goals require that they concentrate on gaining points, and not missing
opportunities to gain points. In contrast, individuals with a prevention focus are expected
to seek feedback about correct rejections and errors of commission, because their
minimal goals only require that they avoid losing points, not that they gain points.

However, when individuals receive negative feedback, the opposite pattern is
expected to hold. Individuals with a promotion focus who receive negative feedback are
expected to reduce their maximal goals to more attainable minimal goals. They are
expected to focus on correct rejections and errors of omission, because avoiding losing
points is sufficient to reach their minimal goals. In contrast, for individuals with a
prevention focus who receive negative feedback, the gap between their current
performance and their goals will be so large that their minimal goals will seem like
maximal goals. Therefore, they are expected to strive to gain points, rather than just to
avoid losing points, because they cannot reach their maximal goal without concentrating
on gaining points.

Although no research has been done on the effects of the interaction of regulatory
focus and feedback sign on feedback seeking, Kluger’s (2000) research suggests that
regulatory focus and feedback sign interact to affect risk preferences. These risk
preferences are relevant to feedback seeking because seeking feedback about hits and

errors of omission indicates a preference for risky action (shooting a target at the risk that

30

the target was peaceful), while seeking feedback about correct rejections and errors of
omission indicates a tendency to avoid risky action (at the price of risky inaction, e. g. not
shooting a target at the risk that the target was hostile).

Kluger (2000) hypothesized that regulatory focus and outcome salience interact
such that when positive outcomes are salient (people have received positive feedback),
those with a promotion focus prefer risky action, and those with a prevention focus prefer
to avoid risky action. When negative outcomes are salient (people have received negative
feedback), those with a promotion focus prefer to avoid risky action, and those with a
prevention focus prefer risky action. This is because, if salient outcomes are consistent
with people’s goals, they will be more motivated and aroused than if the salient outcomes
are inconsistent with their goals. Kluger argues that positive outcomes (such as positive
feedback) are consistent with a promotion focus, because individuals with a promotion
focus concentrate on the presence or absence of positive outcomes. In contrast, negative
outcomes (such as negative feedback) are consistent with a prevention focus, because
individuals with a prevention focus concentrate on the absence or presence of negative
outcomes. So, individuals whose regulatory foci are congruent with salient outcomes tend
to be more motivated and aroused, which leads them to prefer action to inaction.

To test this hypothesis, Kluger (2000) measured regulatory focus using the proxy
variable of undergraduate vs. graduate student. Higgins (1998) holds that growth needs
are antecedents to a promotion focus, and security needs are antecedents to a prevention
focus. Kluger assumed that graduate students have higher growth needs and lower
security needs than undergraduates, so he assumed that graduate students are more likely

to have a promotion focus than undergraduates, and undergraduates are more likely to

31

have a prevention focus than graduate students. Kluger operationalized outcome focus by
flaming scenarios positively or negatively. In the positive outcome focus scenarios,
participants had to choose between program A, in which a moderately positive outcome
was guaranteed, and program B, in which there was a 1/3 chance of a highly positive
outcome, and a 2/3 chance of no positive outcome. In the negative outcome focus
scenarios, participants had to choose between program A, in which a moderately negative
outcome was guaranteed, and program B, in which there was a 1/3 chance of no negative
outcome, and a 2/3 chance of a highly negative outcome. Risk preference was measured
by which option participants chose. Individuals who chose program B were coded as
preferring risky action, and those who chose program A were coded as preferring to
avoid risky action. The results supported the hypothesis that congruence between
regulatory focus and salient outcomes leads to greater risk-seeking than incongruence.
Since maximizing hits and minimizing errors of omission requires risky action, Kluger’s
(2000) findings are consistent with the idea that promotion-focused individuals who
receive positive feedback and prevention-focused individuals who receive negative
feedback may seek feedback about hits and errors of omission, and promotion-focused
individuals who receive negative feedback and prevention-focused individuals who
receive positive feedback may seek feedback about correct rejections and errors of
commission. Therefore, some evidence suggests that regulatory focus and feedback sign
interact to influence feedback seeking. The next section discusses the implications of

feedback seeking for learning and performance.

32

Effects of Feedbaismg on gamingand Perform_a_rm_e

A search of the published literature revealed no studies addressing the effects of
seeking process feedback, outcome feedback, feedback about aspects of the task
performed correctly, feedback about aspects of the task performed incorrectly, feedback
about hits and errors of omission, or feedback about correct rejections and errors of
commission on learning and performance. Therefore, hypotheses about the effects of
seeking these types of feedback on learning and performance were drawn from the
regulatory focus literature.

Hypotheses

The hypotheses outlined in this section correspond to the heuristic model (see
Figure 1). This section first predicts the main effects of regulatory focus on feedback
seeking and affect, and the main effects of feedback sign on affect. Then, the interactive
effects of regulatory focus and feedback sign on feedback seeking are predicted. This is
followed by predictions about the effects of feedback seeking on learning and
performance, which are expected to be fully mediated by affect, cognitive effort, self-
efficacy, and attention to basic/strategic aspects of the task. Finally, predictions are made
regarding the effects of learning on performance, and the effects of both learning and
performance on adaptability.
Main Effects of Reguflm' Focas on Feedbzik Seeking

Individuals with a promotion focus self-regulate around ideal self-guides, and
those with a prevention focus self-regulate around ought self-guides (Higgins, 1997). An
ideal self-guide is a mental representation of the way a person would like to be, or the

way others would like him/her to be, and an ought self-guide is a mental representation of

33

the way a person should be, according to oneself or others (Higgins, as cited in Higgins,
Klein, & Strauman, 1985). Stepper et al. (as cited in Higgins, 1998) hypothesized that
because individuals with a promotion focus self-regulate around ideals, they focus on the
presence or absence of positive outcomes, and because individuals with a prevention
focus self-regulate around oughts, they focus on the presence or absence of negative
outcomes. In order to test this hypothesis, they manipulated participants’ regulatory
focus, then had them read a story. Individuals with a promotion focus remembered more
events in the story related to the presence or absence of positive outcomes, and those with
a prevention focus remembered more events in the story related to the presence or
absence of negative outcomes. Therefore, individuals with a promotion focus are
expected to seek feedback about aspects of the task that they have performed correctly, a
type of positive outcome, and individuals with a prevention focus are expected to seek
feedback about aspects of the task that they have performed incorrectly, a type of
negative outcome.

HI a: Individuals with a promotion focus are expected to seek feedback about

aspects of the task that have been performed correctly.

HI b: Individuals with a prevention focus are expected to seek feedback about

aspects of the task that have been performed incorrectly.
min Effects of Regafatorv Focus and Feedback 8ng on Affect

Higgins et al. (1986) found that individuals with predominant actual-ideal
discrepancies tend to feel dej ection-related emotions such as sadness and disappointment,
because they perceive that they are failing to meet their hopes and ideals. In contrast,

individuals with predominant actual-ought discrepancies tend to feel agitation-related

34

emotions such as anxiety and uneasiness, because they perceive that they are failing to
meet their duties and obligations.

Since individuals with a promotion focus self-regulate around actual-ideal
discrepancies, and those with a prevention focus self-regulate around actual-ought
discrepancies (Higgins, 1997), it follows fl'om Higgins et al.’s (1986) findings that
individuals with a promotion focus who perceive that they are failing to reach their goals
tend to feel dej ection-related emotions, while those with a prevention focus who perceive
that they are failing to reach their goals tend to feel agitation-related emotions. Consistent
with this idea, Higgins, Shah, and Friedman (1997) found that individuals with a chronic
promotion focus tend to experience emotions on the elation-dej ection continuum, and
those with a chronic prevention focus tend to experience emotions on the anxiety-
quiescence continuum. Roney, Higgins, and Shah (1995) found similar effects of
situationally induced regulatory focus on affect.

H20: Individuals with a promotion focus are expected to experience emotions on

the elation/dejection continuum to a greater extent than individuals with a

prevention focus.

H2b: Individuals with a prevention focus are expected to experience emotions on

the anxiety/quiescence continuum to a greater extent than individuals with a

promotion focus.

When individuals perceive that their performance has met a standard, they
experience pleasant affect, and when they perceive that their performance has failed to
meet a standard, they experience unpleasant affect (Bandura & Cervone, 1983, 1986).

Positive feedback is evidence that their performance has met or exceeded a standard, and

35

negative feedback is evidence that their performance is below a standard. After
experiencing large negative discrepancies flom a standard repeatedly, individuals tend to
withdraw flom the task (Bandura & Cervone, 1983, 1986).

H3a: Individuals with who receive positive evaluative feedback are expected to

experience pleasant affect.

H3 b: Near the beginning of the task, individuals who receive negative evaluative

feedback are expected to experience unpleasant afifect.

H3c: Near the end of the task, negative evaluative feedback is not expected to be

related to afifect.
Interactive Effects of Regmtorv Focus gd Feedback Sig on Feedback Seeking

Individuals with a promotion focus self-regulate around ideal self-guides, and
those with a prevention focus self-regulate around ought self-guides (Higgins, 1997). An
ideal self-guide is a mental representation of the way a person would like to be, or the
way others would like him/her to be, and an ought self-guide is a mental representation of
the way a person should be, according to oneself or others (Higgins, as cited in Higgins,
Klein, & Strauman, 1985). A promotion-focused individual’s hopes and ideals serve as
maximal goals, meaning that the individual tries to fulfill them to the greatest extent
possible (Brendl & Higgins, 1996). In contrast, a prevention-focused individuals’ duties
and obligations serve as minimal goals, because once they have met their obligations,
there is no need to expend further effort. Because promotion-focused individuals have
maximal goals, they are likely to seek feedback that will help them to achieve the greatest
success on the task. In this case, process feedback is what allows participants to achieve

the greatest success on the task, because it gives them adaptive guidance about what

36

aspects of the task they should be concentrating on in order to improve their skills.
Outcome feedback may be perceived as sufficient to allow prevention-focused
individuals to reach their minimal goals, whereas process feedback may be perceived as
requiring too much effort to read and implement.

Because individuals with a promotion focus self-regulate around ideal self-guides,
they are eager to attain their hopes and dreams. Therefore, they are willing to take risky
actions in order to maximize their accomplishments. In contrast, individuals with a
prevention focus self-regulate around ought self-guides, and are vigilant against failure to
meet their obligations, and are less willing to take risky actions for fear of failing to meet
the minimal requirements of their obligations. Although prevention-focused individuals
are less willing than promotion-focused individuals to take risky actions, they are more
willing to accept the risks of inaction. Crowe and Higgins (1997) found that promotion-
focused individuals try to maximize hits and minimize errors of omission, and
prevention-focused individuals try to maximize correct rejections and minimize errors of
omission. This is because promotion-focused individuals want to maximize their
accomplishments, and by maximizing their hits and errors of omission, they are
maximizing the number of points that they gain, and minimizing the number of missed
opportunities to gain points. In contrast, prevention-focused individuals want to meet the
minimal requirements of their obligations, and by maximizing their correct rejections and
minimizing their errors of commission, they are maximizing the number of point
deductions that they successfully avoid, and minimizing the number of point deductions
that they incur. Because regulatory focus affects whether individuals try to maximize

their hits or correct rejections, and whether they try to minimize their errors of omission

37

or errors of commission, it is also expected to affect whether they seek feedback about
hits, correct rejections, errors of omission, and errors of commission.

H4: Promotion-focused individuals who receive positive feedback are expected to

seek (a) process feedback and (b) feedback about hits and errors of omission.

H5: Prevention-focused individuals who receive positive feedback are expected to

seek (a) outcome feedback and (b) feedback about correct rejections and errors of

commission.

Individuals with a promotion focus who receive negative feedback are expected to
seek outcome feedback and feedback about correct rejections and errors of commission
early in the task. Later in the task, they are expected to discredit the negative feedback
and partially withdraw from the task. However, their pattern of feedback seeking is not
expected to change as a result of withdrawing from the task, because they are already
seeking the types of feedback that are relevant to minimal goals.

Promotion-focused individuals strive to reach their hopes and ideals (Higgins,
1997) but negative feedback indicates that they are failing to do so. Although promotion-
focused individuals have maximal goals to reach their hopes and ideals to the greatest
extent that they can (Brendl & Higgins, 1996), those who receive negative feedback are
expected to reduce those goals to make them more attainable. Near the beginning of the
task, promotion-focused individuals are expected to seek outcome feedback and feedback
about hits and errors of omission. This is because outcome feedback may be perceived as
sufficient to attain minimal goals, whereas process feedback may be perceived as too
difficult to implement because it requires focusing on certain strategic elements of the

task, which are unlikely to lead to short-term performance gains. Therefore, promotion-

38

focused individuals who receive negative feedback are expected to seek outcome
feedback rather than process feedback.

Because they are focused on minimal goals, promotion-focused individuals who
receive negative feedback are expected to focus on not losing points rather than focusing
on gaining points. For this reason, they are likely to seek feedback about correct
rejections and errors of commission, which gives then information about how they have
successfully avoided losing points, and how they have failed to avoid point losses, rather
than seeking feedback about hits and errors of omission, which would give them
information about points gained and missed opportunities to gain points.

H6: Promotion-focused individuals who receive negative feedback are expected

to (a) seek outcome feedback more ofien than process feedback, (b) seek feedback

about correct rejections and errors of commission more ofien than feedback
about hits and errors of omission.

Individuals with a prevention focus who receive negative feedback are expected
to seek process feedback and feedback about hits and errors of omission early in the task.
Later in the task, they are expected to discredit the negative feedback, and to partially
withdraw fl'om the task by seeking types of feedback that are relevant to minimal goals
rather than maximal goals. However, by the time they have discredited the negative
feedback, they will already have been seeking process feedback and feedback about hits
and errors of omission for long enough to have learned more and started to perform better
than prevention-focused individuals who received positive feedback.

Individuals with a prevention focus strive to meet their duties and obligations

(Higgins, 1997), but negative feedback indicates that they are failing to do so. The

39

desperation that accompanies this failure to meet their minimal goals may cause those
goals to seem so distant and hard to attain that they function like maximal goals.
Therefore, prevention-focused individuals who receive negative feedback are expected to
seek process feedback, because it contains sequenced, adaptive guidance on what aspects
of the task participants should study and practice, so it may be perceived as highly useful
for reaching maximal goals. Individuals with a prevention focus are expected to seek
feedback about hits and errors of omission, because in order to reach their maximal goals,
they must work to gain points, not just avoid losing points. Feedback about hits and
errors of omission gives them information about how many points they successfully
gained, and how many opportunities to gain points they missed, which is more relevant to
their maximal goals than feedback about correct rejections and errors of commission,
which would give them information about points they successfully avoided losing, and
points they lost.

H7: Near the beginning of the task, individuals with a prevention focus who

receive negative feedback will (a) seek process feedback and (b) seek feedback

about hits and errors of omission.

H8: Later in the task, individuals with a prevention focus who receive negative

feedback will (a) seek outcome feedback and (b) seek feedback about correct

rejections and errors of commission.
Effects of Feedback on Affect, ngnitive Effort, Attention to Basic and Strategic
Elements and Self-Efficacy

When individuals perceive that their performance has met a standard, they

experience pleasant affect, and when they perceive that their performance has failed to

40

meet a standard, they experience unpleasant affect (Bandura & Cervone, 1983, 1986).
Feedback about aspects of the task that they have done correctly is evidence that their
performance has met a standard, and feedback about aspects of the task that they have
done incorrectly is evidence that their performance has failed to meet a standard.

H9: Participants who seek feedback about aspects of the task they performed

correctly will experience more positive aflect than participants who seek feedback

about aspects of the task they performed incorrectly.

Participants who seek feedback about aspects of the task they have done correctly
are maintaining or increasing their belief that they can come closer to reaching their
goals, or avoid failing to meet their goals. In contrast, participants who seek feedback
about aspects of the task they are doing incorrectly are less likely to believe that they can
reach their goals, or avoid failing to meet their goals. Therefore, according to Bandura’s
(1997) definition of self-efficacy as beliefs in one’s capabilities to organize and perform
the behaviors required to attain certain outcomes, feedback about aspects of the task done
correctly is expected to be positively correlated with self-efficacy. An additional variable
that is expected to affect self-efficacy is whether participants seek process feedback.
Those who seek process feedback receive specific guidance, which is expected to be
perceived as useful, and therefore is expected to increase self-efficacy.

HI 0a: Seeking feedback about aspects of the task done correctly is expected to be

positively correlated with self-eflicacy.

H10b: Seeking feedback about aspects of the task done incorrectly is expected to

be negatively correlated with self-efi‘icacy.

41

H10c: Seeking process feedback is expected to be positively correlated with self-

efficacy.

Participants who seek process feedback receive adaptive guidance about which
elements of the task they should concentrate on. This guidance would instruct
participants to focus on basic elements during the first block, then the simpler strategic
elements in the second block, and the most complex strategic elements in the third block.
In contrast, participants who seek outcome feedback are expected to focus on basic
elements of the task because learning the basic elements of the task is necessary to
perform with a minimal degree of competence. Focusing on the basic elements of the task
is expected to be the default for participants who are not guided by process feedback to
learn and practice the strategic elements of the task.

H1 1a: Participants who seek process feedback are expected to shifi overtime

from a focus on basic elements of the task to a focus on strategic elements of the

task

H1 1b: Participants who seek outcome feedback are expected to focus on basic

elements of the task

Participants who seek feedback about hits and errors of omission receive
information about members of a category they correctly identified, and missed
opportunities to identify members of that category. This information is relevant to
participants’ maximal goals, because participants gain points by identifying members of a
category. Since feedback about hits and errors of omission is relevant to maximal goals, it
is expected to be motivating. In contrast, participants who seek feedback about correct

rejections and errors of commission receive information about non-members of a

42

category they correctly identified, and non-members they failed to identify. This
information is relevant to participants’ minimal goals, because participants do not lose
points when they correctly identify non-members of a category, and they lose points
when they fail to identify non-members of a category. Participants with minimal goals
focus on not losing points rather than gaining points. Since feedback about correct
rejections and errors of commission is relevant to minimal goals, it is expected to be de-
motivating.

H12a: Seeking feedback about hits and errors of omission is expected to be

positively correlated with cognitive eflort.

H12b: Seeking feedback about correct rejections and errors of commission is

expected to be negatively correlated with cognitive eflort.
Effects of Affect, Cognitive Effort, Attention to Basic and Strategic Elements, aQSelf-
Efficacy on LearnJgand Perform_ance During the Praitice Trig

Unpleasant affect has been shown to inhibit cognitive flexibility, which decreases
performance quality on a complex task (Murray, Sujan, Hirt, & Sujan, 1990). For this
reason, dejection is expected to be negatively correlated with basic and strategic learning
and performance during the practice trials. Anxiety is expected to have negative effects
on learning and performance during the practice trials for three reasons. First, as stated
above, unpleasant affect inhibits cognitive flexibility. Second, anxious individuals tend to
divide their attention between task-relevant and self-relevant variables (Wine, 1971).
Third, according to Easterbrook’s (1959) cue-utilization hypothesis, arousal increases
attention to focal cues and decreases attention to peripheral cues. On complex tasks that

require alternating attention between focal and peripheral cues, arousal may have a

43

negative impact on learning and performance. Although elation and anxiety are both
high-arousal emotions, the task used in the present study is not expected to inspire
feelings of elation strong enough to have a negative impact in learning and performance.

H13a: Dejection is expected to be negatively correlated with basic and strategic

learning and performance during the practice trials.

H13b: Anxiety is expected to be negatively correlated with basic and strategic

learning and performance during the practice trials.

Four variables are expected to affect individuals’ learning and performance on
basic and strategic elements of the task: whether participants focus on basic elements of
the task or shift over time from a focus on basic elements to a focus on strategic elements,
cognitive effort, and self-efficacy. Individuals who study and practice strategic aspects of
the task in the recommended order are expected to learn those elements, which will lead
to better strategic performance. Individuals who focus on basic elements of the task, as
compared to those who shift from a focus on basic elements to strategic elements, expend
a greater percentage of their time studying and practicing basic elements of the task.
Therefore, individuals who focus on basic elements of the task are expected to learn and
perform the basic elements better than those who shifi from a focus on basic elements to
strategic elements. Cognitive effort is positively related to learning and performance
(Fisher, 1995). Therefore, participants with greater cognitive effort are expected to learn
and perform the basic and strategic aspects of the task better. Self-efficacy has been
shown to be positively related to performance (Bandura, 1986). In addition, previOus
studies using the experimental task used in this study have found that self-efficacy for the

task is related to learning and performance (e.g. Kozlowski et al., 1998). Therefore,

44

participants with higher self-efficacy are expected to learn and perform better on both the
basic and strategic elements of the task.
H14a: Participants who shift from a focus on basic elements of the task to
strategic elements of the task over time are expected to learn and perform
strategic aspects of the task during the practice trials better than participants who
focus on basic elements throughout the task
H14b: Participants who focus on basic elements throughout the task are expected
to learn and perform basic elements of the task during the practice trials better
than participants who shift from a focus on basic elements of the task to strategic
elements of the task over time.
H14c: Cognitive eflort is expected to be positively correlated with basic and
strategic learning and performance during the practice trials.
H14d: Self-eflicacy is expected to be positively correlated with basic and strategic
learning and performance during the practice trials.
Effects of Learning During the Practice Trials on Performance Duringthe Pragfice Trizfi
Because the task used in this study is cognitively complex, performance is
expected to be contingent on learning. Although performing some of the strategic
elements requires learning the basic elements first, performing the strategic elements also
requires additional, strategic knowledge.
H150: Learning basic elements of the task during the practice trials will be
positively correlated with basic performance during the practice trials.
H15b: Learning strategic elements of the task during the practice trials will be

positively correlated with strategic performance during the practice trials.

45

Effects of Learningand Perform_arice on the Pgtice Trials on Learningafl Perform_afie
on the Generalization Trial

Basic learning and performance are expected to predict the generalization of basic
skills to a more difficult and complex task. Similarly, strategic learning and performance
are expected to predict the generalization of strategic skills to a more difficult and
complex task. This is because declarative knowledge and training performance are
necessary for the reproduction of trained skills in a new situation, and thus are predictors
of adaptive performance (Kozlowski et al., 2001).

H160: Individuals who learn and perform well on the basic elements of the task

are expected to perform well on the basic elements of the generalization trial.

H16b: Individuals who learn and perform well on the strategic elements of the

task are expected to perform well on the strategic elements of the generalization

trial.

46

METHOD
Design

Overview

This study employed a two (promotion focus vs. prevention focus) by two
(positive feedback vs. negative feedback) fully crossed between-subjects design. The
experiment took place in a single session consisting of three blocks, each block consisting
of three trials, for a total of nine training trials. Blocks (3) and trials (9) both represented
within subjects factors. After the training blocks, trainees engaged in a generalization
trial, in which the workload was increased (there were more targets and the length of the
trial was doubled), and the rules underlying task proficiency were modified, in order to
determine to what extent trainees’ skills are adaptable.

Simulation

A modified version of the PC-based decision making simulation
TEAMS/TANDEM (see Kozlowski & Gully, 1996; Weaver, Bowers, Salas, & Cannon-
Bowers, 1995; Weaver, Morgan, Hall, & Compton, 1993) was used as the experimental
platform. This version, called the Tactical Action Simulation (TAS), is a novel, dynamic
task. During TAS, each participant was seated at a simulated radar console upon which
multiple contacts were presented. Trainees gathered information about each contact by
“hooking” the target, gathering information about its characteristic “cue values,” and
making a final decision about the target’s disposition based on the information gathered.
Three cues were available for each of the component decisions (nine cues overall), which
had to be made in the following order: (1) Type (air, surface, or submarine); (2) Class

(civilian or military); (3) Intent (peaceful or hostile); and (4) Engage (shoot or clear).

47

S_kill Components

For two minutes before each trial, participants had access to a PC-based training
manual. Topics studied and time spent on each topic were recorded by the computer.
Information in the manual fell into three different categories: basic functionality,
declarative knowledge, and strategic knowledge.

Basic fimctionality on the task involved learning those features of the program, in
terms of both software and hardware, which made it possible to perform the task. Task
features had to be learned in order for a participant to perform well on TAS include
hooking targets, accessing cue menus, and “zooming” to alter the display radius. Also,
participants had to be able to use a mouse and keyboard.

Declarative knowledge was also required to process information and make
decisions. Decisions about the target’s Type (air, surface, submarine), Class (civilian or
military), and Intent (peaceful or hostile) were based on three cues each, which are
accessed from pull-down menus. These three component decisions had to be made in the
following order: Type, Class, Intent. Based on the target’s intent, participants had to
make a final engagement decision to shoot the target or clear it flom their radar. Learning
to make correct component decisions and final engagement decisions were prerequisites
for the strategic elements of the task.

Strategic knowledge is the participant’s ability to understand the deeper elements
of the simulation and to develop two critical strategic skills: situational assessment and
target prioritization. Three elements of the task (using the zoom firnction, identifying
defensive perimeters, and locating and utilizing marker targets) were relevant to

situational assessment. Participants needed to expand the radius of their radar screen by

48

“zooming out,” in order to determine the overall situation in which they were operating.
This action was critical because participants lose points when targets cross either of the
two “defensive perimeters” within the task. The inner defensive perimeter, located at
lONM, was clearly marked, but the outer perimeter, located at 256 NM, was outside the
initial viewing range of 32 NM, and was not clearly marked. In order to locate the outer
defensive perimeter, participants had to first “zoom out,” then locate “marker targets”
that served to identify the outer perimeter.

Target prioritization required a separate set of strategic skills. The scenarios were
designed so that there were often multiple targets approaching each perimeter, some of
which were more threatening than others. Therefore, decisions about which targets were
the greatest threats were critical. The priority of targets was based on two factors: their
speed and location. Faster targets had a higher priority than slower targets at a similar
location, because they would cross a perimeter sooner. For the same reason, targets near a
perimeter had a higher priority than targets farther away from a perimeter that were
traveling at the same speed. Targets that were traveling quickly near a perimeter were the
highest priority. In addition, trainees had to “trade off’ targets approaching their inner
and outer perimeters. Strategic decisions related to such trade-offs revolved around the
number of targets at each perimeter, their priority, and their “cost” in points if they
penetrated.

Procedure
Participants
A total of 259 undergraduates at a large Midwestern university participated in this

study. Eight participants were dropped from the final analysesi. Fifty-five percent of

49

participants were female, and eighty-nine percent were between the ages of 18 and 21.
Participants received course credit, and four students won $25 for achieving a score of
1500 or above on the radar tracking task.

Prior to the Exmriment

Before the experiment, participants filled out questionnaires assessing their
demographic information, mental ability, trait goal orientation, and trait regulatory focus
on the University’s intranet.

Informed Coasgn;

Participants were asked to read and sign two informed consent forms. They were
asked to sign the first consent form electronically before they filled out a questionnaire on
the University’s intranet (see Appendix A). The first consent form only pertained to that
questionnaire, and by signing the first consent form participants were not consenting to
participate in the rest of the experiment. When participants arrived at the lab, the
experimenter welcomed them and asked them to fill out the second informed consent
form (see Appendix A) describing the experiment and its risks and benefits. Participants
were fully debriefed (see Appendix B) following the experiment.

Prior to Familiarization Trial

The experimenter welcomed participants, read a short introduction to the
experiment, and briefly demonstrated the task, displaying how to hook targets, use the
pull down menus, use the zoom firnction, and make decisions in the proper order. The
experimenter also demonstrated how to seek feedback, and how to use the on-line
manual. After the demonstration, participants filled out measures of affect and self-

efficacy. Then, the experimenter handed out the regulatory focus manipulation, and read

50

it to participants. Participants were then told that they would have the opportunity to try
the simulation in a one-minute familiarization trial, and that they would subsequently
progress through three blocks of three trials each. Each trial consisted of a two-minute
study period, a practice period, and three minutes to review evaluative feedback and to
seek feedback.
Entities

Participants completed a one-minute familiarization trial in order to allow them to
get familiar with the task. For blocks 1 through 3, the regulatory focus manipulation was
reinforced at the beginning of each block, and then participants filled out a goal
questionnaire. After each block, participants filled out measures of affect, perceived
cognitive effort, and self-efficacy.
Generalization

Trainees’ adaptive performance was operationalized using a generalization trial,
which was more complex and placed a greater cognitive workload on participants than
the previous trials. In order to perform well on the generalization trial, participants had to
adapt the knowledge and skills they learned throughout training to a new, more complex
situation. Before the generalization trial, participants were given instructions describing
how the final session would differ from the previous sessions.

To increase the workload, the number of targets was increased fl‘om 22 to 60, a
172% increase. Task complexity was increased by (1) including more “pop-up” targets,
which appeared suddenly on the screen; (2) creating more defensive perimeter intrusions;
(3) creating “pop-up” targets that appeared very close to defensive perimeters; and (4)

differentially distributing boundary intrusions to create a situation in which many targets

51

threatened the outer perimeter, while fewer targets threatened the inner perimeter,
requiring strategic trade-offs on the part of trainees; and (5) changing the “rules of
engagement” so that the components of strategic score received more weight.

During the generalization trial, strategic score was calculated by summing 100
times the number of marker targets hooked, 100 times the number of high priority targets
prosecuted, and 100 times the number of queries about the speed of targets approaching
the outer perimeter, and subtracting 125 points for targets crossing the inner perimeter,
and 75 points for targets crossing the outer perimeter. This differed considerably fl'om the
previous trials, where strategic score was calculated by summing 10 times the number of
marker targets hooked, 10 times the number of high priority targets prosecuted, and 10
times the number of queries about the number of targets approaching the outer perimeter,
and subtracting 10 times number of perimeter intrusions. The generalization task was
much more demanding, difficult, and complex than the previous trials, requiring trainees
to adapt their strategies and skills.

Manipulations
Regalatog Focus

Regulatory focus was manipulated by framing the task as an opportunity to gain
money, or to avoid losing money. Participants in the promotion focus condition were told
that they would gain $25 if their total score (strategic + basic) was 1500 or greater on the
generalization trial, otherwise they would not gain $25. The manual described how basic
and strategic scores were calculated. Participants in the prevention focus condition were
told that they would receive $25, but if their total score (strategic + basic) was less than

1500 on the generalization trial, they would lose the $25. The cutoff was chosen based on

52

previous studies using this experimental paradigm, which indicated that a score of 1500
was a difficult, yet attainable goal.

Information about how to calculate the basic and strategic score was available in
the on-line manual. These scores were based on the basic and strategic performance
composite variables described in the measures section. Basic score was calculated by
adding 100 points for each target correctly identified and prosecuted, and subtracting 100
points for each target misidentified or prosecuted incorrectly. During all trials except the
generalization trial, strategic score was calculated by summing 10 times the number of
marker targets hooked, 10 times the number of high priority targets prosecuted, and 10
times the number of queries about the speed of targets approaching the outer perimeter,
and subtracting 10 times the number of perimeter intrusions. During the generalization
trial, strategic score was calculated by summing 100 times the number of marker targets
hooked, 100 times the number of perimeter intrusions and 100 times the number of
queries about the speed of targets approaching the outer perimeter, and subtracting 125

points for targets crossing the inner perimeter, and 75 points for targets crossing the outer

perimeter.

Feedback 8ng

After each trial, the computer generated evaluative feedback for each participant.
Participants in the positive feedback condition received the following feedback, “In the
last trial, you improved X percent. You are currently performing above our standards for
what is acceptable progress on this task.” The percent improvement was veridical, and
was intended to make the otherwise unchanging feedback more believable. Participants in

the negative feedback condition received the following feedback, “In the last trial, you

53

improved X percent. However, you are currently performing below our standards for
what is acceptable progress on this task.”
Measures

Demmhics

Before the experiment, participants recorded their gender, age, and experience
with video games.
Aim

Prior to the experiment, participants recorded their highest score on the SAT or
ACT tests. They were assured that their scores would be confidential, and that they would
be used for research purposes only. They were asked to sign a statement giving the
experimenter permission to verify their scores with the registrar’s office. A random
sample of 100 participants who provided their SAT or ACT scores were selected, and
their self-reported scores were compared with their scores on file at the registrar’s office.
According to ETS, coefficient alphas for recent versions of the SAT range between .91
and .93 for the verbal section, and between .92 and .93 for the math section. The
publishers of the ACT report that using the stratified alpha method, the average
reliabilities for over 12 recent ACT forms are .91 for English, .91 for Mathematics, .85
for Reading, and .83 for Science Reasoning.
M OrienLation Measures

Before the experiment, participants filled out Button, Mathieu, and Zajac’s (1996)
goal orientation measure, and a version of VandeWalle’s (1997) goal orientation measure
that was modified to be domain-general, instead of specific to the work domain. Button,

Mathieu, and Zajac’s (1996) questionnaire is based on a two-factor model of goal

54

 

orientation, and is the most commonly used measure of goal orientation in the field of I/O
psychology. VandeWalle’s (1997) questionnaire is based on a three-factor model of goal
orientation. VandeWalle (1997) presents a compelling argument that the three-factor
model is superior for both theoretical and empirical reasons, but the utility of these two
goal orientation measures for predicting feedback seeking, learning, and performance has
not been directly compared in the published literature. Therefore, this study uses both
measures to explore the relationship between goal orientation and feedback seeking,
learning, and performance. Both goal orientation measures are presented in Appendix D.

The Button, Mathieu, and Zaj ac (1996) measure contains 16 items, with responses
made on a 5-point scale ranging flom “strongly disagree” (1) to “strongly agree” (5).
Coefficient alpha was .78 for mastery orientation and .84 for performance goal
orientation. The VandeWalle (1997) measure contains 13 items, with responses made on
a 6-point scale ranging from “strongly agree” (1) to “strongly disagree” (6). Coefficient
alpha was .84 for learning orientation, .84 for prove orientation, and .82 for avoid
orientation. For this study, the responses on the VandeWalle scale were reversed in order
to be consistent with Button, Mathieu, and Zaj ac’s measure, so that one indicated
“strongly disagree”, and six indicated “strongly agree”.
Trait Regalatorv Focu_sMeasures

Two measures of regrlatory focus were collected before the experiment. The first
was Shah, Higgins, and Friedman’s (1998) Selves Questionnaire, on which participants
were given a definition of their ideal and ought selves. Their ideal self was defined as the
type of person they would ideally like to be; the type of person they hope or wish they

could be. Their ought self was defined as the type of person they believe they ought to be;

55

 

the type of person they believe it is their duty or responsibility to be. Participants were
asked to provide five attributes of their ideal and ought self-guides. After each attribute,
they rated how much they would like to have that attribute (ideal extent rating), and to
what extent they already have that attribute (actual extent rating). Then, they did the same
thing for their ought self guides. Actual-ideal discrepancies were calculated by
subtracting the actual extent rating from the ideal extent rating for each attribute, and
summing the differences. The greater the differences, the greater the actual-ideal
discrepancy. Similarly, actual-ought discrepancies were calculated by subtracting the
actual extent rating from the ought extent rating for each attribute, and summing the
differences. The greater the differences, the greater the actual-ought discrepancy. The
test-retest reliability for this questionnaire is .60 for one year (Moretti & Higgins, 1990),
and .48 for three years (Strauman, 1996). The regulatory focus measure is presented in
Appendix D.

The second measure of trait regulatory focus was Davis’s (2001) context-specific
measure. The items assess students’ tendencies to focus on ideals or oughts in their
psychology class. Response options are on a 5-point scale ranging from “strongly
disagree” (1) to “strongly agree” (5). Coefficient alpha was .31 for both the promotion
scale and the prevention scale.

Affect Measure

Affect was assessed at the beginning of the experiment, and after each block,
using a 20 item measure developed by Roney, Higgins, and Shah (1995; see Appendix
E). Responses were made on a 7-point scale ranging from “not at all” (1) to “extremely”

(7). A principal axis factor analysis using varimax rotation was performed in order to

56

determine whether the two factors (anxiety/quiescence and elation/dej ection) proposed by
Roney et al. emerged from the data. Following the Kaiser normalization criterion
guideline of selecting components with eigenvalues greater than one, four factors were
rotated. In order to achieve a more interpretable factor structure, six items with high
cross-loadings were dropped: afl'aid, disappointed, dissatisfied, angry, distracted, and
worried. After dropping these items, another principal axis factor analysis using varimax
rotation was performed. Following the Kaiser normalization criterion guideline of
selecting components with eigenvalues greater than one, three factors were rotated. The
eigenvalues for the first three factors were 4.33, 2.23, and 1.34, respectively. The
eigenvalue for the fourth factor was .76. The first three factors accounted for 33.36%,
17.19%, and 10.31% of the variance, respectively. Thus, the first three factors
collectively explained 60.86% of the variance. All the factor loadings were at least :1: .50,
and the highest cross-loading was :t .36.

The first factor included items such as “dejected” and “discouraged,” and was
labeled “dej ection.” The second factor included items such as “happy” and “cheerful,”
and was labeled, “elation.” The third factor included such items as “tense,” and “calm,”
(reverse scored) and was labeled “anxiety.” Because the factor structure proposed by
Roney et al. (1995) was not supported by the data, three affect scales were created by
summing the items for each factor.

Coefficient alpha for the dejection scale was .83 for time 1, .85 for time 2, and .86
for time 3. Coefficient alpha for the elation scale was .79 for time 1, .80 for time 2, and
.82 for time 3. Coefficient alpha for the anxiety scale was .67 for time 1, .61 for time 2,

and .76 for time 3.

57

 

Feedbagkaeelgiag

After performing in the simulation, participants had three minutes to review the
evaluative feedback and to seek feedback. The six types of feedback that participants
could choose to seek were feedback about aspects of the task performed correctly,
feedback about aspects of the task performed incorrectly, feedback about hits and errors
of omission, feedback about correct rejections and errors of commission, process
feedback, and outcome feedback. They were able to view as many of the six different
types of feedback as they wanted during the allotted time. The six choices appeared on
the feedback menu, and participants were able to click the mouse on the feedback type
they wanted to view first, then press any key to return to the feedback menu (see
Appendix F for examples of the six feedback types). The FASTBACK feedback program
tracked which types of feedback participants chose to view, and how much time they
spend on each type of feedback.

Correct feedback was labeled, “Things I did correctly in the last trial,” on the
feedback menu. If participants clicked the mouse button on correct feedback, they will
received information about what proportion of their type decisions, class decisions, intent
decisions, and final engagement decisions were correct, and what proportion of high
priority targets they correctly prosecuted. Incorrect feedback, labeled “Things I did
incorrectly in the last trial,” was the same as correct feedback, except that participants
were told the proportion of decisions that they made incorrectly.

Feedback about hits and errors of omission was labeled, “Number of hostile
targets correctly shot down and incorrectly cleared fl'om your radar,” on the feedback

menu. Feedback about correct rejections and errors of omission was labeled “Number of

58

peaceful targets correctly cleared from your radar and incorrectly shot down.” If
participants clicked on these feedback types, they received the information that the label
indicated.

Outcome was labeled “score,” and if participants clicked on it, they received all
the information they would need to calculate their basic and strategic scores—
inforrnation about what proportion of their type decisions, class decisions, intent
decisions, and final engagement decisions were correct and incorrect, and the number of
times they zoomed out, the number of marker targets booked, and the proportion of high
priority targets engaged. Process feedback, labeled, “How to improve your performance,”
was sequenced, and adaptive to three levels of performance. This feedback gave advice
about what participants should do to improve their performance. Participants who sought
process feedback during the first block received information about their Type, Class,
Intent, and Final engagement decisions. Those who sought feedback during the second
block received information about zooming in and out, hooking marker targets, engaging
pop-up targets, and defending the inner and outer perimeters. Participants who sought
process feedback during the third block received information about prioritizing targets
that are approaching the inner and outer perimeters and “trading of’ targets approaching
the inner and outer perimeters.

In addition to being sequenced, the process feedback was adaptive to three levels
of performance (split at the 50th and 85th percentile) for each of the skills listed above.
These cutoffs were chosen because previous studies using this paradigm show that
participants can perform at the 50th percentile with minimal effort and practice, but

performing at the 85th percentile is a difficult, yet attainable goal (Bell & Kozlowski, in

59

 

press). The feedback for participants scoring within each of these ranges on a particular
skill or strategy was designed as follows:

Below the 50th percentile. Process feedback for participants scoring in this range
informed them that they had not yet learned to perform the skill or strategy, and that they
should study the material about that skill or strategy in their on-line manual, and practice
the skill in the next practice session.

Between the 50"Larld 85th percentile. Individuals scoring in this range who sought
process feedback were informed that they had reached a level of minimal performance,
but that they needed to become more proficient at the skill or strategy. They were told
what they should study and practice in order to improve their performance.

Above the 85th percentile. Process feedback for participants scoring in this range
informed them that they had mastered the skill or strategy, and that they should
concentrate on improving skills and strategies that they had not yet mastered.
Self-Efficacy

Self-efficacy was assessed after the demonstration, and after each block, using an
8-item self-report measure developed for use in this research paradigm (see Kozlowski,
Gully, Smith, Nason, & Brown, 1995). Self-efficacy was operationalized as a, “task-
focused, self-perception with item content specifically focused on the capability to cope
and to develop methods to effectively deal with the information, decisions, and
challenges of the simulation” (Kozlowski et al., 1996, p. 18). This is consistent with
conceptualizations of efficacy as central to self-regulatory processes (Gist & Mitchell,

1992). Response options for this scale range from “strongly disagree” (1) to “strongly

60

 

agree” (5). Coefficient alpha was .91 for time 1, .93 for time 2, and .95 for time 3. The
self-efficacy measure is presented in Appendix E.
Perceived Cogaitive Effort

After the demonstration and after each block, participants completed Fisher’s
(1995) measure of mental workload, which essentially is a measure of participants’
perceived cognitive effort. This measure contains six items with a response scale ranging
from “strongly agree” (1) to “strongly disagree” (5). Coefficient alpha was .84 for time 1,

.83 for time 2, and .81 for time 3.

61

Basic and Strategic Task Knowledge

After the third block, participants completed a 13-item multiple-choice measure
of declarative knowledge about the task and a 14-item multiple-choice measure of
strategic knowledge about the task, developed by Bell and Kozlowski (in press). The
basic measure focuses on cue values and basic operating features of the task, and the
strategic measure focuses on aspects of the task such as locating the outer defensive
perimeter and prioritizing targets.

A confirmatory factor analysis (CFA) was performed in order to determine
whether basic and strategic knowledge were separate factors. Because the knowledge
items were coded as zero = “incorrect” and one = “correct,” I created continuously scaled
indicators by surmning two or three knowledge variables. This resulted in six indicators
for the basic knowledge scale, and seven indicators for the strategic performance scale.
AMOS was used to assess the fit of the data to the model in which each components was
linked to its respective indicators, and the components were allowed to correlate (see
Figure 2). The fit indices were as follows: x2 (34, N = 251) = 96.26, p < .001, TLI = .96,
RMSEA = .09, SRMR = .03. The SRMR and TLI indicate good fit, but the x2 and
RMSEA indicate poor fit. Since past research using this measure showed that basic and
strategic knowledge are separate factors (e. g. Bell & Kozlowski, in press), separate scales
were created for basic and strategic knowledge. Coefficient alpha for the basic
knowledge scale was .83, and coefficient alpha for the strategic knowledge scale was .69.
Study Sequence

The computerized TAS manual recorded how much time participants spent

studying each page of the manual during each study session. Participants were instructed

62

to exit out of the manual if they were finished studying before the allotted time was up, in
order to discourage them from letting the manual continue to run if they were no longer
studying.
Basic and Strategic Performance

In order to determine whether separate basic and strategic composite variables
could be created by combining the relevant performance indicators, I conducted an
exploratory factor analysis. This factor analysis was conducted on the indicators as
measured during the final training trial to allow time for the factor structure to stabilize.
Principal components analysis using Varimax rotation was used, and components with
eigenvalues greater than one were selected. Two components were rotated, with
eigenvalues of 2.56 and 1.47, respectively. The first component, which corresponds to
strategic performance, accounted for 42.74% of the variance, while the second
component, which corresponds to basic performance, accounted for 24.49% of the
variance. After rotation, the variables all loaded on their respective components greater
than :I: .50 and cross-loaded less than :I: .28. Therefore, basic and strategic performance
composite variables were created by standardizing the performance measures within
training block and summing them using unit weights. The basic performance composite
was created by combining the number of points correct and points incorrect. The strategic
performance composite was created by combining the number of marker targets hooked,
the number of high priority targets prosecuted, the number of perimeter intrusions, and
the number of queries about the speed of targets approaching the outer perimeter. All the
indicators were given a positive weight except for points incorrect and number of

perimeter intrusions, which were given a negative weight.

63

RESULTS
Analysis Plan

A preliminary inspection of the correlation matrix revealed ability, trait, and
method factor covariates that correlated with key processes or outcomes. Specifically,
cognitive ability, VandeWalle’s measure of goal orientation, and the order in which
feedback choices were presented on the menu were correlated with feedback seeking,
learning, or performance. Therefore cognitive ability, VandeWalle’s measure of goal
orientation, and the order in which feedback choices were presented on the menu were
treated as covariates in all the analyses (see method section for a description of the
menu). This section will describe the intercorrelations of the key variables, the RM-
MANCOVA that was conducted to examine the overall effects that manipulated
regulatory focus and feedback sign have on feedback seeking, and the tests of the a priori
hypotheses.

Correlations

A composite variable was created for each variable that was measured at three
time periods, and the correlation matrix of the composite variables was inspected to
explore the relationships among the variables. See Appendix G for both the full
correlation matrix and the correlation matrix based on the composite variables.
Correlations Between IV’s an_d Feed—ba_ck Seek_ing

There was no significant correlation between regulatory focus and seeking correct
feedback (r = -.06, p < .34), which is not consistent with hypothesis 1. Unexpectedly,
there was a negative correlation between prevention focus and seeking feedback about

hits and errors of omission (r = -.14, p < .03). In addition, an unexpected correlation was

64

found between receiving negative feedback and seeking feedback about hits and errors of
omission (r = -.16, p < .01). The order of feedback types on the menu is significantly
related to seeking feedback about aspects of the task performed correctly (r = -.52, p <
.001), and seeking feedback about hits and errors of omission (r = .30, p < .001), which
suggests a method flaw. Therefore, the order of feedback types on the menu was used as
a covariate for the RM-MANCOVA and hypothesis tests.
Corriations Between IV’_sand Affect

Negative feedback is positively correlated with dejection (r = .14, p < .03) which
is consistent with predictions. However, contrary to predictions, there was no significant
correlation between regulatory focus and elation (r = .03, p < .62), dejection (r = .09, p <
.17), or calmness (r = -.05, p < .44).
Correlations Between the IV’s a_nd Outcomes

Cognitive ability is correlated with knowledge (r = .25, p < .001), basic (1 = .31, p
< .001) and strategic performance (r = .33, p_ < .001), and performance on the
generalization trial (r = .30, p < .001). VandeWalle’s mastery orientation scale is
correlated with basic performance (3; = .15, p < .03) and performance on the
generalization trial (r = .19, p < .006) and VandeWalle’s avoid orientation scale is
correlated with strategic performance (r = -.14, p < .04). Therefore, cognitive ability and
VandeWalle’s goal orientation scales were used as covariates in the RM-MANCOVA
and hypothesis tests.
Correlations Between FeedbaLk Seek_ir_rgand the Mediators

Consistent with predictions, there was a positive correlation between seeking

feedback about aspects of the task performed correctly and elation (g = .19, p < .004).

65

However, contrary to predictions, there was a negative relationship between seeking
feedback about aspects of the task performed incorrectly and dejection (r = -.18, p <
.004). Unexpectedly, there was no significant relationship between seeking outcome
feedback and studying basic elements of the task (r = -.01, p < .83), but there was a
positive relationship between seeking feedback about aspects of the task performed
correctly and studying basic elements of the task (r = .14, p < .03). There was a positive
relationship between seeking process feedback and studying strategic elements of the task
(r = .17, p < .01), which is not inconsistent with the hypothesis that individuals who seek
process feedback tend to shift from a focus on basic elements to strategic elements over
time. However, contrary to predictions, there was a positive correlation between seeking
feedback about aspects of the task performed incorrectly and self-efficacy (r = .20, p <
.002). Also contrary to predictions, there were no significant correlations between
feedback seeking and mental workload.
Correlations Between the Mediators and Outcomes

Consistent with predictions, self-efficacy is positively related to knowledge (r =
.26, p < .001), basic (r = .33, p_ < .001) and strategic performance (1 = .24, p < .001) and
performance on the generalization trial (5 = .36, p < .001). Also consistent with
predictions, dejection is negatively correlated with knowledge (r = -.32, p < .001), basic
(r = -.28, p < .001) and strategic performance (1' = -.30, p < .001) and performance on the
generalization trial (r = -.31, p < .008), and calmness is positively related to knowledge (r
= .17, p_ < .001) strategic performance (1 = .18, p_ < .008) and performance on the

generalization trial (r = .23, p < .001).

66

Studying strategic information was positively related to knowledge (r = .49, p < .001)
basic (1' = .30, p < .001) and strategic performance (1' = .19, p < .004) and performance on
the generalization trial (r = .39, p < .001). This is not inconsistent with the hypothesis that
participants who shift flom a focus on basic elements to strategic elements over time will
learn and perform strategic aspects of the task during the practice trials better than
participants who focus on basic elements throughout the task. Unexpectedly, mental
workload was not related to any of the outcomes.
RM-MANCOVA

An RM-MANCOVA was performed to examine the overall and direct effects that
manipulated regulatory focus, feedback sign, and time have on feedback seeking. Using
Wilk’s Lambda, the RM-MANCOVA demonstrated a significant overall effect for
regulatory focus E(6, 209) = 2.21 , p < .04, 112 =.06. However, the RM-MANCOVA did
not demonstrate a significant overall effect for feedback sign E(6, 209) = 1.58, p_ < .16, 112
= .04 or the interaction of regulatory focus and feedback sign F(6, 209) = 0.82, p < .60,
32 =02. The RM-MANCOVA demonstrated significant overall effects for two of the
covariates: VandeWalle’s prove scale E(6, 209) = 2.20, p < .04, :12 = .06 and the order in
which feedback choices were presented on the menu E(6, 209) = 19.02, p < .001 , 112 =
.35. However, no significant overall effects were demonstrated for cognitive ability E(6,
209) = 1.79, p < .10, 11’ = .05, VandeWalle’s mastery scale, E(6, 209) = 1.60, p < .15, :3

= .04 or VandeWalle’s avoid scale E(6, 209) = 1.82, p < .10, 112 = .05.

67

Hypothesis Tests
M_ain Effects of Regalatogv Focus on Feedbagt Seekiag

It was predicted that participants in the promotion-focused condition would seek
more feedback about aspects of the task they performed correctly than aspects of the task
they performed incorrectly (hla). A regression analysis was performed with the total
amount of feedback about aspects of the task performed correctly as the dependent
variable. The covariates (cognitive ability, VandeWalle’s measure of goal orientation,
and the order in which feedback choices were presented on a menu) were entered in the
first step as independent variables. Regulatory focus was entered in the second step.
Regulatory focus had a marginally significant effect on seeking feedback about aspects of
the task performed correctly, Q2 = .01, Bin, (1, 216) = 3.23, p < .07, such that individuals
in the promotion-focused condition sought more feedback about aspects of the task
performed correctly than those in the prevention-focused condition.

It was predicted that individuals in the prevention-focused condition would seek
more feedback about aspects of the task they performed incorrectly than aspects of the
task they performed correctly (hlb). A regression analysis was performed with the total
amount of feedback about aspects of the task performed incorrectly as the dependent
variable. The usual covariates entered in the first step as independent variables.
Regulatory focus was entered in the second step. Regulatory focus did not have a
significant effect on seeking feedback about aspects of the task performed incorrectly,
m2 = .006, a... (1, 216) = 1.32, p_ < .25.

In summary, regulatory focus had a marginally significant effect on seeking

feedback about aspects of the task performed correctly, but did not have an effect on

68

either seeking feedback about aspects of the task performed incorrectly, or the difference
between the amount of correct feedback sought and the amount of incorrect feedback
sought. Therefore, hypothesis 1a was partially but marginally supported, and hypothesis
1b was not supported.
min Effects of Regrflol Focus on Affect

Partial correlations were examined to test the hypothesis that individuals with a
promotion focus tend to experience emotions on the elation/dejection continuum to a
greater extent than individuals with a prevention focus (h2a), and individuals with a
prevention focus tend to experience emotions on the anxiety/quiescence continuum to a
greater extent than individuals with a promotion focus (h2b). After the covariates were
partialled out, regulatory focus was not significantly correlated with elation (r_= .04, p <
.60), dejection (r = .11, p_ < .11), or anxiety (1' = .04, p < .59). Therefore, hypotheses 2a
and 2b were not supported.
Integctive Effects of Feedback 8ng and Time on Affect

An RM-MANCOVA was performed, using the same covariates, to test the
hypothesis that near the beginning of the task, individuals who receive negative
evaluative feedback experience more unpleasant affect than individuals who receive
positive feedback (h3b) and near the end of the task, evaluative feedback is not related to
affect (3c). The interaction between feedback sign and time was not significant using
Wilk’s Lambda, F(6, 190) =1.70, p < .12, n2 = .051, so hypothesis 3b and 3c are not
supported.

The hypothesis that individuals who receive positive evaluative feedback will

experience more pleasant affect than individuals who receive negative evaluative

69

feedback (h3a) was tested using the RM-MANCOVA above. The RM-MANCOVA
demonstrated a significant overall effect for feedback sign, 13(3, 193) = 3.34, p < .02, 112 =
.049. An examination of the between-subj ects effects revealed that only one of the three
affect factors, dejection, was influenced by evaluative feedback, F(l , 195) =8.68, p <
.004, 112 = .043, such that individuals who receive negative feedback feel more dejected
than those who receive positive feedback. So, hypothesis 3a was partially supported. In
summary, there is no significant interaction between regulatory focus and evaluative
feedback on affect.

Interactive Effects of Regu_latory Poor; and Feedbacflgn on Feedbackaeemg

Two hierarchical regression analyses were performed to test the hypothesis that
participants in the promotion-focused condition who receive positive feedback seek
process feedback and feedback about hits and errors of omission more than individuals in
the prevention-focused condition (114). In the first regression analysis, process feedback
was the dependent variable. The usual covariates, regulatory focus, and feedback sign
were entered in the first step as independent variables, and the interaction between
regulatory focus and feedback sign was entered in the second step. The interactive effect
of regulatory focus and evaluative feedback on seeking process feedback was not
significant, g2 = .000, E1... (1, 214) = .097, p_< .76.

In the second regression analysis, feedback about hits and errors of omission was
the dependent variable, the usual covariates, regulatory focus and feedback sign were
entered in the first step as independent variables, and the interaction between regulatory
focus and feedback sign was entered in the second step. The interactive effect of

regulatory focus and evaluative feedback on seeking feedback about hits and errors of

70

omission was also not significant 4&2 = .001, Fin, (1, 214) = .17, p < .68. So, hypothesis 4
was not supported.

Two hierarchical regression analyses were performed to test the hypotheses that
individuals in the prevention-focused condition who receive positive feedback tend to
seek outcome feedback and feedback about correct rejections and errors of commission
more than participants in the promotion-focused condition (h5), whereas participants in
the promotion-focused condition who receive negative feedback tend to seek outcome
feedback and feedback about correct rejections and errors of commission more than
individuals in the prevention-focused condition (h6). In the first regression analysis,
outcome feedback was the dependent variable, the usual covariates, regulatory focus and
feedback sign were entered in the first step, and the interaction of regulatory focus and
feedback sign was entered in the second step. The interactive effect of regulatory focus
and evaluative feedback on seeking outcome feedback was not significant, AB} = .011,
Fine (1 , 214) = 2.45, p_ < .12. The interactive effect of regulatory focus and evaluative
feedback on seeking feedback about correct rejections and errors of commission was also
not significant, $2 = .000, Fine (1, 214) = .08, p < .78. Therefore, hypotheses five and six
are not supported. In conclusion, there were no significant interactions between
regulatory focus and feedback sign on feedback seeking.

Interacgtive Effects of Rengny Focus. Feedback Siga, and Time on Feedbfi Seem

Two RM-ANCOVAs using the same covariates as above were performed to test
the hypothesis that early in the task, individuals with a prevention focus who receive
negative feedback will seek more process feedback and feedback about hits and errors of

omission (h7). The interactive effect of regulatory focus, feedback sign, and time on

71

process feedback was not significant, F(Z, 213) = 2.42, p < .09, 112 = .022. The interactive
effect of regulatory focus, feedback sign, and time on feedback about hits and errors of
omission was also not significant, E(2, 213) =8], 2 < .45, 112 = .008. Therefore,
hypothesis 7 was not supported.

Another two repeated-measures AN COVAs were performed to test the hypothesis
that later in the task, individuals with a prevention focus who receive negative feedback
will seek more outcome feedback and feedback about correct rejections and errors of
commission (h8). The interactive effect of regulatory focus, feedback sign, and time on
outcome feedback was not significant, 5(2, 213) = .83, p < .44, 112 = .008. The interactive
effect of regulatory focus, feedback sign, and time on feedback about correct rejections
and errors of commission was also not significant, F(Z, 213) = .11, p < .90 112 = .001.
Therefore, hypothesis 8 was not supported.

In summary, none of the proposed three-way interactions between regulatory
focus, feedback sign, and time had significant effects on feedback seeking.

Effects of Feedbzflc Seeking on Affect. Self-Efficacy, Studya'ng, and Cogritive Effort

Partial correlations were used to test the hypothesis that participants who seek
feedback about aspects of the task they performed correctly will experience more positive
affect than participants who seek feedback about aspects of the task they performed
incorrectly (h9). After partialling out the covariates, the total amount of feedback sought
about aspects of the task performed correctly was positively correlated with elation (I =
.16, p < .03), which supports hypothesis nine. However, the amount of feedback sought

about aspects of the task performed incorrectly was not significantly correlated with

72

anxiety (; = -.10, p < .16), and was marginally negatively correlated with dejection (r = -
.13, p < .08), which does not support hypothesis nine.

The hypothesis that seeking feedback about aspects of the task done correctly is
positively correlated with self-efficacy (h10a) was not supported. After partialling the
covariates, there was no significant relationship between seeking feedback about aspects
of the task done correctly and self-efficacy (r = .10, p < .14). The hypothesis that seeking
feedback about aspects of the test done incorrectly is negatively correlated with self-
efficacy (h10b) was also not supported, because after partialling out the covariates, a
positive correlation was found (r_ = .15, p < .03). It may be that individuals with higher
self-efficacy were more likely to seek feedback about aspects of the task they performed
incorrectly, because of their confidence that they could improve their skills in those areas.

A hierarchical regression analysis was performed to test the hypothesis that
participants who seek process feedback tend to shift over time fi'om a focus on basic
elements of the task to a focus on strategic elements (hl 1a). A new dummy-coded
variable, “shiftl ,” was created to represent whether participants spent more time studying
basic elements of the task than strategic elements of the task during block 1 (shift 1 = 0 if
a participant did not spend more time studying basic elements of the task than strategic
elements of the task during block 1, and shift 1 = 1 if a participant did spend more time
studying basic elements of the task than strategic elements of the task during block 1).
Another dummy-coded variable, “shift3” was created to represent whether participants
spent more time studying strategic elements of the task than basic elements of the task
during block 3 (shift 3 = 0 if a participant did not spend more time studying strategic

elements of the task than basic elements of the task during block 3, and shift 3 = 1 if a

73

participant did spend more time studying strategic elements of the task than basic
elements of the task during block 3). A variable called “addshift” represents shift] +
shift2. Addshift = zero if participants do not study more basic than strategic information
during the first block, and do not study more strategic than basic information during the
third block. Add shift = 1 if participants either study more basic than strategic
information during the first block, or study more strategic than basic information during
the third block, but not both (almost all participants for whom add shift = 1 studied more
basic than strategic information throughout the task). Add shift = 2 if participants study
more basic than strategic information during the first block and study more strategic than
basic information during the third block.

Addshift was entered as the independent variable, the usual covariates were
entered in the first step as independent variables, and process feedback was entered in the
second step. The effect of process feedback was not significant, _AL2= .003, fine (1, 205)
= .56, p_ < .46.

Two hierarchical regression analyses were performed to test the hypothesis that
participants who seek outcome feedback tend to focus on basic elements of the task more
than participants who seek process feedback (hl 1b). In the first regression analysis, basic
studying was entered as the dependent variable, the usual covariates were entered in the
first step as independent variables, and outcome feedback was entered in the second step.
Outcome feedback did not demonstrate a significant effect on studying basic elements of
the task, AR_2= .000, Em, (1, 205) = .06, p < .81. In the second regression analysis, basic
studying was entered as the dependent variable, the usual covariates were entered in the

first step, and process feedback were entered in the second step. The relationship between

74

process feedback and basic studying was not significant, AR_2_= .001, Ejnc (1, 205) = .16,
p < .69.

The hypothesis that seeking feedback about hits and errors of omission is
positively correlated with cognitive effort (h12a) was not supported by an examination of
the correlations after the covariates were partialled out (r = .03, p < .70), nor was the
hypothesis (h12b) that seeking feedback about correct rejections and errors of
commission is negatively correlated with cognitive effort (I = -.06, p < .40).

In summary, the hypothesized relationship between feedback seeking and affect
was partially supported. The predicted relationships between feedback seeking and self-
efficacy, studying, and cognitive effort were not supported.

Effects of Affect. Cognitive Effort. Smg/inmSelf-Efiicacy on Knowle
Performance Duringthe Practice Triafi

It was predicted that dejection would be negatively correlated with basic and
strategic knowledge and performance during the practice trials (h13a). After partialling
out the covariates, dejection was negatively correlated with both basic (r = -.19, p < .008)
and strategic performance, (r = -.25, p_ < .001) and strategic knowledge (r = -.27, p <
.001), and was marginally negatively correlated with basic knowledge (1' = -.12, p < .09).
Therefore, hypothesis 13a was supported.

The hypothesis that anxiety is negatively correlated with basic and strategic
knowledge and performance during the practice trials (h13b) was partially supported.
After partialling out the covariates, anxiety was negatively correlated with strategic
knowledge (r = -. 14, p < .05), but there was no significant correlation between anxiety

and basic knowledge (r = .03, p < .69), or between anxiety and basic (r = -.02, p < .74) or

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strategic performance (r = .10, p < .16). It is possible that strategic knowledge requires
more concentration to learn than basic knowledge, and therefore learning strategic
knowledge is more easily disrupted by anxiety.

Two hierarchical linear regressions were used to test the hypothesis that
participants who shift fi'om a focus on basic elements of the task to strategic elements of
the task over time learn and perform strategic aspects of the task better than participants
who focus on basic elements throughout the task (h14a). In each regression, the usual
covariates were entered as independent variables in the first step, and the “addshift”
variable described earlier was entered in the second step. In the first regression, the
dependent variable was learning strategic elements of the task, and in the second
regression the dependent variable was strategic performance. Shifting flom a focus on
basic elements of the task to strategic elements of the task over time was related to
learning strategic elements ofthe task m2 = .114, a... (1, 205) = 28.17, p < .001, but was
not related to strategic performance $2 = .000, Fine (1, 205) = .09, p < .77. Therefore
hypothesis 14a was partially supported.

Two hierarchical linear regression analyses were used to test the hypotheses that
participants who focus on basic elements throughout the task learn and perform basic
elements of the task during the practice trials better than participants who shift from a
focus on basic elements of the task to strategic elements of the task over time (h14b). In
each regression, the usual covariates were entered as independent variables in the first
step, and the “addshift” variable described earlier was entered in the second step. In the
first regression, the dependent variable was basic knowledge, and in the second

regression the dependent variable was basic performance. Shifting from a focus on basic

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elements of the task to strategic elements of the task over time was not related to learning
basic elements of the task $2 = .001, Em (1 , 205) = .27, p < .60 or basic performance
$2 = .010, En. (1, 201) = 2.57, p_ < .11.

The hypothesis that cognitive effort is positively correlated with basic and
strategic learning and performance during the practice trials (hl4c) was not supported.
After partialling the effects of the covariates, cognitive effort was not significantly
correlated with basic (_r_ = -.02, p < .78) or strategic (r = .12, p < .09) knowledge, or with
basic (r = -.10, p < .18) or strategic (r = -.12, p < .10) performance.

The hypothesis that self-efficacy is positively correlated with basic and strategic
knowledge and performance during the practice trials was supported (h14d). After
partialling out the covariates, self-efficacy is positively correlated with basic (5 = .33, p <
.001) and strategic performance (r = .21, p < .001), and basic (r = .16, p < .02) and
strategic knowledge (r = .18, p < .008).

In summary, the hypothesized effects of dejection and self-efficacy on knowledge
and performance were supported. The hypothesized effects of anxiety, studying, and
cognitive effort on knowledge and performance were partially supported.

Effects of Lem'ng During themctice Trials on Performance During the Practice Tria_1_s

The hypothesis that learning basic elements of the task during the practice trials is
positively correlated with basic performance during the practice trials (h15a) was
supported (1; = .27, p < .001), after partialling the effects of the covariates. Similarly, the
hypothesis that learning strategic elements of the task during the practice trials is
positively correlated with strategic performance during the practice trials (h15b) was

supported (r = .33, p < .001), after partialling the effects of the covariates.

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Effects of Learning and Performm on the Practice Trials on Learning and Performance
on the Generalization Trial

Two hierarchical linear regression analyses were used to test the hypothesis that
individuals who learn and perform well on the basic elements of the task perform well on
the basic elements of the generalization trial (h16a). In the first regression analysis, basic
performance on the generalization trial was entered as the dependent variable, the usual
covariates were entered in the first step as independent variables, and basic knowledge
was entered in the second step. Basic knowledge demonstrated a significant effect on
basic performance on the generalization trial, _A_Rz = .087, an, (1, 204) = 21.75, p < .001.
In the second regression analysis, basic performance on the generalization trial was
entered as the dependent variable, the usual covariates were entered in the first step as
independent variables, and basic performance on the practice trials was entered in the
second step. Basic performance on the practice trials demonstrated a significant effect on
basic performance on the generalization trials (_A_R2 = .140, fine (1, 204) = 37.18, p <
.001). Thus, hypothesis 16a was supported.

It was predicted that individuals who learn and perform well on strategic elements
of the task tend to perform well on strategic elements of the generalization trial (blob).
To test this hypothesis, two hierarchical regression analyses were performed. In the first
regression analysis, performance on strategic elements of the generalization trial was
entered as the dependent variable, the usual covariates were entered as independent
variables in the first step, and strategic knowledge was entered in the second step.
Strategic knowledge demonstrated a significant effect on strategic performance on the

generalization trial, g2 = .141, am, (1, 204) = 37.41, p < .001.

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In the second regression analysis, performance on strategic elements of the
generalization trial was entered as the dependent variable, the usual covariates were
entered as independent variables in the first step, and strategic performance during the
practice trials was entered in the second step. Strategic performance during the practice
trials demonstrated a significant effect on strategic performance on the generalization
trial, A133 = .19, gm (1, 204) = 52.69, p < .001. So, hypothesis 16b was supported. In
summary, knowledge and performance on the practice trials was generally related to
performance on the generalization trial, as expected.

Exploratory Analyses
Explanation for Lack of Support of Hypptheses

One reason why many of the hypotheses were not supported may be the way that
participants were presented with the six choices for what type of feedback to seek.
Approximately half of the participants were presented with the feedback options on a
menu in the following order: feedback about aspects of the task performed correctly,
process feedback, feedback about correct rejections and errors of commission, outcome
feedback, feedback about aspects of the task performed incorrectly, and feedback about
hits and errors of omission. The other half of the participants was presented with the
feedback menu in the opposite order. The two menu orders were counterbalanced across
conditions. However, the RM-MANCOVA described at the beginning of the results
section revealed that menu had a significant effect on feedback seeking using Wilk’s
Lambda, 5(6, 209) = 19.02, p < .001, 112 = .35. The tests ofbetween-subjects effects
revealed that menu order had a significant effect on seeking feedback about aspects of the

task performed correctly E(l, 214) = 87.15, p < .001, 112 = .29 and feedback about hits

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and errors of omission E(l, 214) = 19.94, p < .001, 112 = .09. Menu order probably had an
effect on these two types of feedback because they are each the first choice on one of the
menus, and the last choice on the other menu. An examination of means revealed that
participants were more likely to seek feedback about aspects of the task performed
correctly and feedback about hits and errors of omission when those types of feedback
were at the top of the menu than when they were at the bottom of the menu.

Since menu order had such a strong effect on feedback seeking, it may have
masked the effects of the manipulations. Even though menu order was entered as a
covariate in the hierarchical regression analyses, menu order may still have affected the
results, because the same two types of feedback were in the center of the list on both
menus. An examination of the average amount of time spent seeking each type of
feedback revealed that participants were less likely to seek the two types of feedback at
the center of the list than the other types of feedback. To test whether this difference was
significant, I performed a series of t-tests comparing the average amount of time spent
seeking each type of feedback per block to the amount of time spent seeking feedback
about correct rejections and errors of commission. The reason I chose feedback about
correct rejections and errors of omission as the reference value is because of the two
types of feedback in the center of the menu, participants spent more time seeking
feedback about correct rejections and errors of commission. Therefore, choosing the
average amount of time seeking feedback about correct rejections and errors of
commission as the reference value creates a more conservative test. The average amount

of time that participants spent seeking each type of feedback differed significantly from

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the amount of time they spent seeking feedback about correct rejections and errors of
commission, (p < .001).

Therefore, in order to explore the relationship between feedback seeking, the
manipulations, and the outcome variables without the effects of menu order, I created a
composite variable summing all the types of feedback seeking. A regression analysis
confirmed that menu order has no significant effect on total feedback seeking, E(1, 249) =
.06, p < .80.

Reduced Model

A reduced model of the relationship between total feedback seeking, the
manipulations, and the outcome variables was constructed based on theory, an
examination of the correlation matrix, and the results of exploratory regression analyses
(see Figure 3). A link was included between regulatory focus and total feedback seeking.
It was expected that participants in the promotion focus condition would seek more
feedback overall than those in the prevention focus condition, because a promotion-
focused individual’s hopes and ideals serve as maximal goals, meaning that the
individual tries to fulfill them to the greatest extent possible (Brendl & Higgins, 1996). In
contrast, a prevention-focused individuals’ duties and obligations serve as minimal goals,
because once they have met their obligations, there is no need to expend further effort.
Participants with maximal goals would be more likely to expend the effort to seek more
feedback than participants with minimal goals.

A link was included between the feedback manipulation and dejection. It was
predicted that participants in the negative feedback condition would feel more dejected

than those in the positive feedback condition. This is because negative feedback is

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evidence that one is failing to progress towards a goal at an acceptable rate, which can
lead to negative affect (Carver & Scheier, 1998).

Consistent with past research, it was expected that mastery goal orientation would
have a positive effect on self-efficacy (e. g. Phillips & Gully, 1997; VandeWalle, Cron, &
Slocum, 2001), and avoid orientation would have a negative effect on self-efficacy (e. g.
VandeWalle et al., 2001). This is because individuals with a mastery orientation believe
that performance quality is due to effort, which means that they believe they can improve
their performance by expending more effort (Dweck, 1975). In contrast, individuals with
a performance orientation believe that performance quality is due to ability, which means
that if they experience difficulties with a task, they may conclude that their ability is too
low to perform the task, and thus have low self-efficacy for the task (Dweck, 1986).

It was predicted that avoid orientation would be positively related to dejection.
This is because an avoid orientation leads to low intrinsic motivation, and therefore low
levels of enjoyment of the task for its own sake (Deci, 1975). Research has confirmed
that avoid orientation is related to negative affect (e. g. worry (Elliot & McGregor, 1999),
fear of negative evaluation (V andeWalle, 1997), state test anxiety (Elliot, & McGregor,
2001), and ernotionality.

Self-efficacy was expected to have a positive effect on the total amount of
feedback sought, because individuals who believe that they can perform a task
successfully are more likely to expend effort (Bandura, 1997). Since seeking feedback
requires effort, individuals with high self-efficacy would presumably be more likely to

seek feedback.

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A link was included between dejection and the total amount of feedback seeking.
It was predicted that dejection would lead to less feedback seeking overall, because
experiencing negative affect over a long enough period of time can lead to lowering one’s
standards and decreasing effort (Carver & Scheier, 1998). For the task used in this
experiment, decreasing one’s effort could manifest itself in seeking less feedback.

Participants who sought more feedback overall were expected to perform better
on the task. This is because feedback allows individuals to monitor their progress towards
their goals and to correct errors in goal-directed behaviors when their performance fails
to meet an important goal (Ashford & Cummings, 1983, 1985; Ilgen & Davis, 2000). In
fact, past research has found that under most circumstances, feedback leads to increased
performance (Ammons, 1956; Mento, Steel, & Karren, 1987). Although Kluger and
DeNisi (1996) argue that feedback does not always lead to increased performance, their
meta-analysis indicates that feedback interventions improve performance on average ((1 =
.41).

The model was tested using AMOS, and the path diagram is presented in Figure
3. The fit indices for the hypothesized model were as follows: x2 (24, I_\I_ = 251) = 98.54,
p < .001, TLI = .97, RMSEA = .12 and SRMR = .099. Since all the fit indices except for
the TLI indicated poor fit, the model was modified to achieve better fit. Modification
indices indicated a relationship between self-efficacy and dejection. This link was added
to the model, because it is consistent with theory. For example, Bandura (1997) holds that
in challenging situations, individuals with low self-efficacy tend to experience greater

depression than individuals with high self-efficacy.

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Modification indices also indicated a direct relationship between self-efficacy and
performance, and between dejection and performance. These links are well established in
the literature. For example, Stajkovic and Luthans’ (1998) meta-analysis of 114 studies
found a significant weighted average correlation between self-efficacy and performance,
G(r+) = .38. Similarly, Judge and Bono’s (2001) meta-analysis found an uncorrected
mean correlation of g = .19, p < .05 between generalized self-efficacy and performance.

The link between negative affect and performance decrements is also well
established. Unpleasant affect inhibits cognitive flexibility, which decreases performance
quality on complex tasks (Murray, Sujan, Hirt, & Sujan, 1990). In addition, experiencing
negative affect over a long enough period of time can lead to lowering one’s standards
and decreasing effort (Carver & Scheier, 1998). This relationship between affect and
effort was discussed earlier in this section as a reason why dejection may lead to less
feedback seeking, but in this task decreased effort may also manifest itself in worse
performance on the task due to prosecuting fewer targets, prosecuting targets more
carelessly, or paying less attention to strategy.

The final model was tested using AMOS, and the path diagram is presented in
Figure 4. The fit indices indicated good fit: x2 (20, E = 251) = 29.62, p < .08, TLI = .99,

RMSEA = .05 and SRMR = .05.

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DISCUSSION

This section will summarize the results of the hypothesis tests, then describe the
final model that was constructed based on theory and structural equation modeling, and
the implications of this model for embedded training systems Then, ideas for how this
experiment could be improved upon for future research will be described. As explained in
the results section, a methodological flaw made testing the relationships between seeking
specific types of feedback and any of the other variables highly ambiguous. Therefore,
only a brief summary of the hypothesis tests follows.

Summary of Hypothesis Tests

Regulatory focus had a marginally significant effect on seeking feedback about
aspects of the task performed correctly, but no effect on seeking feedback about aspects
of the task performed incorrectly. Contrary to predictions, regulatory focus was not
significantly correlated with any of the dimensions of affect. Evaluative feedback had a
significant main effect on dejection in the expected direction, but there was no significant
interaction between regulatory focus and time on affect. In addition, there were no
significant interactions between regulatory focus and feedback sign on feedback seeking,
and none of the proposed three-way interactions between regulatory focus, feedback sign,
and time had significant effects on feedback seeking.

The hypothesized relationship between feedback seeking and affect was partially
supported, but the predicted relationships between feedback seeking and self-efficacy,
studying, and cognitive effort were not supported. The hypothesized effects of dej ection
and self-efficacy on knowledge and performance were supported, and the hypothesized

effects of anxiety, studying, and cognitive effort on knowledge and performance were

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partially supported. As predicted, learning basic elements of the task was positively
correlated with basic performance during the practice trials, and learning strategic
elements of the task was positively correlated with strategic performance during the
practice trials. As predicted, learning and performance during the practice trials was
generally positively related to performance during the generalization trial.
Final Model

Because the hypothesis tests provided little support for the original hypotheses, a
revised model was constructed based on theory, an examination of the correlation matrix,
and exploratory regression analyses. This revised model is explained in the results
section. Structural equation modeling was used to test the fit of the model, and the model
was modified to achieve better fit. This final model, which fits the data well, is described
below.

Effects of Regalatog Focus
This study provides evidence that a promotion focus is adaptive under conditions of

both positive and negative feedback. Specifically, a promotion focus leads to greater
feedback seeking than a prevention focus, and this relationship is not moderated by the
feedback manipulation. This contradicts predictions made by Kluger et al. (2000) and
Higgins (2001), and provides evidence that regulatory focus is more similar to goal
orientation than Higgins has argued.

Higgins (2001) holds that when individuals experience failure, a prevention focus
should lead to greater motivational strength than a promotion focus, because the
experience of failure under promotion focus is not consistent with the (ideal) goal to

approach success, but it is consistent with the (ought) goal to avoid failure. Similarly,

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Higgins argues that when individuals experience success, a promotion focus should lead
to greater motivational strength than a prevention focus. However, if “motivational
strength” means the intensity of motivation, and increases the probability of effortful
activities such as seeking feedback, the results of this study contradict Higgins’
predictions.

The results of this study are consistent with the conceptualization of regulatory
focus as equivalent to goal orientation, although the findings must be interpreted with
caution, because null results do not disprove a theory. Goal orientation theory would
predict that in situations where feedback can provide task information rather than just
serving impression-management purposes, mastery-oriented individuals should seek
more feedback than performance-oriented individuals, regardless of whether they are
experiencing success or failure (V andeWalle, in press). This is because mastery-
oriented/promotion—focused individuals tend to perceive feedback as diagnostic, since
diagnostic information is consistent with their higher-order goals for personal growth and
self-improvement. In contrast, performance-oriented/prevention focused individuals tend
to perceive feedback as evaluative, since evaluative information is consistent with their
higher-order goals for regard and security. The higher-order goals underlying regulatory
focus and goal orientation will be discussed firrther in the section on future research.

Effects of Eva—MW
Self-Efficacy

The model indicates that negative feedback decreases self-efficacy and positive

feedback increases self-efficacy. This is consistent with Bandura (1997) who holds that

repeated failure leads to lowered self-efficacy because past performance is one of the

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indicators people use to estimate how successful they will be at organizing and executing
the courses of action needed to achieve their goals.
Dejection

In addition to its effects on self-efficacy, the model indicates that evaluative
feedback also affects dejection. Specifically, negative feedback increases dejection, and
positive feedback decreases dejection. This is consistent with Carver and Scheier (1998),
who hold that negative feedback is evidence that one’s goals are not being met, and that
failing to progress towards a goal at an acceptable rate can lead to negative affect. Ilgen
and Davis (2000) hold that the link between negative feedback and affect is mediated by
feedback recipients’ cognitive representations of their performance level, and attributions
for why they performed at that level. However, they acknowledge that negative feedback
is unlikely to be encoded in an affectively positive way, which is consistent with this
study’s findings.

Effects of Goal Orientation

Self-Efficag

The results of this study indicate that goal orientation influences self-efficacy,
such that a mastery orientation has a positive effect on self-efficacy and an avoid
orientation has a negative effect on self-efficacy. These results are consistent with
VandeWalle (2001), who explained the positive relationship between mastery orientation
and self-efficacy by saying that a mastery orientation leads to viewing negative feedback
as an opportunity to learn, which prevents self-efficacy from being lowered by negative

feedback. In addition, VandeWalle argues that a mastery orientation has a positive

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relationship with an internal locus of control, which leads to the interpretation of positive
feedback as indicating that one can succeed on similar tasks in the future.

VandeWalle (2001) explains the negative effect of an avoid orientation on self-
efficacy, and the null effect of a prove orientation on self-efficacy by pointing out that an
avoid orientation has an even stronger relationship with an entity theory of ability and
pessimism than a prove orientation, and an entity theory of ability leads to a perception of
negative feedback as indicative of failure.

In contrast to VandeWalle’s conceptualization of self-efficacy as a consequence
of goal orientation, Carr, DeShon, and Dobbins (2001) suggest that self-efficacy is an
antecedent of the choice between prove and avoid goals in achievement situations. Carr et
al. hold that people are oriented towards the goals of control and/or regard. The goal of
control leads to striving to understand and influence one’s environment through learning
and developing one’s skills. A regard goal leads to striving to garner respect and esteem
fi'om oneself or others. When people are primarily motivated by control goals, they
choose growth (mastery) goals in achievement situations, because growth goals help
them to reduce their uncertainty about their environment. When people are primarily
motivated by regard goals, their choice of goals in achievement situations is influenced
by self-efficacy, such that people with high self-efficacy choose prove goals and those
with low self-efficacy choose avoid goals. This is because individuals with high self-
efficacy view the situation as an opportunity, and choose an approach goal, whereas
individuals with low self-efficacy will view the situation as a threat, and choose an

avoidance goal.

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So, there is evidence that goal orientation influences self-efficacy, yet there is also
a compelling theoretical explanation for why self-efficacy influences the choice of prove
or avoid goals in achievement contexts. These two conceptualizations of the relationship
between self-efficacy and goal orientation are not mutually exclusive. It is possible that
mutual causation exists between self-efficacy and state goal orientation, such that self-
efficacy influences the choice between prove or avoid goals, and goal orientation
influences performance, which influences self-efficacy, then the cycle begins again.
Unfortunately, this mutual causation hypothesis cannot be tested using data from this
experiment, because only trait goal orientation was measured.
_Ajfagt

This study found a positive relationship between avoid orientation and dejection.
Dweck and colleagues hold that performance goals leave one vulnerable to negative
affect if one’s perceived ability is threatened (e. g. Dweck & Leggett, 1988). There is
limited research evidence to suggest that an avoid orientation may lead to more negative
affect than a prove orientation (e.g. Elliot & McGregor, 2001, Elliot & McGregor, 1999).
The more negative affective consequences of an avoid as compared to a prove orientation
may be due to several factors. First, individuals experience avoid regulation as less self-
deterrnined than approach regulation, and the less one experiences self-determination, the
less one experiences intrinsic enjoyment of the task (Deci, 1975). Second, an avoid
orientation is undergirded by fear of failure, whereas a prove orientation is undergirded
by both fear of failure and need for achievement (Elliot, 1999). Therefore, an avoid

orientation should lead to more negative affective consequences than a prove orientation.

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Relationship Between Self-Efficacy and Dejection

This study found a strong negative relationship between self-efficacy and
dejection. In order to explore the direction of causality between self-efficacy and
dejection, two multiple regressions were run predicting dejection in the third block from
self-efficacy in the first block, and predicting self-efficacy in the third block from
dejection in the first block. The results indicated that after intelligence, goal orientation,
and the order of items on the feedback menu were partialled out, self-efficacy in the first
block had a negative relationship with dejection in the third block _A_R2 = .034, Em (l ,
207) = 7.79, p < .006, and dejection in the first block had a negative relationship with
self-efficacy in the third block m2 = .039 an, (1, 206) = 9.27, p < .003. These findings
suggest mutual causation between self-efficacy and dejection.

Bandura (1997) holds that self-efficacy and affect do exert mutual causation, but
that self-efficacy has a much stronger influence on affect than affect has on self-efficacy.
This is because a negative sense of arousal has the potential to undermine one’s self-
efficacy for performing a task, but only if one has no other readily available means of
assessing one’s competence for the task, such as remembering past performance or
engaging in social comparisons. However, self-efficacy has a strong influence on affect
because people with high self-efficacy can more effectively regulate their thoughts,
behaviors, and affective states in order to achieve more positive affective outcomes.
Specifically, if people believe they are not capable of dealing with a threat that could lead
to an aversive outcome, anxiety results. If people believe they are incapable of gaining

valued outcomes, depression results.

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Although the results of this study do not support Bandura’s idea that self-efficacy
has a stronger influence on affect than affect has on self-efficacy, this study does support
the idea of mutual causation between self-efficacy and dejection, which is consistent with
Bandura’s social-cognitive theory.

Effects of Self-Efficm

This study found a positive relationship between self-efficacy and both total
amount of feedback seeking, and total performance on the practice trials. The positive
relationship between self-efficacy and feedback seeking is consistent with past research
which found that self-efficacy effects self-set goal level (Locke & Latham, 1990) and the
strategies people use to achieve those goals (Bandura & Wood, 1989). Presumably this
includes the strategy of seeking feedback.

The positive relationship between self-efficacy and performance is strongly
supported by past research. For example, Stajkovic and Luthans’ (1998) meta-analysis of
114 studies found a significant weighted average correlation between self-efficacy and
performance, G(r+) = .3 8. Judge and Bono’s (2001) meta-analysis found an uncorrected
mean correlation of g = .19, p < .05 between generalized self-efficacy and performance.
This relationship between self-efficacy and performance is at least partially due to the
fact that self-efficacy leads to setting higher goals (Locke & Latham, 1990), and the
positive relationship between self-efficacy and the amount of time and effort the person is
willing to expend on the task (Bandura, 1997).

Effects of Dejection
A negative relationship was found between dej ection and both overall feedback

seeking and total performance on the practice trials. These findings are consistent with

92

control theory, which holds that experiencing negative affect over a long enough period
of time can lead to lowering one’s standards and decreasing effort (Carver & Scheier,
1998). For the task used in this experiment, decreasing one’s effort could manifest itself
in seeking less feedback and lower performance.

The negative relationship that was found between dejection and performance is
consistent with past research. For example, Murray, Sujan, Hirt, and Sujan (1990) found
that unpleasant affect inhibits cognitive flexibility, which decreases performance quality
on complex tasks. In addition, research has shown that negative affect can interfere with
the generation and testing of alternative strategies (Cervone, J iwani, & Wood, 1990;
Bandura & Jourden, 1991).

Implications for Embedded Training Systems

The results of this study are expected to generalize to other embedded training
systems that allow trainees to choose how much feedback they seek, and where there are
no impression management considerations associated with feedback seeking. If trainees
can choose how much feedback they seek, and are not attempting to manage others’
impressions of them, individuals with a prevention focus will tend to seek less feedback
than those with a promotion focus. This is consistent with the learner control literature,
which holds that learners often do not make decisions that maximize their own learning
under conditions of learner control. The finding that individuals with a prevention focus
seek less feedback than those with a prevention focus suggests that individuals with a
prevention focus will tend to exhibit less adaptability than those with a prevention focus,
for two reasons. First, training design elements that leverage self-regulatory processes,

such as feedback seeking, lead to increased adaptability (Smith et al., 1997), and

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regulatory focus influences feedback seeking. Second, the maintenance of trained skills is
a prerequisite to the adaptation of those skills to a more complex environment, and this
study shows that regulatory focus influences performance through its effects on feedback
seeking. Therefore, regulatory focus influences adaptability, an important skill in today’s
workplace.

If trainees can seek feedback directly from the embedded training system, without
anyone monitoring their feedback-seeking behavior, then impression management
concerns will not influence their choices, and the results of the current study are expected
to generalize. In contrast, if trainees must seek feedback from a person, or if a person is
aware of their feedback-seeking behavior, trainees may use their feedback-seeking
behavior to manage that person’s impressions of them. Consistent with this idea,
VandeWalle (in press) proposed that individuals with a performance goal orientation
more likely than those with a mastery orientation to seek evaluative feedback fi'om a
person if they perceive that they are performing well. This is because individuals who are
primarily performance-goal oriented are more concerned with managing others’
impressions of them than those who are primarily mastery-goal oriented, and people seek
evaluative feedback when they are performing well as a strategy to draw the evaluator’s
attention to their good performance.

The results of this study suggest a few recommendations for the design of
embedded training systems. First, trainees’ performance and adaptability can be
improved using interventions to increase the amount of feedback they seek. In this study,
the amount of feedback trainees sought had a positive relationship with performance,

which in turn had a positive relationship with adaptability.

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Second, the amount of feedback trainees seek can be increased by using gain/non-
gain flaming instead of loss/non-loss framing. This recommendation is tentative, because
it is not clear whether the gain/non-gain framing induced a promotion focus, so there is
some ambiguity regarding why the flaming manipulation worked.

Future Research

This section outlines how the present study could be improved upon in future
research. First it discusses how the methodological flaws of the experiment could be
ameliorated, then it suggests ways to explore the similarities between regulatory focus
and goal orientation.

Methodological Issues

Four methodological issues to be corrected in future research are the effects of the
order of items on the feedback menu on feedback seeking, the poor reliability of certain
measures, and the manipulation of regulatory focus and evaluative feedback.

Order Effects

As explained in the results section, the order in which items were presented on the
menu influenced seeking certain types of feedback. Therefore, it would be interesting to
replicate this experiment using a different experimental design that would either cancel
out the order effects between subjects, or allow the order effects to be estimated and
statistically controlled.

One option is to randomize the order in which items are presented on the feedback
menu for each participant, for each trial. There would be no systematic effect of order
between subjects, but order may have an effect within subjects, which would not be

measurable using this design (Cochran & Cox, 1957). A better option may be to use a

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randomized blocks design. Items on the feedback menu would be grouped into two or
more “blocks,” and items would be randomized within those blocks. This technique
would allow the order effects to be estimated and statistically controlled.
Measures

Three of the measures used in this experiment had low reliability, even though
previous research using the same measures reported acceptable reliability. Both trait
regulatory focus measures had low reliability in this study. Shah, Higgins, and
Friedman’s (1998) Selves Questionnaire was unreliable because people list their ideal
and ought selves, then rate the extent to which their actual selves are discrepant flom
their ideal and ought selves. Therefore, each person is rating their discrepancies flom a
different standard. Davis’s (2001) regulatory focus measure is a better starting point than
Higgins’s Selves Questionnaire for constructing a more reliable measure.

In addition to the trait regulatory focus measures, Roney, Higgins, and Shah’s
(1995) affect measure was also unreliable. A factor analysis of Roney, Higgins, and
Shah’s affect measure failed to support their proposed factor structure (see method
section for a more detailed description of the factor analysis). Specifically, they proposed
a dej ection/ elation factor and an anxiety/quiescence factor, but the factor analysis
revealed three factors: dej ection, elation, and anxiety. Their anxiety factor only had
marginally acceptable reliability (alpha = .67, .61, and .76 at times 1, 2, and 3,
respectively). Therefore, it would be better to use a more reliable measure of anxiety,

such as Spielberger's State-Trait Anxiety scale.

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Manipulating Regalatogz Focus

A third methodological problem to be addressed in future research is that it is
unclear what effects the flaming manipulation had on regulatory focus and risk-seeking.
The situation was flamed either as an opportunity to gain vs. not gain $25, or as a threat
of losing vs. not losing $25. Regulatory focus has been manipulated using similar
flaming instructions in past experiments, where the manipulation presumably worked
because the hypotheses were supported (e. g. Roney, Higgins, and Shah,1995; Higgins,
Shah, and Friedman, 1997). However, it could be argued that the manipulation was not
strong enough or self-relevant enough to prime participants’ ideal or ought self-
discrepancies. Unfortunately, there is no way to check to see if the regulatory focus
manipulation worked in this experiment. There is a need for a reliable measure of
regulatory focus, so that future researchers can determine whether flaming the situation
in terms of gains vs. non-gains or losses vs. non-losses is an effective way to manipulate
regulatory focus.

Although the flaming manipulation used in this study has been used in past
experiments to manipulate regulatory focus, similar manipulations have also been used to
demonstrate the effects of flaming on people’s propensity to make risky decisions. This
raises the possibility that the flaming manipulation used in this study may have
influenced risk-seeking.Kahneman and Tversky (l 97 9) found that when decisions are
flamed in gain vs. non-gain terms, people tend to be risk averse, whereas when decisions
are flamed in loss vs. non-loss terms, people tend to seek risks. This is because people

tend to value an outcome more highly if there is a chance that they might lose it than if

97

there is a chance that they might gain it. For example, Tversky and Kahneman (1988)
gave people the following two problems:

1. Assume yourself richer by $300 than you are today. You have to
choose between a sure gain of $1 00, or a 50% chance to gain $200
and 50% to gain nothing.

2. Assume yourself richer by $500 than you are today. You have to
choose between a sure loss of $1 00 or a 5 0% chance to lose nothing
and a 50% chance to lose $200.

The majority of people chose the sure gain of $ 1 00 in the first problem, but they
chose the gamble in the second problem. This is because people value the $100 more
highly when they have a chance to lose it than when they have a chance to gain it, so they
are unwilling to accept a sure loss of $100 in the second problem.

Crowe and Higgins (1997) developed hypotheses based on self-discrepancy
theory that directly contradicted the decision flaming literature. They proposed that
gain/non-gain flaming would lead to greater risk-seeking than loss/non-loss flaming,
because gain/non-gain flaming would induce a promotion focus, whereas loss/non-loss
flaming would induce a prevention focus. They tested this hypothesis by examining the
effects of flaming on performance on a signal detection task. They predicted that
participants who received the gain/non-gain flaming would seek to maximize their hits
and rrrimirnize their errors of omission, whereas participants who received the loss/non-
loss flaming would seek to maximize their correct rejections and minimize their errors of
commission. This is because individuals with a promotion focus are eager to reach their

hopes and ideals, so they tend to take action even at the risk of making an error in order

98

to ensure all possible hits. In contrast, individuals with a prevention focus are vigilant
against failure to meet their obligations, so they adopt a more cautious strategy of seeking
to maximize correct rejections and minimize errors of commission. Their hypothesis was
supported.

This study attempted to examine the relationship between flaming and seeking
feedback about hits, errors of omission, correct rejections, and errors of commission,
which would have provided some insight into the relationship between flaming and risk
seeking. However, due to the method flaw, this study’s hypotheses regarding specific
types of feedback could not be evaluated.

One reason why the decision flaming literature and Higgins’ research yield
contradictory results may be because they operationalize risk seeking differently.

In the decision flaming literature, risk seeking is generally operationalized as people’s
responses to situational judgement items asking them what decision they would make in a
hypothetical medical emergency or gambling situation. Specifically, the medical
emergency items generally ask participants to choose between two medical treatments
that differ in terms of how risky they are, and the decision is flamed either in terms of the
percent likelihood that a certain number of people will die, or the percent likelihood that a
certain number of people will be saved. Similarly, the gambling items ask participants to
choose between two games of chance that differ in terms of how risky they are, and the
decision is flamed either in terms of the percent likelihood that a certain amount of
money will be won, or the percent likelihood that a certain amount of money will be lost.

In Higgins’ research, risk seeking is operationalized as people’s propensity to say

that they have or have not seen a stimulus previously on a signal detection task. Perhaps

99

people’s tendency to say that they have seen a stimulus previously is not risk-seeking, but
rather a tendency to approach matches to a desired end state, as opposed to avoiding
mismatches to a desired end state. The risk of saying a signal matches or does not match
is equal, because either answer has an equal likelihood of being wrong. However, saying
that a signal matches is consistent with a promotion-focused individual’s tendency to
approach matches to a desired end state, whereas saying a signal does not match is
consistent with a prevention-focused individual’s tendency to avoid mismatches to a
desired end state.lt would be interesting to conduct a study that manipulated flaming and
measured risk seeking using both Tversky and Kahneman’s medical emergency and
gambling items, and Higgins’ signal detection task, in order to reconcile the contradictory
findings of past research.

Now that the effects of the flaming manipulation on risk seeking hypothesized by
various theorists have been explained, it is possible to return to the issue of whether the
flaming manipulation influenced risk seeking in this study. If the flaming manipulation
had influenced risk seeking, participants who considered seeking feedback to be risky
would have sought more feedback in the loss vs. non-loss flaming condition than in the
gain vs. non-gain flaming condition.

Performance-oriented and prevention focused trainees tend to perceive feedback
seeking as risky, because feedback may indicate that they are failing to meet their goals
of proving their competence, avoiding disproving their incompetence, or avoiding failure
to meet their obligations (V andeWalle, 2001). Therefore, an interaction between the
flaming manipulation and trait goal orientation or trait regulatory focus to influence

feedback seeking would be expected if the flaming manipulation influenced risk seeking.

100

No such interaction was found, so there is no evidence to suggest that the flaming
manipulation influenced risk seeking in this study.
Manipulating Evaluative Feedbac_k

Another methodological issue to be corrected in future research is the way that
success vs. failure on the task was manipulated. Participants were given feedback saying
that they were performing either above or below standards for acceptable performance on
the task. It was assumed that participants who received feedback saying they were
performing below standards would perceive that they were failing to meet their ideals or
obligations, whereas participants who received feedback saying they were performing
above standards would perceive that they were succeeding in meeting their ideals or
obligations. However, the only manipulation check that was collected with regards to the
evaluative feedback was to ask trainees to what extent they believed the evaluative
feedback was accurate. Most trainees perceived that the evaluative feedback was accurate
(the mean accuracy rating was four on a five-point scale), but it is unclear whether they
perceived the “below standards” feedback as indicating failure, and the “above standards”
feedback as indicating success.

A related issue is that it is unclear whether trainees perceived the evaluative
feedback as indicating whether they were likely to succeed or fail on the generalization
trial. Since the $25 reward was contingent on achieving a certain score on the
generalization trial, if participants did not use the evaluative feedback as the basis for
estimating how well they would perform on the generalization trial, the evaluative

feedback probably did not influence their motivation. Unfortunately, it is impossible to

101

determine whether participants’ expectancies for how well they would perform on the
generalization trial were based on the evaluative feedback.
Exploring the Similarities Between Rgtiatory Focus and Goal Oriengtion

As explained earlier, the results of this study raise questions about how similar the
constructs of regulatory focus and goal orientation are to one another. This section will
discuss how regulatory focus and goal orientation might map onto one another based on a
consideration of approach vs. avoidance regulation, antecedents, and consequences.
Appmch vs. Avoida_a_ca

Higgins (1997) conceptualizes a promotion focus as motivation to approach one’s
ideals, and a prevention focus as motivation to avoid failure to meet one’s oughts. Carver
and Scheier (1998) agree that a promotion focus is an example of an approach loop, but
they argue that a prevention focus is an avoidance loop bounded by an approach loop.
They hold that people approach their ought selves in order to avoid failing to meet their
obligations.

Elliot (1999) holds that mastery and performance-prove goals are approach goals,
whereas a performance-avoid goal is an avoidance goal. Specifically, people with
mastery goals approach learning and task mastery, those with prove goals approach being
perceived as competent, and those with avoid goals avoid being perceived as
incompetent.

So, a promotion focus, mastery orientation, and prove orientation are approach
forms of motivation, an avoid orientation is an avoidance form of motivation, and a
prevention focus is primarily an avoidance form of motivation, but involves an approach

loop in the service of an avoidance loop. An examination of the antecedents and

102

consequences of goal orientation and regulatory focus will help to clarify how these
constructs map onto each other.
Antecedents

This section will first discuss how a promotion focus and a mastery orientation
serve similar higher order needs, as do a prevention focus and a performance orientation.
Then, the role of normative vs. internal standards as antecedents to regulatory focus and
goal orientation will be discussed.

Higher-order needs. Van Dijk and Kluger (2001) propose that a promotion focus
and mastery orientation serve nurturance and self-actualization needs, whereas prevention
focus and performance orientation serve security needs. This section presents the
argument that a promotion focus and mastery orientation involve approaching nurturance
(growth) goals that serves higher order control needs, and that a prevention focus and
performance orientation serve security or regard needs.

Higgins (1997) holds that self-regulating around one’s ideals serves nurturance
(e. g. nourishment) needs, whereas self-regulating around one’s oughts serves security
(e. g. protection) needs. Carr, Deshon, and Dobbins (2001) hold that growth (i.e. mastery)
goals serve a higher order control need, which is defined as striving to reduce uncertainty
about the environment through learning, and striving to operate successfirlly in the
environment by improving one’s skills. Carr et al. hold that prove and avoid goals serve a
higher order need for regard, which is defined as striving to garner respect and esteem
flom oneself or others.

Nurturance needs, as described by Higgins (1997), could serve the higher order

goal of control, as described by Carr et al. (2001). In other words, seeking nurturance, or

103

personal growth, could serve the higher order goal of reducing uncertainty about one’s
environment through learning, and striving to operate successfully in the environment by
improving one’s skills. Therefore, a promotion focus and a mastery orientation could
serve similar higher order needs (Van Dijk & Kluger, 2001).

The need for regard, as described by Carr et al., seems similar to the need for
security, as described by Higgins (1997). In Higgins’s discussion of how different child-
rearing practices can lead to a focus on nurturance or security needs, be implies that
security needs have a strong interpersonal component. For eXample, Higgins holds that
when caretakers punish their children by withdrawing their love, this causes the children
to focus on security needs, and thus to self-regulate around oughts. Therefore, an
important aspect of security needs is to have secure interpersonal relationships, a concept
similar to Carr et al.’s regard needs. It follows that a prevention focus and a performance
orientation could serve similar higher order needs (Van Dijk & Kluger, 2001).

Normative vs. internal standards. Although Higgins, Klein, and Strauman (1985)
argue that both ideals and oughts can be self-generated or imposed upon us by others,
oughts seem more likely to be imposed upon us by others than ideals (Van Dijk &
Kluger, 2001). This is because oughts are duties and obligations (Higgins et al., 1985),
and for anyone living as part of a society many of those duties are likely to involve
performing tasks for other people. In contrast, ideals are hopes and dreams, which are
more likely to be internally generated because they serve personal growth goals.

Similarly, a performance orientation can be induced by means of a competitive
goal structure, which focuses people’s attention on normative comparisons of their ability

(Ames, 1984). A mastery orientation can be induced by means of an individualistic goal

104

structure that emphasizes self-challenge, rather than competition. Therefore, normative
standards are antecedents to both a prevention focus and a performance orientation, and
internal standards are antecedents to both a promotion focus and a mastery orientation
(Van Dijk & Kluger, 2001).

Consmuences

A mastery orientation and promotion focus have similar consequences for risk-
taking, perseverance and affect, as do a performance orientation and prevention focus
(Van Dijk & Kluger, 2001).

Risk-taking and perseverance. As explained in the introduction, individuals with a
prevention focus try to decrease their errors of commission (even if it means increasing
their errors of omission) by withdrawing flom the task as soon as possible (Crowe &
Higgins, 1997). In contrast, those with a promotion focus try to decrease their errors of
omission (even if it means increasing their errors of commission) by persevering on the
task. Similarly, individuals with a learning orientation are willing to risk making an error
(of commission) to learn something, so they seek challenges and persevere on difficult
tasks (Elliot & Dweck, 1988). In contrast, individuals with a performance orientation are
more likely to avoid challenges and withdraw flom difficult tasks as soon as possible in
order to avoid making errors (of commission; Crowe & Higgins, 1997).

AM Just as a performance orientation and prevention goals have similar
implications for risk-taking and perseverance, they also have similar consequences for
affect (Van Dijk & Kluger, 2001). Individuals with a promotion focus tend to experience
emotions on the elation-dejection continuum, whereas those with a prevention focus tend

to experience emotions on the anxiety-quiescence continuum (Higgins et al., 1986).

105

Higgins argues that elation and quiescence are positive emotions, and that dejection and
anxiety are negative emotions. However, according to Watson and T ellegen’s (1985)
circumplex model of affect, anxiety and cahnness are at opposite ends of the negative
affect axis, and elation and dullness are at opposite ends of the positive affect axis.
Higgins’s affect dimensions do not map perfectly onto Watson and Tellegen’s
circumplex model, because according to Watson and Tellegen, dejection would fall on
the unpleasantness-pleasantness axis, not the positive affect axis. Still, it is useful to think
of Higgins’s affect dimensions in terms of Watson and Tellegen’s model, because it
provides a way to integrate the findings of the goal orientation literature and the
regulatory focus literature (Van Dijk & Kluger, 2001).

People with a mastery orientation tend to experience intrinsic enjoyment of the
task (Deci & Ryan, 1985), which is an example of high positive affect, whereas those
with a performance orientation tend to experience anxiety and stress (Dweck & Leggett,
1988), which are examples of high negative affect. So, a promotion focus and a mastery
orientation both lead to affect on the positive affect dimension, whereas a prevention
focus and a performance orientation both lead to affect on the negative affect dimension
(Van Dijk & Kluger, 2001).

Proppsitiorflbout the Relationship Between Regmtory Focus apd GogOrientaLtion

 

Therefore, promotion focus and mastery orientation seem similar, because both
involve approaching a developmental goal that serves higher order control needs. In
addition, both can be situationally manipulated by instating an intemally-referenced goal

structure, and both have similar consequences in terms of persevering and taking risks in

106

order to improve, and experiencing affect on the positive affect dimension (Van Dijk &
Kluger, 2001).

A prevention focus and a performance orientation (as conceptualized in Dweck’s
(1978) two-factor model of goal orientation) both incorporate approach and avoidance
goals (Carver & Scheier, 1998; Elliot, 1999) that serve security needs. In addition, a
prevention focus and a performance orientation can be situationally induced using
normative standards, and both have similar consequences in terms of withdrawing flom
difficult tasks in order to avoid errors of commission, and experiencing affect on the
negative affect dimension (Van Dijk & Kluger, 2001).

Conclusions

The purpose of this study was to determine the effects of regulatory focus and
evaluative feedback on the type and amount of feedback trainees seek, and the
consequences of this feedback-seeking behavior for learning, performance, and transfer.
Although the predicted interactive effects of regulatory focus and evaluative feedback on
feedback seeking were not supported, regulatory focus had a significant main effect on
overall feedback seeking, such that a promotion focus leads to more feedback seeking
than a prevention focus. This finding contradicts the predictions of Higgins (2001) and
Kluger (2000), and is consistent with the conceptualization of self-regulation as
equivalent to goal orientation. However, more research is needed to explore the
relationship between self-regulation and goal orientation in a more controlled manner.

A positive relationship was found between overall feedback seeking and total
performance on the practice trials. This is consistent with past research indicating the

positive effects of feedback on performance (e. g. Ammons, 1956, Mento et al., 1987),

107

and reconfirms the importance of understanding the antecedents to feedback seeking in
order to leverage performance. Future research should improve on the methodology of
this study to explore the effects of regulatory focus and goal orientation on seeking

different types of feedback.

108

REFERENCES

Ames, C. (1984). Achievement attributions and self-instructions under

competitive and individualistic goal structures. Journal of Educational Paychology, 76,
478-487.

Ammons, R. B. (1956). Effects of knowledge of performance: A survey and
tentative theoretical formulation. Journal of Genaral Psychology, 54, 279-299.

Ashford, S. J. & Cummings, L. L. (1983). Proactive feedback seeking: The
instrumental use of the information environment. Organizational Behavior and Huma_n
Decision Processes. 32. 370-398.

Ashford, S. J. & Cummings, L. L. (1985). Feedback as an individual resource:
Personal strategies of creating information. Journal of Occmpational Psychology, 58, 67-
79.

Ashford, S. J. & Northcraft, G. B. (1992). Conveying more (or less) than we
realize: The role of impression-management in feedback-seeking. Organizational
Behavior & Human Decision Processes. 53. 310-334.

Ashford, S. J. & Tsui, A. S. (1991). Self-regulation for managerial effectiveness:
The role of active feedback seeking. Academy of Management JoumaL. 34. 251-280.

Bandura, A. & Cervone, D. (1983). Self-evaluative and self-efficacy mechanisms
governing the motivational effects of goal systems. Journal of Personalig; and Social
Psychology, 45, 1017-1028.

Bandura, A. & Cervone, D. (1986). Differential engagement of self-reactive
influences in cognitive motivation. Organizational Behavior and Human Decision
Processes. 38. 92-1 13.

Bandura, A. (1991). Social-cognitive theory of self-regulation. Organizational
Behavior and Human Decision Processes. 50. 248-287.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H.
Freeman and Co.

Bandura, A., & Jourden, F. J. (1991). Self-regulatory mechanisms governing the
impact of social comparison on complex decision-making. Journal of Personality a_n_d
Social Psychology, 60, 941 -951.

Bandura, A., & Wood, R. E. (1989). Effect of perceived controllability and
performance standards on self-regulation of complex decision-making. Journal of
Personality aard Soaial Psychology, 56, 805-814.

109

Bell, B. S., & Kozlowski, S. W. J. (in press). Guiding individuals through
training: The effects of adaptive guidance in a complex training environment.

Brendl, C. M. & Higgins, E. T. (1996). Principles of judging valence: What
makes events positive or negative? In M. P. Zanna (Ed.), Advgces in experimental
social psychology: Vol. 28 (pp. 95-160). New York: Academic Press.

Brown, K. G. (2001). Using computers to deliver training: Which employees
learn and why? Personnel Psychology, 54, 271-296.

Button S. B., Mathieu, J. E. & Zajac, D. M. (1996). Goal orientation in
organizational research: A conceptual and empirical foundation. Organizational Behavior
aad Human Decision Processes. 67. 26-48.

Carr, J. Z., DeShon, R. P., & Dobbins, H. W. (2001). A process model of goal
orientation. In R.P. DeShon (Chair), New Direction_s In Goal Orientation: Exploring The
Construct And Its Mea_surement. Presented in a symposium at the 16th Annual
Conference of the Society for Industrial and Organizational Psychology, San Diego, CA.

Carver, C. S. & Scheier, M. F. (1982). Control theory: A useful conceptual
flamework for personality, social, clinical, and health psychology. Psychological
Bulletin. 92. 111-135.

Carver, C. S., & Scheier, M. F. (1998). On the self-regulation of behavior. New
York: Cambridge University Press.

Carver, C. S., Lawrence, J. W., & Scheier, M. F. (1996). A control-process
perspective on the origins of affect. In L. L. Martin & A. Tesser (Eds), Striving and
feeling: Interactions among goals. affetaand self-regalation (pp. 11-52). Mahwah, N.J.:

Cervone, D. Jiwani, N., & Wood, R. (1990). Goal setting and the differential
influence of self-regulatory processes on complex decision-making performance. Journal
of Personality and Social Psychology, 61, 257-266.

Cochran, W. G., & Cox, G. M., (1957). Experimental Desigpa. New York: John
Wiley & Sons, Inc.

Cook, D. M. (1968). The impact on managers of flequency of feedback. Academy
of Management Journal. 11. 263-277.

Cotton (1995). Participation’s effect on performance and satisfaction: A
reconsideration of Wagner. Academy of Management Review. 20. 276-278.

Crowe, E. & Higgins, E. T. (1997). Regulatory focus and strategic inclinations:
Promotion and prevention in decision-making. Organizational Belmviopand Humg
Decision Processes. 69. 117-132.

110

Davis, C. A. (2001). Reactions to Performance Feedback: The Role of Goa_l
Orientation and Self-Regalatog Focus. Unpublished master’s thesis, Michigan State
University, Lansing.

 

Deci, E. L. (1975). Intrinsic Motivation. New York: Plenum.

Deci, E. L. & Ryan, R. M. (1985). Intrinsic motivatiomd self-deterrnination in
human behavior. New York: Plenum.

Dweck, C. S. (1975). The role of expectations and attributions in the alleviation of
learned helplessness. Joumal of Personality & Social Psychology, 31, 674-685.

Dweck, C. S. (1986). Motivational processes affecting learning. American
Psychologist. 41. 1040-1048.

Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation
and personality. Psychological Review. 95. 256-273.

Earley, P. C. (1988). Computer-generated performance feedback in the magazine-
subscription industry. Organizational Behavior and Human Decision Processes. 41. 50-
64.

Easterbrook, J. A. (1959). The effect of emotion on cue utilization and the
organization of behavior. Psychological Review. 66. 183-201.

Elliot, A. J. (1999). Approach and avoidance motivation and achievement goals.
Educational Psychologist. 34. 169-189.

Elliot, A. J ., & McGregor, H. A. (1999). Test anxiety and the hierarchical model
of approach and avoidance achievement motivation. Journal of Personalig & Social

Psychology, 76, 628-644.

Elliot, A. J ., & McGregor, H. A. (2001). A 2 * 2 achievement goal flamework.
Journal of Permlity and Social Psychology, 80, 501-519.

Elliott, E. S., & Dweck, C. S. (1988). Goals: An approach to motivation and
achievement. Journal of Personality & Social Psychology, 54, 5-12.

Experimental Social Psychology, 26, 108-123.

Fisher, S. L. & Ford, J. K. (1998). Differential effects of learner effort and goal
orientation on two leaming outcomes. Personnel Psychology, 51, 397-420.

Gioia, D. A. & Sims, H. P. (1986). Cognition/behavior connections: Attribution
and verbal behavior in leader-subordinate interactions. Organizational Behavior and
Human Decision Processes. 37. 197-229.

111

Gist, M. E., & Mitchell, T. R. (1992). Self-efficacy: A theoretical analysis of its
determinants and malleability. Academy of Management Review. 17. 183-211.

Gryna, F. M. (1988). Production. InJ. M. Juran&F. M. Gryna (Eds. ),J____uran’ s
girality control handbook (4‘11 ed. ). New York: McGraw-Hill.

Hage, J. (1995). Post-industrial lives: New demands, new prescriptions. In A.
Howard (Ed.), The changing nature of work (pp. 139-174). San Francisco: Jossey-Bass.

Higgins, E. T. & Tykocinski, O. (1992). Self-discrepancies and biographical
memory: Personality and cognition at the level of psychological situation. Personaliiy and
Social Psychological Bulletin. 18. 527-535.

Higgins, E. T. (1987). Self-discrepancy: A theory relating self and affect.
Psimhological Review. 94. 319-340.

Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist. 52.
1 280-1 300.

Higgins, E. T. (1998). Promotion and prevention: Regulatory focus as a
motivational principle. In M. P. Zanna (Ed.), Advances in experimental social
psychology (V 01. 30, pp. 1—46). San Diego, CA: Academic Press.

Higgins, E. T. (2001). Promotion and prevention experiences: Relating emotions
to nonemotional motivational states. In J. P. F orgas (Ed.), Handboofik of affect and socia_l
cogpition. (pp. 186-211). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Higgins, E. T., Roney, C. J. R., Crowe, E., & Hymes, C. (1994). Ideal versus
ought predilections for approach and avoidance: Distinct self-regulatory systems. Journal
of Personality and Social Psychology, 66, 276-286.

Higgins, E. T., Bond, R. N., Klein, R., & Strauman, T. (1986). Self-discrepancies
and emotional vulnerability: How magnitude, accessibility, and type of discrepancy
influence affect. Journal of Personalng and Social Paychology, 51, 5-15.

Higgins, E. T. Klein, R. S., & Strauman, T. (1985). Self-concept discrepancy
theory: A psychological model for distinguishing among different aspects of depression
and anxiety. Social Cognition. 3. 51 -.76

Higgins, E. T., Shah, J ., & Friedman, R. (1997). Emotional responses to goal
attainment: Strength of regulatory focus as moderator. Journal of Personality and Social

Psychology, 72, 515-525.

Ilgen, D. R. & Davis, C. A. (2000). Bearing bad news: Reactions to negative
performance feedback. Applied PsychologLAn International Review. 49. 550-565.

Ilgen, D. R., Fisher, C. D., & Taylor, M. S. (1979). Consequences of individual
feedback on behavior in organizations. Journal of Applied Psychology, 64, 349-371.

112

Ilgen, D. R., Mitchell, T. R., & Fredrickson, J. W. (1981). Poor performers:
Supervisors' and subordinates' responses. Organizational Bahaworfll Human Decision
Processes. 27. 386-410.

Judge, T. A. & Bono, J. E. (2001). Relationship of core self-evaluations traits--
self-esteem, generalized self-efficacy, locus of control, and emotional stability--with job
satisfaction and job performance: A meta-analysis. J ourna of Applied Psychology, 86,
80-92.

Kluger, A. N. & DeNisi, A. (1996). Effects of feedback intervention on
performance: A historical review, a meta-analysis, and a preliminary feedback
intervention theory. Psychological Bulletin. 119. 254-284.

Kluger, A. N. (2000). Needs. self-regulation. and ris sk preference. Paper presented
at the 15th annual convention of the Society for Industrial/Organizational Psychology,
New Orleans, LA.

Kluger, A. N., Van-Dijk, D., Kass, R., & Stein, E. Z., & Lustig, H. (2000a). When
positive/negative feedback makes us try harder? Hebrew University of Jerusalem,
unpublished manuscript.

Kopelman, R. E. (1986). Managing productivity in organizations: A practical,
pepple—oriented perspective. New York: McGraw-Hill.

Kozlowski, S. W. J ., & Gully, S. M. (1996, August). TEAMS/TANDEM:
Examining skill aguisition, adaptability. and effectivenesa. In J. Vancouver & A.
Williams (Chairs), Using computer simulations to study complex organizational
behavior. Symposium conducted at the Annual Convention of the Academy of
Management Association, Cincinnati, OH.

Kozlowski, S. W. J ., Gully, S. M., Smith, A. E., Nason, E. R., & Brown, K. G.
(1995, May). Sequenced mastery training and advance organizers: Effects on learning,
self-efficacy, performance, and generalization. In R. J. Klimoski (Chair), Thinking and
feeling while doing: thUnderstandiag the learner 1n the learning process. Symposium
conducted at the 10th Annual Conference of the Society for Industrial and Organizational
Psychology, Orlando, FL.

Kozlowski, S. W. J ., Toney, R. J ., Mullins, M. E., Bell, B. S., & Weissbein, D.
A., (1998). Guiding the development of deployable shipboard training systems:
Enhancing skill acquisition. adaptability. and effectiveness (Final Report 2; Contract No.
N61339-96-K-0005). Orlando, FL: Naval Air Warfare Center Training Systems Division.

Kozlowski, S. W. J ., Toney, R. J ., Mullins, M. E., Weissbein, D. A., Brown, K
G., & Bell, B. S. (2001). Developing adaptability: A theory for the design of integrated-
embedded training systems. In E. Salas (Ed.), Advances in human performance a_rl_d_
_cagnitive engineering reseaih (V 01. 1, pp. 59-123). Amsterdam: JAI/Elsevier Science.

113

Larson, J. R. (1986). Supervisors’ performance feedback to subordinates: The
impact of subordinate performance valence and outcome dependence. Organizational
Behavior and Human Decisions Processes. 37. 391-408.

Liden, R. C. & Mitchell, T. R. (1985). Reactions to feedback: The role of
attributions. Academy of Management Journal. 28. 291-308.

Locke, E. A. & Latham, G. P. (1990). A theory of goal setting and task
performance. Upper Saddle River, NJ: Prentice-Hall, Inc.

Lord, R. G., & Levy, P. E. (1994). Moving flom Cognition to Action: A Control
Theory Perspective. Applied Psychology: An International Review. 43. 335-398.

Mento, A. J ., Steel, R. P., & Karren, R, J. (1987). A meta-analytic study of the
effects of goal setting on task performance: 1966-1984. Organizational Behavior and
Human Decision Processes. 39. 52-83.

Moretti, M. M. & Higgins, E. T. (1990). Relating self-discrepancy to self-esteem:
The contribution of self-discrepancy beyond actual-self ratings. Journal of

Murray, N., Sujan, H. Hirt, E. R. & Sujan, M. (1990). The influence of mood on
categorization: A cognitive flexibility interpretation. J oumflf Personality anal Social
Psychology, 59, 411-425.

Phillips, J. M. & Gully, S. M. (1997). Role of goal orientation, ability, need for
achievement, and locus of control in the self-efficacy and goal-setting process. Journal of

Applied Psychology, 82, 792-802.

Roney, C. J. R., Higgins, E. T., & Shah, J. (1995). Goals and flaming: How
outcome focus influences motivation and emotion. Persrmrlity and Social Psychology
Bulletin, 21,1151-1160.

 

Shah, J ., Higgins, T., & Friedman, R. S. (1998). Performance incentives and
means: How regulatory focus influences goal attainment. Journal of Personalng and
Social Psychology, 74, 285-293.

Smith, E. M., Ford, J. K., & Kozlowski, S. W. J. (1997). Building adaptive
expertise: Implications for training design strategies. In M. A. Quinones & A. Ehrenstein

(Eds.), Training for a rapidly changing workplace: Applications of psychological research
(pp. 89-118). Washington, DC: American Psychological Association.

 

Spielberger, C. D. (1983). Manual for the State-Trait Anxiety Inventory. Palo
Alto, CA: Consulting Psychologists Press.

 

Stajkovic, A. D. and Luthans, F. (1998). Self-efficacy and work-related
performance: A meta-analysis. Pavehological Bulletin. 124. 240-261.

114

Strauman, T. J. (1990). Self-guides and emotionally significant childhood
memories: A study of retrieval efficiency and incidental negative emotional content.

Journal of Personalig and Social Psychology, 59, 869-880.

Strauman, T. J. (1996). Stability within the self: A longitudinal study of the
structural implications of self-discrepancy theory. Journal of Personality and Social

 

Strauman, T. J ., & Higgins, E. T. (1987). Automatic activation of self-
discrepancies and emotional syndromes: When cognitive structures influence affect.
Journaflf Personalig and Social Psycholo 53, 1004-1014.

Tailby, S. & Tumbull, P. J. (1987, January). Learning to manage just-in-time.
Personnel Management, pp. 16-19.

Tennyson, R. D. (1980). Instructional control strategies and content structure as
design variables in concept acquisition using computer-based instruction. Journal of
Educational—Psychology, 72, 525-532.

Tennyson, R. D. (1981). Use of adaptive information for advisement in learning
concepts and rules using computer assisted instruction. Ameriam Educational Research
Journal 18 425-438.

 

Tennyson, C. L., Tennyson, R. D., & Rothen, W. (1980). Content structure and
instructional control strategies as design variables in concept acquisition. Journal of

Educational Psychology, 72, 499-505.

Van Dijk, D. V., & Kluger, A. N. (2001). Goal orientation versus self-regulation:
different labels or different constructs?

VandeWalle, D. (1997). Development and validation of a work domain goal
orientation instrument. Educational and Psychological Measurement, 57, 995-1015.

VandeWalle, D. C., Cron, W. L., & Slocum, J. W., (2001). The role of goal
orientation following performance feedback. Journal of Applied Psychology, 86, 629-
640.

VandeWalle, D. (in press). A goal orientation model of feedback-seeking
behavior. Human Resource Management Review.

Wagner, J. A. (1994). Participation's effects on performance and satisfaction: A
reconsideration of research evidence. Academy of Management Review. 19. 312-330.

Wall, T. D. & Jackson, P. R. (1995). New manufacturing initiatives and shopfloor
job design. In A. Howard (Ed.), The changing nature of work (pp. 139-174). San
Francisco: Jossey-Bass.

Watson, D. & Tellegen, A. (1985). Toward a consensual structure of mood.
Psychological Bulletin, 98, 219-235.

115

Weaver, J. L., Bowers, C. A., Salas, E., Cannon-Bowers, J. A. (1995). Networked
simulations: New paradigms for team performance research. Behavior Research
Methods. Instruments and Computers. 27. 12-24.

Weaver, J. L., Morgan, B. B., Jr., Hall, J ., & Compton, D. (1993). Team decision
making in the command information center: Development of a low-fidelity team deci§i_or_r
making task for assessingthe effects of teamwork stressors. Orlando, FL: Naval Training
Systems Center, Human Factors Division.

Wine, J. (1971). Test anxiety and direction of attention. Psychological Bulletin.
LC, 92-104.

APPENDICES

116

APPENDIX A

Electronic Informed Consent for On-line Questionnaire

The research study in which you are being asked to participate is about computer-
based training. The study has two parts. The first part is to fill out a questionnaire on the
University’s intranet. The second part is to go to a computer laboratory at room 4 Snyder
Hall and participate in a computer simulation. This consent form relates to the first part of
the research study.

If you agree to participate in the first part of the research study, you will be asked
to fill out a questionnaire, which will take about 20 minutes. It includes questions about
your demographic information, SAT or ACT scores, and other variables related to the
computer simulation you will do if you agree to participate in the second part of the
research study.

Your participation is completely voluntary. You are flee not to answer certain
questions if you choose, and you are flee to discontinue the study at any time for any
reason without penalty. No risks or discomforts are anticipated as a result of filling out
the questionnaire.

You will receive six credits for your participation in of this study. In the second
part of the research study, you may receive $25 based on your scores on the computer
simulation. The score requirements will be explained during the second part of the
research study. Winners will be determined at the conclusion of the second part of the

research study.

117

At the end of your involvement in the second part of this research study, you will
be provided with feedback explaining the purpose of the research in more detail. You
may ask about the results of the study when it is complete by contacting the investigator.
The investigator’s name and phone number are at the bottom of this form. If you have
any question about your participation in this study, ask the investigator before you
indicate your consent to voluntarily participate by signing below. If you have any
questions about your rights as a research participant, please contact David Wright at the
phone number below.

If you agree to participate, you will be asked to type your name at the bottom of
this form. The reason you are asked for your name is to ensure that you receive credit for
participating in the study. Your name will not be associated with your responses, and will
be kept confidential. Your privacy will be protected to the maximum extent allowable by
law.

Consent: You have been informed of the above-described study and its possible
risks. You give permission to participate in the first part of the research study, which
involves filling out a questionnaire on the University’s intranet. You know that the
investigator will be available at the phone number and e-mail address below to answer
any questions you may have. You understand that you are flee to withdraw this consent
and discontinue your participation in this study at any time without penalty.

Invesggator: Heather Dobbins 353-9166 dobbinsh@msu.edu
UCRIHS Chaia: David E. Wright 355-2180

Name Date:

 

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Subject #:
Informed Consent

Tactical Action Simulation (TAS)

The study in which you are being asked to participate investigates your learning
and performance on the Tactical Action Simulation (TAS). TAS is a computer-
simulated, radar tracking task that you will be trained to use, and then you will practice.
Using the computer-mouse, you will assess the attributes of contacts that appear on your
screen and decided what action should be taken for each contact. You will also be asked
to answer questions which will help us understand your task performance and learning.

Your participation in this study requires 2.5 hours of your time. Your participation
is completely voluntary. You are flee not to participate in certain procedures or answer
certain questions if you choose, and you are flee to discontinue the study at any time for
any reason without penalty. No risks or discomforts are anticipated as a result of this
study, other than those associated with working on a challenging task on a computer for
this length of time.

You will be given a subject number at the beginning of the experiment. The
purpose of this subject number is to keep track of the various materials you will complete
throughout the study. Your name will not be associated with your responses and will be
kept confidential. Your privacy will be protected to the maximum extent allowable by
law.

You will receive six credits for participation in this study. You may receive $25

based on your scores on the final trial. The score requirements will be explained later in

119

the experiment. Winners will be determined at the conclusion of today’s session. If you
win, you will be contacted at the address and phone number you indicate below within 3
weeks of the study’s conclusion. Instructions for claiming the award will be provided
when you are contacted.

At the end of your involvement in this study, you will be provided with feedback
explaining the purpose of the research in more detail. You may ask about the results of
the study when it is complete by contacting the investigator. The investigator’s name and
phone number is on the debriefing form you will receive at the conclusion of your
involvement. If you have any question about your participation in this study, ask the
investigator before you indicate your consent to voluntarily participate by signing below.

Consent: You have been informed of the above-described study and its possible
risks. You give permission for your participation in this study. You know that the
investigator will be available to answer any questions you may have. You understand

that you are flee to withdraw this consent and discontinue your participation in this study

 

 

at any time without penalty.
Print name Signature
Address Phone

 

 

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APPENDIX B

Debriefing Form

Tactical Action Simulation (TAS)

The study in which you just participated was designed to examine the effects of
training and feedback on learning and decision-making processes. During this study, you
operated the TAS radar simulation. TAS simulates the complex physical performance,
information processing, and decision-making demands of performing fast-paced, critical
tasks. To perform the TAS simulation, you needed to learn how to operate the task and
develop strategies for effective task performance. TAS required you to gather
information about the objects on the screen, make decisions, and take actions based on
the information you gathered. We will use the information gathered during the study to
link your performance on the task to your knowledge of the task.

If you have any questions about this study or would like to receive a copy of the
results when they are complete, please notify the investigator now. If, in the future, you
have any questions about the study or would like to receive the results when they are
complete, please call the investigator listed below. Finally, thank you for participating in
this study. If you have any other questions or comments please do not hesitate flom
contacting the experimenter. If you have any questions about your rights as a participant
in a research study, please contact the UCHRIS Chair.

Investigator: Heather Dobbins 353-9166

UCRIHS Chair: David E. Wright 355-2180

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APPENDIX C

Regulatory focus manipulation

The following is the promotion focus manipulation:

“This study is about computer-based training. You will be operating a
computer simulation called the Tactical Action Simulation, or TAS. Each
time you operate TAS, the computer gives you a score based on your
performance. You will gain $25 if you score 1500 or higher on the final

trial, otherwise you will not gain the $25.”

The following is the prevention focus manipulation:

“This study is about computer-based training. You will be operating a
computer simulation called the Tactical Action Simulation, or TAS. Each
time you operate TAS, the computer gives you a score based on your
performance. You will receive $25 for your participation, but if your

score is below 1500 on the final trial, you will lose the $25.”

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APPENDIX D

On-line questionnaire
SAT and ACT Scores
In the spaces below we would like you to please record your highest SAT and/or
ACT score. It is important to realize that this score will be used for research purposes
only and will be kept completely confidential.

SAT Score:

 

ACT Score:

 

We would also like your permission to verify these scores with the Registar’s
office here at Michigan State University. By checking the box below you are providing

your consent for us to verify your SAT and/or ACT scores with the Registar’s office.
Work Orientation1
This set of questions asks you to describe your general work orientation. Please make

your ratings by clicking on one of the buttons below each questionz.

1. I do my best when I’m working on a fairly difficult task.

 

‘ Button et al.’s (1996) measure of goal orientation
2 The buttons below each question correspond to a Likert-type scale with anchors ranging
flom 1 (strongly disagree) to 5 (strongly agree).

123

2. When I have difficulty solving a problem, I enjoy trying different approaches to see
which one will work.

3. I try hard to improve on my past performance.

4. The opportunity to do challenging work is important to me.

5. The opportunity to extend the range of my abilities is important to me.

6. The opportunity to learn new things is important to me.

7. I prefer to work on tasks that force me to learn new things.

8. When I fail to complete a difficult task, I plan to try harder the next time I work on it.

9. The things I enjoy the most are the things I do the best.

10. I feel smart when I do something without making any mistakes.

11. I prefer to do things that I can do well rather than things that I do poorly.

12. I like to be fairly confident that I can successfully perform a task before I attempt it.

13. I am happiest at work when I perform tasks on which I know that I won’t make any
errors.

14. I feel smart when I can do something better than most other people.

15. The opinions others have about how well I can do certain things are important to me.

16. I like to work on tasks that I have done well on in the past

Demogaphic Questiona

 

The questions below ask you to provide some basic information about yourself.

Answer the questions by clicking on the appropriate button.

17. What is your gender?

(1) Male (2) Female

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18. What is your age?
(1)<18 yrs (2) 18-19 yrs (3) 20 - 21 yrs (4) 22-23 yrs (5) > 23 yrs
19. What is your overall grade point average?
(1) 0- 1.0 (2) 1.1-2.0 (3) 2.1 -3.0 (4) 3.1 -4.0 (5) >40
20. Have you ever been to this particular lab before?
(1) Yes (2) No
21. Are left or right handed?
(1) Left (2) Right
22. Do you play with video games?

(1) Never (2) Rarely (3) Sometimes (4) Frequently (5) Always

Task Attitudes3
This set of questions also asks you to describe your general task orientation.

Please use the buttons below each question to make your ratings.4

23. I am willing to select a challenging assignment that I can learn a lot flom.
24. I often look for opportunities to develop new skills and knowledge.

25. I enjoy challenging and difficult tasks where I’ll learn new skills.

26. For me, development of my ability is important enough to take risks.

27. I prefer situations that require a high level of ability and talent.

28. I’m concerned with showing that I can perform better than others.

 

3 VandeWalle’s (1997) measure of goal orientation
4 The buttons below each question correspond to a Likert-type scale with anchors ranging
flom 1 (strongly disagree) to 5 (strongly agree).

125

29.

30.

31.

32.

33.

34.

35.

I try to figure out what it takes to prove my ability to others.

I enjoy it when others are aware of how well I am doing.

I prefer projects where I can prove my ability to others.

I would avoid taking on a new task if there were a chance that I would appear rather
incompetent to others.

Avoiding a show of low ability is more important to me than learning a new skill.
I’m concerned about taking on a task at work if my performance would reveal that I
had low ability.

I prefer to avoid situations where I might perform poorly.

Selves Questionnaire

Before you fill out this questionnaire, it is important to understand what your

ideal self is, and what your ought self is.

Your ideal self is the type of person you would ideally like to be, the type of

person you hope or wish to be. Your ought self is the type of person you believe you

should be, the type of person you believe it is your duty or responsibility to be. In this

questionnaire, you will be asked to give examples of characteristics that describe your

ideal and ought selves. The characteristics describing your ideal self should be different

flom the characteristics describing your ought self.

36a. Please list a characteristic of your ideal selfbelow. This should be a characteristic of

the type of person you would ideally like to be:

126

36b. To what extent would you ideally like to have the characteristic you listed in #3 6a
above? Please use the buttons below to make your ratings.5

36c. To what extent to you actually have the characteristic you listed in #3 6a above?
Please use the buttons below to make your ratings.

37a. Please list another characteristic of your ideal selfbelow:

37b. To what extent would you ideally like to have the characteristic you listed in #37a
above? Please use the buttons below to make your ratings.

37c. To what extent to you actually have the characteristic you listed in #3 7a above?
Please use the buttons below to make your ratings.

38a. Please list another characteristic of your ideal selfbelow:

38b. To what extent would you ideally like to have the characteristic you listed in #3 8a
above? Please use the buttons below to make your ratings.

38c. To what extent to you actually have the characteristic you listed in #3 83 above?
Please use the buttons below to make your ratings:

39a. Please list another characteristic of your ideal selfbelow:

39b. To what extent would you ideally like to have the characteristic you listed in #3 9a
above? Please use the buttons below to make your ratings.

39c. To what extent to you actually have the characteristic you listed in #3 9a above?
Please use the buttons below to make your ratings.

40a. Please list another characteristic of your ideal selfbelow:

40b. To what extent would you ideally like to have the characteristic you listed in #403

above? Please use the buttons below to make your ratings.

 

5 The buttons below each multiple-choice question in this section correspond to a Likert-

127

40c. To what extent to you actually have the characteristic you listed in #40a above?

Please use the buttons below to make your ratings.

Now, there are going to be some questions about your ought self. Remember, your ought
self is the type of person you believe you ought to be, the type of person you believe it is

your duty or responsibility to be.

41 a. Please list a characteristic of your ought selfbelow. This should be a characteristic
of the type of person you believe you should be:
41b. To what extent do you believe you should have the characteristic you listed in #41 a
above? Please use the buttons below to make your ratings.

41c. To what extent to you actually have the characteristic you listed in #41a above?
Please use the buttons below to make your ratings.

42a. Please list another characteristic of your ought selfbelow:

42b. To what extent do you believe you should have the characteristic you listed in #42a
above? Please use the buttons below to make your ratings.

42c. To what extent to you actually have the characteristic you listed in #43a above?
Please use the buttons below to make your ratings.

43a. Please list another characteristic of your ought selfbelow:

43b. To what extent do you believe you ought to have the characteristic you listed in

#43a above? Please use the buttons below to make your ratings.

 

type scale with anchors ranging flom 1 (slightly) to 4 (extremely).

128

43c. To what extent to you actually have the characteristic you listed in #43a above?
Please use the buttons below to make your ratings.

44a. Please list another characteristic of your ought self below:

44b. To what extent do you believe you ought to have the characteristic you listed in
#44a above? Please use the buttons below to make your ratings.

44c. To what extent to you actually have the characteristic you listed in #44a above?
Please use the buttons below to make your ratings.
45a. Please list a characteristic of your ought selfbelow:
45b. To what extent do you believe you ought to have the characteristic you listed in
#45a above? Please use the buttons below to make your ratings.

45c. To what extent to you actually have the characteristic you listed in #45a above?

Please use the buttons below to make your ratings.

Alternative measure of regulatory focus
Please rate how important each of the following factors were in your decision to
enroll in the psychology course(s) you are currently taking.
46. I thought it would be fun.
47. I thought it would be interesting.
48. I thought it would be challenging.*
49. I wanted to take this course.
50. I thought I would enjoy this course.
51. I like psychology.

52. It is a requirement for me.

129

53. I was told I should take it.

54. I need it because it is a prerequisite for other classes.
55. I thought it would be easy.

56. Ihad to take this course.

57. I had to take a psychology course.

130

APPENDIX E

Measures assessed during the experiment
Affect
To what extent do you feel these emotions right now? Please use the scale below

to make your ratings on the scantron sheet:

Slightly Extremely

| l | | I I l
< l | | l l I l >

(1) (2) (3) (4) (5) (6) (7)

1. distracted
2. confused
3. cheerful
4. sad

5. aflaid

6. calm

7. dissatisfied
8. nervous
9. angry

10. relaxed
ll. anxious
12. disappointed

l3. dejected

131

14. proud

15. tense

16. discouraged
l7. satisfied

18. worried

l9. hopeless

20. happy

Self-Efficacy Sca_la

This set of questions asks you to describe how you feel about your capabilities for
playing TAG. Please use the scale shown below to make your ratings on the scantron
sheet.

Strongly Strongly

Disagree Disagree Neutral Agree Agree

<: l l I l>

 

(1) (2) (3) (4) (5)

21. I can meet the challenges of this simulation.

22. I am confident in my understanding of how information cues are related to
decisions.

23. I can deal with decisions under ambiguous conditions.

24. I am certain that I can manage the requirements of this task.

25. I believe that I will fare well in this task if the workload is increased.

26. I am confident that I can cope with this simulation if it becomes more complex.

132

 

27. I believe I can develop methods to handle changing aspects of this task.

28. I am certain I can cope with task components competing for my time.

Mental Woraload Scale
This set of questions asks you about how much mental effort you put into the task.

Please use the scale shown below to make your ratings on the scantron sheet.

Never Seldom Occasionally Frequently Constantly

‘< I I I I I 3>

(1) (2) (3) (4) (5)

29. I felt mentally tired and worn out after performing TAS.

30. Learning TAS was a difficult and complex task.

31. The overall mental workload I felt while learning TAS was low.
32. Learning TAS was easy.

33. Learning TAS required a lot of mental activity.

34. I had to work very hard to learn TAS.
Basic Knowledge Test
The following is a knowledge test about TAG. Please use the scantron sheet to

answer these questions.

35. If a Response is Given, what is the likely Intent of the target?

133

a. Military
b. Hostile
c. Civilian
d. Peaceful
36. If a target’s Speed is 25 knots, its Communication Time is 85 seconds, and its
Altitude/Depth is 0 feet, what does this suggest about the target’s Type?
a. The target is a Surface Vessel
b. The target is a Submarine
c. The target is Civilian
d. The target is Military
37. A submarine may have which of the following characteristics?
a. Speed 30 knots, Altitude/Depth - 20, Communication time 85 seconds.
b. Speed 30 knots, Altitude/Depth 0, Communication time 30 seconds.
c. Speed 20 knots, Altitude/Depth 0, Communication time 80 seconds.
(1. Speed 20 knots, Altitude/Depth -20, Communication time 90 seconds.
38. A Maneuvering Pattern of Code Delta indicates the target is which of the following?
a. Air
b. Military
c. Surface
(1. Civilian
39. A Blue Lagoon Direction of Origin indicates the target is which of the following?
a. Unknown

b. Sub

134

c. Civilian

d. Military

40. If a target’s Altitude/Depth is 10 feet, what is the Type of the target?

41.

a. Air

b. Surface

0. Submarine

(1. Unknown

If a target’s Intelligence is Unavailable, what Class does this suggest for the target?
a. Air

b. Civilian

c. Military

(1. Unknown

42. If a target’s characteristics are Communication Time = 20 seconds and Speed = 50

knots, which of the following actions should you take?

43.

a. Choose Intent is Peaceflrl

b. Choose Type is Surface

c. Get another piece of information

d. Choose Type is Air

A Communication Time of 52 seconds indicates that the target is likely:
a. Air

b. Surface

c. Submarine

(1. Unknown

135

44. If a target’s characteristics are Intelligence in Private and Maneuvering Pattern is
Code Foxtrot, which of the following actions should you take?
3. Choose Class is Military
b. Choose Intent is Peaceful
0. Choose Class is Civilian
d. Choose Intent is Unknown
45. If a target’s Maneuvering Pattern is Code Echo, this suggests that the target falls into
which category?
a. Class in unknown.
b. Class is Military
c. Class is Hostile
(1. Class is Peaceful
46. If a target’s characteristics are Response is Inaudible, Threat Level = 3, and
Countermeasures are J arnming, which of the following actions should you take?
a. Choose Intent is Unknown
b. Choose Intent is Peacefirl
c. Choose Intent is Hostile
(1. Choose Intent is Military
47 . If a target’s Speed is 40 knots, what does this suggest about the target?
a. The target is Air.
b. The target is Surface.
0. The target is Civilian

d. The target is Military.

136

 

MC Knowledge Test
48. Your Outer Defensive Perimeter is located at:

a. 64 nm

b. 128 nm

e. 256 nm

(1. 512 nm
49. If a target is outside the current radius of your console, you can view it by doing
what?

a. There is nothing you can do.

b. Wait for the target to enter

c. Zoom out

d. Zoom in
50. If you’ve just noticed three targets near your inner perimeter, which of the following
should you do next?

a. Engage the target nearest the inner perimeter.

b. Engage the fastest target near the inner perimeter.

c. Zoom-Out to check the outer perimeter.

d. Zoom-In to check how close targets are to the inner perimeter.
S 1. If three Targets are about 20 miles outside your inner defensive perimeter, which of
the following should you do to prioritize the Targets?

a. Engage the closest Target

b. Engage the hostile Target

c. Engage the fastest Target

137

d. It makes no difference in what order you engage the Targets.
52. If you Zoom-Out to find three targets clustered around your Outer Perimeter, how
would you determine which target is the marker target?
a. Check to see which target is closest to the outer perimeter.
b. Check the speeds of the targets
c. Check to see which target is Civilian
d. Check to see which target is Hostile
53. Which of the following targets would be the lowest priority?
a. A target which has a speed of 15 knots.
b. A target which has just crossed your inner defensive perimeter.
c. A target which is Peaceful.
d. A target which is Civilian.
54. What is the purpose of marker targets?
a. To determine which Targets are Hostile and which are Peaceful.
b. To locate your Inner Defensive Perimeter.
c. To quickly determine the speeds of targets near your perimeters.
d. To locate your Outer Defensive Perimeter.
55. Which of the following functions is most useful for identifying marker targets?
a. Zoom-in
b. Right-button feedback.
c. Engage Shoot or Clear

d. Zoom-out

138

56. If three Targets are about 10 miles outside your outer defensive perimeter, which of
the following should you do to prioritize the Targets?

a. Engage the fastest Target

b. Engage the hostile Target

c. Engage the closest Target

(1. It makes no difference in what order you engage the Targets.
57. On the average, approximately how many Targets pop-up during each practice trial?

a. l

b. 3

c. 6

d. 9
58. Which of the following would be the most effective strategy for defending your outer
defensive perimeter?

a. Zoom-out to 128 nm, locate the Marker Targets, and check the speed of targets
near the outer perimeter.

b. Zoom-out to 256 nm, locate the Marker Targets, and check the speed of targets
near the outer perimeter.

c. Zoom-out to 128 nm, locate a Hostile Air Target, and check the speed of targets
near that target.

d. Zoom-out to 256 nm, locate a Hostile Air Target, and check the speed of targets
near that target.
59. If all penalty intrusions are cost -100 points, which would be the most effective

strategy?

139

a. Do not allow any Targets to enter your Inner Defensive perimeter, even if it
means allowing targets to cross your Outer Defensive perimeter.

b. Do not allow any Targets to enter your Outer Defensive perimeter, even if it
means allowing targets to cross your Inner Defensive perimeter.

c. Defend both your Inner and Outer Defensive perimeters equally.

(1. None of these are effective strategies.
60. It is important to make trade-offs between targets:

a. That are Hostile and those that are Peaceful.

b. Approaching your Inner and Outer perimeters.

c. That are Civilian and those that are Military.

(1. That have already crossed your Inner Defensive perimeter, and those that are

approaching your Outer Defensive perimeter.

140

 

 

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Example of Process Feedback
Practice Trial Seven
High Priority Targets:
Minimum Performance — In the last practice trial, you only engaged # high priority
targets. This means that you haven ’t learned how to prioritize targets. You should study
the material in your TAG manual on prioritization strategies and practice engaging high

priority targets in the next practice trial.

Medium Performance — In the last practice trial, you engaged # high priority targets.
This is good but you are still missing some of the high priority targets. You should study
the material in your TAG manual on prioritization strategies and practice engaging high

priority targets in the next practice trial.

Maximum Performance — In the last practice trial, you engaged # high priority targets.
Nice work. This shows that you have learned how to prioritize targets. You should
concentrate now on other areas in which you need to improve, but make sure you

continue to apply your prioritization strategies.

159

 

Making Trade-Offs Between Targets Approaching the Inner and Outer Perimeters:
Minimum Performance -- In the last practice trial, you didn ’t check the speed of any of
the targets near your inner defensive perimeter. This means that you have not learned
how to make trade-ofifs between targets approaching your inner and outer defensive
perimeters. You should study the material in your TAG manual on making trade-ofifs

between targets and you should practice this skill in the next practice session.

Medium Performance — In the last practice trial, you checked the speed of # targets near
your inner defensive perimeter, which is good. But, to efi’ectively make trade-ofifs
between targets approaching your inner and outer defensive perimeters, you should study
the material in your TAG manual on making trade-oflfs and practice this skill in the next

practice section.

Maximum Performance — In the last practice trial, you checked the speed of # targets
near your inner defensive perimeter. Hopefully you are using this information to make
trade-offs between targets approaching your inner and outer defensive perimeters. You
should concentrate now on other areas in which you need to improve your performance,

but make sure you continue to make trade-oflfs between targets.

160

 

 

 

 

 

 

 

 

 

 

 

 

 

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170

 

 

 

Footnotes

 

i F our participants’ data was dropped from the final analyses because they appeared to be responding
randomly to the questionnaire items. The other four participants’ data was dropped because they did not
follow the experimenter’s instructions.

171

 

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