Does Self-Regulation Require Cognitive Resources ...

Copyright 1996 by the American PsychologicalAssociation, Inc. 0021-9010/96/$3.00

Journal of Applied Psychology 1996, Vol. 81, No. 5, 595-608

Does Self-Regulation Require Cognitive Resources? Evaluation of Resource Allocation Models of Goal Setting R i c h a r d P. D e S h o n , K e n n e t h G. B r o w n , a n d J e n n i f e r L. G r e e n i s Michigan State University The resource allocation model of goal setting (R. Kanfer & P. L. Ackerman, 1989) maintains that self-regulation initiated through goal setting requires attentional resources that could be more productively applied to skill acquisition and complex task performance. The current study questioned this hypothesis because attentional resources had not been directly manipulated or measured in studies supporting the model. Thus, alternative explanations that are based on other complex task goal-setting models cannot be excluded. As a direct test of the resource allocation hypothesis, dual task methodology was used to measure the attentional resource requirements of self-regulation. Even at the limits of human information processing, participants who were assigned difficult, specific goals performed at least as well on the secondary task as did individuals with do-your-best goals. These findings suggest that self-regulation does not necessarily require attentional resources. Implications for theory and practice are discussed.

The cognitive demands placed on workers in modemday jobs are increasing, and information processing is more important than ever (Goldstein & GiUiam, 1990; Howell & Cooke, 1989). In addition, the boundaries that separate jobs are becoming less defined as jobs change rapidly in today's dynamic and technologically oriented workplace. The responsibilities that once clearly delineated a job description are becoming increasingly broad, ambiguous, and dynamic (Howard, 1995; Ilgen, 1994). In fact, it has been argued that the concept o f a " j o b " is obsolete (e.g., Bridges, 1994). Individuals can no longer expect to master a fixed set o f skills that will be relatively stable over the course o f their careers. Instead, constant learning through skill updating, and retraining is required to maintain performance. These rapid changes in the workplace provide new challenges to established fields of research in the field of applied psychology. The increased complexity and ambiguity o f tasks in modern jobs pose particular problems in the area o f goal setting. Simple, repetitive tasks for which goal setting has proven to be remarkably effective are becoming less com-

mon in the workplace (Howell & Cooke, 1989). Unfortunately, attempts to generalize goal-setting effects to complex, strategy-driven tasks have had little success. The data show that setting difficult, specific performance goals on complex tasks often has little positive effect on performance and has even resulted in performance decrements (Earley, Connolly, & Ekegren, 1989; Kanfer & Ackerman, 1989; Latham & Locke, 1991; Wood, Bandura, & Bailey, 1990). Apparently, goal setting is not an effective motivational technique for an ever-increasing domain o f tasks encountered in the workplace. The surprising and important finding that setting difficult, specific goals on complex tasks can cause performance decrements has generated much research and theory. Currently, resource allocation models o f motivational processes dominate thinking about the source of the performance decrement. Research by Kanfer and Ackerman (1989) and Kanfer, Ackerman, Murtha, Dugdale, and Nelson (1994) is consistent with the notion that setting quantitative performance goals on complex tasks leads to the allocation of attentional resources toward self-regulatory cognitions. According to this view, the allocation of resources to self-regulation "steals" critical resources from complex task skill acquisition and results in poor performance. The purpose of the present research was to directly evaluate the fundamental assumption o f this model, that self-regulation requires attentional resources. The article is organized around the following themes. First, we present Kanfer and Ackerman's (1989) resource allocation model and provide evidence both in favor of and against the assumption that self-reg-

Richard P. DeShon, Kenneth G. Brown, and Jennifer L. Greenis, Department of Psychology, Michigan State University. We thank Dan IIgen for helpful comments on an earlier version of this article. Correspondence concerning this article should be addressed to Richard P. DeShon, Department of Psychology, Psychology Research Building, Michigan State University, East Lansing, Michigan 48824- l ! 17. Electronic mail may be sent via Internet to [email protected]. 595

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ulation requires attentional resources. Then, we discuss the method of measuring attentional resources using concurrent task performance, and report on a pilot study that was performed to verify that the tasks in this research required substantially overlapping attentional resources. Finally, we report on a study that directly assessed the resource requirements of self-regulation. Resource Allocation Model o f Goal Setting Consistent with the early attention models of Kahneman (1973) and Norman and Bobrow (1975), Kanfer and Ackerman (1989) viewed attentional resources as an "undifferentiated pool representing the limited capacity of the human information-processing system" (p. 663). These attentional resources are allocated among competing task demands through both distal and proximal motivation processes. When confronting tasks, individuals must first decide whether to allocate attentional resources toward goal attainment (called distal motivational processes) and then decide how much of the attentional pool should be devoted to task performance (called proximal motivational processes). Proximal motivational processes have three self-regulatory components: selfmonitoring, self-evaluation, and self-reaction. Self-monitoring occurs when individuals allocate attention to the actions and consequences of their behavior. Successful self-monitoring involves attention to goal-related behaviors and strategic allocation decisions to facilitate goal achievement (e.g., allocate more effort to the task). Selfevaluation involves the comparison of one's current performance to the desired level of performance (goal) and the assessment of the size and direction of this goal-performance discrepancy. Self-reaction includes both affective reactions to feedback and self-efficacyjudgments. Kanfer and Ackerman (1989) proposed that the setting of goals automatically initiates self-regulatory activities whereby people monitor and evaluate their performance. Generally, these self-regulatory behaviors are very functional, as they provide information concerning the movement toward a goal. However, self-regulatory activities require attentional resources. Simple or well-learned tasks do not require a great deal of cognitive resources to maintain or increase performance; therefore, the evaluation of one's performance does not interfere with on-task cognitive demands. Novel or complex tasks, on the other hand, require virtually all available attentional resources. In this case, self-regulatory activities use valuable resources that might be better applied to task performance. The result is a decrement in task performance relative to individuals with no goals or do-your-best (DYB) goals. Thus, both simple and complex tasks demand attentional resources, but the demands imposed by self-regulation are more detrimental to performance on complex tasks

because resource demands during skills acquisition are greater. Evaluation o f the Resource Allocation Model To support the resource allocation model, Kanfer and Ackerman (1989) examined the effects of attentional resource demands on skill acquisition in an air traffic control (ATC) simulation. They hypothesized that goal setting would facilitate complex task performance after attentional resources became available for self-regulation through the automatization of the task. In their third experiment, participants received part-task training before performing the full task. One group of participants received declarative training that was expected to reduce the attentional demands of the full ATC task by teaching the decision rules before task performance. The remaining participants received procedural training to improve full-task performance by facilitating the development of motor sequence skills and keyboard response procedures (but not reducing declarative memory cognitive demands). As predicted, a significant Goal x Training interaction occurred such that goal setting benefited performance in the declarative training condition but hurt performance in the procedural training condition. In addition, Kanfer et al. (1994) investigated the effects of goal setting and practice conditions (massed vs. spaced practice) on performance. In the massed-practice condition, trainees assigned specific, difficult goals tended to perform poorer than trainees in the DYB goal condition. In the spaced-practice conditions, the trainees with goals performed better than the control trainees. Kanfer et al. suggested that the beneficial effects of goal setting during the spaced-practice results from the rest intervals that provided opportunities for learners to conduct self-regulatory processes without simultaneously needing to allocate attentional resources to the task. Although the research used to support the attention allocation model of complex task goal setting is consistent with the hypothesis that setting goals initiates selfregulatory processes that divert resources from primary task performance, a direct test of the attention allocation model has not been performed. Moreover, an increasing base of theory and research suggests that goal-performance discrepancy monitoring and affective reactions to stimuli do not place a heavy burden on attentional resources. For instance, Lord and Levy's (1994) work on control theory suggests that monitoring performance to detect goal discrepancies is an automatic data-driven process. Given the goal-directed nature of virtually all behavior, the goal-setting process and performance evaluative cycle should be highly automated through extensive practice in everyday life. Payne, Bettman, and Johnson (1993) presented data suggesting that the basic com-

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parison process inherent in evaluating performance relative to a standard requires about 80 ms. This process is far too fast to be controlled by high-level, symbolic processes that require attentional resources and is likely an automatized perceptual phenomenon (Newell, 1990). Similarly, Bargh (1989) reviewed a large empirical literature that demonstrated that general memory structures, such as situational scripts and complex action sequences, automatically guide attention and behavior when working toward a conscious goal. Because the scripts have been used repeatedly across problem situations, they could be applied flexibly and automatically to novel scenarios. Additional research provides evidence that reactions to goal-performance discrepancies may also occur automatically. Affective reactions to negative feedback are very fast (i.e., 10 ms), often occur outside the awareness of the observer, and place minimal demands on the attentional system (Edwards, 1990; Zajonc, 1980). Similarly, attitude formation and causal attributions often occur without intention or awareness and do not load the cognitive system (Fazio, Sanbonmatsu, Powell, & Kardes, 1986; Smith & Miller, 1979). Together, this research suggests that monitoring, detecting, and reacting to discrepancies can often be performed automatically and without significant cognitive resource requirements. It is also important to recognize that the study of selfregulatory resource requirements on complex tasks confounds resource requirements with other factors that make tasks complex. At least two alternative models of complex task goal setting make similar predictions yet are based on different theoretical positions. First, the work of Earley and colleagues (Earley, Connolly, & Ekegren, 1989; Eadey, Connolly, & Lee, 1989) indicated that assigning difficult, specific goals may induce a perceived pressure for immediate performance. This pressure to perform manifests itself in the selection ofnonsystematic hypothesis testing and rapid strategy switching. Second, DeShon and Alexander (1996) demonstrated that goal setting on complex tasks may lead individuals to frame the task incorrectly. Instead of relying on intuitive or heuristic processes, goal setting may lead the individual to adopt less effective, explicit learning strategies. In the same study, DeShon and Alexander (1996) provided further evidence that conflicts with the resource allocation model. Using dual-task methodology, DeShon and Alexander measured attentional resource allocation during complex task performance and found that individuals with specific, difficult goals used more attentional resources. However, the resources were used productively in the development of better task strategies. The existing data are not capable of distinguishing the correct model, which results in theoretical ambiguity. Although the manipulations used to study self-regula-

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tion were expected to affect task demands and resource requirements, the validity of these manipulations is difficult to verify, and the resource requirements of selfregulation have not been directly tested or measured. A direct test of the resource allocation model of goal setting could avoid task complexity confounds and indirect manipulations by examining resource allocation on simple tasks that require minimal learning or strategy development. The logic behind this notion was stated by Kanfer and Ackerman (1989), who concluded that self-regulation is initiated upon the acceptance of a performance goal on both simple and complex tasks: Complex, novel tasks impose greater attentional demands and reduce the opportunity for benefits of self-regulatory activities. In simple tasks, resource demands are likely to be lower[. . . . and ] goal setting in this context facilitates performance. (p. 687) In other words, simple tasks make less demands on attentional resources, and therefore, sufficient resources are available for self-regulatory cognitions. Given this, the attentional resource requirements of self-regulation can be assessed on simple tasks by increasing the load on the attentional system and examining decrements in task performance (e.g., Nissen & Bullemer, 1987 ). The result is a straightforward assessment of the resources required by self-regulation separate from the complex task environment where alternative explanations exist. Instead of observing the effects of indirect manipulations, this research strategy directly assesses the resource requirements of self-regulatory processes. Measurement o f Attentional Resources Dual task methodology is the best researched and most popular resource assessment technique available. In this methodology, two tasks are performed simultaneously, and a distinction is made between the primary task and the secondary task. Participants are instructed to maximize performance on both tasks, but attention should be focused on the primary task such that the secondary task does not interfere with the primary task even if performance on the secondary task decreases. Decrements in secondary task performance reflect the resource capacity that remains while the primary task is performed (O'Donnell & Eggemeier, 1986 ). The four most common tasks used in dual task methodology are choice reaction time, monitoring, manual tracking, and memorization (O'Donnell & Eggemeier, 1986; Ogden, Levine, & Eisner, 1979). These four tasks have been repeatedly demonstrated to require substantially overlapping attentional resources (Ogden et al., 1979 ). To examine the cognitive load imposed by self-regulation, we selected two of these tasks on the basis of the following criteria. First, the tasks had to be dependent on

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a common pool of central attentional resources. Second, the tasks had to impose continuous information-processing demands on the user (O'Donnell & Eggemeier, 1986). Third, the tasks had to be classified as simple by current task complexity taxonomies (e.g., Wood, 1986). Fourth, the tasks had to reasonably approximate the processing demands that are experienced in a real-world task. To this end, a manual pursuit-tracking task was selected as the primary task because it mimics the resources used to track moving objects on radar displays. This is a common cognitively demanding task performed in many real-world jobs such as aviation control, piloting aircraft, and military tank and ship defense systems. For the secondary task, we wished to mimic the declarative knowledge processing demands placed on the operator who is required to keep several classification or decision rules active simultaneously. This type of cognitive demand is representative of the simple learning tasks common in daily work. Therefore, a letter memorization task was chosen. Summary Although the data used to support the resource allocation model are consistent with the interpretation that self-regulation requires attentional resources that interfere with complex task performance, no direct test of this critical assumption has been performed. The existing research on discrepancy monitoring and affective reactions to feedback suggests that self-regulation should be a largely automatized and resource independent process. Furthermore, there are alternative models of the complex task, goal-setting effect that do not require resource allocation assumptions. Because self-regulation occurs on both simple and complex tasks, dual task methodology can be used to estimate the amount of resources required by self-regulation on simple tasks. Use of multiple, simple tasks places a high demand on attentional resources but avoids potential task complexity confounds, thereby providing an unambiguous evaluation of the resource allocation model. Following this logic, a first pilot study demonstrates that the cognitive resources required to perform the tracking task used in this experiment overlap substantially with the resources that are required by declarative memory demands. Next, the primary study combines the tracking and memorization tasks with a goal-setting manipulation to assess the extent to which self-regulation demands attentional resources. Pilot Study Odgen, Levine, and Eisner (1979) reviewed 146experimental studies that used secondary task methodology to investigate resource requirements in the concurrent per-

formance of multiple tasks. The results of this review clearly indicated that performance on manual pursuittracking tasks is dependent on the same resources used by other cognitive tasks such as verbal memorization, serial learning, mental mathematics, and choice reaction time. According to Sirevaag, Kramer, Coles, and Donchin (1989), "Successful higher-order tracking performance requires considerable attention to time-estimation as well as anticipation of future events" (p. 80). To accomplish this, a pattern must be maintained in working memory, and the individual must continually make predictions and choices to optimize tracking performance. In addition, pursuit-tracking performance has been shown to strongly affect the P300 component of event-related brain potential measured through electroencephalographic activity (Kramer, Wickens, & Donchin, 1983; Sirevaag et al., 1989; Wickens, Kramer, Vanasse, & Donchin, 1983). This finding is important because the amplitude of the P300 wave has been shown to be a reliable indicator of the amount of central cognitive resources devoted to task performance (O'Donnell & Eggemeier, 1986). Thus, the abundant literature on attention allocation and dual-task performance indicates that both of the tasks used in this experiment (i.e., a second-order, twodimensional pursuit-tracking task and a short-term memory task) require central processing resources. Despite this large literature demonstrating that pursuit-tracking tasks and cognitively demanding tasks such as memorization require substantially overlapping attentional resources, it is possible that the particular combination of tasks used in this research might not interfere with each other. Therefore, a pilot study was performed to verify that the tracking task used in this research required attentional resources that overlapped with the resources required by memorizing letter lists. In the pilot study, the priority of the memorization and tracking tasks was reversed such that memorization was primary. If these tasks require overlapping resources for performance, tracking performance should decrease as the memorization task becomes more difficult and places larger demands on attentional resources.

Method Participants. There were 65 undergraduate students enrolled in psychologycourses at a large Midwestern university who received extra credit for their voluntary participation in the study. All participants were between the ages of 18 and 24 and had normal or corrected vision. Tracking task. The tracking task used in this research was a two-dimensional, manual pursuit task with second-order dynamics (acceleration). The entire experiment was presented on an IBM-compatible computer (with Intel chip 80486) with a 14-inch [ 35.5 cm] videographic array color monitor having640 × 480 pixel resolution. Participants used a standard mouse to

RESOURCE ALLOCATIONMODELS track a target (a white circle approximately 0.5 cm diameter) that moved smoothly and continuously across the black background. The target moved quickly and could easily traverse the full width of the monitor in less than 1.5 s if no change in velocity (acceleration) or direction occurred. Before each trial, participants were given a 3-s warning where the target remained in the center of the computer screen. The target then moved, varying in velocity and direction for 30 s. The computer recorded the horizontal and vertical difference between the target and the mouse-controlled cursor 50 times per second. From these data, the average Euclidean distance of the cursor from the target was calculated and used for all comparisons and feedback. All participants received six practice tracking trials. For the actual experiment, three tracking patterns were presented randomly in blocks of three for a total of nine experimental trials. Memorization task. Participants memorized a string of letters presented before each tracking trial. After a 30-s tracking trial, we asked the participants to type into the computer the letter string. Three tracking trials were completed while memorizing three letters, followed by three tracking trials while memorizing five letters, and three tracking trials while memorizing seven letters. The letters for this task were carefully selected to make "chunking" or the use of mnemonics difficult. To create each trial of three, five, and seven letters, a list of random letter strings was generated, and all letter strings that had the same letter twice, vowels, or common acronyms were eliminated. Participants were told that the order of letter recall was unimportant and were given 5 s to memorize the three- and five-letter strings and 10 s to memorize the seven-letter string. Feedback. After each trial, participants were provided with feedback about their memorization and tracking performances on the preceding trial. After entering the recalled list of letters, participants were told how many of the letters they correctly recalled. Participants then received tracking performance feedback as follows: "On average, you were X units away from the target on the last trial" The variable X represents the distance in computer screen pixels. Procedure. The data were collected in groups of 3 participants who were seated at 90° angles to each other. The experimenter explained that the study was designed to investigate how well people can memorize information while performing distracting tasks. Participants were asked to perform both tasks to the best of their ability but to keep the memorization task primary. To emphasize the primacy of the memorization task, we instructed participants to concentrate their attention on memorizing the letters and not to let the tracking task interfere with memorizing the letters. Directions on how to use the computer mouse for the tracking task were provided on the computer, and six practice tracking trials were completed before the nine experimental trials began. Before each block of trials, directions explaining the memorization task were presented including the number of letters they were to memorize and how long they would have to memorize the letters before the tracking trial began.

Results We screened the data for poor memorization on the p r i m a r y m e m o r y task and extreme scores on the sec-

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ondary tracking task before performing the analysis. There were 4 participants who did not correctly m e m o rize any o f the seven-letter strings and were not included in the subsequent analyses. The mean absolute deviation from the target on the tracking task was 37.2 units (SD = 8.4). Tracking errors greater than 80 units from the target (5 SDs from the m e a n ) were not included in the analyses. This criterion eliminated virtually all of the data for 2 additional participants. Therefore, the screened data set contained complete observations for 59 participants. To use the secondary tracking task as an index of the overlapping attentional resources, it was necessary to demonstrate that participants followed instructions and did not allow the secondary task to interfere with primary task performance. Schweikert and Boruff (1986) provided free recall performance data across a wide variety of stimuli that can be used as a baseline for this comparison. In their study, individuals memorized digit lists without performing a secondary task and then free-recalled the digits after a short delay. The percentage of correctly recalled lists in their study was 96.9% for the fivedigit list and 70.0% for the seven-digit list.~ In our study, participants correctly recalled 97.9% of the three-letter strings, 97.4% of the five-letter strings, and 64.5% of the seven-letter strings while simultaneously performing the tracking task. The similarity of these scores indicates that participants successfully focused their attention on memorizing the letter lists and did not allow the tracking task to interfere with recall performance. Performance on the tracking task was measured as the mean absolute tracking error for each 30-s trial. A repeated measures analysis o f variance (ANOVA) was used to test the interference hypothesis. Again, if the tracking task and the memorization task require the same attentional resources, tracking error should increase as the memorization task became more demanding. The mean absolute tracking error increased across the memorization conditions (M3 --- 36.63, M5 = 36,85, and M7 = 38.02, pooled SD -- 8.41 ) and the differences between these repeated means was significant, F(2, 116) = 5.86, p < .01; M S E = 4.88. Correcting the F t e s t for deviations from sphericity ( H u y n h - F e l d t ~ = 0.81 ) did not alter this conclusion. The effect size for the within-subject, tracking error mean differences was between Cohen's standards for a moderate and large effect (nz = 0.09).

Discussion The results from this dual-task study are very consistent with the robust finding in the h u m a n factors literai Schweikert and Boruff (1986) did not use a three-digit list in their experiment. However, on the basis of results from a fivedigit list, it is reasonable to assume that free recall of the threedigit list would be between 97% and 100%.

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ture that performance on pursuit-tracking tasks and m e m o r y span tasks require substantially overlapping attentional resources. Participants maintained performance on the m e m o r y span task while simultaneously performing the tracking task. However, performance on the pursuit-tracking task declined as the difficulty o f the memorization task increased. From the pattern o f tracking error means, we conclude that the critical j u m p in load occurred between the memorization o f five and seven letters. Primary Study In the pilot study it was determined that memorizing letter strings has a moderate-to-large negative effect on tracking performance, indicating that these tasks require substantially overlapping attentional resources. Therefore, it is possible to examine the attentional resource requirements o f self-regulation. To demonstrate performance interference effects in the pilot study, we reversed the task priority by instructing participants to treat the letter memorization task as primary and the tracking task as secondary. In this study, participants were instructed that the tracking task was o f primary importance. The letter memorization task was used to index reserve capacity. A nine-letter memorization condition was included in the secondary task to examine performance as participants reach the 7+2 limit of short-term m e m o r y ( S T M ) . Memorizing nine letters while tracking a complex trajectory is certainly sufficient to push individuals to the limits o f attentional capacity. Consistent with the resource allocation model, it was assumed that attentional resources represented an undifferentiated pool of limited size that can be selectively allocated partially or fully to achieve the individual's current goals (Kanfer & Ackerman, 1989). It was further assumed that self-regulatory processes are initiated upon the acceptance of a difficult specific goal. If these selfregulatory cognitions require a nontrivial amount of attentional resources, individuals will have less attentional resources available to allocate to the secondary task. Finally, it was assumed that the attentional resources required for self-regulation remain relatively constant over time.

Hypotheses Consistent with goal-setting literature relating to simple tasks, individuals assigned difficult, specific performance goals should perform better (smaller average tracking errors) than individuals assigned DYB goals when the tracking task is performed alone. Furthermore, on the basis o f instructions provided to participants to focus on the primary task without letting the secondary

task interfere with the primary task, performance on the tracking task should remain constant as the difficulty o f the secondary task increases. Therefore, the goal-setting effect on the primary tracking task should remain constant over the levels o f secondary task difficulty. The critical hypotheses for this study involved performance on the secondary task. According to the resource allocation model, individuals who are assigned difficult, specific goals engage in self-regulatory activities. The process o f self-regulation leaves fewer resources available for secondary task performance. If self-regulation requires attentional resources that could otherwise be devoted to secondary task performance, then secondary task performance should be differentially affected as a function of goal condition. This implies that there should be an interaction between goal assignment and secondary task difficulty such that performance on the secondary task drops off more rapidly for individuals with difficult, specific goals than it does for individuals having DYB goals, as the difficulty o f the secondary task increases. On the other hand, if self-regnlation does not require attentional resources, then performance on the secondary task should be unaffected by the goal-setting manipulation across all levels o f secondary task performance.

Method Participants. There were 123 undergraduate students enrolled in psychology courses at a large Midwestem university who received extra credit for their voluntary participation in the study. All participants were between the ages of 18 and 24 and had normal or corrected vision. Design and manipulations. Participants were randomly assigned to one of three goal conditions and were given instructions to accurately track the target within 28 units, within 34 units, or simply to do their best to track the moving target. These goal levels, which were based on a pilot study of 25 individuals, were set at approximately 10% and 20% accomplishment for the difficult and moderate goal conditions, respectively. There were 41 participants in the DYB condition, 42 participants in the moderately difficult goal condition (34 units), and 40 participants in the difficult goal condition (28 units). All participants performed the tracking task under five memory conditions: no-letter memorization, three-letter memorization, five-letter memorization, seven-letter memorization, and nineletter memorization. Therefore, the experimental design consisted of three between-goal levels and five within-subject memorization levels. Measures. At the end of the experiment, participants responded to a number of goal-oriented questions. As a manipulation check, participants indicated whether they had been assigned a performance goal, their assigned performance goal, and their personal goal. Commitment to the assigned goal was assessed through the Hollenbeck, Klein, O'Leary, and Wright (1989) seven-item scale. In addition, affective reactions to the performance feedback on the tracking task were assessed with the following three items: "I was dissatisfied with my perfor-

RESOURCE ALLOCATION MODELS mance during the previous trials"; "I felt pressured to meet the suggested goal while performing this task"; and "I felt frustrated with my performance during the previous trials?' With the exception of the question on personal goals, responses to all questions were based on a 5-point Likert scale anchored by strongly agree and strongly disagree. The digit-recoding task developed by Woltz (1988) was used to measure individual differences in working memory capacity. This measure is consistent with Baddeley's (1986) definition of working memory and requires participants to simultaneously store representations and perform mental operations on the representations. On each of 16 trials (plus 3 practice trials), three nonsequential letters were presented for 10 s for memorization. Next, a number between - 2 and 2 was presented in the center of the display. Participants were instructed to move forward or backward in the alphabet to find a new letter for each of the original letters by using the number presented. Participants were allowed 20 s to perform this operation on the stored letters. After the time allotted expired, participants were provided with eight multiple-choice answers. A 10-s deadline was required for response in order to prevent the use of the alternatives to aid in response selection. The measure was presented by computer, and feedback was provided after every trial. This task has been used to successfully assess individual differences in working memory capacity in numerous applications (Kyilonen & Christal, 1990; Woltz, 1988 ) and is highly correlated with measures of general cognitive ability, reasoning ability and processing speed (Kyllonen & Christal, 1990). Feedback. After each trial, participants were provided with feedback about their memorization and tracking performance for the preceding trial. After entering the recalled list of letters, participants were told how many of the letters they correctly recalled. Participants then received the following tracking performance feedback: "On average, you were X units away from the target on the last trial?' The variable X represented the distance in computer screen pixels. Smaller values represented better tracking performance. Procedure. With the exceptions of the task prioritization and the goal-setting manipulation, the procedure in this study was identical to that of the pilot study. The experimenter explained that the study was designed to investigate how well people can track moving objects while performing distracting tasks. Participants were asked to perform both tasks to the best of their ability but to keep the tracking task primary. To emphasize the primacy of the tracking task, we told participants to concentrate their attention on tracking the moving object and not to let the letter memorization interfere with tracking performance. The primary task goal manipulation was initiated immediately before the practice trials. Participants were randomly assigned to one of three goal conditions with instructions to stay within 28 units of the moving object, 34 units of the moving object, or to do their best to track the moving object. This information was repeated before each block of tracking trials. Participants were first provided with three practice tracking trials before having to complete three tracking trials under no memory load. Note that the three tracking trials with no memory load is equivalent to single task performance. After completing the zero memory load trials, participants were instructed to simultaneously perform the memorization task.

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They performed three tracking trials under three-letter, five-letter, seven-letter, and nine-letter memorization load conditions. Participants were told that the order of letter recall was unimportant and were given 5 s to memorize the three- and five-letter strings and 10 s to memorize the seven- and nine-letter strings. The letter strings, which were centered on the computer screen, were presented in lowercase, 14-point font with a space between each letter. After all 15 experimental trials, participants completed the experimental questionnaire.

Results As in the pilot study, the data were initially screened for extreme scores, and mean absolute tracking errors greater than 80 units were treated as missing. This procedure removed 0.8% of the observations in the D Y B condition, 0.6% in the moderately difficult goal condition (34 units), and no observations in the difficult goal condition (28 units). The goal-setting manipulation check in the postexperimental questionnaire was used to determine whether participants had been sensitive to the goal manipulation. There were 7 participants in the moderately difficult goal condition and 5 in the difficult goal condition who reported that they had not been assigned a goal. These individuals were removed from all subsequent analyses, and as a result, the group sample sizes were 41, 35, and 35 for the respective goal conditions o f DYB, 34 units, and 28 units. Manipulation checks. Individuals in the difficult goal condition reported having more difficult personal goals than individuals in the DYB goal condition ( M D y B = 33.59, M28 = 30.38, d -- 0.33 _< 0.51 ~ 0.69). Individuals in the moderate difficulty goal condition reported having easier performance goals than either of the other goal conditions (M34 = 34.91 ). The reason for this difference is unclear, and the results for the moderate difficulty goal condition should be interpreted cautiously. However, the other responses made by individuals in the moderate difficulty goal condition paralleled those provided by individuals in the difficult goal conditions. Individuals in the DYB goal condition reported slightly higher goal c o m m i t m e n t than did individuals in the specific goal conditions ( M D y B = 2.70, M34 = 2.52, M28 = 2.57), but this difference was not statistically significant, F ( 2 , 108) = 1.58, ns, 2 and the confidence interval ( C I ) around the effect size for the D Y B versus difficult goal condition overlapped the zero point substantially ( d = - 0 . 2 3 < 0.23 < 0.69). Similarly, performance on the working m e m o r y task did not differ across the three goal conditions; M D V a = 1 0 . 5 5 , M34 = 10.44, M28 = 10.94, F ( 2 , 108) = 0.17, ns, 7/2 = .00. Finally, Cronbach's a for the affective reactions scale was .7 I. As expected, individuals

2 All nonsignificant p values are > .05.

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in the difficult goal condition reported higher negative affect than individuals in the DYB condition, F ( 1, 75) = 7.38,p < .05, ~2 = .09. Primary task performance. Performance on the tracking task was measured as the mean absolute deviation o f the participant's cursor from the target. The average tracking error on the primary task across the difficulty levels o f the secondary task is presented in Figure 1. A 3 (goal level) × 5 (memorization blocks) repeated measures ANOVA was performed to evaluate performance differences between goal-setting conditions over the m e m o r y load conditions. In support o f the first hypothesis, the goal-setting manipulation resulted in significant differences in average tracking error such that individuals with specific, difficult goals performed better than individuals in the other goal conditions, F(2, 108 ) = 4.08, p < .05, ~/2 __ .08. This result provides prima facie evidence that self-regulation occurred on the primary task. Consistent with the second hypothesis, the average tracking error across goal conditions did not change over the memorization conditions, F(8, 432) = 1.08, ns, T12 = .02. Finally, the average tracking error remained very consistent across the memorization conditions for the most difficult goal condition. The effect size for the

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Figure 1. Averagetracking error across goal conditions and secondary task difficulty. DYB = do your best.

difference in tracking errors between the smallest memory load condition and the greatest m e m o r y load condition was far smaller (n2 = .00) than Cohen's small effect size. These results indicate that participants followed directions and did not allow the m e m o r y load manipulation to interfere with primary task tracking performance. If anything, performance under the m e m o r y load conditions was slightly better than single task performance (i.e., zero memory load). Secondary task performance. The proportion o f correctly recalled letter strings across the levels o f secondary task difficulty is presented in Figure 2. Figure 2 also includes the recall performance data from the pilot study where memorization was the primary task. A repeated measures categorical analysis (Agresti, 1990) demonstrated that there was a difference in recall performance across the two studies; x2( 1, N = 182) = 68.83, p < .01. Figure 2 also shows that the difference in recall performance across the two studies decreased as the m e m o r y load approached the limits o f human short term memory. Even so, the difference in recall performance across the studies remained for the seven-letter memorization condition, X2( 1, N = 182) = 5.09, p < .05. The large difference in recall performance between the pilot study

RESOURCE ALLOCATION MODELS and the present study indicates that the primary tracking task required considerable attentional resources and that recall suffered as a result. Figure 2 also clearly demonstrates that recall performance did not differ across goal conditions. A repeated measures categorical analysis indicated that recall performance decreased as the difficulty o f the secondary task increased, ×2(3, N = 492) -- 710.32, p < .01. However, there was essentially no difference between goal conditions in recall performance, x2(2, N = 123) = 1.19, n s . If anything, individuals with the most difficult goals actually performed somewhat better on the secondary task than individuals in the other goal conditions. A chisquare test examining the difference in the proportion of correct recall across the goal conditions for the nine-letter memorization condition showed that the recall performance o f individuals with the most difficult goals was not significantly different from performance in the other goal conditions, ×2(2, N = 123) -- 4.34, n s . Perhaps most noteworthy was the lack o f interaction between goal condition and secondary task difficulty, x2(6, N = 492) = 4.85, n s . Finally, individual differences in working memory capacity were highly related to performance on the secondary task, F ( 1, 73) = 14.35, p < .05, n 2 = .20, but the goal-setting results were unaffected by partialing out these individual differences. In addition, there was no interaction between working m e m o r y capacity and goal conditions, F ( 1, 73) = 1.02, n s , 72 = .01. The interpretational ambiguity of a null finding can be reduced by following the recommendations o f Cohen (1994) and Schmidt (1996) to reduce the dependence on the binary hypothesis testing paradigm by focusing on effect sizes and CIs. The effect size for the difference in recall performance on the secondary task between the DYB and difficult, specific goal conditions was estimated with the phi coefficient. In the three-letter memorization condition, the effect size and 90% CI 3 for the difference in recall performance across goal conditions (DYB vs. difficult, specific) was - . 1149 < - . 0 0 5 < . 104. In the fiveletter memorization condition, the effect size and 90% CI for the difference in recall performance across goal conditions was - . 1 2 7 < - . 0 1 8 < .090. In the seven-letter memorization condition, the effect size and 90% CI for the difference in recall performance across goal conditions was - . 1 4 2 < - . 0 3 3 < .076. Finally, in the nineletter memorization condition, the effect size and 90% CI for the difference in recall performance across goal conditions was - . 176 < - . 0 6 7 < .042. The conclusion that can be drawn from the effect sizes and CIs is clear. With the exception o f the three-letter memorization condition, the CIs do not even overlap with Cohen's (1988) lower bound for a small effect size (r >__ .10). Therefore, it is highly unlikely (i.e., less than 5% probability) that the data in this experiment could have been generated

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through a process where self-regulation required even a small a m o u n t o f cognitive resources. G e n e r a l Discussion Kanfer and Ackerman's (1989) resource allocation model is based on the assumption that the process o f selfregulation requires attentional resources on both simple and complex tasks. Simple tasks do not require the full pool o f attentional resources, and therefore self-regulation is beneficial. Complex tasks, on the other hand, require all available resources and self-regulation captures a portion of these resources. The result is poor task performance. The present research tested the resource allocation model by using multiple, simple tasks to place a high demand on attentional resources. The use o f simple tasks provided a more direct test of the basic resource allocation hypothesis separate from potential confounds in complex task scenarios. The results of the present research do not support the notion that self-regulatory activities used a significant a m o u n t of attentional resources. Performance on the primary tracking task was consistent with typical goal-setting effects. Specifically, individuals with difficult goals outperformed individuals with moderately difficult and DYB goals. Individuals with moderately difficult goals also performed better than individuals with DYB goals, but this difference did not reach traditional levels of statistical significance. This performance pattern remained stable across the levels o f secondary task difficulty, and tracking performance for the individuals with the most difficult goals barely fluctuated. At the same time, individuals with dit~cult specific goals performed as well or better on the secondary task than individuals with DYB goals. This effect remained stable even at the limits o f human short-term m e m o r y (i.e., nine letters). Either self-regulation does not necessarily require a significant a m o u n t of attentional resources or self-regulation is fundamentally different on simple and complex tasks. The latter possibility is discussed below in relation to limitations of the research. The idea that goal-oriented self-regulation can occur as an automatized process is far from novel. One o f the fundamental propositions of Lord and Levy's (1994) hi-

3 We used 90% confidence intervals (CIs) because of the dear, directional predictions of Kanfer and Ackerman's (1989) theory. There is a 5% error rate in each tail of the CI. This is consistent with a directional assessment of the effect size that is based an alpha of.05. The conclusions would not change if95% CIs that yielded a 2.5% error rate in each tail were used. A negative effect size indicates that the recall performance in the difficult, specific goal condition was higher than recall performance in the do-your-best condition.

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erarchical model of self-regulation is that the monitoring and detection of discrepancies cannot require substantial cognitive resources. Lord and Levy (1994) cautioned applied psychologists not to overemphasize the importance of deliberative resource dependent processes relative to automatic implemental cognitive processes. Wood and Locke's (1990) model of goal-setting effects on complex tasks identifies stored universal plans, such as effort allocation, as mediators of the effects of goals on performance, l_x~ckeand Latham (1990) maintained that these stored universal plans are "highly automatized, so that the individual uses them almost unthinkingly" ( p. 296). Similarly, Bargh and Gollwitzer (1994) argued that environmental events may automatically trigger goals that guide behavior through self-regulation without conscious awareness. Finally, and most importantly, Karoly (1993) defined self-regulation as the "modulation of thought, affect, behavior, or attention via deliberate or automated use of specific mechanisms and supportive metaskills" (p. 25 ). Given the goal directedness of everyday behavior, the process of monitoring task performance should be highly automatized. It is unlikely that monitoring and reacting to one's performance would divert enough attentional resources to routinely hurt complex task performance. Interestingly, Kanfer (1996) and Kanfer and Ackerman (1996) recently presented a modified version of the resource allocation model that is consistent with the present research. In this model, individuals differ in the extent to which they develop, modify, and use two skills: emotional control and motivational control. Emotional control is defined as a self-regulatory skill that can limit intrusions of performance anxiety and other negative emotions while performing a task; whereas motivational control is defined as a self-regulatory skill that helps the individual to maintain task focus despite boredom or satisfaction with current performance. In the above studies, Kanfer and Ackerman stated that these skills are malleable and can be learned through training and practice. Therefore, we believe these skills can become welllearned and relatively resource independent. If these skills are an important component of the self-regulatory process, then research should focus less on the ubiquity of self-regulatory interference and more on the factors that contribute to effective learning and automazation of these skills. Our conclusion that self-regulation did not affect performance on the attentionally demanding tasks used in this research can be construed as accepting the null hypothesis. To reduce the ambiguity of a null result, we based our conclusions on effect size estimates and the corresponding confidence intervals. Instead of drawing a binary conclusion on whether self-regulation requires cognitive resources, we focused attention on the upper limit

of the effect size confidence intervals. T h e upper limit of the effect size confidence intervals indicated that it was extremely unlikely that self-regulation required even a small amount of cognitive resources. In addition to these arguments, the null results of this research can be supported within the traditional hypothesis testing paradigm. Cook and Campbell (1979) and Cook et al., (1979) have addressed the problem of accepting the null hypothesis. Cook and Campbell stated: When an explicit directional hypothesis guides the research, it is sometimes possible to conclude with considerable confidence that the derived effect was not obtained under the conditions in which the testing occurred. This conclusion is easiest to draw when the results are statistically significant and in the opposite direction to that specified in the hypothesis or when the results, though not statistically reliable, are contrary to the derived prediction . . . . But note here that the issue is not acceptance of the hypothesis of no-difference, but acceptance of the hypothesis that a particular predicted effect was not obtained. (p. 45) This critical distinction represents the purpose and resuits of this research very well. A strong situation for assessing the magnitude of the self-regulation effect was designed. The results were opposite, though not statistically significant from the predicted pattern that was based on Kanfer and Ackerman's (1989) theory. Consistent with Cook and Campbell's (1979) recommendation, we concluded that the predicted effect was not obtained under the testing conditions. Cook et al. (1979) provided four conditions that helped us determine whether no-difference findings provide support for the null hypothesis and warrant interpretation. These conditions include: (a) When the theoretical conditions necessary for the effect to occur have been explicated, operationalized, and demonstrably met in the research; (b) when all the known plausible countervailing forces have been explicated, operationalized, and demonstrably ruled out; (c) when the statistical analysis is powerful enough to detect at the least the theoretically maximum effect at a preordained alpha level; and (d) when the manipulations and measures are demonstrably valid. (p. 668) This research meets or exceeds all four criteria. The important theoretical variables and assumptions were identified and the relevant variables were either controlled or measured. The goal-setting manipulation and the dual task methodology used in this research have been commonly used for over 20 years. Instead of relying on the demonstrated robustness of these manipulations, we performed a pilot study where it was demonstrated that the lack of a performance decrement on the secondary task was not due to the secondary task being insensitive to the resources required for performance on the tracking task. Moreover, performance on the letter recall

RESOURCE ALLOCATIONMODELS task in the primary study was uniformly much lower than the reference performance in the pilot study (see Figure 2). The lack of a difficult goal performance decrement on the secondary task was not due to the absence of selfregulation. The success of the goal-setting manipulation indicates that participants were able to effectively monitor their performance relative to the provided goal and to increase performance toward that standard (selfevaluation). Furthermore, participants were able to successfully allocate attentional resources across the two tasks in accordance with the directions supplied by the experimenters (self-monitoring). In other words, participants effectively distributed their attention between the tracking and memorization tasks so that tracking performance was not affected by the different memorization conditions. Finally, the affective reactions to feedback questionnaire indicate that participants in the difficult goal condition had higher negative affective reactions to their task performance (self-reaction). In addition to the demonstrated sensitivity of the design, this research had more than adequate statistical power to detect whatever effect of self-regulation existed. The two most cited articles on the complex task goal-setting effect are Kanfer and Ackerman (1989) and Earley, Connolly, and Ekegren (1989). The average effect size of the goal decrement across the three studies in Earley, Connolly, and Ekegren and Kanfer and Ackerman's third study was very large (d = 1.08). As stated earlier, the effect of self-regulation is manifested by performance differences on the secondary recall task. Using Cohen's ( 1988 ) power tables for the chi-square test, we had more than 80% power to detect an effect slightly larger than a small effect size (.2) in each memory condition (N = 328, four responses and 82 participants). Moreover, this research had 99% power to detect an effect size that is far smaller (>__.3) than the average effect size in Earley, Connolly, and Ekegren (1989) and Kanfer and Ackerman (1989). We emphasize that our results indicate only that selfregulation does not necessarily require attentional resources. The present results do not demonstrate that selfregulation never requires attentional resources. In fact, there are many contexts where self-regulation does require attentional resources. For instance, military pilots must often focus their entire atterltional pool on tracking a moving object with the intent of disabling the object. Simultaneously, the pilot must maintain situational awareness to keep from inflicting harm on nontarget objects and to avoid potential threats from enemy aircraft and the ground. Advances in heads-up display technology are a direct result of attempts to minimize the interference of information seeking for self-regulation on primary task performance. Alternatively, tasks may have been designed so that attempts to self-regulate are pun-

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ished. For instance, Huber ( 1985 ) observed that setting difficult, specific goals results in worse performance on a maze solution task. The primary dependent variable in Huber ( 1985 ) was the number of moves required to complete the maze plus a penalty score for the number of times that the participants looked at a picture of the maze. 4 Consequently, the participants who self-regulated by repeatedly "peeking" at their current state and comparing it with their goal state were penalized. Research is needed to identify the task factors that result in resource dependent self-regulation. From the current literature, it appears that at least four task factors result in attentionally demanding self-regulation. First, some systems are poorly designed and require the operator to switch the focus of attention to acquire information needed for adequate self-regulation. These issues have traditionally been addressed in human factors research, but little attention has focused on individuals' motivation to perform tasks. The design of the feedback systems needs to be altered to provide necessary information without distracting the operator from the primary task. Second, ambiguous feedback may necessitate on-line processes for interpretation. To acquire skills, the individual must be able to understand implications of the feedback. If the automatic process of feedback attribution fails to yield a sufficient interpretation, the individual will actively process the feedback and make conscious, attentionally demanding decisions about it. Third, repeated negative feedback over time may lead the individual to expend cognitive resources evaluating the validity of the current strategy, causal attributions from feedback, and the importance of the particular task. However, these outcomes represent positive on-task cognitions that should eventually result in improved learning and performance. Fourth, in the initial stages of skill acquisition, the individual will likely feel the need to supplement declarative task knowledge with any reference material that has been provided. If attending to the reference material provides too much distraction from primary task attention, short-term performance will suffer. Individuals with difficult, specific goals should not be penalized for seeking input for the self-regulatory process. Our results have at least two potential limitations to the generalizability of the present results to complex tasks. The first limitation revolves around the issue of whether self-regulation on complex tasks is qualitatively different than self-regulation on simple tasks. Like Kanfer and Ackerman (1989), we believe that the basic process of self-regulation is the same across simple and complex tasks. To our knowledge, there are no data that lead to any other conclusion. However, it is possible that 4 We thank John Hollenbeck for bringing this point to our attention.

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DESHON, BROWN, AND GREEN1S

self-regulation on complex tasks is a more effortful process. For instance, complex tasks may require more extensive and attentionally demanding processes such as meta-cognition. Although meta-cognition is certainly an important aspect of complex task performance, it is important to distinguish between the basic process of selfregulation and the process of developing task-specific strategies. Otherwise, circular reasoning occurs. Self-regulation addresses the allocation of effort across on-task and off-task activities, the comparison of current performance to the desired performance level, and reactions to negative performance feedback. Self-regulation makes it possible for task performers to recognize that current strategies are ineffective. Consequently, it is responsible for initiating meta-cognitive processes and further strategy development. Similarly, complex tasks may require more effortful self-regulation because self-reaction to repeated negative feedback may promote higher levels of anxiety, frustration, and off-task cognitions. Whether this is self-regulation or an outcome of the self-regulatory process is unclear. The cognitive interference model states that anxiety-related cognitions after sustained exposure to failure undermine performance because they divert attentional resources toward self-concerned thoughts (Coyne, Metalsky, & Lavelle, 1980; Mikulincer, 1989; Sarason, 1975 ). However, the detrimental effects of off-task cognitions appear only with feedback that is negative and prolonged thus making difficulties appear unsolvable. Recent work has shown that even under sustained failure, the interference effect is not ubiquitous. Mikulincer (1989) demonstrated that performance decrements from off-task cognitions occurred only for individuals prone to cognitive interference (as measured by the Thought Occurrence Scale; TOQ) and for off-task cognitions that involved task escape thoughts. Similarly, Carver and Scheier (1990) suggested that when individuals are faced with obstacles that appear insurmountable they disengage from the attempt by withdrawing behaviorally or mentally. In the context of goal-setting research, these individuals are no longer committed to pursuing the goal. Performance decrements from task disengagement are a motivational consequence of self-regulation, not a result of cognitive interference. In short, most of the potential sources of interference appear to be outcomes of self-regulation and not a necessary result of the basic process of self-regulation. The second limitation of this research is the reliance on the unitary, fixed pool ofattentional resources metaphor. The use of this model in the current study is justified partially because of the research focus. Our purpose was to test the fundamental assumption of resource allocation models of task performance, which is based on the unitary fixed-pool model. However, when Humphreys and

ReveRe (1984) first discussed resource allocation models and motivation, they pointed out the potentially misleading effects of over-reliance on the metaphor. The multiple resource model has been the most frequently discussed alternative to the unitary pool models (Wickens, 1980; 1984). Attentional resources in this model are split into three dimensions: stages of information processing (i.e., perceptual vs. central processing); input modalities (visual vs. auditory); and cognitive representation (i.e., verbal vs. spatial). Wickens (1984) also suggested that there may be a central pool of undifferentiated resources to account for the empirical findings of nonspecific interference. The multiple resource metaphor does not substantially weaken our argument because it has been difficult to support empirically and has come under increased attack over the last decade (Allport, 1989; Navon, 1984; Neumann, 1987). Furthermore, it is now clear that the multiple resource model has too many degrees of freedom thus making it untestable. When theoretical restrictions on the number of resources are placed on the multiple resource model, the empirical results are confusing (Neumann, 1987). In addition, if there are multiple pools of attentional resources, it is necessary to specify exactly the resources required by self-regulation and the overlap of these resources with those required by the particular task under consideration. The tasks studied here were designed to load on a central pool of attentional resources. If self-regulation requires noncentral resources, the issue becomes much less important in skill acquisition on complex tasks. In conclusion, the theoretical and practical implications of these findings are straightforward. Self-regulation is not necessarily a hidden task that should be avoided to preserve cognitive resources during skill acquisition. On the contrary, self-regulation is a natural process that is integral to learning and makes goal achievement possible. Although some situations and particular individuals may require more active forms of selfregulation, it is important to distinguish between the basic process of self-regulation and the outcomes of the selfregulation process such as meta-cognition and strategy development. Researchers who study skill acquisition through training or motivational techniques need to identify and eliminate aspects of the task that require the individual to switch attention from the task to effectively perform self-regulation. Finally, our research suggests that alternative explanations for the lack of a goal-setting effect on complex tasks need to be explored. Recent research on the framing effect of goals (DeShon & Alexander, 1996; Earley, Connolly, & Ekegren, 1989; Wood & Locke, 1990) is a promising alternative to the resource allocation model.

RESOURCE ALLOCATION MODELS

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