11

Optimizing Individual Performance

One of the long-standing concerns of sports psychology has been an understanding of performance under pressure. Unlike the military, the “pressure” in a sport is not usually intended to put someone at risk of life and limb. Although fear of injury may create pressure in some sports, more frequently competitive pressure is associated with a threat to one's self-esteem.

Since the mid-1970s there has been interest among psychologists to use sports as a naturalistic laboratory in which to conduct research on performance under pressure. In this chapter we review the rather expansive sports literature in four areas: the relationship between the mental health model, mood, and motor performance; cognitive-behavioral interventions for sport and motor performance; preperformance routines (i.e., preparation rituals), sports performance and electrophysiological correlates associated with these routines; and the effect of exercise on reactivity to psychosocial stressors. In the last section, we turn to a different area of research, neuroscience, to consider broader issues of the brain and performance.

THE MENTAL HEALTH MODEL OF SPORTS PERFORMANCE

A recent mental health model of athletic performance (Morgan, 1985) posits that success in sports is negatively correlated with psychopathology: that is, anxious, depressed, hysterical, neurotic, introverted, withdrawn, confused, fatigued, or schizoid athletes do not perform as well in sports as athletes with more positive mental health profiles. Scientifi-



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IN THE MIND'S EYE: ENHANCING HUMAN PERFORMANCE 11 Optimizing Individual Performance One of the long-standing concerns of sports psychology has been an understanding of performance under pressure. Unlike the military, the “pressure” in a sport is not usually intended to put someone at risk of life and limb. Although fear of injury may create pressure in some sports, more frequently competitive pressure is associated with a threat to one's self-esteem. Since the mid-1970s there has been interest among psychologists to use sports as a naturalistic laboratory in which to conduct research on performance under pressure. In this chapter we review the rather expansive sports literature in four areas: the relationship between the mental health model, mood, and motor performance; cognitive-behavioral interventions for sport and motor performance; preperformance routines (i.e., preparation rituals), sports performance and electrophysiological correlates associated with these routines; and the effect of exercise on reactivity to psychosocial stressors. In the last section, we turn to a different area of research, neuroscience, to consider broader issues of the brain and performance. THE MENTAL HEALTH MODEL OF SPORTS PERFORMANCE A recent mental health model of athletic performance (Morgan, 1985) posits that success in sports is negatively correlated with psychopathology: that is, anxious, depressed, hysterical, neurotic, introverted, withdrawn, confused, fatigued, or schizoid athletes do not perform as well in sports as athletes with more positive mental health profiles. Scientifi-

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IN THE MIND'S EYE: ENHANCING HUMAN PERFORMANCE cally speaking, Morgan's model may not seem very provocative, but its common sense and intuitive appeal has advantages in its gaining acceptance among the public (see Morgan, 1980). Morgan maintains that his model has an intermediate level of complexity: that is, that the model can specify relationships [e.g., y = f(x)]. Morgan implies that the flow of causality is from psychological states to success in sports, not the other way around. However, he does not provide evidence for this flow, and it could be argued that the flow is in the opposite direction: that self-protective mechanisms may operate for athletes believing that they will not make a team or place first, which subsequently affects psychological states. Morgan (1985:71) further maintains that this type of model “also predicts specific responses will be dependent upon specific stimulus conditions.” Although the model does not clearly spell out all of the stimulus conditions, its empirical basis suggests that it may be limited to certain types of sports. With few exceptions (LeUnes et al., 1986; Morgan, 1981), most of the evidence has come from sports that emphasize muscular or cardiorespiratory endurance —body building, cycling, karate, wrestling, speedskating, soccer, rowing, distance running, and swimming. The model has been applied to other types of performance tasks. (In considering the use of this model for military settings, it seems most appropriate for dynamic, large-muscle activities or cardiorespiratory endurance tasks that involve intense aerobic/anaerobic training over a period of months.) The basic model is a between-subjects static model, consisting of predictions of desirable psychological states for optimal performance. It has also been extended to a within-subjects dynamic model consisting of monitoring athletes throughout different stimulus conditions associated with intense training. Morgan (1985) believes the evolving dynamic model will ultimately prove to be superior to the static model in understanding changes in athletic performance. We first present the static model to better understand its features, which are also incorporated into the dynamic model. Static Model: The “Iceberg Profile” Although Morgan's work is admittedly pretheoretical, the measuring instruments used to test the static model represent existing psychological theory applied to sports (e.g., Eysenck et al., 1982). Specifically, Morgan 's research has primarily focused on several standard psychological instruments: the State-Trait Anxiety Inventory (STAI) (Spielberger et al., 1970); the Profile of Mood States (POMS) (McNair et al., 1971/ 1981); the Eysenck Personality Inventory (EPI) (Eysenck and Eysenck,

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IN THE MIND'S EYE: ENHANCING HUMAN PERFORMANCE 1968, which measures extroversion, neuroticism, and conformity; the Somatic Perception Questionnaire (SPQ) (Landy and Stern, 1971); and the Depression Adjective Checklist (DACL) (Lubin, 1967). The conformity measure (i.e., lie scale) of the EPI was used throughout Morgan' s research to infer the validity of subjects' self-report responses. In some studies (Morgan, 1981; Morgan and Johnson, 1978), the Minnesota Multiphasic Personality Inventory (MMPI) was used in place of the other instruments. To test the model, Morgan defined psychopathology as higher scores relative to college student norms on introversion, neuroticism, trait anxiety, tension, depression, anger, fatigue, and confusion, as well as on the MMPI clinical scales used to diagnose problems dealing with psychological adjustment. Thus, Morgan predicted that athletes displaying psychopathology—defined as one standard deviation above the 50th percentile on the scales—would have lower levels of performance compared with athletes displaying a more “positive mood profile.” In a review of seven of his own empirical studies testing the static mental health model with pre-elite and elite athletes, he found a consistent pattern, relative to college student norms (McNair et al., 1971/ 1981): higher than average vigor and extroversion scores (i.e., positive characteristics for performance) and lower than average scores on the scales indicative of potential psychopathology. For example, on the POMS inventory, which has become most associated with tests of Morgan's mental health model (see LeUnes et al., 1988, for an annotated bibliography), the positive mood profile has become known as the “iceberg profile.” This term refers to the shape of the curve relative to 1967 college student norms when raw scores on the six scales are plotted on the POMS profile sheet; see Figure 2. Since vigor happened to appear on these scoring forms in a middle position among the other moods and since athletes typically score above the population average on vigor, the plot looked like an iceberg. 1 In all this research, the measured relationship of mental health to performance has been somewhat indirect. In no case were actual performances compared with athletes' responses on psychological inventories at the time the inventories were completed. Instead, they were inferred from final placement in competitions (e.g., 1st or 2nd) whether they made or did not make a team, or from expert ratings of elite runners within a 2-year period (Morgan et al., 1987a). In most studies the time span between athletes' completing the psychological inventories and the performance outcome was less than a week. In other studies in which trait measures (e.g., MMPI) were used, this time span was either much longer (as much as 4 years), or the response set used for the POMS (i.e., “past week including today”) did not necessarily include the time in

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IN THE MIND'S EYE: ENHANCING HUMAN PERFORMANCE FIGURE 2 Comparison of profile of mood states of athletes and college student norms. which the performances occurred (Morgan et al., 1987a). In spite of the rather crude performance measures, the findings have been remarkably consistent in showing that better athletes displayed a more positive mental health profile than athletes who were slightly worse (Morgan, 1985). The robust pattern generalized across several muscular and cardiorespiratory endurance-type sports and several samples of athletes. (Morgan also concluded that the findings were not likely due to response distortion since scores on the EPI lie scales were not correlated with any of the psychometric variables or performance.) The accuracy of the prediction was high but less than perfect. Using an a priori clinical analysis as the prediction basis, Morgan (1985) concluded that the accuracy of the prediction was between 70 and 80 percent and always exceeded base-rate or chance expectancies.2 Although Morgan's model suggests a high degree of predictive accuracy, he maintains that this level of precision is not acceptable for selection purposes. Considering that in Morgan's (1985) early research the mental health model was capable of making fine-grain differentiations with reasonable accuracy among high-level performers, an important question is whether the findings have held up in more contemporary research studies by other investigators. Although several of the studies cited in the annotated bibliography of LeUnes et al. (1988) claim to support Morgan's iceberg profile, many of these studies made no statistical comparisons to

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IN THE MIND'S EYE: ENHANCING HUMAN PERFORMANCE a control group. Instead, these studies simply plotted the means of a single group of athletes and contrasted them to the normative sample reported in the POMS test manual (McNair et al., 1971/1981). The unusually consistent findings relative to the test norms are likely due to an inappropriate comparison group; the norms were obtained from participants in psychological tests and students in psychology courses and “should be considered as very tentative” (McNair et al., 1971/1981:19). More recent studies of undergraduate college nonathletes (Boyle, 1987; Dyer and Crouch, 1987) demonstrate that the mean scores for tension, depression, and vigor have decreased over the past 20 years. This instability over time, which represents from 2-7 points on the various POMS subscales, and the current controversy surrounding the factor structure of the POMS (Boyle, 1987; Norcross et al., 1984; Reddon et al., 1985), suggests that the normative sample reported in the POMS manual is an inappropriate comparison group. Thus, studies of this type offer no convincing support for the iceberg profile and the mental health model. A better test of the replicability of Morgan's findings for the mental health model are studies that provide comparison groups similar to those reported by Morgan (1985). We found 14 such studies that compared the POMS scores of two groups of athletes representing two levels of performance outcome (13 in LeUnes et al., 1988; Morgan et al., 1987b). Of the 148 total comparisons made with the six scales, only 27 (18 percent) were statistically significant and 24 (16 percent) in the predicted direction. The pattern predicted by the mental health model may have been present in these studies, but the power of the statistical tests may not have been sufficient to detect differences because of the small number of subjects. To examine this possibility, the directions of the 148 comparisons were examined relative to the predictions of the mental health model. As shown in Table 1, the percentage of supportive versus nonsupportive findings was calculated for each study. Compared to chance expectations of 50 percent supportive, these 14 studies yield an unimpressive overall average percentage of 53 percent supportive findings. As a whole, the findings from other investigators, as well as Morgan's studies (Morgan and Pollock, 1977; Morgan et al., 1987b), fail to clearly support the predictions of the mental health model. In the face of these more recent findings, it is highly questionable that the POMS instrument, which is primarily used to test the mental health model, is sensitive enough to reliably differentiate among athletes who are already highly proficient. In addition to small samples and performance measures that have been indirect and often distally linked in time with psychological measures, the susceptibility of the POMS to distorting influences may also contribute to the lack of sensitivity in differentiating among athletes. For instance, Boyle (1987) has noted that

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IN THE MIND'S EYE: ENHANCING HUMAN PERFORMANCE TABLE 1 Comparisons of the Outcomes and Directions of Performance Changes Predicted by the Mental Health Model         Comparisons in Predicted Direction Study and Year Sport Na Total Comparisons Number Percent Craighead et al., 1986 Basketball 61 6 2 33 Daiss et al., 1986 Football 60 4b 1 25 DeMers, 1983 Diving 60 18 8 44 Dyer and Crouch, 1987 Running 40 12c 8 66 Guttman et al., 1984 Skating 11 18 14 77 LeUnes and Nation, 1982 Football 180 18 14 77 Miller and Miller, 1985 Netball 20 6 3 50 Morgan and Pollack, 1977 Running 19 6d 1 16 Morgan et al., 1987b Running 27 6 0 0 Riddick, 1984 Swimming 79 18 9 50 Silva et al., 1981 Wrestling 15 6 4 67 Silva et al., 1985 Wrestling 86 6 6 100 Tharion et al., 1988 Running 34 6 3 50 Wilson et al., 1980 Running 30 18 18 100 a Number of athletes. b Data not reported for two subscales. c Comparisons between beginning runners and advanced runners at 3 hours and at 10 minutes prior to performance. d Comparisons between world-class middle long distance runners and college middle distance runners. social desirability and other response sets, inadequate self-insight, and item transparency affect POMS scores. Miller and Edgington (1984) have demonstrated the susceptibility of the POMS to response distortion or “faking good” when physical education students were led to believe the test results might influence team selection. The resulting extreme iceberg profile prompts considerable concern that the strong situational demands of team selection and placement in athletic competition may produce distorting influences that override instructions to subjects to answer the POMS as honestly as they can and that may be unique to the POMS and, thus, not detectable by the conformity scale of the EPI. Overall in fact, the static model has had limited success in predicting levels of athletic performance. Dynamic Model: Measuring Overtraining A more viable and methodologically sound approach of the mental health model may be to use measures of mood to monitor athletes across

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IN THE MIND'S EYE: ENHANCING HUMAN PERFORMANCE different conditions of situationally induced pressure brought about by manipulating the training stimulus. This section reviews studies examining this dynamic mental health model. An interesting extension of the mental health model has been in the area of overtraining and staleness. Staleness is an undesirable or pathological condition that, in a sports context, often results when one is not able to fully recover from the acute fatigue caused by overtraining (Morgan et al., 1987a). (Overtraining should not be confused with overlearning, in which the physical demands of the task do not interfere with successful performance.) The most obvious symptoms of staleness include “physiological and psychomotor retardation, chronic fatigue, depressed appetite, weight loss, insomnia, decreased libido, muscle soreness and elevated depression and tension” (Morgan et al., 1987a:110). In most endurance sports, coaches plan times during the year or season when they can increase the volume of training by altering the intensity (i.e., time at a fixed distance) or duration (i.e., constant pace but varied distance). Intensity and duration of training increases nearly four-fold during several weeks or months prior to important competitions. Most coaches believe that with the proper training stimulus, the process of overtraining can lead to enhanced athletic performance as long as the athlete is allowed sufficient time to recover from the acute fatigue brought on by the intense training. Often, however, the training stimulus is not appropriate, and athletes are unable to recuperate fully within 12-24 hours following an intense workout. As the intense training continues, chronic levels of fatigue develop and performance deteriorates. The undesirable effects of overtraining are sometimes checked by monitoring resting heart rate, blood pressure, lactate, creatine kinase, cortisol, or catecholamine levels. If values on these measures deviate from normal, then the training stimulus can be cut back in a dose-response manner. Just as these physiological manifestations of overtraining can be monitored to prevent staleness, Morgan et al. (1987a) argue that staleness can also be prevented by systematically monitoring changes in mood. By examining mood states (e.g., with POMS) during training cycles consisting of light training, intense or overtraining, and tapering phases, staleness can be diagnosed early and prevented by reducing the training stimulus until the athlete once again displays a mood profile conducive to optimal performance (i.e., the iceberg profile). Without physiological or psychological markers to prevent staleness, the only known treatment is a dramatic reduction or change in training or, in extreme cases, complete rest by discontinuance of training. With the exception of one study conducted with wrestlers,3 most of the longitudinal studies by Morgan et al. (1987a) have been with collegiate swimmers. In these studies, varsity swimmers were given the

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IN THE MIND'S EYE: ENHANCING HUMAN PERFORMANCE POMS scale at 1-month intervals from September (training load at approximately 3,000 yards/day) to the peak training load in January (11,000 yards/day) until just before the conference championship in February (peak training load was tapered to 5,000 yards/day). In this research, global mood disturbance was measured with the response set of “how you have been feeling during the past week including today.” Global mood disturbance was calculated by summing the negative mood scales (anger, confusion, depression, fatigue, and tension), subtracting the “vigor” score from this total, and then adding a constant of 100 to prevent the occurrence of negative values. During the 10 years that Morgan and his colleagues monitored mood scores on approximately 200 female and 200 male competitive swimmers, the greatest global mood disturbance typically occurred in late January when athletes were training twice a day to accomplish the peak training load. This increase in global mood disturbance usually resulted from a statistically significant increase in fatigue and a significant reduction in vigor. During this time about 5-10 percent of the swimmers experienced what was considered as staleness: the swimmers' performances had deteriorated, and they were unable to train at customary levels. They were referred to counseling psychology and outpatient psychiatric services, and approximately 80 percent of them “were judged to possess depression of clinical significance” (Morgan et al., 1987a:108). Of the eight studies summarized by Morgan et al. (1987a), only one study controlled for nonathletic stressors—social, economic, and academic—in the life of college students. Throughout the semester (September through early December), 44 collegiate swimmers and 86 nonathletic college students (controls) completed the POMS bimonthly from week 3 to week 13 of the semester. As shown in Figure 3, the swimmers scored significantly lower than the controls at the beginning of the semester. However, as the training volume increased during the ensuing weeks (weeks 5 to 11), the swimmers experienced a significant global mood change; the controls did not change significantly at any point throughout the 13-week period. The swimmers had significantly higher mood disturbance than the controls during weeks 9 through 13. As the swimmers were not yet into the taper phase, there was no support for the findings of other studies (see Morgan et al., 1987a) that a return to normal mood levels occurs once the training stimulus was lowered. In several of the research reports of the static and dynamic mental health models, Morgan has argued that psychobiological tests of the models would provide greater understanding than does a purely psychological approach. Illustrative of this is a recent psychobiological study (O'Connor et al., 1989) conducted with 14 female collegiate swimmers and 8 active college females who served as controls. As in other swim-

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IN THE MIND'S EYE: ENHANCING HUMAN PERFORMANCE FIGURE 3 Moods of student athletes and nonathletes. ming studies, the training volume of the swimmers increased from 2,000 yards/day in September (baseline) to a peak of 12,000 yards/day in January (overtraining), followed by a reduction in training to 4,500 yards/ day by February (taper). The swimmers had more global mood disturbance in comparison with the controls during overtraining followed by a reduction in global mood disturbance after the taper period. The salivary cortisol levels of the controls did not change across time periods, while the swimmers had higher cortisol levels than controls during the baseline and overtraining periods. The swimmers had cortisol levels similar to the control subjects during the taper phase. 4 In this study the team's coach (who was not given the mood and cortisol data) classified three swimmers as being stale because they had a performance decrement of 5-10 percent for 2 weeks or longer. For each of these women, the period of staleness coincided with the end of the overtraining period. Using a nonparametric test to examine the 3 stale and 11 normal swimmers, the results showed that the stale group had significantly higher global mood disturbance and salivary cortisol levels than the normal group. This finding is merely suggestive since the stale athletes may also have had more mood disturbance during the baseline period. The results of this study (O'Connor et al., 1989) are consistent with other studies (Morgan et al., 1988) showing a convergence of physiological and psychometric measures of distress in swimmers during a

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IN THE MIND'S EYE: ENHANCING HUMAN PERFORMANCE period of increased training. The fact that tension remained elevated following the taper period may have been due to anxiety associated with an impending conference championship. The differences in salivary cortisol levels during the baseline are also problematic. This difference may have been due to limitations associated with only collecting one cortisol sample for each training phase and the time frame in relation to training in which these samples were obtained (O'Connor et al., 1989). Although no subjects in this study possessed negative affective states of clinical significance (i.e., one standard deviation of the population average of college females), three subjects had prolonged performance decrements that resulted in their being diagnosed as stale. Global mood disturbance has recently been shown to be related to athletes' running economy (Williams and Krahenbuhl, in press) and mood state following a rapid 6 percent weight loss (Horswill et al., 1990). For example, it has been noted for some time that even in elite runners there is considerable day-to-day variability in ventilation measures, which exercise physiologists use to infer how economically one runs at a constant workload. Comparing the POMS to daily ventilation measures showed a surprisingly high association, r = .80. It appears that these within-subject comparisons of mood state over days or months can detect when athletes may feel undue pressure in their lives that can slightly (e.g., running economy, weight loss) or dramatically (e.g., staleness) affect their performance. The implications of the dynamic model are far-reaching. Summary There are a number of shortcomings in the studies testing the static and dynamic mental health models. These shortcomings include the use of a single-group design in several studies, which greatly limits their internal validity; most of these studies were cross-sectional, exploratory, descriptive, or retrospective in nature. Many of the studies only used comparisons to college norms established more than 20 years ago, and it is possible that these norms are not stable over time and are no longer still representative of college students today. Most of the studies had very small numbers of subjects, which greatly restricted the use of multivariate statistics for testing the predictions of the model and for determining the strength of the predicted relationships. The studies were limited in scope to endurance sports for which the training stimulus was easily quantifiable. Finally, there is meager physiological validation for the predictions of the mental health model. In addition to these shortcomings, another major problem in the series of studies testing the static model has been in the quantification of

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IN THE MIND'S EYE: ENHANCING HUMAN PERFORMANCE performance. The performance measure employed represented an outcome, such as won or lost, made or didn't make the team; but an athlete might have performed very well relative to his or her past performance (e.g., personal best), but still failed to win. Yet Morgan would label the performance as unsuccessful. If, instead, the performance measure was actual swimming times and distances, a ratio scale of measurement could be developed to detect small group differences and enable future investigators to determine if athletes having the greatest performance decrements actually have more global mood disturbance. 5 The results of studies using control groups to test the static model have produced inconsistent results. It may be too much to expect that a self-report measure of mood can be sensitive enough to reliably discriminate among levels of athletic proficiency, let alone predict which athletes will make a team or place first in a given contest. With the use of within-subjects designs with control groups (Morgan et al., 1987a:Study 7; O'Connor et al., 1989), the studies testing the dynamic mental health model have had more internal validity and have shown a greater degree of consistency in supporting the predictions of the model. The dynamic model also has the distinct advantage of being able to specify the situational variables (e.g., training intensity) that lead to predictable mood changes characteristic of what is known as staleness. COGNITIVE-BEHAVIORAL INTERVENTIONS Sports psychologists have studied cognitive-behavioral interventions as a means of facilitating sports performance since the late 1970s. The general assumption is that cognitive-behavioral interventions can help performers achieve greater control of their precompetitive arousal states and maintain attentional focus on the task at hand. The studies reported in this section do not measure arousal/attentional processes directly; rather, they infer these processes from performance changes. Improvements in arousal and attention, which can be achieved by practice in cognitive-behavioral intervention techniques, is believed to result in higher levels of motor performance. As defined by Greenspan and Feltz (1989), “interventions” consist of actions, initiated by someone other than the performer, that focus on psychological skills in an attempt to improve performance. For this review, the interventions typically reported in the sports psychology literature have been put into five categories: (1) relaxation with no attempt to alter cognitions (e.g., progressive muscle relaxation, autogenic training, biofeedback, and hypnotherapy); (2) imagery (including “mental practice”); (3) mental preparation strategies (e.g., association-disassociation strategies and goal setting); (4) skill development

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