the inverted u hypothesis predicts that

The Yerkes-Dodson Law of Arousal and Performance

Charlotte Nickerson

Research Assistant at Harvard University

Undergraduate at Harvard University

Charlotte Nickerson is a student at Harvard University obsessed with the intersection of mental health, productivity, and design.

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On This Page:

The concept of optimal arousal in relation to performance on a task is depicted here. Performance is maximized at the optimal level of arousal, and it tapers off during under- and overarousal.

Key Takeaways

The Yerkes-Dodson law states that there is an empirical relationship between stress and performance and that there is an optimal level of stress corresponding to an optimal level of performance. Generally, practitioners present this relationship as an inverted U-shaped curve.
Research shows that moderate arousal is generally best; when arousal is very high or very low, performance tends to suffer (Yerkes & Dodson, 1908).
Robert Yerkes (pronounced “Yerk-EES”) and John Dodson discovered that the optimal arousal level depends on the complexity and difficulty of the task to be performed.
This relationship is known as the Yerkes-Dodson law, which holds that a simple task is performed best when arousal levels are relatively high, and complex tasks are best performed when arousal levels are lower.
The Yerkes-Dodson law’s original formulation derives from a 1908 paper on experiments in Japanese dancing mice learning to discriminate between white and black boxes using electric shocks. This research was largely ignored until the 1950s when Hebb’s concept of arousal and the “U-shaped curve” led to renewed interest in the Yerkes-Dodson law’s general applications in human arousal and performance.
The Yerkes-Dodson law has more recently drawn criticism for its poor original experimental design and it’s over-extrapolated scope to personality, managerial practices, and even accounts of the reliability of eyewitness testimony.

How the Law Works

The Yerkes-Dodson law describes the empirical relationship between stress and performance.

In particular, it posits that performance increases with physiological or mental arousal, but only up to a certain point. This is also known as the inverted U model of arousal.

When stress gets too high, performance decreases. To add more nuance, the shape of the stress-performance curve varies based on the complexity and familiarity of the task.

Task performance is best when arousal levels are in the middle range, with difficult tasks best performed under lower levels of arousal and simple tasks best performed under higher levels of arousal.

Yerkes Dodson Curve and Task Performance

Original Experiments

The Yerkes-Dodson law has seen a number of interpretations since its inception in 1908. In their original paper, Robert Yerkes and John Dodson reported the results of two experiments involving “discrimination learning” – the ability to respond differently to different stimuli – and dancing mice (Teigen, 1994).

The mice received a non-injurious electric shock whenever they entered a white box but no shock when they entered the black box next to the white box.

In the first set of experiments, Yerkes and Dodson gave the mice very weak shocks; however, they found that these mice took two long to learn the habit of choosing the black box over the white box (choosing correctly 10/10 times over three consecutive days).

When the researchers increased the strength of the shock, the number of trials needed for the mice to learn the habit decreased – until they reached the third and strongest level of electric shock.

When the electric shock was at its strongest, the number of trials needed for the mice to learn which box to enter went up again. This finding went against Yerkes and Dodson” hypothesis that the rate of habit-formation would increase linearly with the increasing strength of the electric shock.

Instead, a degree of stimulation that was neither too weak nor too strong optimized the rate of learning (Yerkes and Dodson, 1908; Teigan, 1994).

Because of this unexpected result, Yerkes and Dodson elaborated on their original experimental design to provide “a more exact and thoroughgoing examination of the relation of strength of stimulus to rapidity of learning” (1908).

The researchers made it easier to discriminate between the white and black boxes by letting more light into the white box and used five rather than three levels of shock.

Contrary to what we now know as the Yerkes-Dodson law, the weakest stimulus gave the slowest rate of learning, while the strongest stimulus led to the fastest rate of learning.

This confused Yerkes and Dodson, who wrote, “The results of the second set of experiments contradict those of the first set. What does this mean?” (1908).

One hypothesis the researchers made was that these contradictory results came from the easiness of the discrimination task.

To test this hypothesis, Yerkes and Dodson made the discrimination task more difficult than in the first set of experiments by allowing less light into the white and black boxes.

The researchers used four levels of shock, but fewer mice in each condition than before – two rather than four. In this set of experiments, the most efficient learning seemingly occurred at the second-weakest shock level (Teigen, 1994).

From these three sets of experiments, Yerkes and Dodson concluded that both weak and strong stimuli can result in low rates of habit formation and that the stimulus level most conducive to learning depends on the nature of the task.

“As the difficultness of discrimination is increased, the strength of that stimulus which is most favorable to habit-formation approaches the threshold” (Yerkes and Dodson, 1908; Teigen, 1994).

Replication Studies

Following the original formulation of the Yerkes-Dodson law, researchers replicated the original study, using animals such as chicks (Cole, 1911) and kittens (Dodson, 1915).

Cole (1911) gave chicks an easy, medium, and difficult discrimination task, with four levels of shock for the medium task and three levels of shock for the other tasks.

In the easy task, the rapidity of learning increased with the strength of shock; in the medium-difficulty task, the strongest shock seemingly decreased the rate of learning, and in the difficult task, the strong shock increased the variability of performance – three chicks learned more rapidly due to the strong shock, while two others failed to learn the discrimination task (the sixth chick died over the course of the experiment).

Although Cole (1911) only observed one U-curve (in the medium-difficulty condition), he concluded that his results were in agreement with Yerkes-Dodson.

Dodson (1915), meanwhile, trained four kittens to discriminate between light and dark-colored boxes by giving them a “medium-strength” shock when they entered the darker box.

These kittens performed better at the discrimination task than those given a “strong” electric shock. When the task was made easier (again, by letting more light into the boxes), the strong and medium-strength shocks proved equally effective. With an easier task, learning improved with shock strength (Teigen, 1994).

Dodson himself later found that both the strength of rewards and punishments were related to the rapidity of learning in a U-shaped manner.

For example, rats who had been starved for up to 41 hours prior to the experiment showed higher rates of discrimination learning than those who were not. However, if they were starved longer (and food was more rewarding as a result), learning became less efficient (Dodson, 1917).

Later scholars generally agreed that the Yerkes-Dodson law was about the relationship between punishment and learning.

Young (1936), following a review of the research of Yerkes and Dodson (1908), Cole (1911), and Dodson (1915), added a later confounding study by Vaughn and Diserens (1930) showing that maze learning was more efficient in human subjects given either light or medium punishments in the form of electric shocks, but not with heavy punishment or no punishment.

To quote Young, “For the learning of every activity, there is an optimum degree of punishment” (1936). The 1930s and 1940s saw an evolution of the Yerkes-Dodson law.

Writers such as Thorndike (1932), Skinner (2019), and Estes (1944) did away with the idea of punishment as a fundamental learning principle, and others introduced a distinction between learning and performance (Teigen, 1994).

Researchers reinterpreted the Yerkes-Dodson law as describing the relationship between motivation and performance.

Some, such as Hilgard and Marquis (1961), concluded that the law was evidence that “under certain conditions, the drive may actually interfere” with learning.

Introductory textbooks as well as scholars on the subject, have described the Yerkes-Dodson law in terms of motivation and performance (e.g., Bourne and Ekstrand, 1973).

In these descriptions, the Yerkes-Dodson law has become more about motivated behavior in general than the psychology of learning.

The shape described by the Yerkes-Dodson law has also changed from U-curves to the inverted U: while learning (as measured by the number of trials needed for mastery) is optimal at the lowest point of a U-curve (the least trials needed), performance is optimal, at its highest, at the highest point of the inverted U-curve.

This expansion in scope, it has been argued, renewed interest in the Yerkes-Dodson law from 1955 to 1960 (Teigen, 1994).

Broadhurst (1957) replicated the original Yerkes-Dodson experiment with a better design by using four motivation levels and three difficulty levels with ten rats in each condition.

Again, the rats had to discriminate between light and dark boxes, but they were motivated by different levels of air deprivation: 0, 2, 4, or 8 seconds.

For the easy discrimination task, the highest performance was seen in the 4-second air deprivation group, while the optimum moved to 2 seconds for the medium and difficult task groups.

Broadhurst also proposed testing motivational differences in individual rats by conducting the experiment on rats differing in “emotionality” (Broadhurst, 1957; Teigen, 1994).

Eyewitness Testimony

Expert witnesses have cited the Yerkes-Dodson law in court.

Witness for the defense: The accused, the eyewitness, and the expert who puts memory on trial, Elizabeth Loftus, a psychologist and expert witness in memory and the fallibility of memory, eyewitness testimony explains,

“I approached the backboard located in front of the jury box and, with a piece of chalk, drew the upside-down U shape that represented the relationship between stress and memory known to psychologists as the Yerkes-Dodson law” (Loftus and Ketcham, 1991).

Although this curve bore more similarity to Hebb’s inverted U-curve of arousal, Loftus used the curve to relate arousal (or “stress”) to the efficiency of memory (rather than, as has been formulated by others, learning, performance, problem-solving, the efficiency of coping, or another concept).

The Yerkes-Dodson effect states that when anxiety is at low and high levels, eyewitness testimony is less accurate than if anxiety is at a medium level. Recall improves as anxiety increases up to an optimal point and then declines.

When we are in a state of anxiety, we tend to focus on whatever is making us feel anxious or fearful , and we exclude other information about the situation.

If a weapon is used to threaten a victim, their attention is likely to focus on it. Consequently, their recall of other information is likely to be poor.

Work Stress

The Yerkes-Dodson law has seen frequent citations in managerial psychology, particularly as researchers have argued that the increase in work stress levels is a “costly disaster” (Corbett, 2015).

Corbett (2015) examines the lineage of this law in business writing and questions its application, calling it a “folk method.”

In particular, Corbett criticizes how the law has been extrapolated from its initially limited animal experiments to almost every facet of human task performance, with studies examining tasks as unrelated as product development teamwork, piloting aircraft, competing in sports, and solving complex cognitive puzzles.

This has proved, Corbett argues, to create a situation where the law has become so ambiguous as to be unfalsifiable (2015).

Corbett argues that the generally uncritical portrayal of the Yerkes-Dodson law in textbooks has added a veneer of scientific legitimacy to the management practice of increasing work stress levels at a time when more robust research is increasingly showing that increasing levels of work-related stress corresponds to decreasing mental and physical health.

Corbett, taking an argument from Micklethwait and Wooldridge (1996) posits that management theory is generally incapable of self-criticism, has confusing terminology, rarely “rises above common sense,” and is riddled with contradictions (2015).

In response, he suggests that managerial psychology embraces evidence-based managerial practices.

Arousal and Performance

The renewal of interest in the Yerkes-Dodson law in the 1950s corresponded to the introduction of the concept of arousal (Teigen, 1994).

Hebb (1955), who wrote seminally on the concept of arousal, introduced the inverted U-curve to describe the relationship between arousal and performance.

This idea of arousal shifted the idea of “drive” from the body to the brain and could be framed as either a behavioral, physiological, or theoretical concept. Although not referenced in Hebb’s original paper, writers continued to describe the Yerkes-Dodson law in terms of arousal in textbooks and research literature (Teigen, 1994).

These reformulations of the Yerkes-Dodson law have used terms such as fear, anxiety, emotionality, tension, drive, and arousal interchangeably.

For example, Levitt (2015) holds that the Yerkes-Dodson law describes “that the relationship between fear, conceptualized as drive, and learning is curvilinear,” reporting findings on human maze learning as support for his view.

Using the arousal concept in the formulation of the Yerkes-Dodson law has also seen the law being linked to phenomena such as personality traits and the effects of physiological stimulants.

For instance, in accounting for the theoretical differences in intellectual performance between introverts and extroverts under time pressure, different noise conditions, and at different times of day (e.g., Revelle, Amaral, and Turriff, 1976; Geen, 1984; and Matthews, 1985) as well as participants differing in impulsivity working under the influence of caffeine (e.g., Anderson and Revelle, 1983).

Critical Evaluation

Yerkes and Dodsons’ original experimental design, scholars generally agree, was deeply flawed by modern standards – so much so that W. P. Brown wrote that the law should be “buried in silence” (Teigen, 1994; W. P. Brown, 1965).

Yerkes and Dodsons’ performance vs. stimulus curves were based on averages from just 2-4 subjects per condition; the researchers performed no statistical tests (Gigerenzer and Murray, 2015), and the highest level of shock used in 3, 4, and 5 shock conditions were of different strengths.

The authors assumed that the linear response curve in the second set of experiments (with the easily discriminated white and black boxes) was simply the first part of a U-curve, which would have been fully uncovered given that they had subjected the mice to higher levels of shocks (Teigen, 1994).

Indeed, this experimental design has been misreported by later scholars, such as Winton (1987), who described the original study as a 3 x 3 design with three different levels of discrimination difficulty and three levels of shock strength.

Additionally, Yerkes and Dodson, as Teigen (1994) points out, failed to discuss the concepts involved in the speed of habit formation. Several of the original replicating studies, such as Dodson’s kitten experiment (1915), also showed poor experimental design.

In this experiment, there were only two kittens in the “less difficult” and “easy” discrimination conditions and no U-curves. Nonetheless, Dodson concluded that the results were compatible with the original Yerkes-Dodson experiment (Teigen, 1994).

Anderson, K. J., & Revelle, W. (1983). The interactive effects of caffeine, impulsivity and task demands on a visual search task. Personality and Individual Differences, 4(2), 127-134.

Bourne, L. E., & Ekstrand, B. R. (1973). Psychology: Its principles and meanings (Dryden, Hinsdale, IL).

Broadhurst, P. L. (1957). Emotionality and the Yerkes-Dodson law. Journal of experimental psychology, 54(5), 345.

Brown, W. P. (1965). The Yerkes-Dodson law repealed. Psychological reports, 17(2), 663-666.

Cole, L. W. (1911). The relation of strength of stimulus to rate of learning in the chick. Journal of Animal Behavior, 1(2), 111.

Corbett, M. (2015). From law to folklore: work stress and the Yerkes-Dodson Law. Journal of Managerial Psychology.

Dodson, J. D. (1915). The relation of strength of stimulus to rapidity of habit-formation in the kitten. Journal of Animal Behavior, 5(4), 330.

Dodson, J. D. (1917). Relative values of reward and punishment in habit formation. Psychobiology, 1(3), 231.

Estes, W. K. (1944). An experimental study of punishment. Psychological Monographs, 57(3), i.

Geen, R. G. (1984). Preferred stimulation levels in introverts and extroverts: Effects on arousal and performance. Journal of Personality and Social Psychology, 46(6), 1303.

Gigerenzer, G., & Murray, D. J. (2015). Cognition as intuitive statistics. Psychology Press.

Hebb, D. O. (1955). Drives and the CNS (conceptual nervous system). Psychological review, 62(4), 243.

Hilgard, E. R., & Marquis, D. G. (1961). Hilgard and Marquis” conditioning and learning.

Levitt, E. E. (2015). The psychology of anxiety.

Loftus, E., & Ketcham, K. (1991). Witness for the defense: The accused, the eyewitness, and the expert who puts memory on trial. Macmillan.

Matthews, G. (1985). The effects of extraversion and arousal on intelligence test performance. British Journal of Psychology, 76(4), 479-493.

Revelle, W., Amaral, P., & Turriff, S. (1976). Introversion/extroversion, time stress, and caffeine: Effect on verbal performance. Science, 192(4235), 149-150.

Skinner, B. F. (2019). The behavior of organisms: An experimental analysis. BF Skinner Foundation.

Teigen, K. H. (1994). Yerkes-Dodson: A law for all seasons. Theory & Psychology, 4(4), 525-547.

Thorndike, E. L. (1932). The fundamentals of learning.

Vaughn, J., & Diserens, C. M. (1930). The relative effects of various intensities of punishment on learning and efficiency. Journal of Comparative Psychology, 10(1), 55.

Winton, W. M. (1987). Do introductory textbooks present the Yerkes-Dodson Law correctly?. American Psychologist, 42(2), 202.

Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit-formation. Punishment: Issues and experiments, 27-41.

Young, P. T. (1936). Social motivation.

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inverted U hypothesis

Quick reference.

Hypothesis that states that performance improves with increasing levels of arousal up to an optimal point beyond which further increases in arousal produce a detrimental effect on performance. Therefore, athletes may perform badly because they are over- or under-aroused. The hypothesis is qualitative, and does not attempt to quantify the relationship between arousal and performance. The optima vary between people doing the same task and one person doing different tasks. A basic assumption in the hypothesis is that arousal is unidimensional and that there is, consequently, a very close correlation between indicators of arousal; this is not the case. See also catastrophe theory.

inverted-U hypothesis

From: inverted-U hypothesis in The Oxford Dictionary of Sports Science & Medicine »

Subjects: Medicine and health — Clinical Medicine

Article contents

Arousal control in sport.

Martin Turner Martin Turner Faculty of Health Sciences, Staffordshire University
and Marc Jones Marc Jones Faculty of Health Sciences, Staffordshire University
https://doi.org/10.1093/acrefore/9780190236557.013.155
Published online: 24 January 2018

Sport and stress are intertwined. Muhammad Ali once said, “I always felt pressure before a big fight, because what was happening was real.” As this quote attests, sport is real, unscripted, with the potential for psychological, and often physical, harm. The response to stress, commonly described as “flight or fight,” is an evolutionary adaptation to dangerous situations. It guides behavior and readies a person to respond, to fight, or flee. However, the stress response is not evoked solely in situations of mortal danger; it occurs in response to any situation with the potential for physical or psychological harm, such as sport. For example, the possibility of missing out on a life-changing gold-medal win in an Olympic Games, or losing an important competition that you were expected to win.

Stress in sport is often illustrated by the archetypal image of an athlete choking; snatching defeat from the jaws of victory. But stress can also help athletes perform well. Stress also plays a role in behavior away from the competition arena, influencing interactions with significant others, motivation and performance in training, and how athletes experience and manage injury and retirement from sport. In sport stress, the psychophysiological responses to stress are not just abstract theoretical concepts removed from the real world; they reflect the thoughts, feelings, and experiences of athletes.

It is important to understand the arousal response to stress in sport. Both theory and research suggest a connection between arousal and athletic performance. Recent approaches propose ideas about how the nature of arousal may differ depending on whether the athlete feels positively (as a challenge) or negatively (as a threat) about the stressor. The approach to seeing stress as a challenge supports a series of strategies that can be used to help control arousal in sport.

cognitive appraisal
psychophysiology
performance
reappraisal
approach focus

The Autonomic Nervous System

The following quote from ex-soccer player David Beckham, about taking a penalty in the 2002 World Cup, illustrates the strong physiological arousal observed in response to stress: “It was an important moment for me, the nation and the team . . . but I’ve never felt pressure like that in a game before. I just couldn’t breathe.” The fact that the stress of competitive sport performance manifests in physical symptoms is due to the interaction between how a person thinks and the workings of the autonomic nervous system (ANS). Key elements of a person’s response to stress are changes in the ANS, which controls functions of the body that are geared to survival and connect with the involuntary muscles, such as lungs, stomach, and kidneys (Lovallo, 2004 ). The ANS is part of the peripheral nervous system, which refers to all the nerves outside of the central nervous system (i.e., the brain and spinal cord). The peripheral nervous system comprises the somatic system (connection with the voluntary muscles) and the ANS. Both the ANS and the somatic system are influenced by the central nervous system.

The ANS can further be subdivided into the sympathetic and parasympathetic branches. The sympathetic branch is responsible for mobilizing the body ready for action, reflected in the classic flight or fight response, which is associated with the emotions of anger and fear (Canon, 1932 ). Because this response is geared toward sustaining an attack or fleeing, it is a short-term response and places a strain on the body, which is why prolonged anger and fear carry the potential for harm (Lazarus, 1999 ). The parasympathetic nervous system is concerned with calming, or reducing the arousal. The activity of the sympathetic nervous system is generally all or nothing, that is, the entire body is affected (Lovallo, 2004 ).

The sympathetic nervous system exerts its influence through hormonal activity. Stimulation of the adrenal medulla (center of the adrenal gland) results in secretion of adrenaline and noradrenaline (epinephrine and norepinephrine). The pituitary gland also releases adrenocorticotrophic hormone (ACTH), which that stimulates the adrenal cortex (outer part of the adrenal gland) to release corticosteroids, and the hormone most often considered in response to stress is cortisol. Cortisol mobilizes energy resources to provide “fuel” for the body and thus plays a crucial role in metabolism, alongside other important functions, including anti-inflammatory effects, inhibiting immune functioning, and impacting the cardiovascular system, for example, through inducing vasoconstriction (Dickerson & Kemeny, 2004 ).

Early Models of Arousal and Sport Performance

There are two early major approaches to explaining the arousal-performance relationship that have been applied to sport: drive theory and the inverted U-hypothesis.

Drive Theory

Drive theory was outlined by Hull ( 1943 ) and then later modified by Spence ( 1956 ); it is sometimes referred to as the Hull-Spence theory of behavior (Spence, 1956 ). Drive theory was originally proposed to explain the relationship between complex tasks and arousal, although it has also been applied to explain the relationship between simple tasks (equivalent to complex tasks that are well learned) and arousal. Performance (P) is a multiplicative function of drive state (D) and habit strength (H):

P	=	D	*	H
Performance		Drive State		Habit Strength

In brief, for well-learned tasks, there is a positive linear relationship between arousal and performance. Drive theory has been used to explain behavior in stressful settings, perhaps most notably by Zajonc ( 1965 ), who adopted drive theory to explain social facilitation. The facilitating effects of arousal occur because heightened arousal increases the likelihood of an athlete’s dominant response tendency (habit strength). If the dominant (well-learned) response is the most appropriate, as is likely for skilled performers, then performance improves. However, the literature does not support for the central tenet of drive theory—that heightened arousal (drive state) is associated with improved performance (Martens, 1971 ). In addition, Martens suggested that it is very difficult to determine habit hierarchies. To explain, it is difficult to know when a task becomes so well learned that arousal will always have a positive influence on performance. Perhaps more damagingly, there are many examples of excessive arousal disrupting performance, so the face validity of the theory does not appear to hold. For example, even highly skilled performers can point to examples in which excessive arousal disrupted performance (e.g., a world class tennis player double faulting on a crucial point).

Furthermore, Oxendine ( 1984 ) did suggest that a linear relationship may exist for gross motor activities. Thus, drive theory may hold for tasks for which power is required but co-ordination is not needed. Anecdotally, this would make sense, but determining when an activity relies solely on power is difficult. For example, even tasks like scrummaging in rugby or weightlifting require coordination. In short, there is limited evidence for drive theory in sport (Zaichkowsky & Baltzell, 2001 ).

Inverted-U Hypothesis

In the inverted-U hypothesis performance is best at a moderate level of arousal. Both low and high levels of arousal are associated with decrements in performance. The original work done on the inverted-U hypothesis related to the strength of stimulus and habit-formation (learning) in mice (Yerkes & Dodson, 1908 ). Mice learned most quickly which chamber of two to enter when the punishment for choosing the wrong chamber was an electric shock of moderate intensity. This finding was supported by later work with rats (Broadhurst, 1957 ). From these rodent-based studies, it is difficult to see how the inverted-U hypothesis has become such a commonly used explanation for the arousal-performance link in humans. Perhaps the idea that moderate levels of arousal are suitable for performance has an intuitive appeal. This hypothesis was supported by some research in attention. For example, under high physiological arousal, the attention field narrows (cf. Easterbrook, 1959 ), which has a positive effect on performance if it blocks out unimportant distractions but a negative effect if the narrowing is so great that task-relevant cues are missed.

Some research evidence shows that anxiety (often associated with high arousal) relates to performance in the manner of an inverted U shape. Specifically, the best performances of 145 high school basketball players occurred under moderate levels of anxiety (Klavora, 1979 ), and the performance of university female basketball players was higher following medium levels of anxiety (Sonstroem & Bernardo, 1982 ). However, despite this support, the inverted-U hypothesis has been met with some criticism (cf. Neiss, 1988 ; Raglin, 1992 ; Zaichkowsky & Baltzell, 2001 ):

This hypothesis describes, but does not explain, the relationship between arousal and performance.

The symmetrical U shape is not a realistic representation of a competitive sport situation. Performance tends to deteriorate much more dramatically with high arousal.

Arousal itself is multidimensional and accordingly, the inverted-U hypothesis may be simplistic.

While the inverted-U hypothesis has some intuitive appeal, research has begun exploring how cognitive and physiological aspects of arousal interact to affect performance and contribute to the experience of athletes under stress. Much of this research has considered the role of arousal as part of the anxiety response.

Contemporary Approaches to Arousal and Sport Performance

Anxiety is characterized by feelings of apprehension and tension along with activation or arousal of the autonomic nervous system (ANS; Spielberger, 1966 ). Two elements of anxiety outlined in this definition, cognitive and physical, explain why more recent approaches to understanding arousal have considered both elements in their approaches.

Multidimensional Anxiety Theory (MAT)

One of the most influential theories in sport research is the multidimensional theory of competitive state anxiety (MAT; Martens, Burton, Vealey, Bump, & Smith, 1990 ).

In MAT cognitive anxiety refers to “fear of failure and negative expectations about performance” while somatic anxiety refers to “individuals’ perceptions of their physiological state” (Hardy, Jones, & Gould, 1996 ; p. 142). Adopting a multidimensional approach to the study of competitive anxiety means that an individual could be high in cognitive anxiety and low in somatic anxiety, or vice versa, high in both or low in both. Furthermore, this approach implies that cognitive and somatic anxiety have separate antecedents and different temporal patterning in the lead up to competition (Parfitt, Jones, & Hardy, 1990 ) and that they are affected differently by different anxiety control techniques (Burton, 1990 ) and crucially have different relationships with performance.

The conceptualization of competitive anxiety as a multidimensional construct meant that new measurement tools had to be developed. Martens and colleagues developed the Competitive State Anxiety Inventory-2 (CSAI-2) and published it alongside the MAT (Martens, Burton, Vealey, Bump, & Smith, 1990 ). The CSAI-2 was originally developed to assess cognitive and somatic anxiety, but an additional cognitive factor (self-confidence) emerged during the development. According to MAT, cognitive anxiety has a negative linear relationship, self-confidence a positive linear relationship, and somatic anxiety an inverted-U relationship with performance. Because the CSAI-2 and MAT were published simultaneously, the theory and the measurement tool are linked. Therefore, limitations in one may affect the other, and it is difficult to determine whether unsupportive research findings are a result of limitations in the theory, the measuring tool, or both.

Some research supports the central tenets of MAT. For example, a sample of swimmers, showed a curvilinear trend, similar to the inverted U, between somatic anxiety and performance. They demonstrated a negative linear trend between cognitive anxiety and performance and a positive relationship between self-confidence and performance (Burton, 1988 ). However, some conflicting evidence has also emerged. For example, in a sample of pistol shooters, a curvilinear trend, similar to the inverted U, between somatic anxiety and performance was observed, but no significant relationship between cognitive anxiety and performance was detected, and there was a negative relationship between self-confidence and performance (Gould, Petlichkoff, Simons, & Vevera, 1987 ; Hardy, Jones, & Gould, 1996 ). Collectively, empirical support for these predictions has been equivocal, with support primarily for the positive association between self-confidence and performance (e.g., Craft, Magyar, Becker, & Feltz, 2003 ; Woodman & Hardy, 2001 ). While the relationship between self-confidence and performance was consistent across sports in their meta-analysis, Craft et al. found that both cognitive anxiety and somatic anxiety seem more influential in individual sports (e.g., tennis, badminton), and data from the highest level of athlete (national competition level or higher) showed a positive relationship between cognitive anxiety and performance and somatic anxiety and performance. This finding suggests that for this more elite group, anxiety may be helpful for performance, and thus, it indirectly supports the drive theory.

In short, it is probably correct to say that the relationship between anxiety and performance is more complex than outlined in MAT. While there is support for some of MAT’s predications (e.g., somatic anxiety does appear to have a curvilinear relationship with performance), this relationship does not appear to be consistent across all groups of athletes (e.g., national level athletes). Perhaps support for MAT has been equivocal because much of it has utilized the CSAI-2 (Martens et al., 1990 ) and the construct validity of the CSAI-2 has been questioned (see Kerr, 1997 ; Lane et al., 1999 ). For example, when Jones and Uphill ( 2004 ) asked university athletes to imagine completing the CSAI-2 as if they were competing in the most important competition of the season as if they were either highly anxious (n = 83) or highly excited (n = 87), both the cognitive and somatic anxiety subscales from the excited group were substantially higher than the norms reported by Martens et al. ( 1990 ). In short, individuals scored highly on the cognitive and somatic anxiety intensity subscales of the CSAI-2 when experiencing an emotion (i.e., excitement) other than anxiety.

Because of concerns that the CSAI-2 does not adequately capture the competitive anxiety experience of performers, several researchers advocated using a modified version of the CSAI-2 that incorporated a directional subscale. The CSAI-2(d) measures not only the intensity of symptoms (as assessed by the original CSAI-2) but also considers the perception of these symptoms (e.g., Jones & Swain, 1992 ; Jones, 1995 ). This directional subscale provides a measure of whether the symptoms reported on the cognitive and somatic anxiety subscales are perceived as being facilitative or debilitative for performance. This modification of the CSAI-2 allowed researchers to test the control model of debilitative and facilitative competitive state anxiety (Jones, 1995 ), which proposes that athletes with a positive belief in their ability to cope, and in goal attainment, will interpret anxiety symptoms as facilitative (helpful), whereas those with negative expectancies will interpret their symptoms as debilitative (unhelpful) for performance (Jones, 1995 ). Both elite and successful competitors have reported more facilitative perceptions of anxiety symptoms in comparison to nonelite and unsuccessful competitors, respectively, when no differences in anxiety intensity levels were present (Jones, Swain, & Hardy, 1993 ; Jones & Swain, 1995 ). Research has generally supported the tenets of Jones’s theory and that athletes with a positive perception of anxiety symptoms perform better (see Cumming & Ramsey, 2008 for a review). However, the conceptual worth of this research has been questioned, and a positive perception of symptoms may simply represent the absence of any real levels of perceived anxiety (Lundqvist, Kentta, & Raglin, 2010 ). That is, in a sample of 84 Swedish athletes, Lundqvist et al. found that most of the anxiety items identified as facilitative for performance were rated at an intensity of “not at all,” and the absence of any perceived anxiety for these items is probably the main reason the athletes in this sample rated them as facilitative to performance.

One further limitation of MAT is that it considers the relationship between cognitive anxiety, somatic anxiety, and performance in a series of two-dimensional relationships (Hardy, 1990 ). But athletes are rarely cognitive anxious in the absence of somatic anxiety and vice versa, so how cognitive anxiety relates to performance may be influenced by somatic anxiety and how somatic anxiety relates to performance may be influenced by cognitive anxiety. The interaction between psychological and physiological arousal is discussed in more detail in the next two approaches outlined, reversal theory and catastrophe theory.

Reversal Theory

In reversal theory (Apter, 1989 ) the experience of arousal is different depending on the metamotivational states (or frames of mind) that an individual is in at any given time. There are four pairs of metamotivational states: telic-paratelic; conformist-negativistic; mastery-sympathy; autic-alloic. When one of each pair is active, the other is inactive. Thus, if a person is in a conformist state, they cannot be in a negativistic state. A person can reverse between opposite states for a number of reasons, including, for example, frustration from not achieving a goal, an external event, or satiation, which is being in the same metamotivational state for an extended period of time (Blaydon, Lindner, & Kerr, 2000 ). Although there are four pairs of metamotivational states, typically one or more of the states will be salient (Frey, 1999 ), reflecting a person’s motives at a particular time. For example, when the telic state is most salient, a person is goal oriented and has a preference for low levels of arousal.

Metamotivational states may be related to participation (e.g., Lindner & Kerr, 2000 ), change at different stages of competition (Males, Kerr, & Gerkovich, 1998 ), and perceptual and cognitive responses to exercise (Thatcher, Kuroda, Thatcher, & Legrand, 2010 ), and they may help explain athletes’ emotional responses to injury (Thatcher, Kerr, Amies, & Day, 2007 ). Reversal theory is supported in sport settings; the application of metamotivational states can explain the range of emotions experienced in sport, and these relate to performance and participation (see Hudson, Males, & Kerr, 2016 , for a review). However, Hudson et al. noted that additional robust research is needed, particularly for evidence to demonstrate that reversals can be controlled or that motivational states can be reliably induced at will in the context of sport and exercise. Another potential limitation of this approach is that interpretation of arousal does not seem to relate to some high-intensity emotions, such as happiness, an emotion frequently experienced in sport settings.

Catastrophe Theory

The catastrophe theory (Fazey & Hardy, 1988 ; Hardy, 1990 ) considers how cognitive anxiety and physiological arousal (not somatic anxiety, which is a perception of physiological state) interact to influence performance. The relationship between cognitive anxiety and performance is different depending on the level of physiological arousal and the relationship between physiological arousal and performance is different depending on the level of cognitive anxiety. Simply, according to catastrophe theory, it is not possible to know how cognitive anxiety relates to performance unless the level of physiological arousal is known and vice versa.

The left-hand side of the three-dimensional relationship where physiological arousal is low shows that increases in cognitive anxiety will help performance, whereas the opposite occurs when physiological arousal is high (the right-hand side). To best explain catastrophe, it is easiest to consider the back and front faces of the three-dimensional relationship as well as the relationship between physiological arousal and performance, in which cognitive anxiety acts as a splitting function. When an individual is experiencing low levels of cognitive anxiety, the relationship between physiological arousal and performance is in the shape of a gentle inverted U. When cognitive anxiety is high, increases in physiological arousal facilitate performance up to an optimum level, but increases past the optimum level result in a severe performance decrement (i.e., a catastrophe). To regain composure and optimum performance, a large reduction in physiological arousal is necessary. Only when cognitive anxiety is high do increases in physiological arousal above the optimum lead to sharp catastrophic decreases in performance.

Hysteresis describes the distinct relationship between physiological arousal and performance under conditions of high cognitive anxiety depending on whether physiological arousal is increasing or decreasing. Hysteresis has been demonstrated in eight crown green bowlers (Hardy, Parfitt, & Pates, 1994 ) who completed a bowling task under conditions of low cognitive anxiety, where their individual data would not be compared, and high cognitive anxiety where they were told their scores would be compared to elite crown green bowlers. Physiological arousal was manipulated using physical exercise, and half the participants did the task with physiological arousal increasing and half with physiological arousal decreasing. While there was evidence of a substantial reduction in performance under conditions of high cognitive anxiety as physiological arousal was increasing, there was no evidence of a substantial decrease in physiological arousal necessary before the bowlers “flipped” back to the upper performance surface of the model. There is other support for the central tenets of catastrophe theory (e.g., Edwards & Hardy, 1996 ; Hardy, Woodman, & Carrington, 2004 ). Although catastrophe theory has been criticized as being too complex to test and therefore of dubious value to sport psychologists (Gill, 1994 ), elements of the theory have been tested, and as Hardy, Jones, and Gould ( 1996 ) point out, complexity is not a reason for rejecting a theory. Indeed, more contemporary theories have not only considered the interaction between psychological variables and physiological states but also that subtle differences in physiological responses may indicate positive or negative approaches to stress.

Challenge and Threat States

Another approach that outlines how a person may respond positively and negatively under stress is the biopsychosocial (BPS) model of challenge and threat (Blascovich & Mendes, 2000 ). Challenge and threat are two distinct psychophysiological responses to stressors that occur in motivated performance situations (like sport) where success is important and there are perceived (via demand appraisals) dangers to esteem, uncertainty, and a requirement for effort (see Blascovich, Mendes, Vanman, & Dickerson, 2011 ; Seery, 2011 ). Building on the BPS model and related work (Obrist, 1981 ; Dienstbier, 1989 ) the theory of challenge and threat states in athletes (TCTSA; Jones et al., 2009 ) and integrative framework of stress, attention, and visuomotor performance (Vine, Moore, & Wilson, 2016 ) offer more recent transactional approaches specific to athletic performance by proposing a framework for psychological, emotional, physiological, and behavioral (attentional) reactions in sport. In the BPS model, the TCTSA, and Vines’ framework, importance is placed on whether an individual experiences a challenge state or a threat state, rather than the magnitude of arousal evinced. Both a challenge and a threat state involve augmented arousal, but in a challenge state this physiological reaction is adaptive, and in a threat state it is maladaptive. In sum, the TCTSA builds on previous theory (Obrist, 1981 ; Dienstbier, 1989 ; Blascovich & Mendes, 2000 ) and offers an integrative, interdisciplinary approach to the understanding of the human stress response in competitive situations,

While the aforementioned challenge and threat approaches place emphasis on the cardiovascular components of challenge and threat states, Vine et al. offer a more detailed account of the attentional consequences of challenge and threat in visually guided motor skills, whereas Jones et al. offer a more detailed account of the cognitive antecedents and performance consequences of challenge and threat.

Specifically in the TCTSA (Jones et al., 2009 ), a challenge state is experienced when sufficient, or nearly sufficient, resources to meet the demands of a situation are perceived, whereas a threat state is experienced when insufficient resources to meet the demands of a situation are perceived. Demand appraisals comprise perceptions of danger, uncertainty, and required effort in a situation, while resource appraisals comprise three interrelated constructs: self-efficacy, perceptions of control, and goal orientation. Jones et al. ( 2009 ) suggest that high levels of self-efficacy, perceived control, and focus on approach goals, represent sufficient resources to cope in a motivated performance situation and are therefore indicative of a challenge state. Conversely, low levels of self-efficacy, perceived control, and focus on avoidance goals, represent insufficient resources to cope in a motivated performance situation and are indicative of a threat state. Physiologically, a challenge state is accompanied by increased sympathetic adrenomedullary (SAM) activity and catecholamine release (i.e., epinephrine and norepinephrine), which is proposed to promote efficient energy use through increased blood flow to the brain and muscles, higher blood glucose levels (fuel for the nervous system), and an increase in free fatty acids that can be used by muscles as fuel (e.g., Dienstbier, 1989 ). A challenge state is fast acting and represents the efficient mobilization of energy for action.

A threat state is also marked by increased SAM activity, but it is also characterized by increased pituitary adrenocortical (PAC) activity accompanied by cortisol release, which tempers the positive effects of SAM activity. Therefore, the mobilization of energy is less efficient than in a challenge state as blood flow (and therefore glucose) to the brain and muscles is restricted (e.g., Dienstbier, 1989 ). A threat state is considered a “distress system” that is maladaptive for performance situations. Indeed, growing research indicates that a challenge state is associated with better cognitive and motor performance than a threat state (e.g., Blascovich, Seery, Mugridge, Norris, & Weisbuch, 2004 ; Moore, Vine, Wilson, & Freeman, 2012 ; Turner, Jones, Sheffield, & Cross, 2012 ) and maintained health (cf. O’Donovan et al., 2012 ). For example, in one study researchers examined the relationship between CV reactivity and the performance of 42 elite male cricketers in a pressured batting test (Turner, Jones, Sheffield, Slater, Barker, & Bell, 2013 ). The batting test required the cricketers to score 36 runs from 30 deliveries and athletes were allocated runs by a national coach. After baseline CV recording, athletes were informed that their performances would be compared to those of all other cricketers and would be seen by all coaching staff, and that their scores would be considered when future team selection was made. The athletes’ CV reactivity to being informed about the batting test was recorded as it is with the netball athletes. Challenge CV reactivity was related to superior performance compared to threat CV reactivity. That is, athletes who exhibited challenge CV reactivity recorded a better score in the batting test than athletes who exhibited threat CV reactivity. In another study (Moore, Vine, Wilson, & Freeman, 2012 ), researchers assessed the cognitive appraisals, emotions (anxiety), CV reactivity (CO and TPR), visual gaze, putting kinematics, muscle activity, and golf putting performance of novice golfers. Participants in a challenge condition, manipulated using instructional sets, exhibited greater challenge CV reactivity and challenge appraisals than participants in a threat condition. Furthermore, participants who exhibited challenge CV reactivity reported more favorable emotions; displayed more effective visual gaze, putting kinematics, and muscle activity; and performed more accurately in the golf putting task than participants who exhibited threat CV reactivity.

Although research investigating the impact of the resource appraisals on challenge and threat states is in its fledgling stage (e.g., Turner et al., 2014 ), the theoretical background for the three resource appraisals is strong and stems from these models: the BPS model (Blascovich & Mendes, 2000 ), the model of adaptive approaches to competition (Skinner & Brewer, 2004 ), and the model of debilitative and facilitative competitive state anxiety (Jones, 1995 ). Therefore, strategies that help athletes to increase their resource appraisals to meet or exceed perceived demands, can promote a challenge state and are therefore valuable (Turner & Barker, 2014 ). In other words, rather than using arousal attenuation or activation strategies, which can be useful at times, athletes should primarily focus on increasing their self-efficacy, perceived control, and approach goals.

Strategies for Confidence, Control, Approach Focus, and Reappraisal

The strategies for promoting a challenge state broadly fit within two themes. The first theme reflects strategies that can be adopted by coaches to create an environment in which a challenge state is more likely. The second theme reflect psychological skills that can be learned by athletes to get themselves into a challenge state.

Creating a Challenge Environment

One method by which challenge has been promoted uses instructional sets. More specifically, past research has used instructional sets to manipulate challenge and threat states. In line with theory (Blascovich & Mendes, 2000 ; Jones et al., 2009 ), challenge instructions typically emphasize the perception of high resource appraisals, and some experiments also attempt to lower the perception of demand appraisals. The use of instructional sets stems from a consistent body of research demonstrating that psychophysiological responses to stressors can be influenced by what participants are told prior to a stressful task (e.g., Allred & Smith, 1989 ) and that instructional sets can modify perceptions of challenge and threat (e.g., Taylor & Scogin, 1992 ; Hemenover & Dienstbier, 1996 ; Alter, Aronson, Darley, Rodriguez, & Ruble, 2010 ; Feinberg & Aiello, 2010 ).

In one study (Feinberg & Aiello, 2010 ), challenge instructions focused on perceiving a cognitive task “as a challenge to be met and overcome” (p. 2079), while threat instructions focused on the difficulty of the task and the importance of working “as quickly and efficiently as possible” (p. 2079). Results demonstrated that challenge appraisals and performance increments followed challenge instructions, while threat appraisals and performance decrements followed threat instructions. However, this study did not include measurements of arousal or physiological reactivity. Growing research demonstrates that challenge and threat instructions can also influence physiological markers of challenge and threat. Another study (Tomaka, Blascovich, Kibler, & Ernst, 1997 ) showed that participants given threat instructions about an upcoming mental arithmetic task experienced threat CV reactivity and cognitively appraised the task as threatening. Conversely, participants given challenge task instructions experienced challenge CV reactivity and cognitively appraised the task as a challenge. Importantly, challenge instructions urged participants to “think of the task as a challenge” (p. 72), while threat instructions reminded participants that the task would be “scored for speed and accuracy” (p. 72). Using similar instructions but in relation to a golf putting task, Moore et al. ( 2012 ) found that those who received challenge instructions appraised the task as a challenge, exhibited challenge CV reactivity, and they displayed more effective visual gaze, putting kinematics, and muscle activity, which aided performance in the putting task, compared to those who received threat instructions.

More recent research (Turner, Jones, Sheffield, Barker, & Coffee, 2014 ) confirmed that challenge instructions only increased the perceptions of resource appraisals from the TCTSA, and threat instructions only decreased the perceptions of resource appraisals, keeping task demands the same between conditions. In other words, challenge instructions promoted high self-efficacy, high perceived control, and a focus on approach goals; threat instructions promoted low self-efficacy, low perceived control, and a focus on avoidance goals. Both sets of instructions increased perceptions of danger, uncertainty, and effort, thus maintaining perceived demands and only altering perceived resources. In two laboratory studies, a throwing across task and a climbing well task, Turner et al. ( 2014 ) found that challenge instructions led to challenge CV reactivity, whereas threat instructions led to threat CV reactivity.

The research suggesting that a challenge state can be promoted using instructional sets has important implications for how leaders manage individual and team approaches to stressors. Leaders can have an important influence on their subordinates’ responses to stressful situations (e.g., Smith, Smoll, & Weichman, 1998 ; Baker, Côté, & Hawes, 2000 ), and therefore they could use this influence to promote a challenge state. Leaders can ensure that their communications with subordinates prior to stressful situations include primers for high confidence, control, and approach goals, while retaining references to the gravity and importance of the occasion. The use of particular instructional sets can also form part of the social support offered by the leader and others within a team setting.

A significant body of research indicates that social support provides a buffer for the adverse effects of stress (e.g., Cohen & McKay, 1984 ; Haslam, O’Brien, Jetten, Vormedal, & Penna, 2005 ). Cobb ( 1976 ) suggests that psychological support reflects the provision of information (e.g., effective coping response) and therefore it is important to provide the right information to those entering stressful situations. Social support can buffer stress in many different ways, but for the purposes of this article, one important mechanism is through informational support (House, 1981 ). Informational support provides those in receipt with coping guidance, similar to challenge instructions, and contributes to positive appraisal by helping those in receipt clarify their understanding of threatening stimuli (e.g., Aspinwall & Taylor, 1997 ). Informational social support can be used to help convince an individual that they can cope with the stressor (Cohen & McKay, 1984 ). Therefore, information offered to individuals that promotes high perceived resources, such as the instructions used in past research (e.g., Tomaka et al., 1997 ; Turner et al., 2014 ), may help those who receive them to enter a challenge state. In addition, Rees and Hardy ( 2004 ) found that social support positively influences performance, regardless of the level of stress, and more recently, Freeman and Rees ( 2008 ) found that high levels of esteem support predicted smaller threat appraisals and greater challenge appraisals, with subsequent better golf performance. The role of social support in challenge and threat states is yet to be fully tested, and some research has found that social support has little effect on challenge and threat states (Moore, Vine, Wilson, & Freeman, 2014 ). However, some suggest that social support could help enhance the resource appraisals, or could actually be a resource appraisal itself (e.g., Haslam & Reicher, 2006 ). Coaches may play an important role in social support, and a recent study (Nichols, Levy, Jones, Meir, Radcliffe, & Perry, 2016 ) of athletes’ perceptions of coach behaviors found a positive association between supportive coach behaviors and challenge and unsupportive coach behaviors and threat. This finding illustrates that coaches can influence the self-reported challenge and threat states of athletes. Interestingly, in a study of soccer coaches coaching behaviors (Dixon, Turner, & Gillman, 2016 ) found positive associations between challenge appraisals and social support and between threat appraisals and autocratic behavior as well as a significantly negative association between threat appraisals and positive feedback. Therefore, the ways coaches appraise stressors are also important for how they behave towards their athletes.

Recently, research has identified a hormone that plays a big role in social bonding that also may influence the human stress response. Oxytocin is produced in the hypothalamus, and in low stress situations, it may physiologically reward those who maintain good social bonds with feelings of greater well-being. But when oxytocin operates in high stress situations, it may encourage people to seek out social contact. Further, oxytocin released during positive (supportive) social contact, even if this social contact is only anticipated, actually reduces the severity of the body’s stress response (Taylor, 2006 ). Recent research (Kubzansky, Mendes, Appleton, Block, & Adler, 2012 ) indicates that when people are put under social stress (e.g., public speaking), oxytocin is associated with a challenge state and a healthier recovery profile after the stress. Research also suggests that oxytocin may help to lower blood pressure and cortisol levels (Light, Smith, Johns, Brownley, Hofheimer, & Amico, 2000 ). In summary, when facing a stressful situation, oxytocin may help people to better deal with stress.

Part of creating a challenge environment may also involve helping individuals to adapt to stressful situations via experiential learning, which places individuals in demanding situations. Past performance accomplishments are a powerful source of self-efficacy (Bandura, 1997 ; Feltz & Lirgg, 2001 ), and, as is recognized in MAT and catastrophe theory, self-efficacy plays an important role in the relationship between arousal and performance. Self-efficacy is also an important resource appraisal in the TCTSA. Therefore, helping athletes to flourish in stressful situations may provide important sources of self-efficacy for subsequence stressors. One way to achieve this experiential learning is through systematic desensitization (Wolpe, 1973 ). In brief, the athlete is subjected to stress regularly and systematically, thus promoting acclimatization to future stressors. This exposure to stress may foster resilience, a construct that has been put forth in relation to challenge and threat states by Seery ( 2011 ). For Seery, the exhibition of a challenge state and potential positive (or less negative) outcomes, is suggestive of resilience in motivated situations, and individuals who have a history of facing some adversity should exhibit greater resilience than those who have experienced no or high adversity. Past research offers some evidence for Seery’s notion of resilience, where repeated exposure to stressors has been shown to lead to an increase in challenge over time (Quigley, Barrett, & Weinstein, 2002 ). In other words, situations that become more familiar may promote a challenge appraisal and challenge CV responses due to enhanced coping perceptions (Blascovich et al., 1999 ; Quigley et al., 2002 ). Also in support of his assertions, Seery, Leo, Lupien, Kondrak, and Almonte ( 2013 ) found that relative to a history of either no adversity or nonextreme high adversity, a moderate number of adverse life events is associated with less negative responses to pain and more positive psychophysiological responses while taking a test. The precise impact of exposure on the demand and resource appraisals is not yet known, and future research should investigate exposure in line with the TCTSA.

Psychological Skills: Imagery, Reappraisal, Relaxation

Psychological skills are techniques that can be applied by individuals to regulate their own internal states such as cognitions and emotions. Two main psychological skills have emerged in literature that can promote a challenge state: reappraisal and imagery.

Reappraisal is an important strategy for regulating emotions (see Gross, 1998 , for review), and two studies have examined the effects of reappraisal on challenge and threat states (i.e., Jamieson, Mendes, Blackstock, & Schmader, 2010 ; Jamieson, Nock, & Mendes, 2012 ). In Jamieson et al.’s ( 2010 ) study, prior to an exam, reappraisal condition participants were told that “recent research suggests that arousal doesn’t hurt performance” and that “people who feel anxious during a test might actually do better.” They were also encouraged to “simply remind yourself that your arousal could be helping you do well” (p. 2). By being prompted to perceive their anxiety as helpful, participants in the reappraisal condition exhibited higher catecholamine levels, indicative of SAM activity (challenge state), perceived their anxiety as helpful, were more confident about performance and demonstrated better performance in the exam compared to a control group. Jamieson et al. ( 2012 ) similarly used a reappraisal condition to encourage participants facing a speech task that their arousal is functional and can help them to succeed. Results showed that participants in the reappraisal condition had higher perceived resources and exhibited higher increases cardiac output as well as lower increases in total peripheral resistance compared to the control group; a psychophysiologically adaptive response. In sport, after responding to a pressure task with a threat state, a reappraisal group shifted toward a challenge cardiovascular response, although this difference was not statistically significant (Moore, Vine, Wilson, & Freeman, 2015 ). The reappraisal group also outperformed the control group during the pressurized task. Importantly, reappraisal does not dampen arousal but aims to reshape how arousal is perceived (Jamieson et al., 2013 ), which contrasts with theories such as MAT and catastrophe theory, where arousal level is seen as important for performance.

Another way to promote a challenge state is through the use of imagery, a technique that involves realistically recreating or creating events in the absence of physical practice. Imagery can be used for a variety of purposes, but notably, it is effective for regulating emotions (e.g., Hecker & Kaczor, 1988 ), enhancing self-confidence (Callow, Hardy, & Hall, 2001 ), and promoting coping under stress (e.g., Vadocz, Hall, & Moritz, 1997 ; for reviews, see Martin, Moritz, & Hall, 1999 ; Cumming & Ramsey, 2008 ), all of which are important aspects of a challenge state. The mechanisms for how imagery works are still under debate, but nonetheless, imagery is a well-researched skill that has been shown to be valuable for motivated performance situations (Durand, Hall, & Haslam, 1997 ). Three studies have expressly applied imagery to enhance a challenge state (Hale & Whitehouse, 1998 ; Williams, Cumming, & Balanos, 2010 ; Williams & Cumming, 2012 ). Hale and Whitehouse ( 1998 ) showed that an imagery-based video and audiotaped manipulation that prompted challenge perceptions resulted in less cognitive anxiety, less somatic anxiety, more self-confidence, and perceptions that symptoms were facilitative. In Williams et al. ( 2010 ) a challenge imagery script that emphasized resources (challenge appraisals), promoted high self-efficacy, high perceived control, and potential gain led to lower threat appraisals, positive emotion perceptions, and higher confidence. Similar scripts were used by Williams and Cumming ( 2012 ) who also found that the challenge script led to challenge appraisals and the threat script led to threat appraisal and a perception that emotional responses were debilitating for performance. Imagery offers a useful way to promote a challenge state, but more research is needed to test the psychophysiological implications of effective imagery use.

Although to date no studies have explicitly explored the effect of relaxation strategies on challenge and threat states, the use of relaxation techniques may be helpful in regulating arousal and may have the potential to reduce the intensity of the felt threat state and potentially its impact on performance, whereas energizing strategies may help enhance the felt experience of a challenge state. This is because increasing or decreasing physiological arousal would appear to have a blanket effect on the intensity of an individual’s emotional state (e.g., Hohmann, 1966 ; Zillmann, Katcher, & Milavsky, 1972 ).

Relaxation techniques have been classified as muscle-to-mind techniques, which are more physical in nature (e.g., breathing techniques), or mind-to-muscle techniques (imaging being in a relaxing environment), which are more cognitive in nature (Harris, 1986 ). However, the autonomic nervous system and cognitive aspects of emotion are linked, illustrated in the fact that an intervention designed to reduce somatic (physical) anxiety also reduced, albeit to a lesser degree, cognitive anxiety in soccer players (Maynard, Hemmings, & Warwick-Evans, 1995 ) and field hockey players (Maynard & Cotton, 1993 ). A number of strategies have been proposed to reduce arousal (e.g., progressive muscular relaxation, centering), whereas strategies to increase arousal include up-beat music and exercise itself (Jones, 2003 ).

One approach that has a particular focus on arousal control in sport is biofeedback (Zaichkowsky & Fuchs, 1988 ). This approach is based on the principle that athletes can learn to voluntarily control their arousal levels by receiving concurrent feedback from an instrument that measures aspects of the autonomic nervous system response. The athlete can experiment with different thoughts and feelings to reduce or increase arousal. It is then anticipated that the ability to control arousal levels transfer to the athletic field. For example, a 20-year-old small-bore rifle shooter underwent an intervention comprising relaxation strategies, thought stopping and biofeedback which resulted in lower levels of urinary adrenaline and noradrenaline (physiological markers indicative of anxiety) in subsequent competitions (Prapavessis, Grove, McNair, & Cable, 1992 ). Biofeedback is also often used as a strategy to regulate Heart Rate Variability (HRV), which is proposed to be a measure of autonomic flexibility. That is, the interplay between sympathetic and parasympathetic influences heart rate and is proposed to represent the capacity for emotional responding (Appelhans & Luecken, 2006 ). HRV training utilizes the link between respiration and HRV wherein breathing in accelerates heart rate and breathing out lowers heart rate. In HRV training, breathing is regulated to around 6 breaths per minute (Vaschillo, Lehrer, Rishe, & Konstantinov, 2002 ). Greater HRV is associated with more positive emotional response to stressors (e.g., Bornas et al., 2005 ) and both performance of stressful tasks (e.g., Hansen et al., 2003 ); there is also some support for the application of HRV training in sport (e.g., Maman & Kanupriya, 2012 ), although more research is needed.

The area of arousal control in sport has moved through initial simplistic theories of arousal toward more complex psychophysiological explanations for the relationship between arousal and performance. More current theory suggests that arousal control is less about arousal magnitude or severity and more about the underlying endocronological processes that regulate arousal. Also, the notion that the perception of arousal symptoms may provide a stronger link to performance has garnered much support. Regardless of the decades of theoretical debate already present in this area, arousal control is a topic that will continue to be examined and discussed, particularly as advancements are made in how psychophysiological reactivity is measure at a biochemical and neurological level. For example, one approach that has received little research in sport is the role of Neuropeptide Y (NPY). NPY has been consistently associated with a positive response under stress, and this has been demonstrated in military settings, including special forces personnel in the United States (Morgan, Wang, Southwick, Rasmousson, Hazlett, Hauger, & Charney, 2000 ). NPY is a 36-amino-acid peptide, and receptors for NPY in the brain are similar (i.e., amygdala, hippocampus, locus coeruleus) to those of ACTH, which ultimately stimulates the release of cortisol. Furthermore, NPY and ACTH have counterbalancing functional effects; thus, NPY may, in effect, attenuate the stress response (Nulk, Schuh, Burrell, & Matthews, 2011 ).

Theoretical developments have also yielded advances in strategies to help performers regulate arousal; the ability to perform under pressure id still considered a fundamental aspect of athletic success. Arousal control is not only about being relaxed, it is also about recognizing the optimum state for performance and finding methods by which to arrive at that state. This idea is perhaps best summed up by Lewis Moody (retired England Rugby Union World Cup winner):

“Before you play, you have to get yourself in the right frame of mind. If you’re not mentally right, you won’t be able to produce your best. Everyone’s different though—you have to do what works for you. Some guys run around shouting and screaming whereas others prefer to chill out” (Moody, 2005 ).

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What Makes Stress "Good" or "Bad"?

The inverted-u hypothesis and 4 factors that influence individual stress levels..

Posted February 18, 2022 | Reviewed by Devon Frye

What Is Stress?
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Stress often has negative effects. But in the right amounts, stress can be good for us.
A theory known as the "inverted-U" hypothesis attempts to explain how varying levels of stress influence us.
Other factors help determine whether stress is "good" or "bad," such as personality, task difficulty, and fear.

Ever since scientists discovered that purely psychological states—such as a feeling of loss of control—could trigger a physical crisis in the body, stress has been a dirty word.

For decades, we have explored the disruptive effects of stress on memory , executive function , and behavior. This discovery also gave way to subfields such as psychoneuroimmunology, the study of the interaction between psychological processes and the nervous and immune systems. And it has led to further discoveries—like that stress in early life, particularly during the time right before and right after birth, can have consequences stretching into adulthood.

Has all of this attention on the negative effects given stress a bad name? Far from being definitively bad, the science actually shows that stress in milder forms is anything but. It’s major stress that’s bad for us. So perhaps rather than running away from stress entirely, we should instead seek out the optimal form of stress.

What Constitutes Optimal Good Stress?

Now, I’m not suggesting that you can click your heels together, like Dorothy in "The Wizard of Oz," and make all of your bad stress magically go away. Simply knowing that there’s an optimal amount of stress won’t dissolve major life stressors like burnout , caring for an aging loved one , or the loss of a job.

What I’m suggesting is that when we experience stress in a setting that feels safe, stress can actually benefit us. Consider the thrill you get from skiing down a mountain, for example. As long as you have taken all the proper precautions and you’re skilled enough to handle the run, the anticipatory stress you feel standing at the top of the hill is good stress.

Additionally, good stress is transient. It’s not by accident that ski runs aren’t designed to last for three days. When you safely make it down the mountain and into the warm and cozy lodge, you feel all those happy endorphins flooding your system, you experience a confidence boost, and you’re excited to jump back on the chair lift to do the whole thing again.

So what do we call mild, transient stress that occurs in a safe setting? We call it: arousal, alertness, engagement, play, stimulation, and thrill.

The Inverted-U and You

What’s going on here? How can stress be both good and bad for us? Well, one hypothesis is actually pretty simple. And in the scientific world, the hypothesis comes in the form of an inverted-U. But you may know it better as a bell curve from your school days.

Scientists didn’t see it at first, though. When viewed from a distance, the effects of stress on the brain and behavior are quite murky. For example, the same stressor, say, having to get up in front of a group to give a presentation, in one setting could increase your heart rate, have no effect in another, and decrease your heart rate in a third. This made the idea of seeing stress as following any kind of linear progression suspect.

However, clarity came with the recognition that the effects of stress on the brain actually form a nonlinear inverted-U. On the left side of the graph, the absence of stress corresponds to boredom or a lack of challenge (which is how you might feel if you’re asked to give the same presentation for the hundredth time). As you transition from the absence of stress to mild stress, though, you begin to feel motivated.

On the right side, extreme levels of stress can cause paralysis or result in feelings of unhappiness or anxiety (which is how you might feel if you’re asked to give a presentation that you feel unprepared to give).

In the middle is where we find the optimal level of stress. Moderate pressure leads to high performance with manageable levels of stress (which is how you might feel when you’re well prepared to give a presentation about something you feel confident and excited to present).

While the Inverted-U Hypothesis gives us a simple explanation for how the same stressor can affect us under different circumstances, there’s another challenge that makes it difficult to predict exactly how much stress is optimal for a given individual.

What Else Influences Stress Levels?

The challenge is that there are four other factors that influence stress levels:

1. Personality

People with different personality types handle stress differently. Generally, psychologists have found that extraverted individuals are more resilient when it comes to handling stress. Introverted people, on the other hand, tend to perform better in more low-stress environments.

This is not to suggest that introverts can’t be trained to become more resilient or that an extravert dealing with a personal challenge will sail along perfectly even-keeled. Also, the duration of stress can affect how we perceive the pressure we’re under.

2. Task Difficulty

The degree of complexity of a task determines the amount of attention and effort required to complete it. You may have noticed in your own life, for instance, that when you’re on a tight deadline, it can be more difficult to deal with complex problems. When the pressure is high, it’s fairly easy to carry out simple activities, like responding to email, but those more challenging tasks need to wait until you have some space to think.

Another influencing factor is your skill level. As you become more skilled, you may find that you need to look for new ways to increase the pressure to keep performing at your peak. After you’ve been doing the same job for a couple of years, it might be time to go for that promotion or apply for a new job that challenges you in other ways.

Finally, the inverted-U theory shows that fear can affect performance. Can you easily set aside or ignore feelings of fear and stay focused on the task at hand? Research shows that those who are better at this (e.g., Olympic athletes) also perform better under pressure, while those who are not good at ignoring fear find themselves choking.

Although we all experience moments of major stress that can cause feelings of overwhelm and burnout , we shouldn’t label all stress as bad. Certain forms of mild to moderate stress can actually keep us on track to hit our goals and the inverted-U hypothesis explains why.

Janse, B. (2019). Inverted-U Theory. Retrieved 2/15/2022 from toolshero: https://www.toolshero.com/human-resources/inverted-u-theory/

Sapolsky, Robert M. Stress and the brain: individual variability and the inverted-U. Nature Neuroscience 18, 1344–1346 (2015). https://doi.org/10.1038/nn.4109

Deana Goldin, Ph.D., DNP, APRN, is an associate clinical professor at Florida International University and an integrative psychiatric and family nurse practitioner.

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Inverted U Theory Explained

The inverted u theory describes the relationship between arousal and performance. The theory hypotheses that arousal levels that are either too high or too low can result in gradual decreases in performance. In between these high and low arousal levels, is an optimum level of arousal for performance, which can be seen in the inverted u curve below.

History of the Inverted u Theory

The inverted u theory may also be referred to as the Yerkes-Dodson law due to its creation by two researchers – Yerkes and Dodson. In 1908, these researchers were trying to understand the relationship between the strength of a stimulus and forming habits in mice. They found that there was a negative relationship between the two i.e. the harder it is to form a habit, the less strong the stimulus needs to be to make the habit stick. This study formed the foundation if the inverted u theory, which has stood the test of time.

Understanding the Inverted-U Curve

The inverted u theory takes its name from the shape of the curve. The peak of the curve highlights the arousal level needed for optimum performance. Either side of the peak, where arousal levels are either too high, or too low, suggests gradual decreases in performance.

What is arousal?

Arousal has been defined as the blend of physiological (i.e. heart rate, muscle tension) and psychological (i.e attention) levels of activation within an athlete, which varies from low (i.e deep sleep) to high arousal (i.e. extreme excitement) (Hackfort, Schinke & Strauss, 2019).

Factors Influencing the Curve

The peak of the inverted u curve, where the optimal levels of arousal are needed for optimal performance, may look different for every individual. There are many factors that might influence where the peak of the curve is, examples of these factors include (1) the individual athlete, (2) the sport, (3) difficulty of the task and (4) the skill level of the athlete – we’ll delve into a few of these factors below…

Task Difficulty

Tasks or sports that involve high levels of coordination may benefit from lower levels of arousal to ensure high focus and attention can be sustained. In contrast, sports or tasks that use major muscle groups may need higher levels of arousal than high-coordination tasks.

Skill Level

Similar to the high-coordination sports and tasks outlined above, beginners may also need lower levels of arousal to maintain focus and avoid distractions and performance declines. In contrast, an expert in a task or sport may not need the same levels of focus and attention as a beginner, and can complete the task with a higher arousal level.

Examples of the Inverted U Theory in Sport

An example of the Inverted u theory can be found in Snooker. This sport requires a high level of fine skill and focus of attention, and therefore players may benefit from a lower arousal level for optimal performance. There are many ways that a lower optimal arousal level can be achieved, such as listening to calm or relaxing music, or using visualisation or meditation to remain composed.

In contrast, sports like boxing and rugby naturally favour higher optimum arousal levels due to their physical nature. Even so, arousal levels that are too high can lead to mistakes and poor performance.

Why is the Inverted U Theory Important in Sport?

From an applied sport psychology perspective, the inverted u theory can help to understand the circumstances in which an athlete can perform at their best. As highlighted, this might look different even for athletes competing in the same sport, but this understanding can benefit athletes and their support staff to achieve and maintain their optimal performance zone – this can include arousal level, mindset, physical fitness and warm ups, and many other factors that influence performance.

How Can This Theory Help Athletes?

Achieving the optimum performance level is important for athletes. This theory can build an understanding of what ideal performance looks and feels like. Through this understanding, athletes can begin to tailor their preparation for competition.

Thinking specifically about how to achieve an optimum level of arousal, athletes can consider ways to increase or decrease their arousal level. Common strategies include listening to music (upbeat to increase arousal, calm and relaxing to lower arousal levels), meditation, and the use of psychological skills such as imagery and self-talk through mental skills training .

How can this theory help coaches?

Coaches also play a part in the preparation to perform. Often, coaches are present at competitions, so understanding how an athlete performs best can help the coach to support in managing the arousal levels. Training sessions can be tailored to replicate demands of competition, encouraging athletes to train under pressure, or perhaps exploring performing at different levels of arousal.

What is the Difference Between Pressure and Stress?

Whilst pressure can be thought of positively, stress is not. Excessive amounts of pressure naturally lead to stress, and excessive or chronic stress can lead to both mental and physical illnesses. It is important to understand the different pressures that we face in different situations in order to manage and use them to our advantage. This links back to athletes and coaches understanding their optimum performance zones.

Responding to stress and pressure

There are several variables that influence the way people respond to pressure and stress. Individual differences in optimal arousal levels, the level of pressure and stress the person is under, and the coping strategies employed can all influence our responses.

Stress can lead to feelings of being overwhelmed, or out of control, therefore it is important to manage and reduce stress as much as possible.

Coping Strategies

The effectiveness of coping strategies is dependent on the individual. Generally, coping strategies can be categorised into 3 types:

Avoidance coping – the problem/situation is avoided. For example, using tv or music to distract from the situation and avoid thinking about it.
Emotion-focused coping – the emotion attached to a problem or situation is dealt with, rather than the problem itself. Practicing meditation and mindfulness are examples of this.
Problem-focused coping – the problem or situation is addressed directly, such as through setting boundaries or seeking support.

There are no right or wrong answers as to which coping strategies to use. For example, avoidance coping might be effective in the short term, but ineffective in the long run, as the problem/situation will continue, whereas problem-focused coping may have more long-term effectiveness in managing a problem or situation.

Final Thoughts

In summary, the inverted u theory describes (but does not explain) the relationship between arousal level and performance. Each individual will have an arousal level that is optimal for peak performance. Arousal levels that are above or below the optimum can lead to gradual declines in performance.

Nicole Wells

Catastrophe Theory in Sport Explained
Arousal In Sport – What Does The Research Suggest?

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Quantifying the inverted U: A meta-analysis of prefrontal dopamine, D1-receptors, and working memory

Matthew a. weber.

1 Department of Neurology, University of Iowa, Iowa City, IA 52242.

Mackenzie M. Conlon

2 Medical Scientist Training Program, University of Iowa, Iowa City, IA 52242.

Hannah R. Stutt

3 Interdisciplinary Graduate Program in Neuroscience, University of Iowa, Iowa City, IA, 52242

Linder Wendt

4 Institute for Clinical and Translational Science, University of Iowa, Iowa City, IA 52242.

Patrick Ten Eyck

Nandakumar s. narayanan.

Dopamine in the prefrontal cortex can be disrupted in human disorders that affect cognitive function such as Parkinson’s disease (PD), attention-deficit hyperactivity disorder (ADHD), and schizophrenia. Dopamine has a powerful effect on prefrontal circuits via the D1-type dopamine receptor (D1DR). It has been proposed that prefrontal dopamine has “inverted U-shaped” dynamics, with optimal dopamine and D1DR signaling required for peak cognitive function. However, the quantitative relationship between prefrontal dopamine and cognitive function is not clear. Here, we conducted a meta-analysis of published manipulations of prefrontal dopamine and the effects on working memory, a high-level executive function in humans, primates, and rodents that involves maintaining and manipulating information over seconds to minutes. We reviewed 646 papers and found that 75 studies met criteria for inclusion. Our quantification of effect sizes for dopamine, D1DRs, and behavior revealed a negative quadratic slope. This is consistent with the proposed inverted U-shape of prefrontal dopamine and D1DRs and working memory performance, explaining 10% of the variance. Of note, the inverted quadratic fit was much stronger for prefrontal D1DRs alone, explaining 26% of the variance, compared to prefrontal dopamine alone, explaining 10% of the variance. Taken together, these data, derived from a variety of manipulations and systems, demonstrate that optimal prefrontal dopamine signalling is linked with higher cognitive function. Our results provide insight into the fundamental dynamics of prefrontal dopamine, which could be useful for pharmacological interventions targeting prefrontal dopaminergic circuits, and into the pathophysiology of human brain disease.

Introduction

Human diseases that affect high-level cognitive processes such as working memory, reasoning, and flexibility can disrupt prefrontal dopamine. For instance, in humans with Parkinson’s disease, hypo- and hyperdopaminergic states have been linked with impaired cognition ( Cools and D’Esposito, 2011 ; Mattay et al., 2002 ; Narayanan et al., 2013 ). In addition, dysfunctioning prefrontal dopaminergic systems may be related to the pathophysiology of attention-deficit hyperactivity disorder (ADHD) ( Bellgrove et al., 2005 ), and prefrontal dopamine has been critically implicated in the pathogenesis of schizophrenia ( Abi-Dargham et al., 2002 ; Goldman-Rakic et al., 2004 ; Okubo et al., 1997 ). Despite these data, the precise relationship between prefrontal dopamine and behavior is unclear. Understanding this relationship is relevant for pharmacological strategies that modulate prefrontal dopaminergic function to improve cognitive function in human disease ( Soriano et al., 2010 ).

Preclinical work in rodents and non-human primates has established that prefrontal dopamine is required for high-level cognitive behaviors ( Brozoski et al., 1979 ; Bubser and Schmidt, 1990 ; Kim et al., 2017 ). One of the most commonly studied cognitive behaviors is working memory, in which information is held for brief periods of time to guide future goal-directed behavior and has been studied extensively to show that decreased or increased prefrontal dopamine is linked with impaired behavioral performance ( Cools and D’Esposito, 2011 ; Floresco, 2013 ; Goldman-Rakic et al., 2004 ). Prefrontal dopamine acts on cortical circuits via D1-type dopamine receptors (D1DRs), which also has been linked with impaired working memory performance ( Floresco and Phillips, 2001 ; Goldman-Rakic et al., 2004 ; Seamans et al., 1998 ; Seamans and Yang, 2004 ). These findings lead to the hypothesis that working memory follows an inverted U-shaped function, in which optimal working memory performance is achieved with optimal levels of prefrontal dopamine and D1DR activation. While inverted U-shaped dynamics have substantial supporting evidence, the contours of this function are not clear. Further, it is not clear whether the inverted U-shape is more strongly dependent on either D1DR levels or overall prefrontal dopamine concentrations, or whether the curve is the same for both dopamine and D1DR manipulations. This is particularly relevant in predicting the degree of behavioral impairment that can be expected with prefrontal dopaminergic manipulations or for interventions that target D1DRs.

To formally quantify the relationship between prefrontal dopamine signaling and working memory, we conducted a meta-analysis of studies in which working memory and either prefrontal dopamine or D1DRs were measured. We report two major results: 1) there was a negative quadratic fit for the relationship between working memory and both prefrontal dopamine and prefrontal D1DR combined; and 2) the relationship was stronger for prefrontal D1DR manipulation and working memory, explaining 26% of the variance, compared to prefrontal dopamine and working memory that explained only 10% of the variance. We interpret these data in the context of prefrontal dopamine dynamics and their relevance for understanding prefrontal function in human disease.

Search strategy and inclusion/exclusion criteria

An electronic search of PubMed, PsychInfo, and Embase was performed on September 15, 2021 using the terms “frontal cortex,” “dopamine,” and “working memory”. Terms such as “human” and “dopamine D1” were also utilized to ensure a comprehensive search was completed. We restricted the search to peer-reviewed articles to ensure that only the most rigorous studies were included. Using functions in EndNote X9, we removed duplicates and literature reviews. resulting in 646 peer-reviewed articles. Two authors independently screened all of the abstracts (M.A.W and M.M.C) to determine appropriateness for this meta-analysis. We sought to synthesize data across multiple domains, including species of the model organism studied, working memory behavioral paradigms, and measure of prefrontal dopamine and D1DRs. Therefore, inclusion criteria were: 1) peer-reviewed original research in either rodents, non-human primates, or humans; that 2) measured prefrontal dopamine or D1DRs and 3) measured working memory performance. Exclusion criteria were: 1) non-original research; 2) case studies; 3) in vitro or computational studies; 4) non-dopamine or D1DR studies; 5) studies that examined executive functions other than working memory; 6) studies that lacked between-group comparisons, control groups, or baseline measures; 7) central or peripheral pharmacology without direct measure of dopamine or D1DRs; and 8) study of genetic polymorphisms without direct measure of dopamine or D1DRs. This screening process resulted in 75 peer-reviewed publications included in the final quantitative analysis. This study’s design and hypothesis were not preregistered.

Data extraction

Several variables were extracted from each study included in the final analysis. Broad characteristics of each study were: 1) article title; 2) authors; 3) publication year; 4) species; 5) experimental manipulation or comparison; 6) type of working memory task; and 7) type of prefrontal dopamine or D1DR measure. Quantitative variables for the measure of working memory and prefrontal dopamine or D1DRs were: 1) number of subjects for each experimental group; 2) group average; and 3) group standard deviation or standard error. Every effort was taken to extract quantitative variables directly from the methods, results, and/or figure captions to ensure exact values were reported. Primary data extraction was completed by M.A.W, but all qualitative and quantitative data was verified independently by two other authors (H.R.S and N.S.N).

When multiple versions of the same working memory task were reported (e.g., the length of the working memory delay period, see Abi-Dargham et al., 2002 ), we extracted the working memory behavior data points with the largest effect size. When multiple dopamine values were presented (e.g., at multiple time points during in vivo microdialysis, see Schmeichel et al., 2013 ), we extracted basal prefrontal dopamine values when available or data that matched the working memory time point as closely as possible when basal prefrontal levels were not reported. When the precise number of subjects in a group was not explicitly reported, we estimated group size based on the information available (e.g. Pietraszek et al., 2009 ). When group average, standard deviation, and standard error were not explicitly reported, we used plot digitizer software (Rohatgi, A., WebPlotDigitizer: Version 4.4, 2020, https://automeris.io/WebPlotDigitizer/ ) to extract relevant statistical data. Several publications contributed multiple data points to the final quantitative analysis because we were able to extract multiple values from these datasets. For example, Adams & Moghaddam, 1998 , tested working memory performance at three time points following peripheral drug injection and included three corresponding prefrontal dopamine measures. Other examples include Novick et al., 2013 (two working memory paradigms), Szczepanik et al., 2020 (multiple doses of the same drug with corresponding prefrontal dopamine values), and Kellendonk et al., 2006 (multiple different measures of prefrontal dopamine - i.e., TH variscosities, D1 mRNA, DA content, c-Fos expression). We compared measures of working memory with prefrontal dopamine concentrations and D1DR activation in control and experimental groups, regardless of the specific statistical analysis that was presented in the publication. Our statistical analysis of control vs. experimental groups was used to generate effect sizes for both 1) difference in working memory performance and 2) difference in prefrontal dopamine or D1DRs between control and experimental conditions.

Following data extraction, we calculated Cohen’s d effect sizes ( Cohen, 1969 ) for each measure of working memory and prefrontal dopamine or D1DRs. This standardized metric of effect size is calculated from differences between group means divided by the pooled standard deviation, and is widely used to compare effects across studies with diverse methodologies. For instance, if administration of a dopaminergic drug or external manipulation affected behavior, then the averages of behavioral performance with or without the experimental drug would be subtracted, divided by the variance. The same comparisons can be made for measures of prefrontal dopamine or D1DR levels by diverse methods. In general, a Cohen’s d value of ~0.1 is considered small, ~0.3 is considered medium, and greater than 0.5 is considered large. Effect sizes were adjusted so that enhanced working memory and increased prefrontal dopamine or D1DRs were reflected by positive values, and impaired working memory and dampened prefrontal dopamine or D1DRs were reflected by negative values. We then sorted effect sizes based on prefrontal dopamine or D1DRs and grouped data to facilitate analysis of working memory performance ( Tables 1 and and2 2 ).

Quadratic equations (aX 2 + bX + (Intercept)) derived from manipulations of prefrontal dopamine or D1DRs and measures of working memory performance.

	Coefficient	95% Confidence Interval	p-value
Prefrontal D1DRs
a	−0.265	−0.545, 0.015	0.063
b	−0.353	−0.539, −0.166	<0.001
(Intercept)	−0.513	−1.097, 0.071	0.082

Prefrontal Dopamine
a	0.059	−0.072, 0.191	0.374
b	−0.091	−0.146, −0.035	0.001
(Intercept)	−0.843	−1.117, −0.568	<0.001

Aggregate
a	−0.830	−0.134, 0.089	0.69
b	−0.120	−0.171, −0.068	<0.001
(Intercept)	−0.830	−1.080, −0.580	<0.001

Studies that reported comparisons of prefrontal cortex D1-type dopamine receptors (D1DRs) and working memory between control and experimental subjects.

Type	Species	Cohen's : Behavior	Author (Year)	Cohen's : D1DR
D1DR	Human	−2.20		−2.47
D1DR	Human	−1.02		−2.40
D1DR	Primate	−1.64		−1.79
D1DR	Rodent	0.88		−1.32
D1DR	Rodent	−3.24		−1.10
D1DR	Primate	−1.31		−0.99
D1DR	Primate	−0.92		−0.97
D1DR	Rodent	−0.55		−0.79
D1DR	Rodent	−1.30		−0.78
D1DR	Rodent	−1.98		−0.77
D1DR	Rodent	−0.02		−0.68
D1DR	Rodent	−0.10		−0.68
D1DR	Rodent	0.68		−0.64
D1DR	Rodent	−0.04		−0.64
D1DR	Rodent	1.06		−0.57
D1DR	Rodent	−0.10		−0.57
D1DR	Rodent	1.34		−0.50
D1DR	Rodent	1.03		−0.32
D1DR	Rodent	0.50		−0.11
D1DR	Rodent	−0.17		−0.11
D1DR	Rodent	0.22		−0.10
D1DR	Rodent	−1.23		0.00
D1DR	Rodent	−0.97		0.00
D1DR	Rodent	0.41		0.00
D1DR	Primate	−1.26		0.19
D1DR	Rodent	−1.06		0.32
D1DR	Rodent	−0.63		0.38
D1DR	Rodent	−1.21		0.47
D1DR	Human	−1.13		0.87
D1DR	Rodent	−2.34		0.95
D1DR	Rodent	−0.45		1.09
D1DR	Rodent	−1.23		1.17
D1DR	Rodent	−0.97		1.17
D1DR	Rodent	−1.17	Areal et al (2015)	1.47
D1DR	Rodent	−1.89		1.74
D1DR	Rodent	−3.14		1.93
D1DR	Primate	−3.34		2.09

Statistical analyses were completed using R software, version 4.1.1. All code and raw data are available at https://narayanan.lab.uiowa.edu . All statistical analyses were performed and verified independently by the Biostatistics, Epidemiology, and Research Design Core within the Institute for Clinical and Translational Science at the University of Iowa.

The primary goal of this meta-analysis was to identify polynomial models (up to order three) that explain changes in working memory performance with changes in prefrontal dopamine and/or D1DRs. We developed models based on the relationship between working memory effect sizes and prefrontal dopamine and D1DR effect sizes. We excluded values greater than or less than a Cohen’s d of +/− 4, as these could have an outsized effect on our models. First, we fit a model based on working memory performance and all prefrontal dopamine and D1DRs. This analysis was followed by stratifying the data set to develop a model fit based on working memory performance and prefrontal dopamine and a model fit based on working memory performance and prefrontal D1DRs. Several publications contributed multiple values to the final data set, and this was accounted for by including a random intercept for each publication. Model fits between different polynomial orders were compared via Akaike Information Criteria (AIC), with lower AICs indicating a better combination of parsimony and goodness of fit.

We used a bootstrap analysis approach to compare R 2 values for prefrontal dopamine and prefrontal D1DRs. This process began by simulating a new dataset for both prefrontal dopamine and prefrontal D1DRs; we resampled the original datasets with replacement to create new datasets the same size as the original. Then, a quadratic model was built on each resampled dataset, and the R 2 value of the dopamine model was subtracted from the R 2 values of the D1DR model. This process was repeated 10,000 times to obtain bootstrap-estimated intervals that reflect 95% confidence for the difference between the two models and that one model’s fit is superior to the other. Here, a positive confidence interval that does not contain zero would indicate that the prefrontal D1DR model provides a superior R 2 value compared to the prefrontal dopamine R 2 value.

Our literature search and screening procedures yielded 75 journal articles that fit our criteria, resulting in 165 data points ( Tables 1 and and2). 2 ). After extreme values (Cohen’s d >+4 and <− 4) were excluded, 156 data points remained. We found that a quadratic function provided the optimal model fit (2 nd order polynomial; p<0.001; AIC = 400.2 vs. linear AIC = 412.7). The R 2 value for the negative quadratic fit was 0.10. A higher order polynomial model did not decrease AIC values (3 rd order AIC = 408.8), suggesting that the 2 nd order model is optimal.

We then stratified our data based on type of prefrontal measure, with a sub-analysis focused on prefrontal dopamine (i.e., dopamine content or turnover, tyrosine hydroxylase, dopamine transporter, etc.). These could include direct manipulations of prefrontal dopamine (e.g., dopamine depletion via 6-hydroxydopamine) or indirect manipulation such as stress or peripheral drug administration. For this analysis, we found 61 studies and 119 data points. A negative quadratic function provided the strongest fit with AIC = 314.4 (p<0.001; vs. linear AIC = 317.2, 3 rd order AIC = 322.7). The R 2 value for our quadratic model was 0.10.

Prefrontal dopamine released from synaptic terminals can powerfully act on prefrontal D1DRs ( Goldman-Rakic et al., 2004 , p.; Seamans and Yang, 2004 ). We examined the role of prefrontal D1DR manipulations on working memory performance in 17 studies with 37 data points. In line with data on prefrontal dopamine, we found that a negative quadratic function again provided the best fit, with AIC = 102.6 (p<0.001; vs. linear AIC = 110.2; 3 rd order AIC = 106.3). The R 2 value for this model was 0.26. Increasing the polynomial order coincided with an increase in the AIC values, suggesting that the negative quadratic model again provided the best combination of parsimony and goodness of fit. Adding an effect for the species being studied did not notably enhance our model’s goodness of fit, possibly due to insufficient sample size to detect this effect. When a variable controlling for species was added to our negative quadratic model, our AIC worsened from 314.4 to 315.5 for the prefrontal dopamine model and from 102.6 to 103.0 for the prefrontal D1DR model.

We then built new quadratic models using the resampling bootstrapped analysis described above for both prefrontal dopamine and prefrontal D1DRs and determined the difference between the two newly-built models. The average difference between R 2 values for the 10,000 iterations was 0.14, where a positive value indicated that the prefrontal D1DR models had a greater R 2 value. The 95% confidence interval for this result was (−0.10, 0.38) and the bootstrapped two-sided p value was 0.31.

Our goal was to quantify the relationship of working memory performance with prefrontal dopamine and D1DRs. We conducted a meta-analysis of 75 studies spanning rodents, non-human primates, and humans. These data suggest that 10% of the variance in working memory behavior was explained by manipulations of prefrontal dopamine, and 26% of the variance was explained by prefrontal D1DR manipulations. These data provide insight into how prefrontal dopamine and D1DRs affects cognitive behaviors.

Our findings are broadly consistent with past work that has proposed an inverted U-shaped relationship between prefrontal dopaminergic dynamics and working memory performance ( Cools and D’Esposito, 2011 ; Floresco, 2013 ). We were able to demonstrate this idea by quantitatively fitting an inverted quadratic function, supporting the idea that there is an optimal regime for dopamine function in the prefrontal cortex that may facilitate a wide range of interacting synaptic and post-synaptic proteins ( Arnsten et al., 2012 ; Arnsten and Li, 2005 ). In establishing this function, we show that prefrontal dopamine has strikingly different signaling principles than striatal dopamine ( Kreitzer, 2009 ; Mohebi et al., 2019 ; Yahr et al., 1969 ), in which striatal dopamine depletion can impair movement ( Burns et al., 1983 ; Schultz et al., 1989 ; Kirik et al., 1998 ) . However, in the striatum there are important differences in that many principal neurons express largely either D1- or D2-type dopamine receptors, and these systems can work in tandem to coordinate a wide range of behaviors. For instance, increasing striatal dopamine or stimulating D1 medium spiny neurons can facilitate or hyperstimulate movement ( Fredriksson et al., 1990 ; Brannan et al., 1998 ; Carta et al., 2006 ; Kravitz et al., 2010). However, both decreasing and increasing striatal dopamine can impair motivation ( Bryce & Floresco, 2019 ; Filla et al., 2018 ; Fry et al., 2021 ; Kamada & Hata, 2020; Salamone et al., 2012 ). Thus, the details of the dopaminergic effects on a behavior depend not just on the complex pharmacodynamics of the dopamine receptor, but how neurons expressing these receptors are precisely integrated into circuits.

While this work supports the hypothesis that working memory performance follows an inverted U-shape function dependent on prefrontal dopamine and D1DRs, our results should be interpreted carefully. For example, the bootstrapped analysis for models of prefrontal D1DRs were not significantly different from models of prefrontal dopamine; however, we note that there were fewer studies for prefrontal D1DRs, which may have affected our statistical power in separating prefrontal D1DRs from prefrontal dopamine manipulations. We also note that there may be important sampling bias; for instance, there are more studies that disrupt prefrontal dopamine/D1DRs than increase dopamine or D1DRs, and very few studies describing increased prefrontal dopamine or D1DRs result in increased working memory function. This insight may suggest that it is challenging to consistently improve working memory with dopaminergic manipulations, at least in intact prefrontal circuits.

Another key constraint is that rodents do not have lateral prefrontal regions that are present in primates ( Laubach et al., 2018 ), although dopamine is strongly released in medial prefrontal regions, and dopamine in these circuits may function according to similar principles ( Floresco, 2013 ; Zahrt et al., 1997 ). It is also important to acknowledge that changes to working memory performance are not only impacted by manipulations of prefrontal dopamine and D1DRs. Other prefrontal dopamine receptors ( Druzin et al., 2000 ; Glickstein et al., 2002 ), neurotransmitter systems ( Monaco et al., 2015 ; Robbins and Arnsten, 2009 ), brain regions ( Bolkan et al., 2017 ; Hart et al., 2018 ), and behaviors (i.e. interval timing, behavioral flexibility – Kim et al., 2017 ; Ragozzino, 2002 ; Zhang et al., 2019 ) are critical for optimal working memory performance. Furthermore, there are other paradigms that can be used to study executive functions, and U-shaped dynamics may be relevant for some behavioral paradigms such as attention, reversal learning, and interval timing ( Floresco, 2013 ; Parker et al., 2015 ; Robbins, 2007 ). However, other behavioral paradigms such as set-shifting or risk-based decision making may have distinct prefrontal dopaminergic dynamics, suggesting that these cognitive paradigms may have distinct relationships between prefrontal dopamine and D1DRs ( Floresco, 2013 ). However, our literature search revealed among manipulations of prefrontal dopamine and cognition that working memory paradigms had the largest number of studies, making it a reasonable starting point for comparisons across metholodogies and species. This work also has limitations that derive from comparing a broad range of studies across several different methodologies and model systems. However, this diversity is also a strength in that we report effects that are consistent across a range of approaches. Finally, publication bias may have affected this analysis, meaning that non-reviewed and unpublished research could have influenced our conclusions. While there are many small effect sizes within our datasets, the wealth of unpublished research possibly reporting nonsignificant prefrontal dopamine, prefrontal D1DR, or working memory changes could alter our interpretation of the inverted U-shape function.

In summary, this study advances the approach of bringing together diverse studies to elucidate patterns in prefrontal dopamine. A key finding here is that, while not statistically significant, the prefrontal D1DRs explained more variance than prefrontal dopamine. Fascinatingly, the initial description of the inverted-U shaped working memory function is based largely on pharmacological activation or inhibition of prefrontal D1DRs. It is possible that working memory performance is more strongly dependent on dopamine receptor activation than specific levels of prefrontal dopamine. This pattern will be useful in designing and interpreting preclinical studies, as well as in designing and optimizing new therapies for diseases such as ADHD, schizophrenia, and PD, which involve profound disruptions in prefrontal dopamine signaling.

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We included studies that measured both working memory performance and either prefrontal D1DRs or dopamine levels. We included studies from rodents, non-human primates, and humans, and expressed effect sizes in Cohen’s d . We found that studies that measured prefrontal D1DRs (red), prefrontal dopamine (blue) were best fit by a negative quadratic function. The model aggregating both prefrontal dopamine and D1DR measurements is shown in grey. Data from 75 studies and a total of 156 data points; 119 that measured prefrontal dopamine levels and 37 that measured prefrontal D1DR levels.

Studies that reported comparisons of prefrontal cortex dopamine and working memory between control and experimental subjects.

Author (Year)	Type	Species	Cohen's : Behavior	Author (Year)	Cohen's : Dopamine
	Dopamine	Rodent	−2.50		−3.76
	Dopamine	Primate	−3.30		−3.68
	Dopamine	Rodent	−3.73		−3.55
	Dopamine	Rodent	−0.37		−3.46
	Dopamine	Rodent	−1.38		−3.31
	Dopamine	Rodent	−1.51		−3.31
	Dopamine	Rodent	−1.49		−3.31
	Dopamine	Rodent	−1.05		−2.71
	Dopamine	Rodent	−0.88		−2.71
	Dopamine	Rodent	−0.96		−2.49
Gibbs & D'Esposito (2006)	Dopamine	Human	−0.63	Gibbs & D’Esposito (2006)	−2.43
	Dopamine	Rodent	−2.62		−2.20
	Dopamine	Primate	−0.94		−1.84
Bertolino et al (2006)	Dopamine	Human	−0.13	Bertolino et al (2006)	−1.83
	Dopamine	Rodent	−1.24		−1.79
	Dopamine	Rodent	−1.13		−1.73
	Dopamine	Human	0.14		−1.67
	Dopamine	Rodent	−2.34		−1.64
	Dopamine	Rodent	−1.89		−1.57
	Dopamine	Rodent	−0.59		−1.54
	Dopamine	Rodent	−1.17	Areal et al (2015)	−1.43
	Dopamine	Rodent	−2.89		−1.43
	Dopamine	Rodent	−0.35		−1.39
	Dopamine	Human	−0.42		−1.36
Zhang et al (2021)	Dopamine	Rodent	−1.68	Zhang et al (2021)	−1.33
	Dopamine	Human	0.29		−1.30
	Dopamine	Human	0.48		−1.25
	Dopamine	Rodent	−0.50		−1.23
	Dopamine	Primate	−0.44		−1.23
	Dopamine	Human	−1.22		−1.16
	Dopamine	Rodent	0.37		−1.15
	Dopamine	Rodent	0.00		−1.12
	Dopamine	Rodent	−0.17		−1.09
	Dopamine	Rodent	0.00		−1.09
	Dopamine	Human	−0.25		−0.98
	Dopamine	Human	−0.49		−0.97
	Dopamine	Rodent	−1.36		−0.76
	Dopamine	Rodent	−0.14		−0.75
	Dopamine	Rodent	0.08		−0.73
	Dopamine	Human	−0.09		−0.64
	Dopamine	Rodent	0.16		−0.61
	Dopamine	Rodent	−0.55		−0.60
	Dopamine	Primate	−2.24		−0.55
	Dopamine	Rodent	−3.10		−0.51
	Dopamine	Rodent	−0.66		−0.46
Yamada et al (1999)	Dopamine	Rodent	−3.18	Yamada et al (1999)	−0.45
Yamada et al (1999)	Dopamine	Rodent	−2.27	Yamada et al (1999)	−0.45
	Dopamine	Human	−0.38		−0.44
	Dopamine	Rodent	1.34		−0.42
	Dopamine	Rodent	−1.18		−0.40
	Dopamine	Human	−1.32		−0.40
	Dopamine	Rodent	−0.62		−0.39
	Dopamine	Rodent	−1.08		−0.36
	Dopamine	Rodent	0.15		−0.35
Baumgartner et al (2012a)	Dopamine	Rodent	−1.93	Baumgartner et al (2012a)	−0.33
	Dopamine	Rodent	0.25		−0.29
	Dopamine	Rodent	0.58		−0.29
	Dopamine	Rodent	0.63		−0.29
	Dopamine	Rodent	−0.52		−0.26
	Dopamine	Rodent	−0.35		−0.18
	Dopamine	Rodent	−2.74		−0.11
	Dopamine	Rodent	1.03		−0.11
	Dopamine	Rodent	0.88		−0.09
	Dopamine	Rodent	−1.07		−0.08
	Dopamine	Rodent	−0.85		−0.08
	Dopamine	Rodent	−0.08		−0.08
	Dopamine	Rodent	−1.68		−0.04
	Dopamine	Rodent	−0.41		−0.04
	Dopamine	Rodent	−0.66		−0.03
Baumgartner et al (2012a)	Dopamine	Rodent	−1.96	Baumgartner et al (2012a)	0.00
Baumgartner et al (2012a)	Dopamine	Rodent	−1.47	Baumgartner et al (2012a)	0.00
	Dopamine	Rodent	−0.71		0.00
	Dopamine	Rodent	−1.27		0.07
	Dopamine	Rodent	−1.22		0.10
	Dopamine	Rodent	−0.89		0.11
	Dopamine	Rodent	−1.23		0.12
	Dopamine	Rodent	−0.97		0.12
	Dopamine	Rodent	−0.14		0.14
	Dopamine	Rodent	−0.46		0.18
Baumgartner et al (2012b)	Dopamine	Rodent	−0.61	Baumgartner et al (2012b)	0.33
	Dopamine	Rodent	−1.35		0.40
	Dopamine	Rodent	−0.59		0.44
	Dopamine	Rodent	−1.23		0.55
	Dopamine	Rodent	−0.97		0.55
	Dopamine	Rodent	−0.04		0.55
	Dopamine	Rodent	0.21		0.58
	Dopamine	Rodent	−1.05		0.65
	Dopamine	Rodent	−0.41		0.67
Baumgartner et al (2012b)	Dopamine	Rodent	−1.42	Baumgartner et al (2012b)	0.67
Baumgartner et al (2012b)	Dopamine	Rodent	−1.03	Baumgartner et al (2012b)	0.67
Zhang et al (2021)	Dopamine	Rodent	0.19	Zhang et al (2021)	0.76
	Dopamine	Rodent	−1.68		0.78
	Dopamine	Rodent	−1.79		0.78
	Dopamine	Rodent	−1.26		0.79
	Dopamine	Rodent	1.00		0.83
	Dopamine	Rodent	−0.29		0.84
	Dopamine	Rodent	−0.96		0.89
	Dopamine	Rodent	−0.19		0.93
	Dopamine	Human	−0.75		0.93
	Dopamine	Rodent	−2.32		0.98
	Dopamine	Rodent	−0.37		1.01
	Dopamine	Rodent	1.50		1.06
	Dopamine	Rodent	−0.54		1.11
	Dopamine	Rodent	−1.00		1.11
	Dopamine	Rodent	−1.31		1.13
	Dopamine	Rodent	−2.43		1.21
Baumgartner et al (2012b)	Dopamine	Rodent	−0.67	Baumgartner et al (2012b)	1.33
	Dopamine	Rodent	−1.54		1.41
	Dopamine	Rodent	0.46		1.43
	Dopamine	Rodent	−1.35		1.53
	Dopamine	Rodent	−3.36		1.63
	Dopamine	Rodent	−1.94		1.74
Zhang et al (2021)	Dopamine	Rodent	0.09	Zhang et al (2021)	1.86
	Dopamine	Rodent	−1.35		2.04
	Dopamine	Rodent	−1.35		2.28
	Dopamine	Rodent	−2.44		2.28
Zhang et al (2021)	Dopamine	Rodent	−0.13	Zhang et al (2021)	2.58
	Dopamine	Rodent	−1.80		2.94
	Dopamine	Rodent	−1.19		3.44

Acknowledgements and Author Note:

MAW and NSN designed the meta-analysis. MAW and MMC independently screened abstracts for appropriateness. MAW collected the data, which was independently checked by NSN and HRS. LW, PTE, and NSN wrote the code and checked the analysis. MAW and NSN wrote the manuscript. HRS, LW, and PTE reviewed the manuscript. All code and raw data are available at https://narayanan.lab.uiowa.edu . This work was funded by NIH R01s MH116043, NS120987 to NN, and UL1TR002537.

This work was funded by NIH R01s MH116043, NS120987 to NSN. This study was partially supported by NIH UL1TR002537 to the Institute for Clinical and Translational Science at the University of Iowa.

Conflict of Interest:

There are no conflicts of interest.

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DOI: 10.1080/02701367.2003.10609113
Corpus ID: 7886976

Arousal, Anxiety, and Performance: A Reexamination of the Inverted-U Hypothesis

S. Arent , D. Landers
Published in Research Quarterly for… 1 December 2003

176 Citations

Relationship between arousal and choice reaction time, regulatory fit: impact on anxiety, arousal, and performance in college-level soccer players., the influence of cortisol, flow, and anxiety on performance in e-sports: a field study, the relationship between test anxiety and cognitive performance : mediated by state and trait self-control, the relationship between arousal zone, anxiety, stress and sports performance, arousal and activation in choice reaction time task, the effects of caffeine on arousal, response time, accuracy, and performance in division i collegiate fencers, a multidisciplinary investigation of the effects of competitive state anxiety on serve kinematics in table tennis, relationship between athletes' emotional intelligence and precompetitive anxiety, exploring the relationship between exercise-induced arousal and cognition using fractionated response time.

Highly Influenced

39 References

The relationship between the competitive state anxiety inventory-2 and sport performance: a meta-analysis, a catastrophe model of anxiety and performance., relationship between competitive state anxiety inventory-2 subscale scores and pistol shooting performance, do anxious swimmers swim slower reexamining the elusive anxiety-performance relationship.

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Conceptual and Methodological Considerations in Sport Anxiety Research: From the Inverted-U Hypothesis to Catastrophe Theory

Confirmatory factor analysis of the competitive state anxiety inventory-2., reexamining the factorial composition and factor structure of the sport anxiety scale, measurement and correlates of sport-specific cognitive and somatic trait anxiety: the sport anxiety scale, test anxiety and direction of attention., motor performance under stress: a test of the inverted-u hypothesis., related papers.

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The Inverted-U Hypothesis -depicting the Relationship between
Inverted U hypothesis
The inverted-U hypothesis
Inverted U hypothesis
The inverted-U hypothesis
Inverted U Theory Explained

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Sport Psyc Exam 3 Flashcards
D. a and b E. b and c, The inverted-U hypothesis predicts that A. as arousal increases, performance decreases B. arousal can be either too low or too high C. top performance occurs at a moderate level of arousal D. a and c E. b and c, A highly trait-anxious athlete (compared to a less trait-anxious athlete) would perceive competition as A. more ...
What the Yerkes-Dodson Law Says About Stress and Performance
The inverted-U curve looks a little different for each person and probably even changes at different points in your life. ... The Yerkes-Dodson law is the theory that there's an optimal level of ...
The Yerkes-Dodson Law of Arousal and Performance
How the Law Works. The Yerkes-Dodson law describes the empirical relationship between stress and performance. In particular, it posits that performance increases with physiological or mental arousal, but only up to a certain point. This is also known as the inverted U model of arousal. When stress gets too high, performance decreases.
Inverted U hypothesis
The optima vary between people doing the same task and one person doing different tasks. A basic assumption in the hypothesis is that arousal is unidimensional and that there is, consequently, a very close correlation between indicators of arousal; this is not the case. See also catastrophe theory. inverted-U hypothesis
The Inverted-U Theory
The Inverted-U Theory helps you to observe and manage these four factors, aiming for a balance that supports engagement, well-being, and peak performance. You can use the model by managing these four influencers, and by being aware of how they can positively or negatively influence your people's performance.
Inverted U hypothesis
The Inverted U Hypothesis is an appealing explanation for performance flaws. In many ways this explanation fits into the observations from sport performers but in reality is too simplistic. In addition to what the Inverted U hypothesis predicts, it is important to consider that beginners usually need a greater amount of attention to the ...
Arousal and performance: revisiting the famous inverted-U-shaped curve
Abstract. Arousal level is thought to be a key determinant of variability in cognitive performance. In a recent study, Beerendonk, Mejías et al. show that peak performance in decision-making tasks is reached at moderate levels of arousal. They also propose a neurobiologically informed computational model that can explain the inverted-U-shaped ...
Stress and anxiety in sport.
The longest-standing approach to the relationship between stress, anxiety and performance in sport is probably the inverted-U hypothesis, derived from the work of Yerkes and Dodson (1908). This hypothesis predicts that performance improves with increases in arousal until a peak is reached, after which further arousal leads to a deterioration in performance. Although arousal and anxiety are not ...
Arousal Control in Sport
Inverted-U Hypothesis. In the inverted-U hypothesis performance is best at a moderate level of arousal. Both low and high levels of arousal are associated with decrements in performance. The original work done on the inverted-U hypothesis related to the strength of stimulus and habit-formation (learning) in mice (Yerkes & Dodson, 1908). Mice ...
Arousal, Anxiety, and Performance: A Reexamination of the Inverted-U
As predicted by the Inverted-U hypothesis, optimal performance on the simple task was seen at 60 and 70% of maximum arousal. Furthermore, for the simple task used in this study, only somatic ...
What Makes Stress "Good" or "Bad"?
Stress often has negative effects. But in the right amounts, stress can be good for us. A theory known as the "inverted-U" hypothesis attempts to explain how varying levels of stress influence us ...
Inverted U Theory Explained
The inverted u theory may also be referred to as the Yerkes-Dodson law due to its creation by two researchers - Yerkes and Dodson. In 1908, these researchers were trying to understand the relationship between the strength of a stimulus and forming habits in mice. They found that there was a negative relationship between the two i.e. the ...
Quantifying the inverted U: A meta-analysis of prefrontal dopamine, D1
These findings lead to the hypothesis that working memory follows an inverted U-shaped function, in which optimal working memory performance is achieved with optimal levels of prefrontal dopamine and D1DR activation. While inverted U-shaped dynamics have substantial supporting evidence, the contours of this function are not clear.
Arousal, Anxiety, and Performance: A Reexamination of the Inverted-U
As predicted by the Inverted-U hypothesis, optimal performance on the simple task was seen at 60 and 70% of maximum arousal. Furthermore, for the simple task used in this study, only somatic anxiety as measured by the SAS accounted for significant variance in performance beyond that accounted for by arousal alone. These findings support ...
Arousal, Anxiety, and Performance: A Reexamination of the Inverted-U
Findings support predictions of the Inverted-U hypothesis and raise doubts about the utility theories that rely on differentiation of cognitive and somatic anxiety to predict performance on simple tasks that are not cognitively loaded. Abstract Until recently, the traditional Inverted-U hypothesis had been the primary model used by sport psychologists to describe the arousal-performance ...
Chapter 12
Study with Quizlet and memorize flashcards containing terms like There is a direct relationship between one's level of, The importance placed on an event and the uncertainty that surrounds the actions of that event are sources of, The inverted-U hypothesis predicts that and more.
Arousal: The Inverted-U Hypothesis Flashcards
Yerkes-Dodson Law (The Inverted-U Hypothesis) - Performance rises as arousal levels rise, up to an optimum point, after which the person becomes over-aroused and their performance level decreases. - Sports requiring fine motor skills such as golf require low arousal for optimum performance, whereas high strength and less skillfull sports such ...
Somatic anxiety
The Multi-dimensional Theory of Anxiety is based on the distinction between somatic and cognitive anxiety. The theory predicts that there is a negative, linear relationship between somatic and cognitive anxiety, that there will be an Inverted-U relationship between somatic anxiety and performance, and that somatic anxiety should decline once performance begins although cognitive anxiety may ...
Chapter 12: Stress Flashcards
The inverted-U hypothesis predicts that. False (T/F) The inverted-U hypothesis is the only theory used to explain stress and performance. an optimal level of state anxiety and other emotions.
The "Inverted-U Hypothesis" suggests that optimal ...
The Inverted-U Hypothesis predicts that engagement will be highest at a moderate level of challenge, neither too hard nor too easy (Figure 1). In these studies, we operationalized challenge as the ...
Sports Psychology- Chapter 12 Flashcards
A negative emotional state. anxiety. A nondirective, generalized bodily reaction-activation. arousal. Using stress in a constructive manner that benefits performance. eustress. An individual's anxiety at a particular moment. state anxiety. A specific to sports, multidimensional measure of trait anxiety.