Models Of Emotion – Performance

Emotion is an integral part of human functioning and can enhance or hinder individual and team performance. This entry defines emotion and then provides an overview of theories and models that have been used to explain and describe the relationship between emotion and performance. These include multidimensional anxiety theory (MAT), the individual zones of optimal functioning (IZOF) hypothesis, and catastrophe models, as well as some of the associated elements of the emotion–performance relationship, including interpretation of emotion and the cognitive–motivational–relational theory (CMRT) of emotions.

Richard Lazarus proposed that emotions are organized psychophysiological reactions to news about ongoing relationships with the environment. Emotions arise when people attend to situations that are deemed important to their goals (e.g., competing, supporting a team). Although the terms emotion and feeling are often used interchangeably, they can be differentiated on the grounds that emotions have both “feeling” and “doing” aspects; feelings do not necessarily involve action or the impulse to act. Most researchers agree that it is the action or the impulse to act that leads to changes in the autonomic nervous system (ANS) and the neuroendocrine system. The ANS is responsible for the control of involuntary or unconscious bodily functions, and the neuroendocrine system is the system that involves both nervous stimulation and endocrine secretion. Both of these systems anticipate a behavioral response to the emotion experienced, and humans can develop an awarness of their emotions and the associated changes. It is this awareness that led William James, a philosopher and psychologist considered by many to be the father of American psychology, to view emotions as response tendencies that may be controlled in a number of ways. Within sport and exercise it is this control feature of emotions, or the ability to regulate emotion, that has received most attention.

Researchers have typically conceptualized emotions in one of two ways. The first is to conceptualize emotions as lying along two dimensions: hedonic tone (pleasure or displeasure) and activation (relaxation or tension)—often referred to as Russell’s circumplex model of affect. In this model, each emotion (or affect) is considered as a combination of high or low hedonic tone and high or low activation such that all emotions can be categorized in one of four quadrants: high hedonic tone and high activation (e.g., excited), high hedonic tone and low activation (e.g., happy), low hedonic tone and high activation (e.g., anxious), and low hedonic tone and low activation (e.g., sad). Although appealing, this model does not allow one to distinguish between some emotions that might otherwise be considered distinct. For example, anger and anxiety are thought to be distinct emotions but both are classified as low in hedonic tone and high in intensity in the circumplex model.

The second way that researchers have conceptualized emotions is to consider them as primary discrete states that vary in important ways from each other. The ways in which these discrete emotions vary is influenced by individuals’ different appraisals of a situation. An appraisal is the cognitive evaluation a person makes regarding the nature and significance of an event. The idea that people can respond with different emotions to the same situation depending on how they appraise (interpret) the situation is one of the central features of cognitive appraisal theories of emotions.

Within such an appraisal framework, the modal model of emotion has been utilized to understand emotion and its behavioral responses. The modal model of emotion is a sequential model, the starting point of which is an individual in a situation that is important to him or her. The interaction between the individual and this situation forces the individual to attend to what is happening. The individual then interprets what is happening and forms an appraisal of the situation. This appraisal results in a multisystem response to the individualsituation interaction (e.g., fight or flight). Thus, in this model, it is the cognitive appraisal that underlies all emotional states and different individuals can display different responses to the same situation. The view that individuals can display different emotional responses to the same situation led researchers to begin to identify specific discrete emotions.

Paul Ekman and colleagues pioneered research that examines emotions in relation to facial expression and identified seven basic emotions on that basis: anger, disgust or contempt, fear, happiness, interest, sadness, and surprise. Other researchers have extended this list and proposed other discrete emotions. For example, Carroll Izard proposed that guilt, shame, and distress are also discrete emotions; Richard Lazarus further proposed shame, envy, pride, jealousy, anxiety, relief, hope, love, and compassion as discrete emotions. Within sport and exercise, a range of emotions have been observed, such as anxiety, frustration, disappointment, happiness, hope, and anger. It is anxiety that has received by far the most attention especially in relation to the emotion–performance relationship.

Anxiety

Anxiety is considered an unpleasant emotion that can be situation-specific (state anxiety [SA]) or dispositional (trait anxiety [TA]). Charles Spielberger suggested that SA is subjective with conscious feelings of tension and apprehension associated with arousal of the ANS. SA is normally relatively transitory, as it is the individual’s response to a specific threatening situation or event. Conversely, TA is a general disposition to respond to a variety of situations with high SA. Early theoretical accounts of anxiety were from a one-dimensional perspective. It is now accepted, however, that anxiety has at least two distinguishable components: a mental component normally termed cognitive anxiety or worry (e.g., I am worried about not performing well); and a physiological component normally termed somatic anxiety or physiological arousal (e.g., an increase in skin conductance [SC], often manifest in sweaty hands). Outlined next are a number of models and theories that have been proposed for the anxiety–performance relationship.

Multidimensional Anxiety Theory

Although it is referred to as a theory, Matthew Martens and colleagues’ MAT is largely a model that describes the relationships between anxiety and performance. MAT posits that cognitive anxiety and somatic anxiety are related to performance in two independent ways. According to MAT, cognitive anxiety has a linear negative relationship with performance. This relationship is proposed on the basis that worrying thoughts (cognitive anxiety) take up some of an athlete’s cognitive resources. As the anxious athlete has fewer cognitive resources, he or she is no longer able to meet the cognitive demands of the task and as a result his or her performance deteriorates. In other words, the more athletes allocate cognitive resources to worry, the fewer resources they have for their performance and poorer performance results. According to MAT, somatic anxiety has an inverted-U relationship with performance whereby performance is optimal at moderate levels of somatic anxiety. The somatic anxiety–performance relationship is based on the Yerkes–Dodson inverted-U arousal–performance model. According to this adaptation of the inverted-U model, performance improves as an athlete becomes more physiologically aroused (e.g., they become faster, more efficient, more accurate, more alert, more focused), but this performance improvement is only up to a point; this point is termed the point of optimal arousal, as it is the point at which best performance is achieved. Beyond that point, if the athlete continues to become more anxious then performance will gradually start to decline (e.g., become slower, less efficient, less accurate, less alert, less focused).

Interpretation of Emotions

Most emotions are conceptualized as being either negative or positive. For example, anxiety is considered a negative emotion and happiness is considered a positive emotion. However, a negative emotion does not simply lead to a negative performance, and athletes also do not typically interpret their emotions in this simplistic fashion. For example, an athlete can have uncomfortable anxious thoughts and feelings about an upcoming sporting event and feel that these “negative” feelings are desirable, even necessary, for optimal performance. Studies by Hardy and colleagues with basketball players and of crown green bowlers in the United Kingdom have shown that a negative emotion (e.g., cognitive anxiety) can indeed sometimes have a positive effect on performance. In the specific context of anxiety interpretation, Mahoney and Avener were the first to report that performers could interpret their anxiety in different ways: The more successful gymnasts tended to use their anxiety as a stimulant to better performance, whereas less successful athletes seemed to reach a state of near panic. Furthermore, Graham Jones and colleagues have reported that successful athletes tend to view their anxiety as helpful to performance.

Individualized Zones of Optimal Functioning

In the 1970s and 1980s, Yuri Hanin proposed the IZOF hypothesis. The central tenet of the IZOF hypothesis is that each athlete has his or her own optimal zone of preperformance emotion within which he or she is more likely to attain optimal performance. If the level of an emotion lies outside of this zone of optimal functioning, performance will be impaired. To illustrate, a person’s optimal level of anxiety is specific to that particular individual and if the athlete is in his or her “zone,” peak performance will follow. Athletes may thus perform differently from each other even if they are experiencing the same degree of anxiety. Investigations including emotions such as anger, disappointment, frustration, excitement, and joy to derive IZOFs have supported the applicability of the IZOF concept in the emotion–performance relationship. While literature on the IZOF has predominantly focused on anxiety, researchers have contended that a recipe of emotions should be used for optimal performance. For example, some athletes may flourish under high levels of anxiety with high levels of hope (or other positive emotions), while other players will do better with lower anxiety in combination with higher or lower levels of other emotions.

Catastrophe Models

In the early 1990s, Lew Hardy and his colleagues developed the cusp catastrophe model as a description of the relationship between anxiety and performance. The cusp catastrophe model (Figure 1) predicts that an athlete’s performance will depend on a complex interaction between cognitive anxiety and physiological arousal. The cusp catastrophe model is a three-dimensional relationship among cognitive anxiety, physiological arousal, and performance with the following predictions: (a) when physiological arousal is low, increases in cognitive anxiety will be beneficial to performance; (b) when physiological arousal is high, increases in cognitive anxiety will be detrimental to performance; (c) when cognitive anxiety is low, changes in physiological arousal will result in small and continuous changes in performance in the form of a mild inverted-U; and (d) when cognitive anxiety is high, physiological arousal will either help or hinder performance, depending on the level of physiological arousal that is experienced. Specifically, when cognitive anxiety is high, performance will increase as a function of physiological arousal, up to a point. If physiological arousal increases beyond this point, the athlete suffers a dramatic drop in performance: a catastrophe. This large and dramatic drop in performance is reversible only if the athlete’s physiological arousal reduces to a point that was lower than the point at which the performance catastrophe occurred.

Figure 1 Two-Surface Catastrophe Model

Thus, a catastrophe can only be reversed by a substantial reduction in physiological arousal. When cognitive anxiety is high, the small changes in physiological arousal that can result in large and discontinuous changes in performance is termed hysteresis and changes in performance are different depending upon whether physiological arousal is increasing or decreasing.

Hardy also developed the butterfly catastrophe model, which is an extension of the cusp catastrophe model. The butterfly catastrophe model incorporates self-confidence as a fourth factor, which is called the bias factor. The bias factor (self-confidence) moderates the interaction between cognitive anxiety and physiological arousal on performance such that the front face of the model (see Figure 1) swings to the right (high self-confidence) or to the left (low self-confidence). Under high cognitive anxiety, highly self-confident performers are able to withstand higher physiological arousal before suffering a sudden drop in performance (i.e., the front face of the model swings to the right) than their less self-confident counterparts. Hardy and colleagues’ studies with golf and basketball players have shown support for the catastrophe model. In addition, Stuart Beattie and colleagues have provided further evidence for the catastrophe model and hysteresis in a rugby context. Operationalizing a performance catastrophe as disengagement from the task, these researchers found that an increase in physiological arousal led to sudden disengagement from the task when cognitive anxiety was high.

Cognitive–Motivational–Relational Theory of Emotions

Richard Lazarus proposed the cognitive– motivational–relational theory (CMRT) of emotions in sport. According to Lazarus, cognitive refers to knowledge and appraisal of what is happening in an encounter: Knowledge is both situational and generalized beliefs about how things work; appraisal is an evaluation of the personal significance of an encounter. Motivational refers to two interrelated components. First, it is a characteristic of a person: a dispositional variable that one brings to every encounter. Second, the disposition to attain a goal has to be activated by the demands, constraints, and resources that the environment provides (i.e., a suitable environment to achieve that goal must be present). Relational suggests that emotions are always about person– environment relationships involving harms or benefits. Emotions are created by person–environment relationships that change over time and circumstances. A central premise of the theory is that each emotion involves a distinct core relational theme that describes the interaction between the individual and the environment. The core relational theme is a summary of the appraisals that individuals make in assessing the risk and reward involved in a particular situation. For example, the core relational theme of anger involves the perceived commitment of an offence. Each core relational theme has an associated action tendency that directly represents the manifestation of the person’s appraisal of the stimulus in relation to the self. For example, according to Lazarus, the associated action tendency for anger is the impulse to retaliate with a view to restoring self-esteem.

In CMRT, it is proposed that the core relational theme and the associated action tendency will influence performance depending on the complex relationship between the athlete and the situation. For example, anger may negatively impact performance if it draws resources away from the primary task at hand. However, if the task requires a “lashing out” motion toward an aggressor or opponent, performance may be facilitated due to its close association with anger’s action tendency. Tim Woodman and colleagues have offered support for CMR in a “kicking out” task where anger was primed via an imagery script. Performance on the physical task was significantly greater in the anger condition compared with happiness and emotion-neutral conditions. In contrast, on a cognitive task, which does not match anger’s action tendency, anger did not have a facilitative effect. From this research, it appears that in order for anger to be an effective performance-enhancing emotion, the task needs to be aligned with anger’s lashing out action tendency. The perspective that action tendencies have matching themes has also been applied to other emotions (e.g., hope, pride, happiness).

Conclusion

Researchers of emotion and sport performance have proposed a number of models and theories examining how emotions impact performance. While there has been a predominant focus on anxiety, more recently researchers have begun to focus their attention on the role of other emotions. Understanding the complexity of emotions continues to be of value to researchers, athletes and applied practitioners with a view to optimal performance. Although the early models of emotion–performance conceptalized the emotion–performance relationship as a series of two-dimensional relationships, it is the interaction between different components of emotions or between emotions themelseves that stands to further our understanding in this fascniating and complex research domain.

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