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 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.

models-of-emotion-performance-sports-psychologyFigure 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).


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.


  1. Crocker, P. R. E., Kowalski, K. C., Graham, T. R., & Kowalski, N. P. (2002). Emotion in sport. In J. M. Silva III & D. E. Stevens (Eds.), Psychological foundations of sport (pp. 107–131). Boston: Allyn & Bacon.
  2. Ekman, P. E., & Davidson, R. J. (1994). The nature of emotion: Fundamental questions. New York: Oxford University Press.
  3. Gross, J. J. (Ed.). (2007). Handbook of emotion regulation. New York: Guilford Press.
  4. Hanin, Y. L. (Ed.). (2000). Emotions in sport.Champaign, IL: Human Kinetics.
  5. Hardy, L., & Parfitt, G. (1991). A catastrophe model of anxiety and performance. British Journal of Psychology, 82, 163–178.
  6. Hardy, L., Woodman, T., & Carrington, S. (2004). Is selfconfidence a bias factor in higher-order catastrophe models? An exploratory analysis. Journal of Sport & Exercise Psychology, 26, 359–368.
  7. Lavallee, D., Thatcher, J., & Jones, M. V. (Eds.). (2004).Coping and emotion in sport. New York: NovaScience.
  8. Lazarus, R. S. (1991). Emotion and adaptation. NewYork: Oxford University Press.
  9. Lazarus, R. S. (2000). How emotions influence performance in competitive sport. The Sport Psychologist, 14, 229–252.
  10. Posner, J., Russell, J., & Peterson, B. S. (2005). The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and Psychopathology,17, 715–734.
  11. Woodman, T., Davis, P. A., Hardy, L., Callow, N., Glasscock, I., & Yuill-Proctor, J. (2009). Emotions and sport performance: An exploration of happiness, hope, and anger. Journal of Sport & Exercise Psychology, 31, 169–188.
  12. Woodman, T., & Hardy, L. (2001). Stress and anxiety. In R. N. Singer, H. A. Hausenblas, & C. M. Janelle (Eds.), Handbook of sport psychology (2nd ed.,pp. 290–318). New York: Wiley.

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