To be published in: Psychology of Emotions, Nova Publisher, 2013
Emotions and Performance: Valuable insights from the sports domain
Sylvain Laborde (1, 2), Markus Raab (1), Fabrice Dosseville (2)
(1) German Sport University, Cologne, Germany
(2) UFR STAPS, EA 4260, University of Caen, France
This chapter is aimed to understand the influence of emotions on human performance.
As a background, we chose to present research done in the sport domain, a real-world and
highly complex environment. Indeed sport appears to be a perfect context to understand the
link between emotions and performance: In competition, athletes almost always have to face
high levels of pressure, which might trigger a full range of emotional experiences.
In this chapter, we first review the theories helping to understand how emotions might
influence performance, integrating them according to an original perspective, considering the
trait and state levels. For the trait level, we present individual differences linked with
emotions, such as trait emotional intelligence, the propensity for reinvestment… At the state
level, we show how specific emotions (e.g., anxiety, anger, hope…) might influence
performance, considering the cognitive and the motor parts of performance. In particular, we
included very recent work on a specific aspect of cognitive performance, decision making;
and innovative work on kinematics regarding motor performance.
After this theoretical review, we address the methodological advances in the field,
showing how to elicit and how to assess emotions. This part is aimed to be particularly
helpful to researchers when designing their experiments concerning this topic.
The last part is dedicated to the applied implications of the findings reviewed so far.
This part stresses the applied benefits of knowing how to deal with one’s emotions in order to
improve one’s performance. Topics like emotion regulation, stress management, appropriate
use of coping strategies, biofeedback… are presented here. Finally, we provide an illustration
of how the findings presented in this chapter can be extended to performance in other
domains, such as organizational, economic, artistic… thus being an inspiring source of ideas
Emotions are part of the life experience of each individual, thus understanding them
and knowing how to deal with them can be a smart and definitive advantage for the one who
aims at reaching a better performance.
1 Introduc tio n+
Emotions not only color our lives and give meaning to them (S. Scott, 2009) but also
have a strong impact on human performance (Eccles et al., 2011). Why is it so important to
consider the relationship between emotions and performance? From an evolutionary
perspective, emotions have been at the core of our struggle to survive and, more prominently
in today’s world, to perform. We chose to study this relationship in sports, a high-pressure
domain that reflects, to some degree, survival fights and contests (Lazarus, 2000). In addition,
sports is an applied field that is concerned with enhancing performance (Eccles et al., 2011).
In this chapter, we will use sports examples to illustrate (a) a select set of theories that
have been developed to explain the emotion–performance relationship, (b) the methodologies
used to study this relationship, and finally (c) the applied interventions that can be derived
from previous theoretical considerations. Our hope is that the chapter will improve
comprehension of the role of emotions in performance.
At the theoretical level, we will draw on two foundational works of the early 2000s:
the work of Hanin (2000) and Lazarus (2000), as well as on the more recent work of Jones
and colleagues (Jones, Meijen, McCarthy, & Sheffield, 2009), to see how the field has
evolved over the last decade. At the methodological level, we provide a guide to eliciting and
assessing emotions. We stress the fact that sports researchers are increasingly considering the
mind–body interaction (Laborde & Raab, submitted). In the last section, we discuss practical
interventions that have been inspired by the theories reviewed. For example, following
Lazarus’s theory, (e.g., Jones, 2003) recognized that it is possible to change cognitions to
change the emotional state, and following Hanin’s theory, others recognized that changing
emotions alters choices in risky tasks (Robazza & Bortoli, 2005; Robazza, Bortoli, Carraro, &
Affects, moods, emotions, feelings, preferences, affective states, and attitudes are
often used as synonyms, but they are not (for a review, see Gray & Watson, 2007; Scherer,
2005). Affect is generally an umbrella term covering the different phenomena that can be
categorized as either good or bad in valence (Gross, 1998). In sports science research,
affective states have been studied mainly under two broad terms: mood (e.g., Beedie, Terry,
& Lane, 2000) and emotions (e.g., Hanin, 2000). Emotions and moods are similar in that they
both refer to feeling states that can be characterized as pleasant or unpleasant (i.e., positive or
negative); however, mood reflects more the overall affective state of the individual (Gray &
Watson, 2007), whereas emotions can be linked to a specific event or moment that causes the
response (Davidson et al., 1994). In this chapter we focus on the concept of emotions.
Two approaches have been adopted to study emotions: the discrete approach and the
dimensional approach. Within the discrete approach, emotions are categorized based on their
qualitative content, with the notion of appraisal being central (Scherer, 2005). They can be
identified according to their core relational themes (Lazarus, 1999). Lazarus (1999)
established a list of 15 emotions classified as positively toned (i.e., compassion, gratitude,
happiness, hope, love, pride, relief) or negatively toned (i.e., anger, anxiety, envy, fright,
guilt, jealousy, sadness, shame). The dimensional approach sees emotions on a continuum
varying from negative to positive, where intensity differentiates the emotions (Feldman
We introduce here three main theories explaining the emotion–performance
relationship in sports: Lazarus’s (2000) cognitive-motivational-relational theory (CMRT),
Hanin’s (2000) individual zone of optimal functioning (IZOF), and (Jones et al., 2009) theory
of challenge and threat states in athletes (TCTSA).
The CMRT (Lazarus, 2000) postulates that individuals continuously appraise their
ongoing relationship with the environment. The appraisal will depend on the integration of a
set a six separate appraisal judgments: goal relevance, goal congruence, type of ego
involvement, options for coping, coping potential, and future expectations. There is a core
relational theme for each emotion, and when the appraisal corresponds to a core relational
theme an emotion will arise. The core relational theme for each emotion is linked to a specific
action tendency. According to Lazarus (2000), the function of emotion is to facilitate
adaptation and, by extension, performance.
According to the CMRT, how will emotions influence performance?
“One main mechanism whereby performance is affected negatively is the self-statements
and ruminations produced by emotional struggles that interfere with attention and
concentration, without which a top performance is not possible” (Lazarus, 2000, p. 249).
Another influence is lessening motivation, which increases the tendency to give up. This will
depend on the core relational theme of each emotion. Also, the way an emotion is expressed
will change its influence on performance (Lazarus, 2000).
The CMRT postulates that the influence of emotion on performance will depend on
the match between the action tendencies derived from the core relational theme and the task
demands (Lazarus, 2000). This prediction was empirically supported with the emotion anger
(H. Davis et al., 2008; Woodman et al., 2009), that is, “a demeaning offense against me and
mine” (Lazarus, 2000, p. 234). One action tendency derived from the emotion anger is “a
powerful impulse to counterattack in order to gain revenge for an affront or repair a wounded
self-esteem” (Lazarus, 2000, p. 243). Two studies (P. A. Davis, Woodman, & Callow, 2010;
Woodman et al., 2009) showed that on a maximal force task, anger has a positive influence on
Regarding the perspectives offered by this theory, research examining the relationship
between Lazarus’s action tendency predictions and performance is warranted. A promising
recent research direction is also the extension of CMRT to moderators such as individual
differences. For example, P. A. Davis et al. (2010) studied the influence of trait anger on the
anger–performance relationship, and Woodman et al. (2009) showed the influence of
extraversion on the anger–performance relationship. P.A. Davis et al. showed that trait anger
was positively associated with performance on a peak-force task when associated with anger-
out, while an association with anger-in decreased the performance on the same task.
Woodman et al. (2009) showed that extroversion moderated the anger–performance
relationship: when angry, extroverts’ peak force increased more than introverts’ with a small
effect size. Finally, applications can be derived from the CMRT, such as the different emotion
regulation strategies aimed to modify cognition proposed by Jones (2003), which we detail in
The CMRT can be considered a nomothetic approach, as the core relational theme of
each discrete emotion does not change according to the person. Taking the example of anger,
different people can be angry about different things and can also express anger differently, but
anger still corresponds to a situation appraised as “a demeaning offense against me and
mine.” Note, however, that Lazarus’s (2000) discrete emotions approach was not designed
specifically for the sports context. Another perspective was taken by Hanin (2000), who
stressed the idiosyncratic nature of emotion in a sports-specific theory, the IZOF.
The IZOF theory (Hanin, 2000) postulates that precompetitive emotional states will
influence performance during competition. It adopts a multidimensional approach to describe
emotional experiences in athletic performance: form (e.g., cognitive, bodily-somatic,
behavioral), intensity, content, time (e.g., duration, frequency), and context (Hanin, 2007).
This theory also assumes that the influence of emotions on performance involves not only
hedonic tone but also emotion functionality. A traditional methodology of the IZOF theory is
to establish individualized performance profiles for each athlete from retrospective recall
about good and bad performance.
According to the IZOF theory, how will emotions influence performance?
According to Hanin (2000, p. 84), “two constructs related to energizing and organizing
aspects of emotion may account for the impact of emotions upon performance process: energy
mobilization (demobilization) and energy utilization (misuse).” As a general rule, the
interaction of specific emotional content (anxiety, anger, etc.) with specific emotional
intensity (high, moderate, or low) will produce specific optimal or dysfunctional effects on
athletic performance (Hanin, 2007, p. 48). In addition, the functionality of emotion is
expected to be determined according to a resource-matching hypothesis, that is, an evaluation
of the individual resources at hand in association with the demands of the task (Hanin, 2007).
The influence of emotions on performance in terms of energy mobilization and utilization has
been detailed by Martinent and Ferrand (2009). In a descriptive qualitative study in table
tennis, Martinent and Ferrand suggested five main categories: confidence, sensations,
motivation, concentration, and adaptation of the behavior of individuals to the constraints and
characteristics of the situation. For example, optimizing emotions are expected to be linked
with increased concentration, increased motivation, increased confidence, positive sensations,
and adaptive behaviors. A promising direction for IZOF could be to use it together with
objective measures, such as physiological variables (Bertollo et al., 2012).
What are the differences between CMRT and IZOF?
IZOF and CMRT share some basics: “any disagreement between us seems to be more
a matter of emphasis than one of basic substance,” said Lazarus about Hanin’s work (Lazarus,
2000, p. 238). The main difference is Lazarus’s emphasis on relational meaning and the
coping process. These topics are not absent from Hanin’s work: First, with the IZOF the
relational meaning is seen as more implicit, in the sense that any emotion can be seen as
facilitative of or debilitating to performance. Second, with IZOF, coping is not central but is
referred to as emotion regulation (Lazarus, 2000). There are also discrepancies in the lists of
emotions. Lazarus identified 15 discrete emotions with their core relational themes; the IZOF
list (Hanin, 2000) contains not only emotions but also words reflecting motives and attitudes
(e.g., slack, lazy).
In the CMRT and the IZOF, emphasis is placed on appraisal, of the emotion core
relational themes for Lazarus (2000) and of emotion functionality with the resource-matching
hypothesis for Hanin (2007). Another theory, the Theory of Challenge and Threat States in
Athletes (TCTSA; Jones et al., 2009), puts the emphasis on different appraisals components,
the challenge and threat appraisals.
The TCTSA (Jones et al., 2009) is concerned not with emotions directly but with the
appraisal of challenge and threat in a situation, which will have different motivational,
emotional, and physiological consequences for performance. The challenge or threat state in
response to competition is determined by self-efficacy, control perception, and achievement
goals (Jones et al., 2009). The TCTSA assumes that positive and negative emotions can occur
in a challenge state, while only negative emotions are supposed to occur in a threat state
(Jones et al., 2009). In challenge states, emotions (positive or negative in valence) are
perceived as functional, and in threat states, emotions (negative in valence) are perceived as
According to the TCTSA, how will emotions influence performance?
As indicated earlier, the TCTSA does not deal primarily with emotions but relates
challenge and threat states to performance. A challenge state can be accompanied by either
positive (e.g., hope) or negative emotions (e.g., anxiety) and is expected to be helpful to
performance. A threat state is accompanied by only negative emotions and is thought to harm
performance. This is different from the IZOF, where positive (in terms of hedonic valence)
emotions are thought to have the potential to harm performance, as well. In addition,
challenge and threat states are thought to influence effort, attention, decision making, and
physical functionality, and hence athletic performance. Finally, the dichotomy of challenge
and threat is important to take into consideration but alone cannot describe the richness of the
There seems to be a consensus to consider not only the hedonic valence but also the
functional impact of emotions on performance. However, what mechanisms are involved in
the influence of emotions on performance have yet to be clarified. Most studies have been
focused on the descriptive level, using retrospective reports (Martinent, Campo, & Ferrand,
2012; Martinent & Ferrand, 2009) or correlating pregame emotional reports with performance
(Hanin, 2000). Such studies can claim some ecological value, but they are of little help in
understanding the mechanisms at hand, because conditions are generally not manipulated.
Therefore causal empirical evidence to support such descriptive findings should still be
encouraged, because causality cannot be assumed from former studies. Testing for correlation
with either subjective or objective performance (Pensgaard & Duda, 2003) is not sufficient
either, because the processes involved in performance are still unknown. Some interesting
empirical work in this direction examined the influence of emotions on specific components
of performance, such as visual attention (Janelle, 2002; M. R. Wilson, 2008), maximal peak
force (P. A. Davis et al., 2010; Woodman et al., 2009), and decision making (Laborde &
Raab, submitted). Such research endeavors should be encouraged to improve our
understanding of the influence of emotions on performance. Finally, the influence of
individual moderators in the emotion–performance relationship should be examined further
(P. A. Davis et al., 2010; Woodman et al., 2009).
One focus has been the focus on the emotional state of the athlete before competition,
at the psychophysiological level. If precompetitive emotional states are repeatedly found to
have an influence on athletic performance (Cerin, 2003; Hanin, 2000), emotions are more
likely to change during competition (Cerin, Szabo, Hunt, & Williams, 2000). In fact,
emotions are expected to be constantly modified through cognitive appraisal (Lazarus, 2000;
Martinent & Ferrand, 2009). In addition, it has been argued that various positive and negative
emotions can occur at the same time, with different hedonic valences, especially if they are of
mild intensity (Cerin, 2004). Consequently, the frequency, intensity, and direction of
emotions are expected to be transient (Martinent et al., 2012), and rather than acting alone,
emotions are expected to act together with other transitory subcomponents of performance,
such as motivation, self-efficacy, and perceived control (Jones et al., 2009).
This previous focus raised some methodological problems. While it is relatively easy
to assess the psychophysiological response before a competition, with self-report
questionnaires and physiological variables, it is harder to do so during a competition. This is
why some researchers used a retrospective qualitative methodology to explore how emotions
evolve during competition (Martinent et al., 2012). McCarthy (2011, p. 53) indicated that
“measures with sufficient temporal resolution and proximity are necessary for each specific
emotion” and that self-report, observer ratings, and facial, autonomic, brain-based, and vocal
measures should be used in combination (Larsen & Fredrickson, 1999). Research on the
emotion–performance link will benefit from a combination of different methodologies within
the same research project (Cooke, Kavussanu, McIntyre, & Ring, 2010), such as collecting
qualitative and quantitative data (e.g., Laborde, Dosseville, Wolf, Martin, & You, submitted);
A fourth theory (beyond CMRT, IZOF, and TCTSA) was recently proposed to
explain the emotion–performance relationship (McCarthy, 2011): the broaden-and-
build theory of positive emotions in sports (Fredrickson, 2001). This theory assumes that
positive emotions “broaden people’s momentary thought-action repertoires and build
enduring personal resources” (Fredrickson, 2001, p. 218). Empirical evidence in sports for
this theory is still needed. Building enduring personal resources might make sense for an
athlete from a long-term perspective, but it is difficult to predict how emotions might alter
movements. In addition, a recent study (Laborde & Raab, submitted) showed that the
influence of emotions on cognitive performance in sports was not about hedonic tone but
about physiological activation. In a decision-making task the decision time and decision
quality were similar under positive and negative emotion (i.e., hedonic valence) conditions,
whereas the physiological state allowed discriminating them.
The theories presented thus far deal with emotions in general; what do we learn
from theories addressing the relationship with performance of one emotion in
particular, such as anxiety (M. R. Wilson, 2008)? The processing efficiency theory
(Eysenck & Calvo, 1992) assumes that anxiety has two functions: emptying working memory,
and providing motivation to allocate additional effort to maintain task performance. This
theory motivated the more recently developed attentional control theory (Eysenck, Derakshan,
Santos, & Calvo, 2007), which assumes that “anxiety reduces attentional control by
increasing the influence of the stimulus-driven attentional system at the cost of goal directed
control.” (M. R. Wilson, 2008, p. 184). Both theories have received support in sports settings
(Oudejans & Pijpers, 2010; M. R. Wilson, 2008), and such theoretical considerations should
be encouraged for other emotions, as well.
Finally, the theories presented here focus on the influence on performance of current
emotions, neglecting the role of anticipated emotions. The influence of anticipated
emotions on performance has been demonstrated at the neurological level (Watanabe, 2007).
When a reward is expected, the activity of working memory is enhanced, inducing changes in
attention, and this state is therefore able to modify behavioral performance. In cognitive
psychology anticipated emotions have also been shown to influence decision making
(Mellers, 2001). In sports, anticipated emotions are strongly linked with motivation; studying
them would help us understand what drive athletes to be involved in strenuous and painful
training (Bagozzi, Dholakia, & Basuroy, 2003). Anticipated emotions are also linked with
goal setting; when people work on accomplishing their goals they anticipate feeling good
about themselves in the future (Brown & McConnell, 2011; Perugini & Bagozzi, 2001).
Anticipated emotions still need to be considered in sports research. Research has examined
anticipated emotions and physical activity intention (Wang, 2011) but not the role they play in
In this section, we introduced a general definition and several theories of emotion and
highlighted open questions that could be addressed in further research in the field. We are
now ready to give a definition of emotions that is specific to the domain of sports.
To measure a construct, there is a need for an operational definition specific to the
domain being studied. In competitive sports, performance is the main objective, and Hanin
(2000) showed that beyond hedonic tone (i.e., positive and negative), the functionality (i.e.,
optimizing or dysfunctional) of emotions has to be considered. This is in line with (Lazarus,
1999) evolutionary perspective that emotions played a beneficial role in the adaptational
struggle to survive and flourish but could also be counterproductive in such struggles.
We propose here an operational definition of emotions in sports that is based on the
work of(based on Hanin, 2000; Lazarus, 2000):
An emotion is a phenomenon that is an organized psychophysiological
reaction to the appraisal of ongoing relationships with the environment. This
reaction consists of responses at three levels of analysis: subjective, behavioral,
and neurophysiological: (a) Introspective reports are generated at the
subjective level; (b) at the behavioral level are overt actions or impulses to
act; and (c) at the neurophysiological level bodily symptoms and
physiological changes make the emotion organismic. Each emotion can be
characterized by a hedonic tone (i.e., positive and negative) and by its
functional impact on performance (optimizing or dysfunctional).
As does any definition, this one has its limits, but it can provide a starting point for
discussions of emotions in sports. As emotion is assumed to be part of a single conceptual
unit together with stress and coping (Lazarus, 2000; Nicholls, Polman, & Levy, 2012), we
now define these two closely related concepts.
When athletes are asked how they feel before a competition, in most cases the answer
will be “stressed.” When an athlete says stress, it often means anxiety (based on Lazarus,
2000), which has been the focus of most of the research in the sports literature to date (Hanin,
2000). However, the emotional landscape is much wider, and looking only for anxiety may
not be the optimal way to predict performance (Cerin, 2003). Athletes sometimes talk about
“good” and “bad” stress; they might be referring to (a) the influence of stress on performance,
as in the IZOF theory (Hanin, 2000), (b) the extent to which emotions’ action tendencies
match the task at hand, as in the CMRT (Lazarus, 2000), (c) the appraisal of the competition
as a challenge or a threat, as with the TCTSA (Jones et al., 2009), or (d) more likely a
combination of the above. Inevitably when discussing stress the topic of coping is raised.
Coping refers to the way athletes handle stress, as discussed below.
Stress represents both the degree of pressure faced by an organism and the
reaction/adaptation of the organism to this pressure (Lazarus, 2000; Selye, 1951). Stress has
remained a unidimensional concept, and all its interesting characteristics are encompassed by
the concept of emotion. Studying emotion provides a broader and richer way to understand
the role of affective experiences in human adaptation and performance, particularly with the
concept of discrete emotions and (Lazarus, 2000) core relational themes. Moreover, stress is
not only linked with negative emotions; it represents the psychological basis of certain
positive emotions, such as relief and hope (Lazarus, 2000). Coping is how we manage or
regulate emotions; it is not a separate process (Lazarus, 2000). It will influence which
emotions occur according to one’s particularities, and how emotions change. “The competitor
must learn how to cope with strong and counterproductive action tendencies that are part of
any emotion. Coping is a crucial component of the solution” (Lazarus, 2000, p. 241). Finally,
Lazarus pointed out that the emotion–performance relationship represents a dynamic process,
because “appraisal, coping, and the emotions they result in are influenced by continual
feedback from our performance” (2000, p. 237).
Now that we have defined stress and coping, we will review emotions at two levels,
trait and state. Individuals can be driven by emotional dispositions (i.e., trait level), and these
dispositions will have an influence on the current emotions experienced (i.e., state level).
Lazarus (2000, p. 236) differentiated the psychological structure of emotion, which is
stable over time, from the process, which is about how things change. When we think about
the influence of emotions on performance, we “should focus on process as much or more than
structure” (p. 236). Two notions that are similar to structure and process are trait and state,
We first consider emotions at the state level, relying mostly on the CMRT of Lazarus
(2000). For example, according to Lazarus, at first glance we might think that there is overlap
between anxiety and fright, but they actually differ in what causes them and in their subjective
feel, as well as in their behavioral and physiological implications. The questionnaire
developed by Jones and colleagues (Jones, Lane, Bray, Uphill, & Catlin, 2005) pointed out
the five main emotions experienced in sports settings: anger, excitement, anxiety, dejection,
and happiness. The prevalence of such emotions might vary according to the sport and the
level considered; for example, with high-level table tennis players, anger, joy, and anxiety
accounted for more than 80% of the emotional experience during games (Martinent et al.,
2012). Although for methodological and ethical reasons (Tenenbaum, Lloyd, Pretty, & Hanin,
2002) emotions have been studied mostly before competitions, they are by nature subject to
vary during the activity (Cerin et al., 2000). It also seems possible that various emotions are
experienced at the same time, a phenomenon that is referred to as emotional blend (Martinent
et al., 2012). In contrast, the experience of such emotions cannot be simultaneous and they are
assumed to be processed sequentially (Brehm & Miron, 2006). Martinent and colleagues
(2012) found some evidence of this in high-level table tennis players, using retrospective
reports. The most common pairs of emotions were joy/relief, joy/pride, self-oriented
anger/anxiety, and self-related anger/discouragement. State emotions have been found to
influence different parameters of performance, such as attention and concentration (Vast,
Young, & Thomas, 2010), sensorimotor skills (Vast, Young, & Thomas, 2011), perception
(Cañal-Bruland, Pijpers, & Oudejans, 2010), visual attention (Nieuwenhuys, Pijpers,
Oudejans, & Bakker, 2008), and movement (Pijpers, Oudejans, & Bakker, 2005).
The state level of emotions can be influenced by many contextual factors, such as
“ambient mood (e.g., depressed, irritable), recent life events (e.g., bereavement), emotion-
related personality-traits (e.g., pessimistic), [and] diurnal and circadian influences on mood”
(McCarthy, 2011, p. 53). Among these factors, the so-called emotion-related personality traits
are of particular importance. We consider them either as trait emotions (e.g., trait anger,
trait anxiety) or as individual differences that influence the emotional state (e.g.,
personality, trait emotional intelligence). They might influence the emotion–performance
relationship at different phases of behavior, such as during the appraisal or emotion
Trait anger predicts the tendency of an individual to experience anger (Spielberger,
Jacobs, Russell, & Crane, 1983). Trait anger also has two components that determine its
influence on performance, according to how anger is regulated, anger-in and anger-out (P. A.
Davis et al., 2010). A sports-specific conceptualization of anger has been realized with
competitive aggressiveness and anger (Maxwell & Moores, 2007), that is, a propensity to
engage in acts of aggression during athletic competitions (Maxwell & Moores, 2008). Trait
anxiety represents the tendency of an individual to experience anxiety (Spielberger, 1983).
Again, we find a specific conceptualization in sports: Competitive trait anxiety is defined as
an athlete’s “tendency to perceive competitive situations as threatening and to respond to
these situations with (increased state)-anxiety” (Martens, Vealey, & Burton, 1990, p. 11).
Trait anger and trait anxiety are defined as the tendency of individuals to experience on a
regular basis the corresponding state emotions.
Personality is most often assessed according to the Big Five personality dimensions
(extroversion, agreeableness, conscientiousness, neuroticism, and openness; (McCrae & John,
1992). Each of the dimensions is related to the stress and coping appraisal (Allen, Greenlees,
& Jones, 2011; Kaiseler, Polman, & Nicholls, 2012). Neuroticism, for instance, seems to
negatively affect the direction of precompetitive anxiety (Cerin, 2004); and extroversion has
been found to moderate the anger–performance relationship: Woodman et al. (2009) found
that when extroverts were angry, their peak force increased more than introverts’. Trait
emotional intelligence (trait EI) is a constellation of emotional self-perceptions situated at the
lower levels of personality hierarchies (Petrides, Pita, & Kokkinaki, 2007). Trait EI has been
found to promote positive emotions and reduce negative emotions in athletes with high stress
before a performance (Laborde, Dosseville, & Scelles, 2010). During the event, trait EI might
continue to influence emotions through a greater use of adaptive coping strategies (Laborde,
You, Dosseville, & Salinas, 2012), moderating the physiological parameters linked with
emotions (Laborde, Brüll, Weber, & Anders, 2011). In summary, these effects at the trait
level should be taken into account because they may influence the frequency, intensity, and
duration of state emotions (Verduyn & Brans, 2012).
In this methodological section, we provide the reader with an overview of how to elicit
and assess emotions in order to study their influence on performance.
In sports research, elicitation of positive emotions has been realized mainly in
real-competition settings and elicitation of negative emotions in experiments. A look at
the research about affective state manipulation in sports shows that there have been many
more studies focusing on the elicitation of negative states than on the elicitation of positive
states. The studies focusing on negative states have been aimed mostly at understanding the
impact of anxiety on performance (e.g., Oudejans & Pijpers, 2010), while the studies about
positive states have been more application oriented, aimed at understanding how to make
athletes feel better before competition (e.g., Bishop, Karageorghis, & Loizou, 2007).
Two approaches have been used to help athletes cope with stress in precompetitive
settings. One uses a technique to decrease negative emotions and the other focuses on
provoking positive emotions. Techniques for the latter have been suggested by Jones (2003)
and are presented in Section 4.1. It should be noted that by positive or negative most of the
studies do not refer to the hedonic tone alone but to the functional effect of emotions on
performance, as well. Therefore, a great deal of work has also been done to change the
interpretation of emotional symptoms seen first as debilitative in optimizing emotions (e.g.,
Thomas, Hanton, & Maynard, 2007). We summarize these techniques in Table 1.
Insert Table 1 near here
In sports psychology research, elicitation of negative emotions is often
synonymous with elicitation of anxiety. To the best of our knowledge, only a few studies
have sought to elicit other negative discrete emotions (P. A. Davis et al., 2010; Woodman et
al., 2009). Originally, the elicitation of negative emotions in the laboratory was aimed at
understanding the processes underlying choking under pressure (e.g., Baumeister & Showers,
1986). More recently researchers have explored the cognitive functioning associated with
anxiety with the processing efficiency theory and the attentional control theory (for a review,
see M. R. Wilson, 2008). Negative emotions have been elicited in sports research with the
stressors presented in Table 2.
Insert Table 2 near here
In the sports literature techniques for eliciting negative states seem to outnumber those
for eliciting positive states. Moreover, in laboratory experiments, the purpose has almost
always been to compare functioning under a negative state to functioning under a “neutral”
state. A notable exception can be found in Woodman et al. (2009), where the authors elicited
three discrete emotions, two positively and one negatively toned (i.e., happiness, hope, anger).
A systematic consideration of emotions of different hedonic valence (i.e., positive, neutral,
negative) would allow a greater understanding of the influence of emotions on athletic
To ensure that participants do not try to respond with what they think the experimenter
expects, researchers do not want participants to be aware that they are trying to manipulate
their emotional state. Therefore, using a cover story that reinforces the purported main aim of
the experiment (main aim for the participant, that is, not for the researcher) allows the true
object of investigation, emotional states, to be kept hidden. In a study about anger, P.A. Davis
et al. (2010) told their participants that they were involved in an experiment aimed at
assessing their peak-force performance under different conditions. Laborde and Raab
(submitted), when assessing the influence of emotions on decision making performance, told
their participants that they were involved in an experiment about concentration and that they
had to remain as focused as possible for all the tasks. To reinforce this cover story, they used
a concentration grid (Harris & Harris, 1984). In future, researchers should include a
manipulation check, to check whether their participants were motivated to uncover the true
nature of the experiment, and an open question, to check whether the participants believed the
cover story (e.g., Laborde & Raab, submitted). They should ensure that participants leave the
laboratory in a good mood, for example, by telling them jokes. Finally, debriefing participants
after emotional manipulation studies is also important for ethical reasons.
Understanding the antecedents of emotions is crucial to being able to act on emotions
(Laborde et al., submitted). Existing research has focused mainly on potential stressors as
antecedents and on one emotion in particular, namely, anxiety (e.g., Hammermeister &
Burton, 2001). Stressors are often investigated through qualitative methodologies that assess
both competitive and noncompetitive stress (Hanton, Fletcher, & Coughlan, 2005) and elite
and nonelite athletes (Mellalieu, Neil, Hanton, & Fletcher, 2009). By examining stressors, it is
also possible to explore the coping strategies used (Holt & Hogg, 2002) and how emotions
developed (Neil, Hanton, Mellalieu, & Fletcher, 2011). If identifying stressors is important
per se, understanding how athletes can appraise these stressors is perhaps even more
To date, there is no psychometrically validated inventory that measures appraisal in
sports. The Stress Appraisal Measure (Peacock & Wong, 1990) was used in a recent study
(Nicholls et al., 2012). It contains 28 items and assesses six dimensions of appraisal including
both primary and secondary appraisal and relational meaning with challenge and threat. Due
to the lack of complete instruments, research in sports has used single items to assess
separately different dimensions of appraisal, such as challenge and threat with two items
(Cerin, 2003), and stressor intensity and controllability with two items (Nicholls, Levy, Grice,
& Polman, 2009).
For self-report measures of emotions, two approaches exist: individualized (i.e.,
idiosyncratic) and group oriented (i.e., nomothetic).
The individualized approach was initiated by the work of Hanin and colleagues, with
the IZOF (Hanin, 2000; Jokela & Hanin, 1999), which we introduced above in Section 2.2.2.
An individualized approach allows the researcher to get closer to the real emotional
experiences of athletes, capturing their idiosyncratic nature by generating content relevant to
each athlete. The IZOF was further developed to use probabilistic methods and has been
coupled with physiological measures (Bertollo et al., 2012). Since theory testing and the
synthesis of data across different studies is difficult using this ideographic approach (Jones et
al., 2005), researchers often adopt more group-oriented approaches.
For the group-oriented approach, several standardized sport-specific measures exist
that focus on a specific emotion, namely, anxiety. These include the Competitive State
Anxiety Inventory-2 (Martens, Vealey, Burton, Bump, & Smith, 1990), which has been
recently revised (Martinent, Ferrand, Guillet, & Gautheur, 2010), and the Sport Anxiety
Scale-2 (Smith, Smoll, Cumming, & Grossbard, 2006).
To assess a broader range of affective states, two non-sport-specific scales were used:
the Profile of Mood States (McNair, Lorr, & Droppleman, 1971), with a derivative used in
sports, the Brunel Mood Scale (Terry, Lane, & Fogarty, 2003); and the Positive and Negative
Affect Schedule (Watson, Clark, & Tellegen, 1988). Although the POMS and the PANAS
were used in sports contexts, they were not designed to assess emotions in sports (Jones et al.,
2005). A sport-specific questionnaire has therefore been developed, the Sport Emotion
Questionnaire (Jones et al., 2005), containing 22 emotional adjectives representing five
dimensions (i.e., anxiety, dejection, excitement, anger, and happiness). This questionnaire was
validated for use in precompetition settings. A self-rating measure to assess emotions during
and after the competition is still needed, especially because specific emotions are elicited at
these times (Jones et al., 2005; Woodman et al., 2009).
Several instruments exist to assess emotions as dimensions. They usually take into
account the hedonic valence (i.e., positive vs. negative) and sometimes the intensity, as well.
The Affect Grid (Russell, Weiss, & Mendelsohn, 1989) assesses hedonic tone and intensity.
The Sport Affect Grid is based on the same principle (Woodman et al., 2009). It assesses two
independent dimensions of affect: intensity and pleasantness. It is presented as a 9 × 9 grid,
the vertical axis assessing intensity (from extremely low to extremely high) and the horizontal
axis assessing hedonic tone (from unpleasant feeling to pleasant feeling). “Participants are
asked to mark an X on the part of the grid that best represents how he/she feels right now.
Scores for the intensity and hedonic tone of the emotions (were) are calculated separately by
converting the location of the X on each axis to a value from 1 to 9” (Woodman et al., 2009,
p. 177). The Feeling Scale (C. J. Hardy & Rejeski, 1989) is a bipolar rating scale commonly
used for the assessment of affective responses during exercise. And finally the Visual
Analogic Scale is a two-axis orthogonal grid (200-mm axes each anchored by not at all and
very much so) that measures the dimensions of arousal and hedonic tone. It has been used in
particular as a quick and effective measure to control for emotional manipulation (P. A. Davis
et al., 2010). In summary, these instruments convey less information than the inventory with
discrete emotions, but nevertheless they can be helpful for within-competition designs.
Combining ideographic and nomothetic measures seems very interesting for research
and sports applications (e.g., Robazza, Bortoli, Nocini, Moser, & Arslan, 2000). In addition,
to predict performance better (like in P. A. Davis et al., 2010), it seems important to consider
emotions in general rather than focusing on only one emotion (e.g., anxiety, Hammermeister
& Burton, 1995, 2001). Self-rating measures are easy to use, cost effective, and can capture
the subjective nature of the emotional experience of the individual, but they are subject to
social desirability bias (Pedregon, Farley, Davis, Wood, & Clark, 2012). This is why the
assessment of emotions needs to be combined with the study of other components, such as
physiology and behavior. In addition, the development of implicit assessment of emotions is
warranted, as with the Implicit Positive and Negative Affect Test (Quirin, Kazen, & Kuhl,
2009). The question of using a sports-specific instrument versus a general one is also worth
addressing. Perhaps different instruments could be used within the same research design to
see how performance can be best predicted.
Finally, when manipulating emotions in the laboratory it is important to have a
manipulation check. A combination of discrete and dimensional subjective instruments (like
in P. A. Davis et al., 2010) allows the collection of additional discriminant information about
the success of the emotional manipulation. The manipulation check is also an indication that it
is possible to draw valid conclusions from the emotion manipulation. Subjective measures are
easy to implement both in the laboratory and in the field because they require nothing other
than a paper and a pencil to be filled out. However, they are somehow limited to the
subjective experience of the emotion, and to understand emotions better it is necessary to go
to the neurophysiological level, which “makes emotions organismic” (Lazarus, 2000).
Subjective experiences of emotions, appropriately assessed by self-report measures,
provide an incomplete view of the emotional experience of the individual. One must also look
at the neurophysiological manifestations of emotions.
By measuring electrodermal activity we can provide a tonic–phasic distinction. The
tonic level of skin resistance or conductance is the absolute level of resistance or conductance
at a given moment in the absence of a measurable phasic response, and it is referred to as SRL
(skin resistance level) or SCL (skin conductance level). Superimposed on the tonic level are
phasic decreases in resistance (increases in conductance; (Dawson, Schell, & Filion, 2000).
Electrodermal activity is an indication of arousal, but not of the valence. The skin
conductance response (SCR) is thought to be linked solely with the sympathetic nervous
system, whose output results in a broad state of activation (Venables & Christie, 1980).
Indeed, the sympathetic innervations of sweat glands induce changes in skin conductance
(Gutrecht, 1994), which is associated with emotional arousal in a wide range of psychological
states and processes (Dawson et al., 2000). Self-reports on the emotional valence and arousal
dimensions are correlated with autonomic and somatic responses to emotional stimuli, while
SCR positively correlates with the emotional arousal independently of valence (Bradley &
Lang, 2000). In sports, SCR has rarely been used as a measure of emotions because of
potential confounding induced by movement activity (e.g., Collet, Guillot, Bolliet,
Delhomme, & Dittmar, 2003; Rada et al., 1995).
Heart rate can indicate emotional valence and intensity. Bradley and Lang (2000)
reported a study about the response to emotional pictures, where a classic tri-phasic pattern of
heart rate was obtained: an initial deceleration, an acceleratory component, and a secondary
deceleration. Affective valence contributes to the amount of initial deceleration and
acceleratory activity, with unpleasant stimuli producing more initial deceleration, and pleasant
stimuli producing greater peak acceleration. However, heart rate is just one of the many
interacting variables in the cardiovascular system, which include posture, respiratory
anomalies, and individual physical differences. Any of these variables can contribute to
hiding the affective covariation (Bradley & Lang, 2000). Nevertheless, when the processing
context is controlled and the subject is passive and oriented, it is possible to observe the effect
of affective valence. This explains why it is difficult to use this measure as an emotional
indicator in moving individuals, such as in the sports context in the field. However, some
studies in sports have related an increase in heart rate to anxiety, for example, in climbing
(Oudejans & Pijpers, 2010) and in gymnastics (Tremayne & Barry, 1988).
From heart rate beat-to-beat data the heart rate variability (HRV) can be calculated.
This is an interesting variable that can describe parasympathetic and sympathetic influences
of the autonomic nervous system on the heart, and thus it serves as an indirect indicator of
The heart rate is never constant but varies from beat to beat. HRV corresponds to the
variability of RR intervals (i.e., intervals between consecutive R-R peaks; Niskanen,
Tarvainen, Ranta-Aho, & Karjalainen, 2004). HRV identifies the branch of the autonomic
nervous system that actually mediates the heart rate. Measuring HRV allows us to assess the
sympathetic–vagal balance of an organism (Camm et al., 1996). The sympathetic and
parasympathetic branches of the autonomous nervous system are involved in emotions
(Levenson, 2003). Therefore, HRV can be considered an objective measure of emotional
responding (Appelhans & Luecken, 2006).
The analysis of HRV can be divided into time-domain, frequency-domain, and
nonlinear methods. The first two have been the most commonly used to reflect emotional
states (Appelhans & Luecken, 2006). It should be noted that it is inappropriate to compare
time-domain measures obtained from recordings of different durations (Camm et al., 1996, p.
357). Frequency data are generally extracted through fast Fourier transform (Appelhans &
Luecken, 2006), which can usually provide easily interpretable results in terms of
physiological regulation (Camm et al., 1996, p. 364). These methods are detailed in Table 3,
together with their expected links with the autonomous system and their supposed evolution
under stress during stressful events. According to the physiological stress model, stress often
comes with an increase of sympathetic tone caused by an increased catecholamine level
(Axelrod, 1984) and a reduction of the vagal tone (Watkins, Grossman, Krishnan, &
Sherwood, 1998). Overall, a decrease in HRV, indicating a disturbed autonomic nervous
system function, has been associated with mental stress and is a sign of an inability to respond
to physiological variability and complexity (Horsten et al., 1999).
Insert Table 3 near here
In sports, HRV has been used successfully as an indicator of precompetitive anxiety
(Murray & Raedeke, 2008) and of the response to a stressful event (Laborde et al., 2011).
Further research is warranted to investigate the links between HRV and specific emotional
responses in the sports context, using, for example, different calculations, such as the
traditional fast Fourier transform (Costa, Galati, & Rognoni, 2009).
Measuring hormones can help detect stress, and especially measures of cortisol
(Denson, Spanovic, & Miller, 2009a, 2009b). Salivary cortisol measurement is noninvasive,
pain-free, and thus ethically acceptable. It can be performed without medical expertise
(Kirschbaum & Hellhammer, 2000). A rise in the cortisol level reflects that the individual is
experiencing stress, as has been found during athletic competitions (Filaire, Duché, Lac, &
Robert, 1996; Filaire, Le Scanff, Duche, & Lac, 1999; Filaire, Maso, Sagnol, Ferrand, & Lac,
2001). The closer to the competition, the higher the cortisol level has been found to be
(Strahler, Ehrlenspiel, Heene, & Brand, 2010). The cortisol rise is less important in elite than
in nonelite athletes (Moya-Albiol et al., 2001). Although cortisol is a well-known marker in
stress research (Hellhammer, Wüst, & Kudielka, 2009), research integrating its measurement
in studies of the emotion–performance relationship is still lacking (e.g., Lautenbach &
Brain studies are a recent and promising advance in the field of emotion and
performance research. In sports, functional magnetic resonance imagery (fMRI) studies have
been used to show the effects of cognitive interventions at the neurological level, when
helping athletes to cope with failure (H. Davis et al., 2008). This first study was followed with
a study that combined fMRI with measures of neuroendocrine responses, to explore the
correlates of neural activation with cortisol and testosterone (H. Davis et al., 2012). This
combination of different measures was advocated by Daamen and Raab (2012) in their review
of how to assess affect in the context of exercise, where they underlined that implicit and
explicit measurements of affective responses should both be used. Such a combined approach
addresses the fact that reflecting consciously about an emotional experience to fill out
subjective reports can influence brain activation (Taylor, Phan, Decker, & Liberzon, 2003).
One challenge faced by researchers willing to use brain measurements in their research
is the adaptation of their emotional tasks to the specificities of fMRI (Daamen & Raab, 2012),
given that a large number of observations is required to derive reliable brain activation
patterns. Researchers must grapple with how to assess emotions, which are by definition of
short duration, and which are expected never to be identical. Despite the methodological
challenges, brain research in this domain is warranted to further our understanding of the
According to Bradley and Lang (2000), behavioral events can be assessed either by
direct actions (e.g., approach, avoidance, escape, attack, defensive reflexes) or by task
enhancement and deficits analyses (e.g., response latency, amplitude).
Effects of emotions on motor behavior and movement patterns - The effects of
emotions can be estimated in controlled observations of changes in movement patterns and
muscular tension and under different emotion intensity levels (e.g., Pijpers, Oudejans,
Holsheimer, & Bakker, 2003; Pijpers et al., 2005). These studies showed that it was possible
to use electromyography data to check for qualitative differences in motor behavior and
movement patterns. Taking the behavioral component into account allows researchers to
focus more on the quality of the movement, rather than on the outcome, that is, the
performance. Finally, a recent study showed that muscle activity and kinematic parameters
mediated the pressure–performance relationship in a golf putting task (Cooke et al., 2010).
Emotions can influence the tendency to engage in certain risk-taking activities,
through emotion regulation. Engaging in specific activities, for example, mountaineering,
was found to have an emotion regulation purpose (Castanier, Le Scanff, & Woodman, 2010,
2011; Woodman, Cazenave, & Le Scanff, 2008). Here the influence of emotions on
performance is not direct but might influence certain types of risk-taking behaviors.
Finally, some research examined emotion behavior at the trait level. Anger, for
example, may be related to aggressive behaviors, and research using the Competitive
Aggressiveness and Anger Scale (Maxwell & Moores, 2007) showed that scores on this scale
correlated with illegal aggressive behavior in rugby (Maxwell & Visek, 2009). It should also
be noted that the emotional disposition to take part in motor activity can be assessed with a
questionnaire, the Motor Activity Anxiety Test (Bortoli & Robazza, 1994), which has been
used in sports to assess the tendency to participate in adventurous sports (Robazza et al.,
In this section, we reviewed ways to elicit and assess emotions, providing researchers
in the field with guidelines to design their studies. Some of the ideas presented can also be
used in the applied field by sports psychology consultants willing to develop interventions
based on emotions, providing them with instruments for evaluating the efficacy of such
interventions. In the next section, we show how to transfer knowledge from theories to
effective, practical interventions, answering the question, “How do I deal with athletes’
emotions?” We also elaborate on the possible extension of theories presented in domains
other than sports.
“If I am right about the way emotion and coping influence performance in competitive
sports, it would help if athletes understood which emotions are aroused in competition, their
individual vulnerability to them, and how best to cope” (Lazarus, 2000, p. 241). What seems
so important to Lazarus is that people become aware of their emotions and of the influence
they might have on their performance.
The three main theories that we reviewed above (i.e., CMRT, IZOF, TCTSA) can inform
this applied perspective. Lazarus’s (2000) CMRT indicates that cognitive interventions might
influence the way we deal with our emotions. In an applied paper, “Controlling Emotions in
Sport”, Jones (2003) presented strategies aimed at changing cognitions, with the goal of either
eliciting a more adapted emotional response or suppressing emotional expression and any
maladaptive behavioral consequences. He proposed a number of different techniques,
including self-statement modification, imagery, Socratic dialogue, storytelling metaphors and
poetry, reframing, and the use of problem-solving skills. The IZOF (Hanin, 2000) can
provide good insight into emotion regulation that fits the individual’s needs (Woodcock,
Cumming, Duda, & Sharp, 2011), allowing to act at the physiological and subjective levels
(Cohen, Tenenbaum, & English, 2006). The TCTSA (Jones et al., 2009) also stresses the
importance of appraisal. To create a “challenge state” that benefits performance (Nicholls et
al., 2012), athletes should be encouraged to enhance self-efficacy and focus on the aspects of
the situation under their control, and on approach goals (Jones et al., 2009).
To design applied interventions that address emotions and performance, practitioners are
encouraged to go beyond coping, which focuses particularly on negative emotions, and direct
their interest toward emotion regulation, which is concerned with both positive and negative
emotions (Gross, 1998). Emotion regulation can be seen not only from a hedonic point of
view (positive and negative) but also from an instrumental point of view. This means, for
example, that people can actually try to regulate their emotions not to experience positive
ones but to experience those that can be helpful for performance, even if they are negative in
hedonic tone, such as anger and anxiety (Lane, Beedie, Devonport, & Stanley, 2011).
Different methods can be implemented to make the best use of one’s emotions in
competition. Some researchers advise experiencing the emotion beforehand in training, so
as to deal with it better at competition. For example, training with mild anxiety may prevent
choking under higher levels of anxiety (Oudejans & Pijpers, 2009, 2010). This principle is
similar to the theory of stress inoculation (Mace & Carroll, 1985, 1986; Stetz, Wildzunas,
Wiederhold, Stetz, & Hunt, 2006). Recent applications with biofeedback appear very
promising in optimizing relaxation methods (Beauchamp, Harvey, & Beauchamp, 2012;
Shaw & Zaichkowsky, 2012; Zaichkowsky, 2012). In particular, biofeedback aiming to
modify the vagal tone should be encouraged, because vagal tone has been recognized as an
index of emotion regulation (Morgan, Aikins, Steffian, Coric, & Southwick, 2007). Another
method is the development of emotional competencies. We talked earlier about trait EI as a
potential moderator of the emotion–performance relationship. Research has shown that
interventions aimed at modifying abilities linked with trait EI were successful in modifying
trait EI (Nelis et al., 2011; Nelis, Quoidbach, Mikolajczak, & Hansenne, 2009).
In this chapter, we focused on sports as a showcase for studying the relationship of
emotions and performance. Yet this relationship is pertinent to many other domains, as well.
In organizational contexts, both emotional dispositions (e.g., O'Boyle, Humphrey,
Pollack, Hawver, & Story, 2011; B. A. Scott, Colquitt, Paddock, & Judge, 2010) and state
emotions (Wiltermuth & Tiedens, 2011) have been found to play a role in performance. In
particular, the role of EI in managing teams has been emphasized (Mikolajczak, Balon, Ruosi,
& Kotsou, 2012; O'Boyle et al., 2011). Thus, particular care should be taken in the
development of such competencies. State emotions should be taken into account as well, for
example, during negotiations, because of their role in manipulation (Andrade & Ariely, 2009;
Filipowicz, Barsade, & Melwani, 2011). This role is reinforced by displays of emotion, which
can reflect an ability to influence others (Cote & Hideg, 2011).
In the military domain, the need to keep the mind sharp under pressure is an
indispensable survival tool. Soldiers with better emotion regulation measured at the
physiological level reach a higher performance (Morgan et al., 2007). Such emotion
regulation competencies are also required for the police (van Gelderen, Bakker, Konijn, &
Demerouti, 2011). Recent research showed that the gaze pattern when firing a weapon is
crucial when performing under pressure and can differentiate elite and rookie police officers
(Vickers & Lewinski, 2012).
In the medical domain, EI has been shown to play an important role in dealing
efficiently with patients (Zijlmans, Embregts, Gerits, Bosman, & Derksen, 2011). People
working in the healthcare system should receive more training regarding emotional
competencies, because they will be facing daily strong emotional issues, such as pain
experienced by patients and their families (Hilliard & O'Neill, 2010). Moreover, in addition to
managing their own emotional issues, people working in healthcare should be able to provide
emotional support to patients and their families.
In school/academia, emotions have a strong influence on learning performance and
academic achievement, and emotions of both learners and teachers are important. Concerning
teachers, evidence has shown that students learn better when they are in a good mood, when
their teachers make them laugh (Roth, Ritchie, Hudson, & Mergard, 2011). At the
dispositional level, academic success is not only about cognitive abilities but also about
emotional competencies. In fact, trait EI has been found to influence positively academic
performance during one exam (Laborde et al., 2010) and during the whole academic year
(Parker et al., 2004). At the state level, students have better grades when they experience
more positive affect before an exam and when they learn, as was shown in a study
manipulating students’ affect with classical music in the background during lectures
(Dosseville, Laborde, & Scelles, 2012). The way emotions influence students’ learning and
performance has been studied with the Achievement Emotions Questionnaire (Pekrun, Goetz,
Frenzel, Barchfeld, & Perry, 2011), and interesting intervention ideas can be found in a
chapter by Beilock and Ramirez (2011).
In the artistic domain, emotions are also important. In music, the emotional display is
important for those seeing a performance (Nakahara, Furuya, Masuko, Francis, & Kinoshita,
2011), and the role of the performer in making listeners experience emotions is fundamental
(Juslin & Timmers, 2010). Finally, in any tasks where creativity is required, people should
know how to deal with their emotions in order to optimize their creativity according to the
task (Baas, De Dreu, & Nijstad, 2008; M. A. Davis, 2009).
Athletes, referees, coaches, and managers daily face situations of high pressure,
making the sports domain an appropriate showcase for our examination of the emotion–
performance relationship. After reviewing and comparing the three main theories in the field,
we provided researchers with an up-to-date review of how to elicit and assess emotions.
Finally, we examined the potential practical implications of the theories mentioned.
The scope of this chapter did not include other variables that can be interesting to take
into account to understand the emotion–performance link, such as motivation. According to
the self-determination theory, when basic psychological needs (autonomy, competence,
relatedness) are satisfied, this state allows for a better response to stress (Quested et al., 2011).
In addition, we did not study the influence of group emotions on group performance. Finally,
we focused here on the influence of emotions on athletic performance, but we did not address
their influence on sports participation, which can have a long-term significant effect on
performance (Mohiyeddini, Pauli, & Bauer, 2009).
Future research could be carried out on several levels. At the theoretical level, an
integration of the different theories proposed could lead to a clearer and more global
understanding of the emotion–performance relationship. Empirical research on this topic
often relies on the combination of different theories, such as the CMRT and the IZOF
(Martinent et al., 2012; Pensgaard & Duda, 2003). Providing such integration more
systematically would ensure the emergence of an integrated theory aimed at a better
understanding of both the antecedents and consequences of emotions, as well as their
relationship with performance. At the methodological level, we encourage researchers to
combine different emotion components in their research and to explore the possibilities
offered by the promising field of neurosciences. At the applied level, the findings about
emotions and performance in sports seem likely to inform other fields, such as organizational
development and education, integrating methodologies that help guide people to higher
efficacy in emotion regulation, such as biofeedback.
To conclude, emotions are a very important parameter to take into account for anyone
trying to reach peak performance, whatever the domain considered. Following Lazarus’s
recommendation (2000), taking the first step—becoming aware of our emotions and their
impact on our performance—opens the door to a more exciting and accomplished life.
Methods for Inducing Positive Emotions in Sports
(Vast et al., 2011)
(Page, Sime, & Nordell, 1999; Woodman et
(Bishop, Karageorghis, & Kinrade, 2009;
Bishop et al., 2007)
(Raudenbush, Corley, & Eppich, 2001)
Psychological skills training intervention
With a focus on three emotional dimensions,
arousal, pleasantness, and functionality:
(Hanin & Syrja, 1995), and using metaphors:
(Lindsay, Thomas, & Douglas, 2010)
Success and failure manipulation
(G. V. Wilson & Kerr, 1999)
(Dunn & Holt, 2004)
Methods for Inducing Negatives Emotions in Sports
(Baumeister, 1984; Oudejans & Pijpers,
Being judged by experts
(Oudejans & Pijpers, 2009)
(Beilock & Carr, 2001; Lewis & Linder,
1997; Oudejans & Pijpers, 2009), in order to
increase self-awareness and therefore self-
evaluation: (Maxwell, Masters, & Poolton,
Arithmetic task: (Acevedo et al., 2006; Lewis
& Linder, 1997) or Stroop color-word task:
(Acevedo et al., 2006)
(Williams & Elliott, 1999)
(Balmer et al., 2007; Laborde et al., 2011;
Laborde & Raab, submitted)
“The trials that you did to warm up before
the task will be integrated in your
performance” (Williams & Elliott, 1999)
“The [performance on the] task reflects your
ability in real life”: (Behan & Wilson, 2008;
Murray & Janelle, 2003)
Emphasis on time
(Masters, Polman, & Hammond, 1993)
(P. A. Davis et al., 2010; Oudejans & Pijpers,
2009; Woodman et al., 2009; Woolfolk,
Parrish, & Murphy, 1985)
Manipulating task’s perceived risk
Climbing a wall to various heights: (Cañal-
Bruland et al., 2010; L. Hardy & Hutchinson,
2007; Nieuwenhuys et al., 2008; Oudejans &
Pijpers, 2009, 2010)
Loud beep if participants failed: (Masters et
Noncontingent feedback (success and failure
Critical feedback is given regardless of actual
performance: (Williams & Elliott, 1999; G.
V. Wilson & Kerr, 1999; M. R. Wilson,
Vine, & Wood, 2009)
Financial incentive: (Baumeister, 1984;
Behan & Wilson, 2008; Beilock & Carr,
2001; Masters et al., 1993; Williams &
Elliott, 1999; M. R. Wilson, Wood, & Vine,
Preparation shortened before performing the
(Masters et al., 1993)
Results shown to others
(Behan & Wilson, 2008; M. R. Wilson,
Wood, et al., 2009)
(Behan & Wilson, 2008; M. R. Wilson,
Wood, et al., 2009)
With group inclusion or exclusion: (Boyes &
Trier Social Stress Task
(Lautenbach & Laborde, submitted)
Heart Rate Variability (HRV) Parameters and Their Evolution During Stressful Events
stressful event (with
examples from the
PNN50 (percentage of
sinus RR intervals
more than 50 ms)
activity (Camm et al.,
Decrease (e.g., Filaire,
Ramat, & Teixeira,
RMSSD (root mean
square of the
sinus RR interval
Decrease (e.g., Filaire
et al., 2010)
deviation of all normal
sinus RR intervals)
(Camm et al., 1996) or
a mix between
Stein, Bosner, &
Increase of SDNN
(e.g., Lehrer et al.,
VLF (very low
stressful event (with
examples from the
the distribution of
oscillations in at
the power in each
of these bands)
correlate of VLF is
still unknown, and this
parameter is not
considered a reliable
measure (Camm et al.,
LF (low frequency)
A complex interplay
influences (Camm et
Dishman et al., 2000)
HF (high frequency)
activity (Camm et al.,
Decrease (e.g., Sloan
et al., 1994)
systems (Camm et al.,
Increase (e.g., Kristal-
Boneh, Raifel, Froom,
& Ribak, 1995;
Laborde et al., 2011).
Due to the likely
contamination of LF
stressful event (with
examples from the
influence, the LF/HF
ratio has been shown
to be a more
individuals than LF
and HF (Sloan et al.,
Acevedo, E. O., Webb, H. E., Weldy, M. L., Fabianke, E. C., Orndorff, G. R., & Starks, M.
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