Psychological Imagery in Sport and Performance
- Krista J. Munroe-ChandlerKrista J. Munroe-ChandlerThe University of Windsor
- and Michelle D. GuerreroMichelle D. GuerreroThe University of Windsor
Imagery, which can be used by anyone, is appealing to performers because it is executed individually and can be performed at anytime and anywhere. The breadth of the application of imagery is far reaching. Briefly, imagery is creating or recreating experiences in one’s mind. From the early theories of imagery (e.g., psychoneuromuscular) to the more recent imagery models (e.g., PETTLEP), understanding the way in which imagery works is essential to furthering our knowledge and developing strong research and intervention programs aimed at enhanced performance. The measurement of imagery ability and frequency provides a way of monitoring the progression of imagery use and imagery ability. Despite the individual differences known to impact imagery use (e.g., type of task, imagery perspective, imagery speed), imagery remains a key psychological skill integral to a performer’s success.
All individuals, regardless of age, gender, or skill level, are capable of using imagery as a means to enhance cognitive, behavioral, and affective outcomes. In the sport domain, athletes use imagery in training, competition, and rehabilitation. Elsewhere, imagery has been widely utilized by other performers including military personnel, surgeons, and musicians.
Everything I make as a producer, I visualize it as a DJ first. And all those beats, I test them as a DJ. (David Guetta)
I have a system of ridding my mind of negative thoughts. I visualize myself writing them down on a piece of paper. Then I imagine myself crumpling up the paper, lighting it on fire, and burning it to a crisp. (Bruce Lee)
The breadth of the application of imagery is far reaching, as demonstrated by these quotations from famous musician David Guetta and legendary martial artist Bruce Lee, illustrating that imagery can be used in different disciplines and for different functions. An often cited definition of imagery is:
an experience that mimics real experience. We can be aware of “seeing” an image, feeling movements as an image, or experiencing an image of smell, tastes, or sounds without actually experiencing the real thing. Sometimes people find that it helps to close their eyes. It differs from dreams in that we are awake and conscious when we form an image.(White & Hardy, 1998, p. 389)
As just described, imagery is multisensory such that it can include the sense of sight, taste, sound, smell, and touch. This description provides insight into why the term imagery is used instead of “visualization,” which denotes only the sense of sight. In addition, the individual is awake and consciously aware when imaging and as such not dreaming. In essence, imagery is creating, or recreating, the entirety of an experience in one’s mind.
From early theories of imagery to more recent imagery models, the ways in which imagery is used to enhance performance will be explored. Measurement of imagery ability and frequency, which has been assessed primarily through the use of self-report, will be discussed, along with various factors influencing imagery use, including ability, speed, age, skill level and perspective. The uses of imagery in sport, exercise, and performance domains will be examined and avenues for future research suggested.
Theories and Models
For many years, researchers have been interested in the way in which imagery is used and applied by individuals. When individuals image they first retrieve information from memory to create or recreate an experience in their mind (Morris, Spittle, & Watt, 2005). Through a combination of imagery sub-processes, such as image transformation (e.g., rotation of an image), scanning (e.g., detecting details of an image), and maintenance (e.g., sustaining an image for some time), vivid and controllable images are generated. Despite the appeal of the simplistic explanation, a deeper understanding of how imagery works is necessary. As such, several theories have been proposed (psychoneuromuscular, bioinformational, triple code). Notwithstanding support and criticism of each of these theories, together they provide a foundation that continues to guide the development and refinement of imagery research and therefore warrant exploration and explanation. The most commonly discussed theories in sport, exercise, and performance psychology are presented along with an overview on the conceptual models of imagery.
The psychoneuromuscular theory (Jacobson, 1930) notes that when an individual mentally imagines a skill, the activated neural pathways are identical to those activated when physically performing the skill. The feedback one receives from the muscle innervation of the imagined skill enables the individual to make adjustments in motor behavior. Through measurement of electromyographical (EMG) activity, wherein the innervations when imaging are much smaller in magnitude than when physically performing, empirical support for the psychoneuromuscular theory has been found. Despite this, Hall (2001) has noted the failure of the psychoneuromuscular theory to examine the various types of imagery and Feltz and Landers (1983) have criticized the validity of this theory because of methodological concerns.
In bioinformation theory, Lang (1979) suggests that mental images comprise both stimulus proposition and stimulus response. Stimulus proposition refers to the content or characteristics of the image, such as a competitive swimmer imagining her surroundings and her opponents. Stimulus response, on the other hand, refers to the physiological and affective reaction experienced by the individual imaging. For example, that same swimmer may feel tightness in her shoulders due to the anxiety experienced when imagining the swim meet or she may neglect external stimuli such as the crowd cheering after imagining a personal best time. Images that contain both stimulus proposition and response are most effective in enhancing performance. Although not often acknowledged, Lang introduced the concept of meaning to the image, enhancing the relevance of the theory. Research supporting the bioinformational theory has found that imagery scripts containing more frequent use of response propositions, compared to stimulus propositions, elicit greater physiological reactions (Bakker, Boschker, & Chung, 1996). Although an improvement over earlier theories, the bioinformational theory lacks explanation regarding the motivational types of imagery (Hall, 2001).
Elaborating upon the bioinformational theory’s stimulus proposition and response characteristics, Ahsen’s (1984) tripe code theory added a third characteristic—the meaning of the image. Ahsen argued that no two people would have the same imagery experience even if provided with the same imagery instructions. Individuals bring their own unique set of experiences with them and view these experience through their individual lenses, thereby allowing for a different meaning of the image to emerge. As such, the most effective images are those that are realistic and vivid, evoke psychophysiological responses, and impart significance to the individual. However, as noted in the literature (Morris et al., 2005), this model neglects the cognitive effects of imagery, which is an important consideration for skill acquisition and learning.
The aforementioned concepts provide theoretical underpinning for imagery use; however, exploration of this topic also requires an examination of the different models of imagery, which are also essential for furthering our understanding of imagery use. Indeed, most of the recent performance imagery research (e.g., sport, exercise) has developed as a result of Paivio’s (1985) analytic model. It is well established that imagery has cognitive and motivational functions that operate at a general or specific level. The cognitive general (CG) function entails imaging strategies, game plans, or routines (e.g., a fast break in basketball), whereas the cognitive specific (CS) function involves imaging specific skills (e.g., follow through on a free throw). The motivational general (MG) function of imagery involves imaging physiological arousal levels and emotions (e.g., staying calm when taking a penalty shot), and the motivational specific (MS) function of imagery includes imaging individual goals (e.g., winning the championship). In an extension of Paivio’s work, Hall, Mack, Paivio, and Hausenblas (1998) further divided the motivational general function into a motivational general–arousal (MG-A) function, encompassing imagery associated with arousal and stress, and a motivational general–mastery (MG-M) function, representing imagery associated with being mentally tough, in control, and self-confident.
Guided by Paivio’s (1985) model, Martin, Moritz, and Hall (1999) developed the Applied Model of Imagery Use in Sport (AMIUS) to explain the way in which athletes use imagery to improve athletic performance. According to AMIUS, the sport situation influences the types of imagery used, which are then associated with various cognitive, affective, and behavioral outcomes. Further, the relationship between the imagery type (five functions of imagery as noted: CS, CG, MS, MG-A, MG-M) and the outcome is moderated by various individual differences, such as imagery ability.
As a model of imagery use, the AMIUS offers several benefits. From a research perspective, the AMIUS provides simple, practical, and testable relationships. From an applied perspective, the model offers guidance for imagery interventions. There is ample support for the AMIUS such that the type of imagery should match the desired outcome, or as summarized by Short, Monsma, and Short (2004), “what you see, is what you get” (p. 342). That is, if a performer wishes to improve his confidence, he should engage in MG-M imagery. However, some researchers (e.g., Bernier & Fournier, 2010; Nordin & Cumming, 2008) have found that images can serve multiple functions for an athlete and have argued that function (why athletes image) and content (what athletes image) are not identical and therefore should be separated. Indeed, the original belief that the type of imagery should match its intended outcome is not as clear as was once thought.
Drawing on the AMIUS, Munroe-Chandler and Gammage (2005) developed an applied model for exercise settings. The exercise model differs from the AMIUS in that the antecedents include factors beyond the physical setting (e.g., exerciser’s goals and experiences), efficacy beliefs mediate the function-outcome relationship, and the individual differences that moderate the relationship extend beyond imagery ability (e.g., frequency of exercise, age). This model has allowed for the refinement and development of exercise imagery research (e.g., Andersson & Moss, 2011; Najafabadi, Memari, Kordi, Shayestehfar, & Eshghi, 2015).
With over a decade of research guided by the AMIUS, Cumming and Williams (2013) proposed a revised model of deliberate imagery use applicable for many performers (e.g., athletes, dancers, musicians). The revised model considers “who” is imaging (age, gender, competitive level), “what” is being imaged (the type), and “why” performers use imagery (the function). Most important, however, the revised model recognizes the personal meaning as the link between the imagery type and function. Cumming and Williams note that the types of imagery are often combined to achieve a specific outcome (e.g., cognitive and motivational types of images are important sources of confidence; Levy, Perry, Nicholls, Larkin, & Davies, 2014), and therefore offers a more flexible framework than the original AMIUS.
Apart from the previously mentioned models, some sport psychology researchers have called for models of imagery to be grounded in neuroscience; the PETTLEP is one such model (Holmes & Collins, 2001). The PETTLEP model was developed to guide imagery interventions and is based on functional equivalence, which suggests that processes that occur in the brain during imagery mimic the processes that occur during actual movement. Seven key factors are identified to help guide imagery interventions; physical, environment, task, timing, learning, emotion, and perspective. Although there have been some studies examining the model’s components in isolation (e.g., O & Munroe-Chandler, 2008), more research is needed testing multiple elements of the model (cf., Smith, Wright, Allsopp, & Westhead, 2007) and in different contexts. Sophisticated neuroimaging techniques such as functional magnetic resonance imagery (fMRI) and positron emission tomography (PET), as well as mental chronometry (informs about the temporal coupling between real and simulated movements), have allowed researchers to test functional equivalence and to gain a greater understanding between imagery and movement.
The measurement of imagery ability and imagery frequency have often been assessed in the sport, exercise, and performance imagery research. Given that imagery is an internal mental skill, its assessment has typically relied on the self-report questionnaires allowing individuals to subjectively report their imagery use and ability. More recent research, however, has combined self-report with other indices of imagery experiences such as chronometry or functional magnetic resonance imagery (fMRI) (Guillot & Collet, 2005).
As noted in the Applied Model of Imagery Use in Sport (AMIUS), imagery ability is one of the most important factors impacting imagery effectiveness. One’s ability to image includes various dimensions such as vividness, controllability, and maintenance (Morris, Spittle, & Watt, 2005). Although some performers may initially be better imagers than others, imagery is a skill that can be improved with practice (Rodgers, Hall, & Buckolz, 1991). From an applied perspective, the measurement of imagery ability is important as it leads to more individualized, and therefore effective, imagery interventions. Further, the measurement of imagery ability can be used as an imagery intervention screening procedure, thereby ensuring adequate imagery ability prior to the commencement of the intervention. Although there are numerous imagery ability questionnaires, the focus will be on the two most commonly used in the performance (sport) domain due to their inclusion of both movement and visual imagery.
The Movement Imagery Questionnaire (MIQ; Hall & Pongrac, 1983) assesses both visual and kinesthetic imagery. Although it was readily used for some time as a measure of imagery ability, Hall and Martin (1997) revised the MIQ (Movement Imagery Questionnaire–Revised; MIQ-R), reducing the number of items and thus minimizing the amount of time needed to complete the questionnaire. Those completing the MIQ-R are instructed to physically complete the movement sequence (i.e., knee raise, arm movement, waist bend, and jump) and then resume the starting position and recreate the experience using visual imagery, and finally using kinesthetic imagery. Participants are then asked to rate the quality of imagery on a 7-point Likert scale from 1 (very easy to picture/feel) to 7 (very difficult to picture/feel). Given that the MIQ and MIQ-R did not distinguish between internal and external visual imagery perspective, Williams et al. (2012) developed the MIQ-3 to more fully capture an individual’s imagery ability. The MIQ-3 assesses external visual imagery (e.g., looking through your own eyes while performing the movement), internal visual imagery (e.g., watching yourself performing the movement), and kinesthetic imagery (e.g., feeling yourself do the movement). Although the MIQ-3 has shown to be a reliable and valid measure (Williams et al., 2012), because of the recentness of its development, more research is warranted using this measure.
The Vividness of Movement Imagery Questionnaire (Isaac, Marks, & Russell, 1986) assesses one’s ability to use visual imagery. It requires the participant to rate the 24 items on the vividness of imagery from 1 (perfectly clear and as vivid as normal vision) to 5 (no image at all; you only know that you are thinking of the skill). The revised VMIQ-2 (Roberts, Callow, Hardy, Markland, & Bringer, 2008) assesses the vividness of both visual and kinesthetic imagery. The 12-item VMIQ-2 scale asks respondents to imagine a variety of motor tasks (e.g., running, kicking a stone) and then rate the image on two perspectives of visual imagery (external and internal), as well as kinesthetically. All items are measured on a 5-point Likert scale ranging from 1 (perfectly clear and as vivid as normal vision) to 5 (no image at all; you only know that you are thinking of the skill). The VMIQ-2 has shown adequate reliability as well as adequate factorial, concurrent, and construct validity (Roberts et al., 2008).
All measurement tools are subject to criticism, and the imagery ability measures are not exempt. The instructions from the VMIQ-2 ask participants to draw on their memory of common movements, whereas the MIQ-3 requires participants to execute a movement first prior to imagining it, thereby relying on short-term memory. It may be argued that imaging a common movement (kicking a ball; VMIQ-2) may be easier for the participant than imaging a less common movement (raising your knee as high as possible so that you are standing on your left leg with your right leg flexed [bent] at the knee; MIQ-3). Conversely, a more common movement such as running up the stairs may elicit varying interpretations from the participant, thus leading to discrepancies in imagery content.
Gregg and Hall (2006) developed the Motivational Imagery Ability Measure for Sport (MIAMS) to assess motivational imagery abilities, which had yet to be included in any previous imagery ability measure. The MIAMS assesses the ability of an athlete to use MG-A and MG-M imagery, wherein the participant images the scene and then rates the image on an ease subscale 1 (not at all easy to form) to 7 (very easy to form) and an emotion subscale 1(no emotion) to 7 (very strong emotion). Psychometric properties of the questionnaire have proved favorable, with acceptable model fit and adequate internal consistencies for the subscales (Gregg & Hall, 2006).
Of course, the various measures of imagery ability can be employed together to provide a more comprehensive assessment of an athlete’s overall imagery ability. Individuals who are more adept at imagery are more likely to engage these practices, and greater imagery use will likely result in enhanced imagery ability (Gregg, Hall, McGowan, & Hall, 2011). This is significant because research conclusively demonstrates that individual differences in imagery ability will have an impact on the effectiveness of imagery, and that high imagery ability leads to the ultimate goal: improved performance on a variety of motor tasks (Hall, 2001).
In addition to imagery ability, measuring a performer’s use of imagery allows researchers, and practitioners, to determine one’s frequency of a specific type of imagery and also enables them to see changes from pre- to post-intervention. The various questionnaires assessing the frequency of imagery use in sport, exercise, and active play will be addressed.
The Sport Imagery Questionnaire (SIQ; Hall, Mack, Paivio, & Hausenblas, 1998; Hall, Stevens, & Paivio, 2005) is the most widely used measure of imagery frequency in the sport domain (Morris et al., 2005). It is a general measure of imagery used for athletes of any sport at any competitive level. The self-report questionnaire comprises 30 items assessing the five functions of imagery (CS, CG, MS, MG-A, MG-M). All items are scored on a 7-point Likert scale anchored by 1 (rarely) and 7 (often). The SIQ has shown strong psychometric properties (i.e., reliability, validity) for athletes 14 years and older (Hall et al., 2005).
Given the research evidence supporting young athletes’ use of imagery (e.g., Munroe-Chandler, Hall, Fishburne, & Shannon, 2005), the Sport Imagery Questionnaire for Children (SIQ-C; Hall, Munroe-Chandler, Fishburne, O, & Hall, 2009) was developed for those young athletes aged 7–14 years. The SIQ-C includes 21 items, which assesses the same five functions as those identified in the adult version (CS, CG, MS, MG-A, MG-M). The items are rated on a 5-point Likert scale anchored at 1 (not at all) and 5 (very often), making it more appropriate for young children. Since its development, the SIQ-C has reported adequate internal consistencies for all subscales (Hall et al., 2009).
For researchers in the field of exercise imagery, two questionnaires have dominated: the Exercise Imagery Questionnaire (EIQ; Hausenblas, Hall, Rodgers, & Munroe, 1999) and the Exercise Imagery Inventory (EII; Giacobbi, Hausenblas, & Penfield, 2005). The nine-item EIQ was developed from qualitative responses from exercisers reporting their use imagery for three main purposes: appearance, energy, and technique. Exercisers are asked to rate their imagery use on the three aforementioned subscales using a 9-point scale, anchored by 1 (never) and 9 (always). Strong reliabilities are reported for all three subscales (Hausenblas et al., 1999; Rodgers, Munroe, & Hall, 2001).
The EII was developed as a result of qualitative evidence indicating exercisers’ use of imagery for purposes beyond those of appearance, energy, and technique. In fact, exercisers were found to use imagery for the following purposes: appearance or health, exercise technique, exercise self-efficacy, and exercise feelings. As a result of these findings, the EII includes questions that assess appearance, energy and technique imagery as well as exercise self-efficacy and exercise feeling imagery. The EII is a 19-item self-report measure of exercise frequency rated on a 7-point Likert scale (1 = rarely and 7 = often). Support for the four-factor model across a variety of samples has been reported (Giacobbi et al., 2005).
The revised version of the EII (EII-R; Giacobbi, Tuccitto, Buman, & Munroe-Chandler, 2010) measures the same four subscales of the original version, in addition to exercise routines. This modification allowed for the measurement of the five functions of imagery, which were suggested in the applied model of exercise imagery use (Munroe-Chandler & Gammage, 2005). Results from a confirmatory factor analysis for the EII-R has demonstrated good fit indices (Giacobbi et al., 2010).
The Children’s Active Play Imagery Questionnaire (CAPIQ; Cooke, Munroe-Chandler, Hall, Tobin, & Guerrero, 2014) assesses the frequency of imagery use in children during their active play. The measure consists of 11 items, each rated on a 5-point Likert scale from 1 (not at all) to 5 (very often), assessing one of the three subscales (capability, fun, and social). Capability imagery refers to the practice of movements, social imagery refers to the engagement of active play activities either by oneself or with others, and fun imagery refers to feelings of satisfaction. The items were developed from active play research as well as qualitative focus groups with children examining their use of imagery during their leisure time physical activity (Tobin, Nadalin, Munroe-Chandler, & Hall, 2013). The CAPIQ has demonstrated adequate internal consistencies for all three subscales (Cooke et al., 2014) and contributes to the measurement of imagery use in a physical activity setting other than organized sport.
Factors Affecting Imagery
Researchers have identified a wide range of factors that have been found to influence imagery effectiveness, including imagery ability, image speed, age, skill level, and perspective.
Both Martin, Moritz, and Hall (1999) and Munroe-Chandler and Gammage (2005) have proposed that the relationship between imagery use and desired outcome is moderated by various individual differences, especially the ability to image. That is, better imagery ability leads to better performance on a variety of motor tasks (Hall, 2001). This was supported in an applied study wherein tennis players with better imagery ability showed greater improvements in tennis serve return accuracy than those athletes with lower imagery ability (Robin et al., 2007). Individual differences in imagery ability has been noted in early imagery research (cf., MacIntyre, Moran, Collet, & Guillot, 2013). Some have noted that novice performers may not be as skilled at imagining given their lack of ability to develop knowledge of the spatial and kinesthetic requirements of the task (Driskell, Copper, & Moran, 1994). Regardless of individual differences in imagery ability, there is sufficient evidence to show that imagery ability can improve with practice (Cooley, Williams, Burns, & Cumming, 2013).
Cumming et al. (2016) developed a structured, imagery exercise known as layered stimulus and response training (LSRT) designed to improve imagery ability. By generating images in a layered fashion, starting with a simple image and gradually incorporating additional information in subsequent layers, imagery ability improves. After each layer, the individual evaluates the image by reflecting on various aspects of the image. For example, what aspects were strong, easy, vague, or difficult to image? Earlier studies have implemented LSRT in a single imagery session, with the intent of enhancing individuals’ imagery ability prior to receiving an imagery intervention (e.g., Cumming, Olphin, & Law, 2007), and more recently for improving actual motor skill performance (Williams, Cooley, & Cumming, 2013).
Regarding the Timing element of the PETTLEP model, Holmes and Collins (2001) have recommended that athletes image primarily in real-time speed, due to the accurate representation of movement tempo and relative timing duration in one’s images. In a large-scale study examining athletes’ voluntary use of image speed (O & Hall, 2009), both recreational and competitive athletes reported using three image speeds depending on the function of imagery being employed and the stage of learning of the athlete. Real-time images were used most often by athletes regardless of imagery function or stage of learning. However, when learning or developing a skill or strategy, slow-motion images were used most often (which supports recent findings with novice golfers; Shirazipour, Munroe-Chandler, Loughead, & Vander Laan, 2016), and when imaging skills or strategies that had been mastered fast-motion images were used most often. Subsequent qualitative research by O and Hall (2013) substantiated those findings and defined voluntary image speed manipulation as that which “occurs when an athlete consciously and purposefully selects a speed at which to image” (p. 11).
The cognitive development of the individual, most often distinguished by age, is another factor influencing imagery use. Much of the research conducted by Kosslyn and colleagues (e.g., Kosslyn, Margolis, Barrett, Goldknopf, & Daly, 1990) in the general psychology domain notes differences in imagery use between children and adults. More specifically, it is not until age 14 that children are able to image similarly to their adult counterparts. Age differences also holds true in the sport, exercise, and active play domain. For example, child-specific imagery measures have been developed to adequately assess their use of imagery in various domains (i.e., SIQ-C, CAPIQ). Findings from an imagery intervention study (Munroe-Chandler, Hall, Fishburne, Murphy, & Hall, 2012) did identify age-related results, such that only the younger athletes (7–10 years) performed faster on a soccer task, when compared to the older athletes (10–14 years). Noted age differences are also evident in the active play setting such that only the older age cohorts (11–14 years) reported picturing themselves playing alone rather than with others (Tobin, Nadalin, Munroe-Chandler, & Hall, 2013). In the exercise domain, Milne, Burke, Hall, Nederhof, and Gammage (2006) found that younger exercisers (Mage = 22 years) reported using more appearance imagery than the older exercisers (Mage = 71 years). Although these findings offer some preliminary evidence for age differences, further research is needed in order to truly understand the effects of age on performers’ use of imagery.
One of the most consistent findings from the performance imagery literature is that higher skilled performers report using imagery more often than lower skilled performers (Cumming & Hall, 2002; Hall, Mack, Paivio, & Hausenblaus, 1998; Hausenblas, Hall, Rodgers, & Munroe, 1999). In the sport domain, although it had been suggested that novice athletes should use imagery more frequently than elite athletes, simply for the purposes of the learning, and development, of new strategies and skills (Hall, 2001), research supports benefits for highly skilled athletes (e.g., Arvinen-Barrow, Weigand, Thomas, Hemmings, & Walley, 2007). This finding is consistent in the exercise imagery field, wherein experienced exercisers use imagery more often than less experienced exercisers (Gammage, Hall, & Rodgers, 2000), and in the performing arts field wherein higher level ballet dancers report using more imagery than their lower level counterparts (Nordin & Cumming, 2008). Moving forward, researchers should consider other ways to assess skill level. Currently, skill level has been dichotomized as novice vs. elite or experienced vs non-experienced. This is problematic given the self-report nature of this dichotomy and the possibility that minimal differences in skill may exist between those two groups (Arvinen-Barrow et al., 2007). In the revised model of deliberate imagery use, Cumming and Williams (2013) suggest that in addition to the skill level of the athlete, other relevant individual characteristics to consider are experience with and confidence using imagery.
Morris and Spittle (2012) noted that imagery perspective is a key factor impacting an athlete’s use of imagery. Indeed, a special issue of the Journal of Mental Imagery (2012) was dedicated solely to imagery perspective. Performers can image the execution of a skill from their own vantage point (internal imagery) or they can view themselves from the perspective of an external observer, as if they were a spectator in the stands watching a performance (external imagery). Early sport imagery researchers advocated the use of an internal perspective (Vealey, 1986), while others have found the perspective to be dependent upon the task. That is, tasks relying heavily on the use of form (e.g., gymnastics) are most effective when imaged from an external perspective (White & Hardy, 1995). Some researchers (Munroe, Giacobbi, Hall, & Weinberg, 2000; Smith, Wright, Allsopp, & Westhead, 2007) support athletes using a combination of internal and external perspectives. In the academic domain, Vasquez and Buehler (2007) found that students demonstrate increased motivation when they imagine the task from a third-person perspective. In a study examining imagery in five different disciplines (i.e., education, medicine, music, psychology, and sport), imagery was most often performed from an internal perspective (Schuster et al., 2011).
Scholars have recently acknowledged the scant research assessing the influence of personality characteristics on imagery use and its effectiveness (Roberts, Callow, Hardy, Woodman, & Thomas, 2010). In an effort to fill this gap, Roberts et al. (2010) examined the interactive effects of imagery perspective and narcissism on motor performance. Given that narcissists enjoy looking at themselves from the point of others, it was hypothesized that those high in narcissism would score higher on external visual imagery and better on their motor performance when compared to those low in narcissism. This hypothesis was supported using two independent samples. As such, it seems as though personality characteristics (i.e., narcissism) may influence the effectiveness of psychological skills and thereby require additional investigation.
Another factor that has recently been examined within the imagery domain is emotion regulation. Anuar, Cumming, and Williams (2016) believed that athletes’ emotion regulation may be associated with their imagery ability given that both imagery and emotion regulation are linked with emotions and memory. Indeed, their results indicated that athletes who change how they think about a particular situation scored higher on imagery ability. This study is the first of its kind, and future research examining individual characteristics and imagery is warranted.
Imagery as a Means to Improving Performance
Drawing on the various imagery models and empirical support, athletes use imagery for various motivational purposes (i.e., motivational general–mastery [MG-M], motivational general–arousal [MG-A], motivational specific [MS]). Most of the motivational imagery interventions have targeted the MG-M imagery function, and results from these studies are promising. In one study, a MG-M imagery intervention was implemented with four elite junior badminton players (Callow, Hardy, & Hall, 2001). The imagery scripts were designed to elicit images of being focused and confident, and included both response and stimulus propositions. Following the completion of the intervention, all but one badminton player showed significant improvements in their sport confidence. Other researchers employing single-subject multiple-baseline designs have found that MG-M imagery improved young squash players’ self-efficacy (O, Munroe-Chandler, Hall, & Hall, 2014) and high-performance golfers’ flow states (Nicholls, Polman, & Holt, 2005). Recently, MG-M imagery sessions were delivered to young athletes with an intellectual disability in an attempt to increase their perceptions of their sport competence (Catenacci, Harris, Langdon, Scott, & Czech, 2016). Results indicated that perceptions of sport competence improved from baseline to post-intervention for three of the five athletes, with two of the three athletes maintaining these changes upon commencement of the intervention. The benefits of MG-M imagery have also been underscored in several cross-sectional studies, providing evidence for a positive link between MG-M imagery and performance, state and trait sport confidence, self-efficacy, collective efficacy (see Cumming & Ramsey, 2009, for review), and mental toughness (Mattie & Munroe-Chandler, 2012).
Imagery has also been used as a means to achieve desirable somatic and emotional experiences associated with sport-related stress, arousal, and anxiety (MG-A imagery). It is generally argued that MG-A imagery may be more beneficial for athletes who experience debilitative interpretations of pre-competitive anxiety as opposed to those who experience facilitative interpretations (Martin, Moritz, & Hall, 1999). For example, a female fencer who is feeling unusually sluggish prior to competition might use MG-A imagery to psych herself up, while a male mixed martial arts fighter who is abnormally restless before the start of a competition might use MG-A imagery to reduce his anxiety. Though MG-A images have been negatively associated with athletes’ self-reported cognitive and somatic anxiety (Monsma & Overby, 2004), few studies have examined the direct effects of MG-A imagery on competitive anxiety. Investigators of past studies have typically delivered multicomponent interventions, which have included MG-A imagery along with other psychological skills (e.g., relaxation, breathing; Thomas, Maynard, & Hanton, 2007). Adopting a multicomponent psychological skills package makes it virtually impossible to determine precisely how much MG-A imagery contributed to any observed changes. Nevertheless, findings from other studies have contributed to researchers’ existing understanding of the MG-A imagery–competitive anxiety relationship (Cumming, Olphin, & Law, 2007; Mellalieu, Hanton, & Thomas, 2009). Specifically, imagery scripts that contained MG-A images (psyching up imagery, anxiety imagery, and coping imagery) led to greater increases in athletes’ heart rate and anxiety intensity (Cumming et al., 2007), while individualized MG-A imagery scripts led to more facilitative interpretations of symptoms related to competitive anxiety (Mellalieu et al., 2009).
Within the sport psychology literature, few interventions have focused exclusively on goal-based images (MS imagery). This is likely because goal- or outcome-based images (e.g., qualifying for a competition, winning a medal) are least often used by athletes. Rather, coaches and sport practitioners often encourage their athletes to focus on process goals (e.g., completing stretching exercises prior to competition) rather than outcome goals. In a sample with beginner golfers, participants who imaged executing the perfect stroke as well as sinking the golf ball (performance and outcome imagery group) had better performance and set higher goals for themselves compared to participants who imaged executing the perfect stroke only (performance group) and the participants who received no intervention (control group; Martin & Hall, 1995). Additionally, athletes who used MS imagery more frequently also reported greater goal achievement, state and trait sport confidence, and self-efficacy (Cumming & Ramsey, 2009).
In addition to motivational purposes, athletes have reported using imagery for cognitive purposes (i.e., cognitive specific [CS] and cognitive general [CG]). Using cognitive imagery to enhance skill acquisition and performance (CS imagery) has received the most attention among researchers (Morris, Spittle, & Watt, 2005). Investigators examining the positive effects of CS imagery have found significant improvements in young soccer players’ time to complete a soccer task (Munroe-Chandler, Hall, Fishburne, Murphy, & Hall, 2012) as well as adult equestrian riders’ performance and self-efficacy for a specific skill (Davies, Boxall, Szekeres, & Greenlees, 2014). In another study, 7- to 10-year-old athletes who imaged the proper execution of a table tennis serve significantly improved their serve accuracy and quality (Li-Wei, Qi-Wei, Orlick, & Zitzelsberger, 1992). Furthermore, CS imagery has been positively associated with gymnasts’ performance at competition (Simonsmeier & Buecker, 2017) and trait confidence (Abma, Fry, Li, & Relyea, 2002).
Evidence for imagery as a means to learn and improve execution of strategies, game plans, and routines (CG imagery) has been equivocal (see Westlund, Pope, & Tobin, 2012, for review). For instance, while improvements in basketball athletes’ strategy execution were observed following a CG imagery intervention (Guillot, Nadrowska, & Collet, 2009), soccer athletes who participated in a seven-week CG imagery intervention showed no improvements in strategy execution from baseline to post-intervention (Munroe-Chandler, Hall, Fishburne, & Shannon, 2005). However, researchers adopting correlational-based studies have shown that athletes who used CG imagery reported higher levels of confidence, self-efficacy, imagery ability, and cohesion in team sports (Westlund et al., 2012).
Imagery has long been recognized as a viable psychological technique that can directly modify exercise-related cognitions. Self-efficacy is a particularly good example of one cognition that continues to receive attention in literature. Weibull, Cumming, Cooley, Williams, and Burns (2015) examined whether a brief (one week) imagery intervention could increase barrier self-efficacy among a group of women who were interested in becoming more active. Findings indicated that participants who performed daily imagery for one week (experimental group) reported greater increases in barrier self-efficacy compared to those who did not perform imagery (control group). Note, however, that when preexisting exercise levels were controlled, there were no significant differences in barrier efficacy between groups. Nevertheless, findings from this study support the notion that imagery can have an influential effect on barrier self-efficacy in a short time frame. Evidence for the effectiveness of using imagery to increase exercise self-efficacy has also been found in other intervention studies, including Duncan, Rodgers, Hall, and Wilson (2011).
Imagery has also been used to modify individuals’ motivation toward exercise. Duncan, Hall, Wilson, and Rodgers (2012) implemented an eight-week imagery intervention and found that participants who listened to guided imagery scripts showed significantly greater increases in self-determined motivation than those who listened to health information sessions. In another study, imagery scripts combined with peer-mentoring led to significantly greater increases in self-determined motivation to exercise at the end of the intervention compared to those whose participation was limited to peer-mentoring only (Giacobbi, Dreisbach, Thurlow, Anand, & Garcia, 2014). Additional benefits of employing imagery in an exercise domain include increased revitalization and post-exercise valence (Stanley & Cumming, 2010) and implicit attitudes toward exercise (Markland, Hall, Duncan, & Simatovic, 2015).
Beyond changing individuals’ attitudes toward exercise, imagery can also significantly impact exercise behavior. For example, audio-administered imagery scripts led to significantly greater increases in self-reported exercise behavior in both adult (Andersson & Moss, 2011) and older adult (Kim, Newton, Sachs, Giacobbi, & Glutting, 2011) samples. Chan and Cameron (2012) also tested the effects of different imagery content on physical activity participation by looking at imagery’s impact on a group of inactive adults. Their findings indicated that imagery scripts linking images of participation in physical activity with achievement of goals were most effective in increasing self-reported physical activity as well as greater increases in goal orientation, intentions, and action planning.
Although few imagery interventions have utilized objective measures of physical activity, the research that has been conducted in this area illustrates positive impact of imagery. In a sample of adolescent girls, Najafabadi et al. (2015) developed imagery scripts that focused on benefits obtained from exercise (e.g., improved appearance, enhanced energy). Following the intervention, significantly greater levels of physical activity (as measured by accelerometers) and physical self-concept were found among females in the imagery group compared to those in the control group. In a separate study, school-aged children who were assigned to an imagery group showed greater levels of active play and self-determined motivation following a four-week intervention compared to children assigned to a control group (Guerrero, Tobin, Munroe-Chandler, & Hall, 2015).
The effects of mental imagery with video-modeling on front squat strength and self-efficacy was recently examined in a sample of adults (Buck, Hutchinson, Winter, & Thompson, 2016). From pre-test to post-test, participants who received the imagery script and video-modeling showed significant increases in their self-efficacy and front squat performance compared to those who received no intervention. In a recent systematic review examining the effects of various cognitive strategies (e.g., imagery) on strength performance, imagery was found to positively influence maximal strength (Tod, Edwards, McGuigan, & Lovell, 2015).
Along with sporting arenas and fitness facilities, researchers have explored the effects and application of imagery in other performance domains. For example, in musical settings, imagery use coupled with physical practice increased pianists’ and trombonists’ movement timing, music memorization, and self-efficacy (see Wright, Wakefield, & Smith, 2014, for review). Imagery use, in the absence of physical practice, has also shown to have promising effects on performance. In this respect, auditory practice (listening to an audio recording and imagining finger movements) led to significantly fewer errors in pianists’ performance than with those who did not engage in auditory practice (Highben & Palmer, 2004). More recently, Braden, Osborne, and Wilson (2015) tested the effectiveness of a multi-component, preventative skills-based program in reducing musical performance anxiety. The intervention program in this study comprised various components, including psychoeducation, cognitive restructuring, relaxation techniques, identification of strengths, goal-setting, positive self-talk, and imagery. Students who received the eight-week program reported significantly less musical performance anxiety than participants who did not receive the program.
In medical settings, researchers have employed imagery interventions to improve two primary outcomes: skill acquisition and levels of stress. With respect to skill acquisition, researchers found that medical students who received two imagery sessions demonstrated greater skill in performing surgery on live rabbits than students who had studied a textbook (Sanders et al., 2008). Similar findings were established in a study with gynecology residents, with those in the imagery group showing significantly better performance of cystoscopies as well as higher self-perceived level of preparedness compared to those in the control group (Komesu et al., 2009). In another study, student nurses who received PETTLEP training performed significantly better on a psychomotor skill (i.e., blood pressure measurement) than those who did not (Wright, Hogard, Ellis, Smith, & Kelly, 2008).
Given its successful use in the medical context, it is perhaps unsurprising that imagery has also been shown to be an effective stress management technique for other healthcare professionals (Arora et al., 2011) who also experience high levels of performance stress (Prabhu, Smith, Yurko, Acker, & Stefanidis, 2010). Compared to their control counterparts, novice surgeons who received imagery training demonstrated reduced self-reported stress as well as decreased objective stress (heart rate and salivary cortisol; Arora et al., 2011). In a very recent intervention study, Ignacio et al. (2016) developed, implemented, and evaluated an imagery intervention designed to improve nursing students’ clinical performance and reduce stress. Although no changes in subjective or objective stress were found, participants did significantly improve their performance from pre- to post-test.
Similar to healthcare professionals, police officers are often faced with a variety of stressors and potentially traumatic events, making imagery an appropriate psychological technique for members of law enforcement. Arnetz, Arble, Backman, Lynch, and Lubin (2013) implemented a 10-week imagery and relaxation intervention designed to help police officers develop effective coping skills. Compared to those in the control group, participants who received imagery training reported better general health and problem-based coping as well as reduced stomach problems, sleep difficulties, and exhaustion. Similarly, an imagery training program with rookie police officers led to significantly less negative mood and stress compared to standard police training (Arnetz, Nevedal, Lumley, Backman, & Lubin, 2009). Additionally, participants who received imagery training also demonstrated better performance during a live critical incident simulation (Arnetz et al., 2009).
That imagery is a powerful psychological technique is undeniable. Imagery allows individuals to search through, skip over, and select images from their memories in order to re-experience past events. Imagery also allows individuals to travel through time to create and manipulate never-experienced events. As illustrated, there is ample evidence documenting the effectiveness of imagery in sport, exercise, and performance settings. However, less is known about the potential negative consequences of imagery. For instance, engaging in self-generated imagery of a task requiring physical self-control (i.e., handgrip squeeze) led to performance decreases in a subsequent handgrip task for those who performed imagery compared to those who rested quietly (Graham, Sonne, & Bray, 2014). Furthermore, under certain conditions, imagery has been shown to have a negative effect on golf putting performance (Beilock, Afremow, Rabe, & Carr, 2001) and levels of aspirations and academic performance (Pham & Taylor, 1999). Together these findings indicate that there may be a dark side to imagery that should be explored to ensure that potential deleterious practices do not counteract the positive benefits associated with imagery use. Thus, future research should specifically explore possible negative effects of imagery on behavior and cognitions, including whether specific types of imagery should be avoided in certain environments and, if so, whether this caveat would hold true for all performers (e.g., professional dancer vs. surgeon)? While some researchers have begun to answer these questions (e.g., Nordin & Cumming, 2005), a more thorough examination of when and what imagery types facilitate or hinder performance would certainly contribute to the existing imagery research.
Along similar lines, there is a considerable gap in the imagery research investigating the impact involuntary, intrusive images have on performance. Imagery is considered to be intrusive as it can capture attention, cause distractions, and provoke unpleasant physiological and emotional reactions (Brewin, Gregory, Lipton, & Burgess, 2010). Indeed, there is some evidence indicating that performers do experience intrusive images (e.g., Nordin & Cumming, 2005; Parker, Jones, & Lovell, 2015). For instance, professional dancers reported experiencing irrelevant images, which may be intrusive, spontaneous, and debilitative (Nordin & Cumming, 2005). More recently, a small percentage of university students who participated on either recreational, university, county, or national competition levels reported experiencing intrusive visual imagery (Parker et al., 2015). Clearly, more research is needed in order to develop a greater understanding of the existence and effect of intrusive imagery within performance settings.
While the body of literature on imagery in performance settings continues to grow, more research exploring the usefulness and applicability of imagery among diverse performers is needed. Virtually all individuals, regardless of their occupations, are required to perform at some point or another. Successful lawyers need to deliver persuading and emotionally moving closing statements to the members of the jury; stand-up comedians are required to provide entertainment by mastering the pace and timing of every joke. Individuals of such occupations could undoubtedly benefit from imagery. Furthermore, as eSports (online competitive gaming) and competitive eating continue to gain popularity, exploring the potential for imagery as a performance enhancement technique for competitive gamers and eaters appears timely. Competitive gamers could use imagery to learn or improve their ability to make crucial decisions and to effectively cope with pressure, whereas competitive eaters could use imagery to improve execution of new strategies and maintain motivation during a contest.
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