Modeling in Sport and Performance
Summary and Keywords
Modeling and imagery are distinct but related psychological skills. However, despite sharing similar cognitive processes, they have traditionally been investigated separately. While modeling has shown similar psychological and physical performance benefits as imagery, it remains an understudied technique within applied sport psychology. Social cognitive and direct perception approaches remain often-used explanations for the effectiveness of modeling on skill acquisition; however, emergent neuropsychological explanations provide evidence to support these earlier theories and a link to the imagery literature.
With advances in technology and the development of applied frameworks, there is renewed interest in exploring modeling effects and how they parallel imagery use in applied settings. Specifically, modeling research has expanded beyond controlled laboratory settings to explore the effect of various theoretical models on motor performance and related cognitions within practice and competitive settings. The emergence of affordable video editing technology makes it easy for coaches and athletes to incorporate modeling into practice. The accessibility of video technology has sparked applied research on how various forms of modeling influence motor performance and cognitions, such as confidence and motivation. These applied investigations demonstrate the complementary nature of modeling and imagery in enhancing sport performance and skill acquisition, while highlighting the challenges in separating modeling and imagery effects. Both literatures offer possibilities for new methodological approaches and directions for studying these psychological skills in tandem as well as independently. Thus, there is much that imagery and modeling researchers can learn from each other in sport and other performance settings.
What is the value of video or a picture in assisting a gymnast’s learning of a new floor routine, in perfecting a pitcher’s throwing technique, in motivating an exerciser, or in enhancing a stroke patient’s rehabilitation? The use of video or pictures to enhance skill acquisition and performance is the foundation of much of our human experience. Humans continually observe others, and ourselves, and acquire new beliefs, attitudes, and behaviors through this process. Traditionally, modeling was not examined in the sport psychology literature despite its pervasiveness in our everyday lives. Indeed, modeling research in sport was primarily examined from a motor learning perspective, which remains relevant into the 21st century. Anecdotally, athletes, teams, and coaches use programs such as Dartfish (software that allows individuals to capture and analyze movement and to edit, review, and share video) to create self-as-a-model and “other” modeling videos with a variety of editing features (e.g., split screen, slow motion, stromotion super-imposed video). Unfortunately, much of this practice remains undocumented and lacks rigorous evaluation. Therefore, there is a need for more applied sport psychology research exploring naturalistic uses of modeling. While diverse designs, tasks, and approaches have been used to explain the modeling process, a chasm has been created whereby researchers within sport psychology and motor learning do not always work together to craft a complete picture of modeling, its antecedents, consequences, behavioral, and psychological impact on sport performance and skill acquisition.
Additionally, modeling has been identified as both a reemergent theme in kinesiology research (Weiss & Gill, 2005) as well as a “forgotten psychological skill” (McCullagh & Weiss, 2002) due to its omission from applied sport psychology research and texts (McCullagh & Wilson, 2007)—remedied only slightly within the last 5 to 10 years. Researchers exploring modeling within applied sport psychology have often relied on the imagery research to inform their investigations due to the inherent similarities between the two. This has advanced our understanding of how modeling compares to imagery and continues to offer novel approaches for studying modeling, but it has also limited our investigation of modeling. Recently, several comprehensive reviews have addressed modeling within sport and motor learning contexts (Holmes & Calmels, 2008; Keil, Holmes, Bennett, Davids, & Smith, 2000; Maslovat, Hayes, Horn, & Hodges, 2010; McCullagh, Law, & Ste-Marie, 2012; McCullagh, Ste-Marie, & Law, 2014; Ste-Marie, Law, Rymal, O, Hall, & McCullagh, 2012). Thus, this article will not attempt to duplicate these reviews. Instead it seeks to identify 21st-century issues in modeling research within sport psychology and highlight areas where modeling research should differentiate itself from imagery studies as well as where we can learn from or complement imagery research.
Defining Modeling and Imagery
What Is Modeling?
To capture both long-term effects (Bandura, 1986) as well as immediate effects (Scully & Newell, 1985) of modeling, this entry takes an inclusive approach and defines modeling as the process of modifying, consciously or subconsciously, one’s thoughts, beliefs, attitudes, and behaviors through the observation of oneself or another. As outlined by the Applied Model for the Use of Observation (AMUO; Ste-Marie et al., 2012), modeling can take many forms. It may be presented using various modalities (i.e., live, video, mirror, point light display), speeds (i.e., real-time, slow motion), angles (i.e., front, back, side, combinations), and can serve various functions (i.e., skill, strategy, performance) with the characteristics that create an effective demonstration being dependent upon contextual factors: the setting (i.e., training, competition, rehabilitation), the nature of the task, and the characteristics of the learner (i.e., stage of learning, developmental factors). The central component of modeling is the nature of the model itself.
Models may vary based on their skill level (i.e., expert vs. learning) as well as a combination of skill and self-beliefs (i.e., mastery vs. coping) and may involve observing another individual or observing the self (i.e., self-as-a-model). Self-as-a-model interventions emerged within therapeutic settings (Dowrick, 1999, 2012) but have also garnered interest within sport psychology. These interventions may rely on simple video replay (i.e., self-observation), may involve editing the video to show the best performance the learner is capable of to date (i.e., positive self-review), or may employ a number of editing techniques to show the learner performing at a level he or she has not yet achieved (i.e., feedforward) through altering how the setting is portrayed or the skill level (e.g., combining skills into a novel sequence, combining elements of a skill to create a complete execution). Finally, modeling can also occur through viewing a combination of models (e.g., self and expert) and can be driven by the learner (i.e., self-regulated) as part of their learning experience or can be reliant upon the instructions of the experimenter or practitioner. Thus, modeling can take many forms within the sport context. A more detailed discussion of the effects of these interventions appears in the “Current State of Modeling Research” section.
Imagery has been defined as the creation or re-creation of a polysensory experience in the mind (Vealey & Forlenza, 2015; White & Hardy, 1998). As highlighted by Munroe-Chandler and Guerrero, imagery is a dynamic and multilayered process that involves different perspectives (i.e., internal and external), speeds (i.e., slow motion and real time), functions (e.g., cognitive and motivational), and sensations (i.e., vision, kinesthetic sensations, etc.). Imagery is a flexible skill that can be used at various times during competition and practice, to meet different purposes (e.g., perfect an existing skill), and to prepare for upcoming events (MacIntyre & Moran, 2007; Post & Wrisberg, 2012; Post, Simpson, Young, & Parker, 2014). The complex and dynamic nature of imagery suggests that performers must regularly practice this skill in order to perfect it. While the current chapter highlights ways in which the imagery and modeling literatures are complementary yet distinct, readers are referred to Munroe-Chandler and Guerrero for a detailed discussion of imagery.
Similar but Different Psychological Skills
Based on the definitions provided for modeling and imagery, it is clear that these psychological skills share some similarities, yet are distinct. The key distinction between the two is stimuli based; modeling involves an external stimuli (e.g., demonstration, video, mirror) while imagery involves an internal stimuli (i.e., generating a mental image). A second distinction is that imagery involves multiple senses (e.g., kinesthetic, vision, audition) while modeling largely uses vision and/or audition. However, it is often difficult to separate the two skills. For example, PETTLEP (i.e., Physical, Environment, Task, Timing, Learning, Emotion, Perspective) imagery interventions often employ modeling through images or video to stimulate athletes’ imagery of a particular skill or scenario (Holmes & Collins, 2001; Smith, Wright, & Cantwell, 2008). Likewise, according to Bandura’s (1986) description of the modeling process, learners must transform information acquired from the demonstration into a cognitive representation of the task, completed through either verbalizations or images. There is also considerable overlap between the psychological and physical performance benefits incurred through modeling and imagery.
Theories and Frameworks of Modeling
Various theories and frameworks have been employed to explain the effects of modeling on performance and learning. Historically, two approaches have dominated this work: social cognitive theory (Bandura, 1986), and the visual perception perspective (Scully & Newell, 1985). Bandura’s (1986) social cognitive theory posits that learners acquire new beliefs and skills through a four-step process (attention, retention, motivation, and reproduction) following observation of a model. Modeling is also theorized to be an important source of one’s self-efficacy beliefs and outcome expectancies (Bandura, 1997). Much of the 21st-century modeling research conducted from a social cognitive perspective applies Zimmerman’s (2000) cyclical model of self-regulated learning.
Alternatively, Scully and Newell’s (1985) visual perception perspective suggests that cognitive processing of the visual information presented via demonstration is not necessary, rather the information is perceived directly and integrated by the visual system in order to replicate movements. This approach was commonly used in studies exploring the nature of what is attended to during observation and focuses on relative motion of body segments (Al-Abood, Davids, Bennett, Ashford, & Marin, 2001; Hodges, Hayes, Breslin, & Williams, 2005; Hayes, Hodges, Scott, Horn, & Williams, 2006), although findings suggest that while relative motion contributes to motor learning, other sources of information are also important (Maslovat et al., 2010). GOADI (goal-directed imitation theory; Wohlschläger et al., 2003) and the common coding theory have been used to frame modeling studies within the motor learning and control domain (e.g., Hayes, Ashford, & Bennett, 2008; Hodges & Coppola, 2015). While these approaches focused on action observation provide valuable information concerning what is acquired through modeling, they are limited in explaining the role of motivational and affective factors in the modeling process. It is likely that greater integration of these perspectives will create a more complete picture of the complexity of learning through observation.
Neuropsychological approaches also explore the effects of modeling and how it influences the brain (for reviews see Holmes & Calmels, 2008; Vogt & Thomaschke, 2007). Specifically, discovery of the Mirror Neuron System (MNS) has provided support for the notion that observation activates areas of the brain responsible for motor planning and execution as well as emotional responses, thereby “mirroring” the brain activation during execution of that experience (Grèzes & Decety, 2001). This provides support for aspects of both social cognitive and action observation approaches and creates exciting new venues for researchers exploring modeling.
Two frameworks have been proposed within the modeling literature that integrate these approaches and provide guidance for future modeling research as well as practical application. The first is McCullagh, Weiss, and Ross’ (1989) integrated approach for the use of modeling. This framework prioritizes the role of the observer and acknowledges that both action perception and the learner’s interpretation of the demonstration contribute to successful modeling. This framework includes a feedback loop, whereby feedback following behavioral outcomes influences the observer’s future behavior, interpretation of the demonstration, and the rehearsal strategies employed throughout the learning process. The second is the Applied Model for the Use of Observation (AMUO; Ste-Marie et al., 2012). This model provides a framework for organizing our knowledge of the factors influencing modeling (e.g., the task, learner characteristics, setting, what is observed, why, potential outcomes) and provides a template for factors to consider when designing modeling experiences for various skill acquisition settings.
Related Theories and Models of Imagery
Several theories have been posited to explain the influence that imagery has on sport performance, skill acquisition, cognitions, and affect (for a comprehensive discussion of imagery theories and models, see Munroe-Chandler and Guerrero’s). Of these theories, Jeannerod’s (1994) functional equivalence and Lang’s bio-informational (1977, 1979) theories have attracted the most attention. Jeannerod’s functional equivalence theory (1994) suggests that imagery and physical practice are functionally equivalent because imagery and overt practice share common neural mechanisms associated with perception and motor control (Holmes & Collins, 2001; Jeannerod, 2001; Roth et al., 1996). That is, imagery recruits similar neural networks to those used to perceive, plan, and execute an actual motor skill (Jeannerod, 2001; Kosslyn, Ganis, & Thompson, 2001). Functional equivalence theory shares similar tenets as the neuropsychological theories of modeling. Taken together, it appears that brain regions responsible for motor learning and performance are activated when performers observer or create an image of specific movement.
Alternatively, Lang (1977, 1979) hypothesized that during imagery individuals could access stimulus, response, and meaning propositions stored in long-term memory. Stimulus propositions describe the context of an event, response propositions comprise the various physiological and affective reactions associated with a particular event, and meaning propositions represent the perceived importance of the image. With repeated imagery practice performers can become more adept at matching the most effective response propositions to particular sets of stimulus propositions. However, Lang (1985) indicated that greater activation of stimulus and response propositions occurred when the image was relevant to the individual (i.e., meaning propositions). Therefore, imagery should be personally relevant to the individual. The importance of long-term memory in the bio-informational theory is similar to modeling’s social cognitive theory. Both theories suggest that performers must attend to specific features of the environment, retain relevant information associated with the motor skill, and then be able recall that information when executing or imaging the desired motor skill. These theories also suggest that for the intervention to be effective, the information being presented or created has to be personally relevant.
Similar to the modeling literature, two frameworks integrate imagery theories to give practitioners guidance on how to apply the mental skill (see Munroe-Chandler and Guerrero’s chapter). The first is the PETTLEP model created by Holmes and Collins (2001). PETTLEP is an acronym for seven factors (i.e., Physical, Environment, Task, Timing, Learning, Emotion, and Perspective) that practitioners should consider when implementing an imagery intervention. The PETTLEP imagery model shares commonalities with the AMUO (Ste-Marie et al., 2012) modeling framework. Both frameworks consider several factors (e.g., the environment, task, learner) that impact motor performance and offer a template for designing effective interventions.
The Applied Model of Imagery Use in Sport (AMIUS) developed by Martin, Moritz, and Hall (1999) suggested that the most effective imagery function for a given athlete depends on the desired cognitive, behavioral, or affective outcomes. Martin et al. (1999) posited that the imagery function used by the athlete should match the desired outcome. Cumming and Williams (2013) recently revised the AMIUS model to be more consistent with current research. The new model considers who is imaging, what is being imaged, why performers are using imagery, and the personal meaning between the imagery type and function. Essentially, the new model suggests that performers can combine cognitive and motivational images to achieve a desired outcome. Overall, the PETTLEP and AMIUS models share commonalities with McCullagh et al.’s (1989) integrated modeling approach as well as the AMUO (Ste-Marie et al., 2012). These frameworks prioritize the role of the performer’s interpretation of the imaged function or observed demonstration, with a focus on matching the function of the image or observation to the desired behavioral and affective outcome.
Current State of Modeling Research: What Can Imagery Learn From Modeling?
Research has established that modeling is an effective tool for enhancing motor skill acquisition and performance among both adults and children (Ashford, Bennett, & Davids, 2006; Ashford, Davids, & Bennett, 2007). Comparatively, fewer studies have explored the psychological effects of modeling, with the literature overall demonstrating equivocal effects for a number of outcomes (e.g., self-efficacy, motivation, anxiety; for a review see McCullagh et al., 2012). This may be due to the variety in tasks, models, and learners examined within these studies. In the subsequent sections, we review this literature using the categories applied by Ste-Marie et al. (2012) in their review of the modeling literature and development of the AMUO. The findings related to why individuals observe and what is effective parallel those seen in the imagery literature.
Modeling Context (Where)
While much of the modeling research has occurred within lab settings, there have been a number of studies focused on sport skills within naturalistic settings, many of which have focused specifically on the self-as-a-model. These studies support that modeling can improve skill learning within training (Clark & Ste-Marie, 2007; Martini, Rymal, & Ste-Marie, 2011; Ste-Marie, Vertes, Rymal, & Martini, 2011) and tentatively support enhanced performance within competition (Ste-Marie, Rymal, Vertes, & Martini, 2011; Vertes & Ste-Marie, 2013), and recovery within injury rehabilitation (Maddison, Prapavessis, & Clatworthy, 2006). What is more encouraging is that these studies have explored psychological factors associated with modeling using both qualitative and quantitative methods, with findings suggesting that modeling has a positive impact on self-regulatory processes (Clark & Ste-Marie, 2007; Rymal, Martini, & Ste-Marie, 2010; Ste-Marie, Rymal, et al., 2011). However, it is unclear which modeling conditions will consistently produce these effects, as findings in the literature are mixed. Recently, studies have also explored the effectiveness of a commonly used motivational tool within sport settings—that of the highlight video, typically in the form of positive self-review. This has been highlighted as an area where consultants can improve their use in the field (Ives, Straub, & Shelley, 2002) and reflects use of modeling within a truly “applied” setting.
Of the limited studies examining the use of highlight videos, findings suggest that positive self-review does not produce greater improvements in performance compared to traditional training (Jennings, Reaburn, & Rynne, 2013). However, findings are more promising with respect to psychological variables. Tracey (2011) found that a positive self-review motivational video led to self-reported improvements in confidence, motivation, emotional management, and facilitated imagery use; while Kingsbury and Tauer (2009) found that youth who observed a highlight video consisting of NBA footage with an expert model high in similarity (i.e., same race) placed higher value on more difficult skills, felt more optimistic about playing post-secondary basketball, and reported more positive arousal levels. These studies suggest that highlight videos can have a psychological impact on observers, yet more research on this technique is merited within the context of modeling theory.
Functions of Modeling (Why)
To address how modeling works within applied settings, Cumming, Clark, Ste-Marie, McCullagh, and Hall (2005) developed the Functions of Observational Learning Questionnaire (FOLQ). This questionnaire allows athletes to self-report the reasons why, or anticipated outcomes, for their modeling use. The FOLQ was based upon the Sport Imagery Questionnaire (Hall, Mack, Paivio, & Hausenblas, 1998), and through a series of validation studies identified three primary functions for modeling: skill (i.e., to learn or improve skill execution), strategy (i.e., to learn or improve strategies, game plans, and routines), and performance (i.e., to influence psychological variables such as confidence, anxiety, focus). Overall, studies using the FOLQ suggest that athletes of all levels employ the skill function to a greater extent than the strategy and performance functions (Cumming et al., 2005; Hall, Munroe-Chandler, Cumming, Law, Ramsey, & Murphy, 2009; Law & Hall, 2009a, 2009b; Wesch, Law, & Hall, 2007). These findings are consistent with, gymnasts’ reports of using self-modeling video for skill-related functions (Hars & Calmels, 2007). These results may be due in part to the focus within sport settings by coaches and athletes on observation as a skill acquisition tool, rather than a motivational tool.
Use of the functions also appears to be associated with different outcomes. For example, Law and Hall (2009b) found that use of the skill function predicted self-efficacy beliefs to learn skills and strategies among adult novices learning an independent sport, whereas use of the performance function predicted self-efficacy to regulate mental states among adults learning an interactive sport. This is an area ripe for exploration as there is scant research on functions of modeling beyond the skill function.
Limited research has explored the use of modeling by important others in the sport environment, such as coaches and officials. Hancock, Rymal, and Ste-Marie (2011) used the FOLQ (Cumming et al., 2005) to examine differences in coaches, officials, and athletes use of modeling. For all three groups, the skill function was employed most, followed by the strategy and performance functions. Interestingly, coaches reported greater use of the skill function than athletes and officials, while officials reported greater use of the performance function than the other two groups.
Model Types (Who)
A common question posed by researchers and practitioners, is which type of model is best? It appears that there are a number of effective model types for enhancing sport and motor skill performance. Models that differ based on skill level—that is, experts (i.e., correct, skilled) and learners (i.e., unskilled, some errors) are both effective for enhancing skill acquisition (e.g., McCullagh & Caird, 1990; Pollock & Lee, 1992). Similarly, those that differ in terms of both skill and psychological states, such as mastery (i.e., skilled and demonstrate high confidence) and coping (i.e., learning and demonstrate gradually improved confidence) can be effective for enhancing skill acquisition (e.g., Clark & Ste-Marie, 2002; Kitsantas, Zimmerman, & Cleary, 2000), although coping models may be more effective for modifying psychological responses, such as decreasing anxiety (e.g., Weiss, McCullagh, Smith, & Berlant, 1998). Recent modeling research has shifted the focus from comparing “other” modeling types to examining the self-as-a-model.
Learning from viewing the self builds upon Bandura’s (1986, 1997) proposal that effective models are those that the learner views as highly similar to him/herself in some way. What could be more similar than yourself? Ste-Marie and colleagues have conducted much of this research within applied sport settings (Ste-Marie, 2013). Their studies have explored the effects of self-observation as well as positive self-review and feedforward self-modeling (Dowrick, 1999). These studies demonstrated that both feedforward self-modeling and self-observation enhanced skill acquisition when compared with practice only for both children and adults (Clark & Ste-Marie, 2007; Martini et al., 2011, Ste-Marie, Vertes, et al., 2011), and that feedforward self-modeling produced greater improvement in intrinsic motivation and self-satisfaction than self-observation and practice alone for children (Clark & Ste-Marie, 2007). Other research employing feedforward self-modeling has found positive results; however, it relied on pre-post comparisons with a small sample (Steel, Adams, Coulson, Canning, & Hawtin, 2013). In contrast, the limited studies examining feedforward self-modeling within competitive settings have found equivocal results (Rymal et al., 2010; Ste-Marie, Rymal, et al., 2011) and studies employing positive self-review self-modeling have not shown similar performance and psychological benefits (Law & Ste-Marie, 2005; Ram & McCullagh, 2003; Winfrey & Weeks, 1993). Tentatively, this suggests that feedforward self-modeling may be the more powerful form of self-as-a-model interventions.
Recently, the effect of combining model types on skill acquisition has been explored. Laboratory studies suggest that viewing a combination of novice and expert models enhanced learning more than viewing either a novice or expert alone (Andrieux & Proteau, 2013, 2014; Rohbanfard & Proteau, 2011). Using sport tasks (i.e., rowing and gymnastics), a combination of expert models and self-modeling enhanced performance compared to a no modeling control group (Anderson & Campbell, 2015; Baudry, Leroy, & Chollet, 2006); however, these studies do not inform whether multiple models are superior to a single model. Barzouka, Sotiropoulos, and Kioumourtzoglou (2015) compared the effect of expert models, expert plus self-observation, and no model control groups on novices’ volleyball pass acquisition. The expert plus self-observation group performed significantly better than the other two groups at retention, and both modeling groups demonstrated greater task orientation than the control group. This suggests that multiple models may be superior to single models and that viewing a model may foster increases in task orientation. Similarly, Robertson, St Germain, and Ste-Marie’s (2017) comparison of self-observation with an expert model group showed that gymnasts who viewed both an expert as well as themselves performed superior to the self-observation alone group at the end of acquisition as well as at post-test, suggesting that viewing an expert model in conjunction with one’s own performance confers additional skill acquisition benefits.
Other self-modeling research has examined performance on endurance tasks rather than specific skill acquisition. Concurrent self-modeling during running tasks has led to lower perceived exertion, longer time to exhaustion, and decreased oxygen consumption (Hagin, Gonzales, & Groslambert, 2015; Gonzales, Hagin, Dowrick, & Groslambert, 2015). While these studies may not inform how prior viewing of a self-modeling video may influence endurance in a subsequent task (e.g., pre-race preparation), it may be feasible for athletes who train alone and do not have the benefit of training groups.
Other Modeling Characteristics (What & How)
Within the AMUO, Ste-Marie et al. (2012) also highlight that what and how a demonstration is portrayed are important considerations for effective modeling. While there has been relatively little sport psychology research in this area compared to that on who, the literature does offer some insight into effective modeling practices.
Characteristics associated with what is portrayed include instructional features, the focus of attention, modality, and content of the demonstration. Instructional features (i.e., augmented feedback), typically in the form of verbal cues or knowledge of results and performance have received the most attention, with the literature showing that they can enhance modeling effects by providing learners with cues regarding their own performance as well as important aspects of skill execution (see Ste-Marie et al., 2012; McCullagh et al., 2012). Recent research by Andrieux and Proteau (2016) highlights that providing novices with advance information about what they will see in modeling demonstrations enhances their ability to learn from the model.
The modality and specific content of the demonstration have received relatively little attention within applied sport psychology. Of the limited studies directly comparing modalities, some have suggested that video, stick figures, and point light displays (e.g., Ghorbani & Bund, 2016; baseball pitch task) as well as video and live demonstrations (e.g., Kernodle, McKethan, & Rabinowitz, 2008; fly casting) all confer equal benefits, while others suggest that video may be preferable to live demonstrations, particularly early in acquisition (Lhuisset & Margnes, 2015; judo). An under-studied modality within both modeling and imagery research is that of audition. The literature suggests that auditory modeling can also be effective for enhancing skill acquisition and performance in laboratory (McCullagh & Little, 1989) and applied sport tasks (for a review see O, Law, & Rymal, 2015).
In contrast, how the demonstration is presented refers to the speed, viewing angle, frequency of modeling, and control of viewing the demonstration. Speed of the demonstration is likely the least studied aspect of how. Based on the limited literature (McCullagh et al., 2012; Ste-Marie et al., 2012) we are unable to draw conclusions about whether slow motion, real-time, fast motion, or a combination of speeds confers benefits for skill acquisition, performance enhancement, or psychological effects in applied sport situations. Certainly much more research is needed in this area.
Much of the research examining viewing angle has been conducted by Ishikura and colleagues (Ishikura & Inomata, 1995, 1998; Ishikura, 2012) and suggests that viewing a model from behind (i.e., subjective view) enhances motor skill sequence learning compared to other viewing angles. In contrast, Smith (2013) found that participants learning a Zumba dance sequence performed equally after receiving modeling delivered from a subjective view point or looking glass (face-to face) viewpoint. However, the looking glass group performed with fewer errors at post-test and were better able to identify errors in a dance sequence. This suggests that varying degrees of cognitive processing may occur when learning from different viewing angles.
Similarly, frequency of model viewing has received relatively little research attention in applied sport psychology and prevents us from making strong conclusions regarding optimal delivery (Ste-Marie et al., 2012). In contrast, the effect of self-control (i.e., providing learners with control over when they view the model) has received much more attention in the 21st century. Studies examining self-control within the context of modeling have often compared a group that has self-controlled viewing of a modeling video (SC) with a group that is yoked (YK) to a member of the SC group in terms of the frequency of model viewing. Overall these studies suggest that providing observers with control over when they view a demonstration provides superior skill learning (Ste-Marie, Vertes, Law, & Rymal, 2013; Wulf, Raupach, & Pfeiffer, 2005) as well as enhanced intrinsic motivation and perceived choice (Ste-Marie et al., 2013). However, path analysis of the pattern of physical and psychological effects of SC versus YK conditions revealed that self-efficacy and motivation do not explain the learning benefits of SC, at least in children viewing a self-modeling video (Ste-Marie, Carter, Law, Vertes, & Smith, 2015), highlighting that not only is further examination of psychological variables associated with modeling effects merited but also that these variables should be explored in the context of self versus experimenter controlled contexts.
There are several areas where modeling research may stimulate future imagery studies. First, the majority of modeling interventions employ specific models (e.g., self, peer, feed-forward-model) to serve a specific function (e.g., learn coordination patterns, timing elements). However, many imagery interventions combine various imagery functions (i.e., motivation and cognitive) and do not always specify who is being imaged. While simultaneously using multiple functions of imagery mimics real-world experiences, the lack of specificity within controlled studies makes it difficult to understand how various imagery functions impact sport performance and makes it difficult to replicate the intervention. Second, research has extensively explored modeling intervention effects on different tasks (i.e., discrete, serial, and continuous task) and demands (cognitive vs. motor), while imagery research has largely focused on discrete/serial skills and task containing greater cognitive elements. Finally, modeling research has directly compared imagery and modeling interventions, whereas imagery research has ignored these comparisons or confounded the imagery intervention by incorporating video replay into the imagery interventions.
Current State of Imagery Research: What Can Modeling Learn From Imagery?
The imagery literature offers insight into how modeling research may evolve; specifically, in terms of the diversity of populations examined, the methods used, and the measurement of its use and effects. Imagery has been shown to enhance sport performance with skilled performers (Callow, Roberts, & Fawkes, 2006; Driskell, Copper, & Moran, 1994; Post, Wrisberg, & Mullins, 2010; Rushall & Lippman, 1998; Weinberg, 2008; Smith et al., 2008) and benefit novices’ motor skill acquisition (Post, Williams, Simpson, & Berning, 2015; Wohldmann, Healy, & Bourne, 2007; Wright & Smith, 2007). Furthermore, clinical studies suggest that imagery aids motor recovery in stroke and ACL rehabilitation programs beyond physical therapy alone (Maddison et al., 2011; Page, Levine, & Leonard, 2005). Imagery also positively influences exercise-related cognitions such as self-efficacy (Weibull, Cumming, Cooley, Williams, & Burns, 2015) and motivation (Duncan, Hall, Wilson, & Rodgers, 2012). Taken together, these findings suggest that imagery benefits motor learning, sport performance, rehabilitation, and important exercise-related cognitions in various populations.
While imagery interventions have been explored with a variety of populations, modeling research has largely been conducted with learners attempting to acquire a novel motor skill. Few investigations have examined the impact that modeling has on skilled performers’ sport performance (Baudry, Leroy, & Chollet, 2006; Leavitt, Young, & Connelly, 1989; Ste-Marie, Vertes, Rymal, & Martini, 2011; Templin & Vernacchia, 1995) or in clinical populations (Flint, 1993; Maddison, Prapavessis, & Clatworthy, 2006; Standal & Jespersen, 2008). Unfortunately, very little modeling research has been conducted among exercisers and non-exercisers. This is an area where additional modeling research is needed, specifically examining how modeling impacts important exercise-related cognitions and behavior. Additional investigations in these areas would assist practitioners in understanding how modeling impacts motor performance beyond skill acquisition.
Imagery researchers have utilized an array of methodologies, including experimental interventions, descriptive investigations, single-subject designs, action research designs, and qualitative research designs (Bell, Skinner, & Fisher, 2009; Evans, Jones, & Mullen, 2004; Jordet, 2005; MacIntyre & Moran, 2007; Post et al., 2014; Weinberg, 2008). Most modeling research has implemented experimental designs to examine the efficacy of a specific modeling intervention on motor learning and/or sport performance (Anderson & Campbell, 2015; Clark & Ste-Marie, 2007). While experimental designs are effective in determining the impact of an intervention, they are often difficult to conduct in field settings or when there are a limited number of participants. Three methodologies used in previous imagery research seemed to be most applicable to assist modeling research conducted in field settings: single-subject multiple baseline, action research designs, and qualitative investigations.
Single-subject multiple baseline designs offer the benefit of allowing all participants to receive the treatment while serving as their own control and are especially effective when examining the impact of psychological interventions in field settings (Hrycaiko & Martin, 1996). In general multiple baselines designs incorporate three phases: baseline, intervention, and post-intervention phases, with participants entering the intervention phase in a staggered fashion once their performance has reached a stable baseline (Tawney & Gast, 1984). As a result, these designs help to control for extraneous variables (Satake, Jagaroo, & Maxwell, 2008). Single subject designs are effective in examining the influence of imagery on sport performance (Bell et al., 2009; Jordet, 2005; Post et al., 2010; Post, Muncie, & Simpson, 2012), and such an approach could be adopted in future modeling research.
Castel (1994) suggested that action research designs could be used to solve day-to-day problems and to intervene in real life situations to improve practice. A central component of action research is the collaboration and feedback between the researcher and the participant (Hult & Lennung, 1980), as opposed to applying a rigid intervention that was predetermined by the investigator (Cohen & Manion, 1994). Thus, with only a few participants a researcher can implement a specific intervention (e.g., self-model), work with the participant to modify the intervention to meet his or her needs (e.g., adjust timing of delivery, viewed angles) and examine the impact of the intervention. Previous imagery investigations have utilized action research designs with elite athletes (Evans et al., 2004) and this approach may be fruitful for examining the impact of modeling interventions.
To explore skilled athletes’ imagery in greater depth, researchers have utilized qualitative interviewing (MacIntyre & Moran, 2007; Munroe, Giacobbi, Hall, & Weinberg, 2000). While several forms of qualitative interviewing exist, imagery researchers have typically utilized semi-structured or phenomenological interviewing (MacIntyre & Moran, 2007; Munroe et al., 2000; Post & Wrisberg, 2012). Semi-structured interviews have sought to understand the characteristics of athletes’ imagery use, as well as the meta-imagery processes underlying their skill use. A similar approach can be adopted to understand the where, when, why, and what related to athletes’ and coaches’ use of modeling. Alternatively, phenomenological interviewing seeks to understand the participant’s experience of a phenomenon. To some extent modeling research has captured participants’ experience through think-aloud protocols that require participants to report what they are thinking during the modeling intervention (Ram & McCullagh, 2003; Rymal et al., 2010). Think-aloud protocols enable the researcher to capture in the moment experiences but do not allow the participant time to reflect on the experience and articulate important insights gleaned from the experience. Thus, future modeling research incorporating phenomenological protocols may enable the researcher to capture important athlete and coach insights. Overall, modeling research has utilized qualitative methods sparingly (Hars & Calmels, 2007; Tracey, 2011), but greater use of these methods may enable researchers to uncover rich details about the use of modeling.
Several instruments have been developed to assess imagery ability and frequency of use (see Munroe-Chandler and Guerrero’s chapter). Typically two questionnaires have been used to assess imagery ability: the Movement Imagery Questionnaire (MIQ; Hall & Pongrac, 1983) and the Vividness of Movement Imagery Questionnaire (VMIQ; Isaac, Marks, & Russell, 1986). Over the past two decades each of these measures has been revised to better assess a person’s imagery, with the most recent versions assessing both visual and kinesthetic imagery ability for adults (MIQ-3; Williams, Cumming, Ntoumanis, Nordin-Bates, Ramsey, & Hall, 2012; revised VMIQ2; Callow & Roberts, 2010), children (MIQ-C; Martini, Carter, Yoxon, Cumming, & Ste-Marie, 2016), and in rehabilitation (MIQ-RS; Gregg, Hall, & Butler, 2010). These are important instruments since imagery ability is a known factor influencing the effectiveness of an imagery intervention (Hall, 2001) and may also moderate the effectiveness of modeling interventions (Rymal & Ste-Marie, 2017). Other instruments have been developed to assess the frequency and functions of imagery in sport, exercise, and rehabilitation settings as well as among children and adults (see Munroe-Chandler and Guerrero’s chapter). The information obtained from the different imagery assessments can be invaluable when developing effective interventions.
To date, only the Functions of Observational Learning Questionnaire (FOLQ, Cumming et al., 2005) has been developed to assess self-reported reasons for using modeling in applied sport settings. The questionnaire enables athletes to report functions of modeling. However, similar to imagery, additional questionnaires could be developed for children, exercise and rehabilitation settings, and to assess individuals’ ability to use modeling techniques. It is possible that there are specific visual search patterns or information that enables a person to acquire more information when using modeling techniques. Developing a modeling ability questionnaire would assist practitioners in understanding the strengths and weakness of their client’s ability to use modeling.
Applying Modeling Beyond Skill Acquisition
Given the pervasiveness of modeling in our everyday experiences, it is no surprise that it has been employed in skill recovery (Flint, 1993; Maddison et al., 2006) and achievement domains beyond sport and exercise (e.g., music, dance, serious gaming, medicine; see McCullagh et al., 2012 for a review). Although psychological skills training is employed within military research (Taylor et al., 2011), we were unable to locate literature exploring modeling techniques within this population. However, suggestions that modeling and imagery might be important techniques for learning are highlighted in two reports commissioned by the Army Research Institute who asked the National Research Council to examine the effectiveness of popular performance enhancement techniques (Druckman & Bjork, 1991; Druckman & Swets, 1988).
One rarely mentioned application of modeling in the sport psychology literature is that of role modeling. Within the existing sport literature there is debate regarding the exact definition of role modeling (Armour & Duncombe, 2012; Payne, Reynolds, Brown, & Fleming, 2002); however, it may be useful to view it as any person identified as a role model by the observer and classified on a continuum ranging from little/no contact/exposure to frequent contact/exposure (Payne et al., 2002). Morgenroth, Ryan, and Peters (2015) have provided a more detailed definition of role modeling, such that role models are “individuals who influence role aspirants’ achievements, motivation, and goals by acting as behavioral models, representations of the possible, and/or inspirations” (p. 468).
Within sport, research has identified that high status models are more effective than low status models for skill acquisition (McCullagh, 1986); however, little is known about what qualities makes a high status model effective, or what conditions are necessary to be deemed “high status.” Studies have confirmed that children do identify role models for sport and physical activity behavior (Biskup & Pfister, 1999; Vescio, Wilde, & Crosswhite, 2005) and that models can also have a negative influence on beliefs (e.g., unsportsmanlike conduct; Weiss, Kipp, & Goodman, 2010). However, in-depth exploration of what characteristics are most salient and the factors that influence the effect of role models on the observer’s behavior is lacking. This may be particularly important for modeling interventions with distinct populations, particularly those who are under-represented within sport and physical activity settings (e.g., individuals with a disability or chronic health condition, individuals from diverse cultural groups and age cohorts). Further, understanding role model influences may also help to address the issue of how maladaptive behaviors are acquired in sport and physical activity settings (e.g., antisocial behavior, disordered eating patterns).
In fields such as medicine and psychology, recent studies have explored perceptions of which qualities are valued in role models (Haider, Snead, & Bari, 2016) and their influence on career aspirations (Parent & Oliver, 2015; Rosenthal, Levy, London, Lobel, & Bazile, 2013). Similar investigations have also been conducted within organizational psychology, exploring the effect of supervisors as work-life role models for employees (Koch & Binnewies, 2015), and the effect of ethical role models on ethical leadership behavior (Brown & Treviño, 2014). Studies have also explored the potential effects of negative role modeling experiences within medical (Mileder, Schmidt, & Dimai, 2014) and business training settings (Baden, 2014). Certainly similar investigations could provide valuable insight into role models for athletes, coaches, and sport psychologists.
Within education, research has explored how factors such as gender and ethnicity influence the impact of positive versus negative role models on individuals’ perceptions and behaviors (Lockwood, 2006; Lockwood & Kunda, 1997; Lockwood, Marshall, & Sadler, 2005; Lockwood, Sadler, Fyman, & Tuck, 2004) as well as the effect of role models within stereotyped threat situations (e.g., female versus male role model effects on math performance; Marx, Munroe, Cole, & Gilbert, 2013). This literature could inform examinations of female participation in traditionally male-dominated positions (e.g., coaching, sport administration), sports, and within the exercise domain (e.g., combatting stereotypes related to age, gender, body composition). Indeed, Lockwood, Chasteen, and Wong (2005) identified that older adults may be more motivated by a negative role model to change their health behavior than a positive role model; whereas for younger adults the opposite was true.
Recently, Morgenroth, Ryan, and Peters (2015) reviewed the role modeling literature within education and occupational settings and proposed a Motivational Theory of Role Modeling to explain how role modeling functions. Their theory draws upon expectancy-value theories of motivation and considers that both attributes of the role model and of the role aspirant (i.e., the individual who seeks to emulate the role model) inform how the role model is perceived. While this theory requires empirical testing, it may provide a framework for exploring role modeling influences across a number of achievement domains, including sport and exercise.
Overall, researchers within sport psychology may benefit from exploring examinations of role modeling within the broader psychological literature. We encourage researchers to explore methodologically sound investigations of role modeling effects within sport, heeding the criticisms of this literature to date; namely that many programs focused on role models are not adequately evaluated, that the various dimensions of role models should be explored (e.g., degree of contact, definition of role model; Payne et al., 2002; Lyle, 2013), and that agreement on a definition and use of stronger theoretical frameworks are required to provide cohesion in this literature (Morgenroth et al., 2015). In particular, much of the role modeling research across domains is qualitative or correlational in nature. Longitudinal and experimental designs would provide much needed clarity on the impact of role models on observers’ beliefs and both short- and long-term behaviors.
Opportunities to Work Together and Future Directions
While modeling research within applied sport psychology is still an emerging area, studies to date suggest that it should be considered alongside other psychological skills when designing mental training programs for athletes. In particular, there is considerable conceptual and practical overlap between modeling effects and those of imagery, which is perhaps the most commonly used psychological skill. Therefore, it seems logical to explore these skills in combination and separately within applied sport psychology settings (McCullagh & Weiss, 2002). To date, there have been limited studies exploring imagery and modeling within applied sport settings (Coelho et al., 2014; deRenne & Morgan, 2013; Ramirez, 2010). Identifying the unique contributions of distinct psychological skills on outcomes is important for effective design of multimodal interventions. This also poses methodological issues for researchers; for example, how do you prevent someone from imaging after viewing a demonstration within an applied setting?
Similarly, researchers are encouraged to consider how imagery and modeling may be complementary in other ways. For example, Williams and Cumming’s (2012) cross-sectional study found that athletes with higher imagery ability employed greater use of the functions of modeling, and skill imagery ability and strategy imagery ability were the strongest predictors of the skill and strategy functions of observational learning, respectively. Perhaps athletes who have higher imagery ability may be more prone to engage in modeling and may benefit more from modeling, it may also be that modeling can help to develop athletes’ imagery ability (Rymal & Ste-Marie, 2017). Rymal and Ste-Marie (2017) demonstrated that young gymnasts’ imagery ability moderated the effect of a feedforward self-modeling intervention delivered at competitions, such that only gymnasts with lower visual imagery ability showed competitive performance benefits from the intervention. However, these effects were only apparent at competitions in the latter portion of the season. Further exploration of the links amongst imagery ability, imagery use, modeling use, and modeling ability is merited to inform the design and implementation of effective psychological skills training programs employing these two skills.
The AMUO (Ste-Marie et al., 2012) provides a framework for researchers and practitioners when investigating and applying modeling in sport situations. However, there are several areas of this model where research is lacking. In particular, observer and task characteristics have received relatively little attention within the sport psychology literature. Within the broader psychology literature, modeling has been studied extensively from a developmental perspective; however, little modeling research within sport has specifically taken this approach (McCullagh, Stiehl, & Weiss, 1990; Weiss, 1983; Weiss, Ebbeck, & Rose, 1992; Weiss, Ebbeck, & Wiese-Bjornstal, 1993). Understanding modeling effects specific to athletes’ stage of learning as well as age and other developmental factors would provide valuable information for coaches and sport skill instructors working with athletes across the lifespan. For example, Law and Hall (2009a) found that among adult golfers, age was more influential in predicting their use of the functions of modeling than skill level. Specifically, younger golfers employed more of the skill function than older golfers and this discrepancy was larger as skill level decreased. Further, younger golfers employed more of both the strategy and performance functions than older golfers, regardless of skill level. These differences may be due to availability of appropriate models for these age groups but it may also be reflective of younger athletes being exposed to greater use of video during their skill acquisition and/or engaging in modeling using new technologies. In addition to exploring developmental factors, researchers should also consider exploring modeling influences with a greater diversity of populations across several dimensions (e.g., ethnicity, gender, skill level, parasport). There is considerable modeling research, and particularly self-modeling research, within therapeutic and educational settings (Dowrick, 1999, 2012) that may inform applied sport psychology research. To date, we found only one study (Luiselli, Duncan, Keary, Nelson, Parenteau, & Woods, 2013) exploring modeling as a sport performance enhancement strategy for individuals with intellectual and developmental disabilities. Their positive findings suggest that more work is merited in this area.
Researchers are also encouraged to explore a wider diversity of tasks. Currently, much of the modeling research within sport psychology has explored independent/individual sport performance (e.g., figure skating, gymnastics, cycling, running, swimming). Consistent with McCullagh et al.’s (2012) call for modeling investigations beyond sport, modeling researchers may also consider the growing literature suggesting that imagery is beneficial within team and interactive sport, exercise, and active play settings for children. To date there is scant modeling research exploring these areas (Fox & Bailenson, 2009; Obrusnikova & Rattigan, 2016).
Further research is also needed to clarify how modeling should be integrated into practice. It has been well documented that imagery should not take the place of physical practice but should supplement it (Post et al., 2015; Waskiewicz & Zajac, 2001). However, it is uncertain if this is true of modeling interventions. Prior modeling research has largely integrated the interventions into physical practice, by either asking participants to view a demonstration of the to be learned task or by incorporating videos as feedback during skill acquisition, and supports this notion. However, the ratio of modeling to practice that benefits skilled athletes and enhances psychological outcomes remains unknown. Future research addressing these questions will assist researchers in understanding the separate and combined contributions that modeling has on motor learning and performance.
Similar to how modeling research within sport psychology has grown out of modeling research within the motor learning domain and been influenced by sport imagery research, recent studies highlight the potential for inter-disciplinary modeling investigations through the use of physiological (Coelho et al., 2014; Gonzales et al., 2015) and biomechanical (deRenne & Morgan, 2013) measures. With the pervasiveness of modeling as a teaching and performance enhancement tool, there is great opportunity for researchers across kinesiology domains to work together.
Finally, as highlighted within this review, there is a need for more rigorous experimental designs within applied sport psychology and for studies using alternative designs to advance our understanding of modeling influences. Similarly, we could find no published research exploring modeling of maladaptive behaviors since McCullagh et al. (2012) highlighted this gap. Behaviors that are viewed as unsportsmanlike or antisocial within sport (e.g., aggression, cheating) and linked to aspects of moral development (e.g., doping) may be susceptible to modeling effects. More controlled research on role models in sport may advance our understanding not only of the effects of witnessing these behaviors but also on the factors that contribute to the exhibition of these behaviors.
Over the last 25 years there have been numerous reviews of the modeling research, and there is no doubt that watching demonstrations can influence a wide variety of behaviors. Despite this growing body of literature, modeling is often overlooked as an important performance enhancement technique within the sport psychology literature. Oftentimes practitioners reveal that they use video when working with athletes but rarely do they refer back to the modeling literature to provide a theoretical or conceptual rationale for this technique. Imagery is no doubt a valuable performance enhancement technique (Weinberg & Gould, 2015); most sport psychologists, Olympic-level athletes, and coaches use imagery. For nearly 30 years, researchers have suggested that modeling and imagery (see McCullagh & Weiss, 2002) may be similar processes but rarely are these techniques differentiated in the research literature. We hope that the evidence we have provided in this chapter will shed new light for not only future researchers but for the use of modeling as an intervention in applied sport psychology.
The authors would like to thank Devyn Richards for her assistance with references.
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