Automatic Regulation Used in Sport and Exercise Research
Summary and Keywords
Much of our sport and physical activity behavior is regulated by processes occurring outside of conscious awareness. In contrast, most sport and physical activity research focuses on processes that are easily accessible by conscious introspection. More and more, however, research is demonstrating that automatic regulation is instrumental to our understanding of how to get people to maintain a physically active lifestyle and how to get the most out of people’s sports performance potential. Automatic regulation is the influence on our thoughts and actions that result from the mental network of associations we use to make sense of the world around us. Habits are automatic associations of cues with behavioral responses. Automatic evaluations are automatic associations of cues as being good or bad. Automatic schemas are automatic associations of cues with actual or ideal self-identity. These processes have been assessed with implicit measures by making indirect inferences from self-report or response latency tasks. Emerging research demonstrates that automatic associations influence sport performance and physical activity behavior, but further work is still needed to establish which type of automatic regulation is responsible for these influences and how automatic regulation and reflective processes interact to impact movement.
Performing well in sports and living a physically active lifestyle seem like very different actions, but, these behaviors might be based on the same types of underlying processes. According to some theories, all of our thoughts and actions are dictated by two regulatory systems: the reflective system, which is slow, straightforward, and deliberate; and the automatic system, which is spontaneous, unintentional, and a little bit stealthy in that it biases our thoughts and actions without us necessarily being aware of the influence. These “dual-process” systems are said to interact to influence our thoughts and actions, with the automatic system eliciting spontaneous biases, which may then be deliberated on, filtered, or redirected by our reflective system (Chaiken & Trope, 1999; Evans & Frankish, 2009; Furley, Schweizer, & Bertrams, 2015; Strack & Deutsch, 2004).
Friese and colleagues (2011) elegantly describe this dual-process perspective of behavior regulation, with the automatic system represented by a horse and the reflective system represented by a rider. When the dual processes are concordant, the rider is able to work in unison with the horse’s urges, but if the systems are conflicting, the rider will either attempt to redirect the wild impulses of the horse or just go along for the ride. The determination of the horse is representative of the strength of the biases of the automatic system, and the rider’s efforts to redirect the horse are reflective of an individual’s self-regulatory capacities in that moment. It is important to note that the “horse’s” actions can either be beneficial or costly for health. That is, a person with a strong habit of walking the dog each morning does not need to redirect that “horse” for health benefits, but persons who are trying to start being more physically active will need to consistently redirect their “horse” from temptations of inactivity to achieve their goal.
There is far more empirical evidence about the reflective system processes than about the automatic system in sport and physical activity. And almost no research has considered the complex interplay between these dual processes in these fields. The comparative lack of insight about the automatic system is not surprising, given that people can access and report their reflective processes, but asking people for insight about their automatic system is likely to be as enlightening as a conversation with a horse.
Prior to any influence from reflective processes, the automatic system influences actions and decisions via a network of automatic associations—mental representations of the connections between objects and events in the environment and the way we feel about them. This network of automatic associations reflects individuals’ understanding of the world and acts as their internal compass—biasing their attention, perception, and behavioral reactions to objects and events. For example, seeing a fast, inside baseball pitch will activate a series of related automatic associations. To some, it may elicit an automatic association with fear of being beaned, so they may react by flinching and leaving their bat resting on their shoulders. To others, this same pitch may elicit a behavioral automatic association of a correctly adjusted swing of the bat, resulting in a decent base hit.
There is evidence that automatic regulation is not a single process. Just as there are multiple distinct reflective processes influencing sport and physical activity behaviors (e.g., self-efficacy is distinct from a person’s attitudes toward sport), so too are there different forms of automatic processes influencing these behaviors (Bargh, 1994; Moors & De Houwer, 2006). Some examples of the different types of automatic regulatory processes are presented in Table 1.
Table 1. Examples of types of automatic regulatory processes and their influences on behavior and decisions
cue to behavior
behavioral response to cue
cue to good/bad
approach/avoid bias to cue
cue to self/other
approach/avoid bias to cue
cue to cue
shared responses to both cues
Habits are the processes by which cues automatically generate impulses toward behaviors, based on the well-learned automatic associations of cues to behaviors (Gardner, 2015). There can be habits of the instigation of behavior or of the execution of behavior (Phillips & Gardner, 2016). For example, a person may have an instigation habit to go on a walk every day after dinner but take different routes each time (no execution habit), whereas another person may have an execution habit of taking the same route every time on a walk but not have the habit of walking regularly (no instigation habit).
Automatic evaluations are the nonconscious connection between cue and “good” or “bad,” which elicits behavioral approach or avoidance tendencies, respectively (Bargh, Chaiken, Raymond, & Hymes, 1996; Rebar et al., 2016). Unlike habits, automatic evaluations do not elicit specific behavioral responses; rather, they lead people to approach or avoid opportunities of behaviors associated with the cue. For example, a person with favorable automatic evaluations of physical activity (i.e., a strong link between “physical activity” and “good”) might be more likely to take the stairs instead of the elevator when given the choice.
Automatic schemas are the nonconscious link between a cue and a person’s mental representation of his or her own real or ideal identity. Similar to automatic evaluations, automatic schemas do not elicit specific behavioral responses, but only the tendency to approach or avoid opportunities to behave in ways aligned with the cue. People are more likely to take up opportunities to behave in line with their mental representations of themselves (Banting, Dimmock, & Lay, 2009). For example, if a young girl has not developed an automatic schema of herself as an athlete, she may be less likely to enroll in sports programs or want to develop sports skills.
The last type of automatic regulation outlined in Table 1 is automatic pairings—the mental binding or bridging between two or more cues that can result in a phenomenon of the perception of a single cue eliciting multiple types of automatic regulatory responses. Automatic associations are all interconnected via automatic pairings. An example of this type of automatic regulation is given in Figure 1. People likely have automatic pairings between their mental representations of themselves as “good” and others as “bad” (represented by double-weighted lines in Figure 1). The automatic pairings then link automatic schemas (dashed lines) to automatic evaluations (dotted lines), in that a cue representative of “self” will elicit behavioral approach tendencies from an automatic schema as well as from an automatic evaluation (solid lines at top of Figure 1), and a cue representative of “other” will elicit parallel behavioral avoidance tendencies (solid lines at bottom of Figure 1).
What Are Cues?
The commonality across automatic regulation processes is that they are all elicited by the perception of a cue. A cue is the external or internal source that activates the automatic association. Locations or contexts can be cues. For example, a person with a strong habit of running the same path each day may find himself making the turns of the path without reflecting on the behavior—that is, acting in an “auto-pilot” manner. People and objects can be cues. Some people are far more likely to approach physical activity opportunities when they are around their “work-out buddy” or certain sporting equipment (Pimm et al., 2015). Events or behaviors can be cues. Many motor reactions in quick-paced sports can be attributed to automatic responses to the perception of opponents’ behaviors (Kibele, 2006).Emotions or feeling states can also be cues. For example, energetic, positive moods can sometimes elicit spontaneous dance sessions.
So what are not cues? Reflective processes are not cues. The literature contains some dispute regarding whether the reflective process of goals can trigger the automatic process of habits (e.g., Aarts & Dijksterhuis, 2000; Neal, Wood, Wu, & Kurlander, 2011; Wood & Neal, 2007). Neal and colleagues (2012) demonstrated that habits can be aligned with goals but are actually triggered by cues (in their study it was performance environments). The present consensus is that reflective processes can make certain cues more or less salient but are not cues in their own right (Wood, Labrecque, Lin, & Rünger, 2014). Wood and Rünger (2016) present the interface between cues, goals, and habits in a straightforward model, which demonstrates how, if the full interface is not accounted for, reflective processes such as goals can be confused as determinants of habit. According to these authors, the confusion likely arises because behaviors may start out as being regulated by reflective processes such as goals but over time have become automated as a habit through consistent cue-behavior pairings.
Automatic Regulation, Sport, and Physical Activity
Each person’s network of automatic associations is unique, as is the way his or her automatic system impacts individual thoughts and actions. In the horse/rider analysis, the strength of the automatic associations is represented by the stubbornness of the horse. The magnitude of automatic associations is based on people’s experiences, personal beliefs, and perceptions of what others think and do (Rudman, 2004). For example, Robertson and Vohora (2008) found that physical education teachers and trainers had stronger automatic antifat biases if they had never been overweight themselves or believed that body weight was determined by personal control. In the context of sport, Brand, Wolff, and Thieme (2014) showed that elite bodybuilders with a recent history of substance abuse (tested via biochemical testing of urine samples) had less favorable automatic evaluations of doping than those who had no recent history of substance abuse. Although some variability in automatic associations is stable as a trait, they also fluctuate across time depending on context or mood and motivational states (Hyde, Elavsky, Doerksen, & Conroy, 2012). So, automatic associations will likely be more influential in some instances than in others.
Certain events, feelings, or environmental cues might make some automatic associations more salient, and therefore more prone to influence behavior, than others (Gawronski & Bodenhausen, 2006). For example, some evidence suggests that physical fatigue and psychological stressors can harm sports performance, unless it was learned implicitly (Poolton, Masters, & Maxwell, 2007). To illustrate how motivational states can impact the saliency of automatic association influences, Banting et al. (2012) primed people to have motivational biases via a word-scramble sentence game during a cycling task. They found that priming people with autonomous motivation with words like “interested” led to more enjoyment of the exercise, higher maximum heart rate during the exercise performance, and a lower perceived exertion of the task. Also, Stone and colleagues (1999) reported that priming people with racial stereotypes prior to performing a sport impacted performance in line with racial stereotypes. Black participants performed worse when a golf task was framed as a “sports intelligence” test, and white participants performed worse when the golf task was framed as a test of “natural athletic ability.” Some evidence suggests that the way automatic associations influence behavior can be manipulated through enhancing the saliency of certain automatic associations. The effectiveness of mental imagery training has been attributed to this type of priming effect (Holmes & Calmels, 2008).
In addition to the influence of momentary states and contexts, the automatic system will likely be more or less influential depending on individual differences. Some people are probably largely driven by their automatic system, driven to act on impulse or habit, in line with their automatic associations, whereas others are probably far more deliberative about their decisions and override their automatic regulatory system. In the horse/rider analogy, these individual differences are representative of the determination of the rider in its efforts to redirect the horse. Sometimes, being too deliberative is disadvantageous. For example, in sport, efforts are made to avoid the phenomenon of reinvestment—the interference of the reflective system with the successful performance of the automatic system (Masters, 1992). Certain training regimes are directed to avoid reinvestment through implicit learning strategies that build motor skill development with minimal reliance on the performer’s conscious introspection (Francesconi, 2011; Poolton & Zachry, 2007). In other circumstances, reinvestment of the reflective system is beneficial, and individual differences that increase deliberative processing can be useful. For example, people with generally good self-control tend to be more physically active overall than people with less self-control (Hagger, Wood, Stiff, & Chatzisarantis, 2010). Relatedly, the construct of mental toughness, which has become popular in research predicting successful sports performance, seems to mostly reflect good self-control (Crust, 2007). Such individual differences provide tools for overcoming unhealthy or deleterious automatic associations. As such, self-control training programs provide one method for improving physical activity engagement and sports performance if aspects of a person’s automatic system guide their “horse” in an undesirable way (Hester & Miller, 1989).
The Measurement of Automatic Regulation in Sport and Physical Activity
The emerging evidence base on automatic regulation in sport and physical activity shows great promise (Furley et al., 2015; Rebar et al., 2016). That these types of behavior are partially directed by automatic regulatory processes opens possibilities for novel strategies to intervene with physical inactivity and enhance sports performance (e.g., Marteau, Hollands, & Fletcher, 2012; Sheeran, Gollwitzer, & Bargh, 2013). However, the credence of this research and any forthcoming intervention strategies is dependent on the validity and trustworthiness of the measurement of these illusive constructs. There remains controversy regarding the measures being utilized to capture automatic associations (e.g., Calanchini & Sherman, 2013; Hagger, Rebar, Mullan, Lipp, & Chatzisarantis, 2015; Sniehotta & Presseau, 2011). Justifiably, criticisms are being raised, and researchers are being consistently asked to defend the construct validity of the implicit measures that they are applying in research on automatic regulation of sport or physical activity. Some measures are criticized for being too indistinguishable from its behavioral influence (e.g., Sniehotta & Presseau, 2011), whereas others are criticized for being too disconnected from the behavioral influence (e.g., Mierke & Klauer, 2001, 2003). Researchers aiming to assess any construct must walk the fine line between demonstrating accessible discriminant and convergent validity; however, the watchdogs seem to be a bit more diligent in this regard for implicit measures. Measures of reflective behavioral determinants such as intentions or goals are far less scrutinized, even though their construct validity is reliant on post hoc metacognition, and people’s retrospections are likely overestimations of how volitional their behavior was (i.e., “yes, I intended to do that!”) (Wood & Rünger, 2016).
Implicit measures aim to capture the behavioral precursors that are inaccessible via direct, explicit self-report. Only a small portion of the actual regulatory drivers of decisions and behaviors is accessible to a person in any given moment because people may not be motivated or have the opportunity, ability, or awareness to accurately report all these influences (Nosek, Hawkins, & Frazier, 2011). This level of metacognition would be incredibly taxing on a person’s working memory and self-regulation, leaving little capacity for any other cognitive or behavioral processes. Whereas a person might be able to accurately reflect on their reflective goals and beliefs, automatic associations and their influence on behavior may not be as accessible via introspection. As such, researchers cannot solely rely on people to directly report all of their automatic associations or the degree to which automatic regulation influences their behavior. Instead, studies have been assessing automatic regulatory constructs via implicit measures—measures that make inferences based on indirect assessment (Nosek et al., 2011). Implicit assessments can be delivered via a variety of modes. For example, inferences about automatic regulation are based on response latencies on computer-based categorization tasks (e.g., Rebar, Ram, & Conroy, 2015) or on thematic analysis of self-reported impressions of the meanings of vague vignettes (e.g., Bernecker & Job, 2011). It is how the automatic regulatory constructs are inferred from the measures that distinguishes implicit measures.
Implicit Self-Report Measurement of Automatic Associations
Given that automatic associations are not accessible via introspection, the validity of self-report measures of automatic regulation remains controversial (Gardner, Abraham, Lally, & de Bruijn, 2012; Hagger et al., 2015; Schultheiss & Pang, 2009; Sniehotta & Presseau, 2011). Some argue that self-report of automatic regulation may not be possible at all (e.g., Sniehotta & Presseau, 2011). If this is the case, this majorly handicaps research of automatic regulation because self-report assessments are uniquely accessible and cost-effective means for large-powered survey studies, which are instrumental aspects of how we understand the way people think and act (Paulhus & Vazire, 2007). Fortunately, some self-report measures seem to have validity for assessment of automatic regulation.
Self-Reported Habit Indices
In physical activity research (and other health behavior research), a self-report habit index has become quite prominent (Verplanken & Orbell, 2003), as well as subsets of the full index, including Gardner and colleagues’ four-item automaticity index (Gardner, Abraham, Lally, & De Bruijn, 2012). The scores represent the degree to which a behavior is habitual for that person (weak to strong). These scores have been shown to be independent from past behavior and to explain future behavior beyond a wide variety of reflective processes such as intentions or self-efficacy (Gardner, de Bruijn, & Lally, 2011; Rebar et al., 2016). In general, scores from these self-report measures are linked to physical activity behavior with a medium-sized effect (d = 0.67, Rebar et al., 2016). The initial self-report habit index was developed to capture how much respondents agreed with several facets of habit including history of repetition (“[the behavior] is something I do frequently”), automaticity (“. . . is something I do automatically”), and expression of identity (“. . . is something that’s typically me”). There was some resistance to the habit index based on critiques, including the unnecessary redundancy of some items and the controversy over whether identity is part of the definition of habit (Gardner, de Bruijn, & Lally, 2012; Gardner et al., 2011; Sniehotta & Presseau, 2011). Additionally, it was argued that by including assessment of past behavior, the meanings of relationships between habit and behavior were hard to interpret. As such, Gardner and colleagues developed a subscale of the habit index that is a more generic scale of automaticity (Gardner, Abraham, Lally, & De Bruijn, 2012). For this scale, people are asked to report their level of agreement with how automatic the behavior is to them. Orbell and Verplanken (2015) point out that by removing items of behavioral repetition, the scale scores no longer can be conceived as reflections of habit, but rather as a measure of self-reported automaticity. Untested to this point is what types of automatic associations this scale captures. There is no specificity of the scale to the influences of habit, so it may be that the scale scores represent a person’s perception of the general strength of the effect of all forms of automatic associations on behavior (i.e., automatic evaluations, pairings, schemas, and habit).
Implicit Motivation Measures
In sport performance literature, implicit self-report measures do not focus on habit, but rather they assess what is referred to as implicit motives (Woike & Bender, 2009). Implicit motives are people’s more nonconscious biases toward certain types of emotional or motivational states and are linked to intrinsic incentives (e.g., enjoyment). Implicit motives reflect behavioral, cognitive, and perceptual biases that originate from a node of automatic associations involving automatic evaluations, automatic schemas, and automatic pairings. For example, what is referred to as the implicit achievement motive (i.e., a bias toward aiming for personal standards of excellence) (McClelland, 1965) may be the product of a node of automatic associations, including an automatic evaluation of “achievement” as “good,” and an automatic schema between “self” and “achievement.” Although the exact structure of these nodes has yet to be established, it seems evident that the self-report measures used to infer implicit motives are starkly different from their explicit motive counterparts.
The most common self-report measure of implicit motives is the Picture Story Exercise or the Thematic Apperception Test (McClelland, Koestner, & Weinberger, 1989). For this exercise, respondents are asked to write imaginative stories to describe scenes within ambiguous pictures. The self-reported measure is then scored using rigorously tested thematic analysis style coding systems. Implicit motives include the need for achievement, the power motive (i.e., biases to control or influence others), and the need for intimacy (i.e., biases toward warm, close, communicative exchanges with others; Schultheiss & Pang, 2009; Woike & Bender, 2009). These motives have been shown to be distinct from explicit motives and to be related to the outcome scores of implicit association tests—response latency measures of automatic associations (Sheldon, King, Houser-Marko, Osbaldiston, & Gunz, 2007). Implicit motives have been linked to sports-related behavioral outcomes, including heightened attentional sensitivity, perceptual biases, and behavioral tendencies toward motive-related cues, all underpinned by shifts in hormonal markers/neurotransmitters (Schultheiss & Brunstein, 2010; Woike & Bender, 2009). Although the underlying structure of the automatic associations remains unclear, what is clear is that these implicit motives provide insight into behavioral influences important for sporting performance.
Implicit Response Latency Measures of Automatic Associations
The self-report measures used to assess automatic associations in sport and physical activity are quite distinctive, in that there is little overlap in the use of these measures across fields. This is not the case when it comes to response latency measures of automatic associations. The same types of response latency measures are prevalent across research on both physical activity and sport, although their use is still rare. Mostly adapted from social psychology, these tasks utilize the speed and accuracy of people’s responses to stimuli to make inferences about the strength of relevant underlying automatic associations. For example, the implicit association test has been used to test for automatic evaluations of doping in sport (Brand et al., 2014) as well for automatic evaluations of physical activity (Hyde et al., 2012).
Implicit Association Tests
The implicit association test originated as a social psychology measure of non-conscious biases (Greenwald, McGhee, & Schwartz, 1998). It came under heavy scrutiny in the early 2000’s, particularly when it was applied as a measure of racial prejudice; however scrutiny and revisions of design and scoring algorithms have helped quell some of these concerns (Cai, Sriram, Greenwald, & McFarland, 2004; Greenwald, Nosek, & Banaji, 2003; Karpinski & Steinman, 2006; Nosek, Greenwald, & Banaji, 2007). The measure is a computerized task in which respondents sort stimuli into categories. For the initial test block of the task, categories are composed of one of two targeted concept categories and one of two contrasting attributes; then for the second test block, these are reversed. For example, in the first block, people may be asked to categorize stimuli into the categories of either “self + good” or “other + bad”; then in the next block, they would be asked to use the categories of “self + bad” and “other + good.” The stimuli being categorized are typically a series of pictures or words that represent the concepts and attributes (e.g., myself, them, excel, unpleasant). Relative strengths of automatic associations are inferred based on a comparison of performance between test blocks. So, someone who was quicker/more accurate during the “self + good”/“other + bad” as opposed to the “self + bad”/“other + good” block would be deemed to have a positive automatic schema (i.e., stronger mental link between self and good than between other and good).
Implicit association tests can be adapted to assess the strength of any form of automatic association. These tests have been used to show that automatic exercise schemas (links between “self” and “exerciser” relative to “not self” and “sedentary”) were related but distinct from explicit, self-reported exercise schemas, and that each predicted independent variability in physical activity behavior (Banting et al., 2009). An implicit association test with the concepts high flight/low flight and the attributes pleasant/unpleasant was used to show that automatic evaluations of “high flight” predicted a pilot’s risk-taking behavior to a greater extent than myriad related reflective processes (Molesworth & Chang, 2009). Given that not all automatic associations are comparisons between two opposing concepts, single-category adaptations of the implicit association test have also become quite common (Karpinski & Steinman, 2006). For example, Conroy et al. (2010) tested automatic evaluations of physical activity with a single-category implicit association test in which the categories of one test block was “physical activity + good” and “bad” and the categories of the other test block were “physical activity + bad” and “good.”
Other Implicit Response Latency Measures
Many other types of response latency measures have been used to assess sport or physical activity relevant automatic associations. Nosek and Banaji’s (2001) Go/No Go Association task has been used as a measure of sport expertise for sport-specific decision making (Nakamoto & Mori, 2008) and automatic evaluations of exercise (Berry & Shields, 2014). De Houwer’s (2003) Extrinsic Affective Simon Task has been used as a measure of automatic evaluations of exercise (Calitri, Lowe, Eves, & Bennett, 2009). An evaluative priming task (Eves, Scott, Hoppé, & French, 2007; Fazio, Jackson, Dunton, & Williams, 1995) has been used to assess automatic evaluations of exercise (Bluemke, Brand, Schweizer, & Kahlert, 2010).
Given the nature of the tasks and the variety of the measures available, response latency measures can be used to assess all forms of automatic associations. However, their use has been limited to small-scale studies, likely because the tests are usually time consuming and require controlled measurement of the timing of responses. Now, there are web-based systems that make large-scale studies more accessible (e.g., Millisecond Software, 2002), so it is likely that the use of tasks like these will become even more popular in the sport and physical activity fields.
The regulation of maintaining a physically active lifestyle and the intricacies of successful sport performance is the result of a complex interplay between reflective and automatic processes. Like a horse and rider, these systems can work to uniformly or oppositionally influence behavior. A comprehensive understanding of the entire behavioral process requires an understanding not only of the dynamics between horse and rider, but also of the inner workings of both. Presently, much more is known about the forces behind the rider (i.e., the reflective system) than about those of the horse (i.e., the automatic system).
All that is known about the automatic system’s influence on physical activity or sport is that it exists above and beyond reflective processes. This lag in understanding of the automatic regulation of physical activity and sport is partially due to practical measurement issues. In some cases, it is unclear which type of automatic association is being captured with the current measures being used to assess these constructs. For example, when someone is asked to what degree a behavior is automatic for them (e.g., Gardner et al., 2012), how can it be determined whether the automatic influence being reported on is habit instead of automatic evaluation, automatic schema, or a combination of the influence of them all? Automatic regulation is far less accessible to direct measurement and, instead, most likely must be inferred indirectly from implicit measurement. Compared to their more direct counterparts, implicit measures are difficult to create, implement, and interpret. Currently, implicit self-report and response latency measures are being relied on, but it may be that these implicit measures start to give way to, or are used in unison with, other possible implicit measures such as responses of brain activity or blood biomarkers to cues. As the ease and accessibility of valid implicit measures prevail, a more thorough understanding of what drives physical activity and sports behaviors will be obtained. Only with a more developed understanding of automatic regulation of sport and physical activity will the true potential of intervention tools be realized.
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