Risky Moves: Faster Movements Increase Perceived Thought Speed,but Do Not Lead to Riskier Behaviour on the Ballon Analogue Risk Task

Abstract

Background: When an individual perceives their mind is racing or overcrowded with thoughts, they are experiencing increased thought speed. Thought speed can be increased when watching fast moving images or text and has been linked to greater risk-taking behaviour. Purpose: Embodied cognition provides a perspective linking movements and cognition, and abstract concepts such as risk. The purpose of this study was to use this perspective to assess whether perceived speed of thought can be changed using self controlled body movements rather than observing movements, and if these manipulations have effects on risk taking behaviour as measured by the Balloon Analogue Risk Task (BART). Research Design: In two experiments, participants performed a Fitts’ tapping task (Experiment 1) or a reciprocal foot stepping task (Experiment 2) using either fast or slow movements. Participants then completed the BART, in which they received money for more virtual pumps of a computerised balloon, however each pump risked bursting the balloon and losing any money already earned. This task generates several measures such as the number of pumps, time between pumps, and number of explosions. In both experiments, the fast movements induced significantly faster perceived thought speeds than the slower movements. Sample: The study tested 30 participants in Experiment 1 (18 males, 12 females) and 36 in Experiment 2 (17 males, 19 females). Analysis: One-way ANOVAs were used to compare fast and slow movement conditions. Results: Despite the significant differences in thought speed in both experiments, neither found a pattern of greater risk-taking behaviour on any of the BART measures for the faster movement groups. Conclusions: These findings contrast with previous work and provide an updated perspective from embodied cognition on how movements affect thought speed. This opens avenues for future work, including interventions that may influence the risk level of tactical game play in sport.

Keywords

embodied cognition thought speed risk taking sport cognitive psychology

Introduction

A series of investigations in Cognitive Psychology have linked perceived cognitive states and subsequent decision-making behaviour. One line of research has investigated the influence of thought speed, specifically. Thought speed or the perceived speed of thought is defined as “…the momentary pace of one’s own thoughts” (Chandler & Pronin, 2012, p. 370), measured using self-report. Speed of thought, and specifically racing and crowded thoughts is part of the experience of mania (Bertschy et al., 2020). Risky behaviour is also associated with mania in mood disorders (Fletcher et al., 2013) In two studies investigating the relationship between thought speed and risk-taking behaviours, Chandler and Pronin (2012) showed that faster perceived thought speed induces greater risk-taking behaviour. In Experiment 1 they manipulated thought speed by asking participants to read text scrolling at either twice the normal reading speed or half the normal reading speed. Perceived thought speed was measured with a self-report item, rating the speed of thoughts on a 9-point Likert scale from 1 (very slow) to 9 (very fast). The fast -scrolling text induced faster perceived thought speed (M = 6.05) and the slow scrolling text induced slower perceived thought speed (M = 4.71). Subsequent performance on the Balloon Analogue Risk Task (BART) showed greater risk-taking behaviour by the group who read out loud from the faster scrolling text (greater average number of pumps). In Experiment 2, participants watched fast, slow, or normal speed movie clips. Subsequent responses on the Cognitive Appraisal of Risky Events Questionnaire (CARE), in which participants rate the likelihood that they will engage in several risky behaviours over the upcoming months, as well as the likelihood that these behaviours will result in positive or negative consequences, showed greater intentions to engage in risky behaviours for those who watched fast speed clips.

Chandler and Pronin’s (2012) work illustrates that thought speed and risk taking can be changed by manipulating the speed of visually processed stimuli. This raises the question about whether a similar relationship holds for other types of stimuli. In particular, while research within movement science shows the strong link between perception and action (e.g., Knoblich & Flach, 2001), there is no research that we are aware of that has examined the impact of movement speed on thought speed and behaviour. Yet, there is evidence that supports such a relationship. For example, a large body of research shows the effects of body positions on thoughts. In particular, Duclos et al. (1989), Niedenthal et al. (2001), and Price and Harmon‐Jones (2015) have all shown that adopting postures associated with particular emotions elicits these very emotions. The classic paradigm uses a pencil held in the mouth vertically with the tip facing up, which is a neutral position, compared with different smile-inducing postures such as holding the pencil in the mouth vertically while lifting the lips to prevent touching the mouth (a non-Duchenne or non-genuine smile), and holding a pencil in the mouth width-wise, which involves the muscles around the eyes, eliciting a Duchenne or genuine smile. The Duchenne or genuine-smile posture is associated with greater happiness. The impact of these effects was shown by Kraft and Pressman (2012). In a stressful task, participants who mimicked genuine smiles showed less stress and faster physiological recovery. The effects are also shown with other postures, as evidenced by Schubert and Koole’s (2009) finding that men’s self-esteem increased after making a fist.

These classic studies are the foundation for a larger body of research in embodied cognition, showing the link between movements and processing. Specifically, embodied cognition emphasizes the bidirectional link between motor processes and higher-level cognitive functions like judgement and decision making (Raab et al., 2023). In embodied choices, the body and its state or action dynamics, such as the speed of movement, informs choices (Gordon et al., 2021). Faster movements may prompt less deliberate and faster intuitive decision making (Kahneman, 2011), characterised by greater impulsivity and risk. In addition, within an embodied cognition perspective, arguments about how abstract concepts such as success are grounded in movements, have been put forward (Friedrich et al., 2024). Thus, embodied cognition can provide a new lens through which we can examine risk-taking behaviours such as risk of losing money (Klupp et al., 2023). The explanation for how movement, mediated by thought speed, may influence these choices and behaviours would provide an alternative to Chandler and Pronin’s work (2012).

The role of movement and sport in inducing risky non-sport behaviour has also been an area of interest to researchers, and is similarly related to understanding the relationship between movement speed and behaviours. The question often asked is whether individual personality traits are linked to sport involvement and behaviour (see Aidman & Schofield, 2004, for a review) and, of particular interest here, whether participation in sport is linked to subsequent behaviour characteristics (e.g., Cronin, 1991). An example is a study by Black et al. (2013), who tested performance on the BART after tennis playing. Overall, participants exhibited more risk-taking behaviour in the BART after playing 60 minutes of tennis compared to after a quiet rest period of 20 minutes. This finding was counter to the authors’ expectations, which were based on previous research showing decreased tobacco and alcohol cravings post exercise (e.g., Van Rensburg et al., 2009). Furthermore, this previous work proposed that reduced cravings are linked to a decreased willingness to take risks. In light of the findings of Chandler and Pronin (2012), however, it is possible that Black et al.’s (2013) finding of increased risk taking in the BART after playing tennis was moderated by perceived thought speed. Unfortunately, given this was not a focus of the study, it is unknown whether tennis playing increased thought speed. Investigating this provides a means of understanding and potentially intervening in behaviour. For example, if tennis play increases risk-taking behaviour through thought speed, interventions may provide an interruption. This also opens up avenues for sport tactical behaviour; if a coach perceives a player needs to increase or decrease risk within game play, a simple manipulation to increase or decrease perceived thought speed may aid. Towards this end, the nature of movements and their speed can be controlled to investigate the relationship between movements, thought speed, and risk taking.

Based on the reviewed literature, the present study was designed to address two main questions: a) do faster or slower movements differentially influence thought speed? And b) do faster and slower thought speeds induced by movements influence risk taking behaviour? We hypothesized that faster movements would induce faster perceived thought speed, and that faster movements would also be associated with more risk-taking behaviour as illustrated using the BART. This was tested using the methods of Chandler and Pronin’s (2012) Experiment 1, changing the manipulation of thought speed from a purely perceptual task to a movement task. To constrain the movements used and avoid the lack of control identified in the use of an open game of tennis in Black et al. (2013), we designed two experiments. In the first experiment we used the Fitts tapping task as a relatively fine motor movement, and in the second experiment we used a stepping task to investigate relatively gross lower body movement. The second experiment also used a control to limit eye movements, present in both the Fitts tapping task and in Chandler and Pronin’s (2012) processing of visual stimuli. We incorporated a metronome to enforce movement speed and constrain the movements, compared to the open nature of tennis (c.f. Black et al., 2013). The methods used in both experiments were approved by the University ethics committee. We hypothesized that faster movements would induce greater thought speed and greater risk-taking behaviour as measured in the BART. To guide our participant recruitment sample sizes, we referred to the samples used in Chandler and Pronin (2012), calculating an a priori sample size using g*power to estimate sample size for a one-way ANOVA with two groups. Using a medium effect size of .5, a power of 80% and a p of .05, g*power resulted in approximately 30 participants in line with Chandler and Pronin’s Experiment 1 sample size.

Experiment 1

Method

Thirty participants, 18 males and 12 females, with an average age of 21.8 years were recruited from the general population of a University. Each participant was randomly assigned to perform a Fitts tapping task for a duration of 2 minutes in either slow speed (N = 15) or fast speed (N = 15). The Fitts task requires participants to tap a pencil on a sheet of paper, reciprocally targeting two adjacent boxes. Boxes were approximately 5.5 cm long and 3 cm high, spaced 14.5 cm apart. Participants were asked to synchronize their tapping to the tones from an electronic metronome playing at either a fast tempo (230 beats per minute) or a slow tempo (40 beats per minute). Previous pilot testing showed these speeds and this manipulation to be effective in inducing significantly different thought speeds (p = .03), coinciding with the speed of movements (i.e., fast movement associated with faster thought speed, slow movement associated with slower thought speed). Figure 1 illustrates the experimental set up. Prior to completing the tapping task, participants were given the opportunity to familiarise themselves with the tones and required speed. Adherence to the speed was enforced by the experimenter through verbal reminders if participants became unsynchronised.

Figure 1.

Illustration of a participant completing the Fitts’ tapping task. This task was completed in either a fast or slow condition

After completion of the tapping task, participants indicated their thought speed on a 9-point scale (1 = very slow, 9 = very fast) and completed the PANAS (Positive and Negative Affect Schedule, Watson et al., 1988) as manipulation checks (data from three participants were lost due to technical difficulties). The PANAS is a well-validated tool to measure affect (Watson et al., 1988), and was included alongside the thought speed scale, which, while not validated, provided comparison to the data in Chandler and Pronin (2012). Participants then reported their age and sex, followed by 18 trials of the BART. In this computer-based task, participants click on a virtual balloon to inflate it, receiving 5c for each click. At any point, participants can decide if they would like to ‘bank’ the money for that balloon, or continue to click to increase their payment. Continued inflation of the balloon results in greater payment, but also increases the risk that the balloon will burst, and the money accumulated on that balloon will be lost. The likelihood of a balloon bursting is randomly determined (refer to Lejuez et al., 2002, for more details on the programming of this task). The probability of popping was set at 1-32, meaning that the average balloon burst after 16 pumps, and all balloons burst after 32 pumps (c.f., 1-64, Chandler & Pronin, 2012). Participants were not told the probability of popping. Participants were guaranteed payment of $5 for their participation, with a further maximum of $5 based on their ‘winnings’ in the BART, for a total maximum of $10. After completion of the experiment, participants were paid the $5 in addition to the amount that they had earned throughout the task.

Results

Data were analysed using one-way Analyses of Variance comparing Condition (fast, slow). Participants completing the Fitts tapping task in a fast condition reported faster thought speed (M = 5.93, SD = 1.14) compared to those in the slow condition (M = 4.69, SD = 1.38), F (1, 26) = 6.48, p = .017, partial η² = .21. These values are similar to those reported in Experiment 1 of Chandler and Pronin (2012) (slow: 4.71 (1.72); fast: 6.05 (1.84)), albeit with slightly faster fast condition values here (see Figure 2). Positive and negative mood, as derived from the PANAS, did not differ significantly between the fast and slow movement conditions (F values <1, p values >.05), although scores for positive affect were higher for the fast movement condition (M = 32.4, SD = 7.6), compared to the slow movement condition (M = 29.5, SD = 8.8), again consistent with previous research. Despite the differences in thought speed, there were no significant differences in any of the BART measures used, including total, average, and adjusted (only unpopped balloons) pump count, time between pumps, number of explosions, and amount of money earned (all F values >1, all p values >.05). Figure 3 illustrates this result using average pump count.

Figure 2.

Results from Experiment 1: Raincloud plot showing the statistically significant difference in perceived thought speed mean ratings by condition.

Figure 3.

Results from Experiment 1: Raincloud plot showing no statistically significant difference in average pump count in the Balloon Analogue Risk Task (Lejuez et al., 2002) in the fast and slow perceived thought speed conditions

Discussion

In this experiment, we hypothesised that faster movements would result in faster perceived thought speed. This hypothesis was supported. However, we also hypothesised that faster movements (and perceived thought speeds) would be associated with greater risk-taking behaviour as measured by behaviours in the BART. This was hypothesis was not supported. Several avenues for further exploration opened up. First, the task used a relatively small and fine movement on only one limb. Within the embodied learning literature, the embodied level refers to the degree of body movements during learning or encoding (Xu et al., 2022). Higher embodiment uses more of the body during movement, and in learning tasks, is associated with more embodied learning, or encoding (Guo & Goh, 2015). Thus, we hypothesized that a larger body movement involving both lower limbs may lead to stronger encoding of speed, and stronger association with risk as measured through behaviours in the BART. In addition, the probability of popping within the BART was relatively narrow or high at 1–32. With frequent popping, the BART at this level may have been a less sensitive measure for risk-taking and less able to pick up variance in participant behaviour that linked to the movement. Thus, the next steps identified were to use a stronger movement to encode speed and thought speed that might then be transferred to the BART, with a lower probability of popping, set to pick up more impulsivity in behaviour (Schonberg et al., 2011).

Second, as argued above, the hand movement task may prompt participants to relate to the perceptual effects (e.g. moving stimuli speed as in Chandler & Pronin, 2012), and moving the movement away from the hand to the foot allows for testing the motor component of the explanation more directly.

The follow up experiment therefore concentrated on a gross body movement involving the lower body, to assess whether using full-body movements increases the embodied cognition effect on thinking speed. In addition, Experiment 2 improved on Experiment 1 by using a lower probability of popping in the BART task (the same probability as in the original Chandler & Pronin, 2012 study) to maximise the possibility of picking up pumping and therefore risk-taking behaviour.

Experiment 2

Method

The same power calculation was used from Experiment 1, which indicated a target sample of approximately 30 participants as used in Chandler and Pronin (2012). Thirty-six participants, 17 males and 19 females, with an average age of 21.9, were again recruited from the general University population and tested in this second experiment. Participants were first screened and excluded if any leg, back, or general injuries were present which might interfere with participation. The methods followed the same general form as Experiment 1, with a few adjustments. First, the thought speed manipulation was changed to a gross body movement in which participants were asked to take a standing position and reciprocally tap their foot to the side in a touch tapping movement. That is, participants moved the right foot out, touched the ground and then stepped back to centre, before repeating on the left foot. To encourage a standard movement, markers were placed on the ground, with the top of the feet (toes) touching a central marker. Participants were asked to achieve the tapping movement by tapping the inside of the right foot to the outside corner of the right marker and back, and repeating on the left side, and to then continue alternating (left, centre, right, centre, etc…), in time to the metronome. Figure 4 illustrates the experimental set up. The distance from the outside corner of the central marker to the outside corner of the right or the left marker was 51.5 cm. Given that this grosser body movement is more generally taxing, the fast and slow speeds were adjusted from the Fitts tapping task to 24 beats per minute (slow) and 140 beats per minute (fast). The distance between these two speeds is the same as in the Fitts tapping manipulation, with the overall slower tempo reflecting the restricted possible movement speed for a grosser movement. Pre-testing showed that these two tempos were achievable without inducing a significant amount of fatigue.

Figure 4.

Experimental set up of Experiment 2 using a gross body Fitts’ tapping task at slow or fast speed

In addition, participants were given a ten second break between two blocks of 1 minute each of this movement. While performing the foot tapping task, participants were instructed to fixate a point on the wall in front of them, to minimise eye movements. The distance on the ground from the central marker to the wall with the visual fixation point was 237.5 cm, and the fixation point was placed at an average eye level height (156.7 cm). A final adjustment to the procedure in Experiment 1 was the lower probability of popping for the balloons in the BART task, which was now set at 1–64, meaning that the average number of pumps before popping was 32, and all balloons popped by 64 pumps. Similar to Experiment 1, participants were guaranteed payment of $5 for their participation, with a further maximum of $5 based on their ‘winnings’ in the BART, for a total maximum of $10. After completion of the experiment, participants were paid the $5 in addition to the amount that they had earned throughout the task.

Results

The same analyses were used as in Experiment 1, with a one-way ANOVA comparing Condition (fast, slow). There was a significant difference in reported thought speed between the fast stepping (M = 5.72, SD = 1.18) and slow stepping (M = 4.22, SD = 1.43) groups, F (1, 35) = 11.73, p < .01, partial η² = .26 (see Figure 5). In performance on the BART, however, there were no differences in total pump count, average pump count, adjusted total pump count, or adjusted average pump count (all F values <1; all p values >.05). Figure 6 illustrates this pattern using the average pump count measure. There were also no differences in the positive or negative scores on the PANAS (F values <1; p values >.05) (Figure 6).

Figure 5.

Results from Experiment 2: Raincloud plot showing statistically significant difference in mean ratings of perceived thought speed by condition

Figure 6.

Results from Experiment 2: Raincloud plot showing risk-taking behaviour as a function of thought-speed condition showing no statistically significant difference. Risk-taking behaviour was assessed using the Balloon Analogue Risk Task (Lejuez et al., 2002), in which greater risk taking is indicated by a greater number of balloon pumps on each trial

Discussion

In this follow-up experiment, we did not show a link between movement speed and risk-taking behaviour. Although the lower body, gross body movement showed a significant difference in ratings of perceived thought speed, there were no differences in the risk-taking behaviours as measured using the BART. The difference between fast and slow movements in this experiment was similar to that between the fast and slow tapping movements in Experiment 1. Thus, there is no reason yet to believe that the lower body movement resulted in stronger encoding of thought speed, as we speculated might happen. It may be, however, that the length of the manipulation, which was relatively brief, and the simplicity, involving only one repetitive small movement, may have limited effects for risk taking behaviour. In previous manipulations involving observation of moving stimuli (e.g., Chandler & Pronin, 2012), the variety of the stimuli may have had an effect. The nature of the movement used here, in both experiments was fast but repetitive, self-controlled, and predictable. While this produced higher perceived thought speed, the effects on risk taking behaviour may have been limited compared to a manipulation involving fast but reactive and more complex movements. Indeed, Black et al.’s (2013) study showing greater risk taking in the BART after 60 minutes of tennis play suggests there may be effects due to the reactive nature of tennis play, with complex whole-body movements. Thus, next steps may involve the use of a fast thought speed condition that involves actions that are reactive and complex, such as those in tennis, with laboratory-based controls and measurement of thought speed. Given complexity, This is in line with recent discussions that indicate structured research programs are needed for quantification and specification for embodied cognition effects (Raab et al., 2023).

Summary and Concluding Discussion

This is the first study, from examination of the literature, that has used movement speed as a manipulation of thought speed to examine subsequent behaviour. For both a fine motor (Fitts’ tapping task) and a more gross body movement task (stepping task), speed of movement was an effective manipulation of perceived thought speed, in the predicted directions (i.e., faster movements, faster thoughts). In contrast to previous work, however, the subsequent risk-taking behaviour, as measured in the BART, did not differ between groups. Mood was also not significantly altered by thought speed, whereas previous work has found some cases of increased positive mood with greater thought speed¹. There are a number of possible explanations for the difference between previous findings and those here, and how movement may differ from previous manipulations. These possibilities provide direction for future work.

As mentioned above, the first possibility for the failure to elicit greater risk-taking behaviour in this study is that the manipulation involved controlling movements rather than observing or following the movements of other stimuli or actors. The difference between observing or controlling movements has been shown by Langer & Roth, 1975. In their study, the predicted outcome of the next role of a die differs depending on whether the participant is observing someone else or rolling the die themselves. Similarly, Raab and MacMahon (2015) showed in a volleyball decision making task that taking the perspective of a player or a spectator changes the selection of who to pass the ball to. Indeed, Chandler and Pronin (2012) and Pronin and Jacobs (2008) propose that increased thought speed may signal the need for action. It is possible, then, that in addition to limitations in the complexity of the movements, actually performing the movements themselves, rather than the previous manipulations in which movement speed of words or images were observed, satisfies the need for action and thus mitigates risk taking behaviours. If this is the case, it may be fruitful to explore movement as an outlet for manic or anxious episodes with increased thought speed that otherwise may lead to risk-taking behaviours (e.g., in bipolar disorder or Post Traumatic Stress Disorder). Similarly, it may be worthwhile directly comparing the effects of thought speed on risk taking behaviour when the manipulation is passive, based on observation of movement versus active, based on performance of movements. This will help compare how much actual movement explains effects. The data from Black et al. (2013) suggests that complex reactive movements in tennis can generate risk taking behaviours, however there was no measure of perceived thought speed, with data collected post play, which includes multiple other factors (e.g., opponent, performance), and thus more exploration and control is warranted. Rather, a controlled examination may target mechanisms for the link between thought speed and risk taking from previous work to understand whether the need for action is satisfied in one scenario and not the other, with risk taking an outcome of an unsatisfied need. Preference or desire for movement may provide an additional informative measure. The State Urge to be Physically Active Questionnaire (SUPA-Q) (Amin et al., 2023) provides a potential tool.

A second possibility for the absence of riskier behaviour after movement speed manipulations of thought speed is that the effects may be either shorter lived than those elicited through previous manipulations, or that the BART is not the most appropriate risk-taking task to capture the effects. Although Chandler and Pronin (2012) found greater risk-taking using both the BART and the CARE questionnaire, only specific measures may reveal the type of risk associated with movement-induced faster perceived thought speed. In addition, the subjective nature of self-report creates a vulnerability to participant bias; moving faster may have prompted participants to report faster thinking as an expectancy, rather than a true reflection of their thought state. The saliency of the speed differences between the two conditions, given that participants were directly involved in controlling this speed, may have been a stronger signal of the expected effects than in designs in which participants are passive observers. Therefore, although the previous research in this area has relied on subjective measures, future work will be strengthened with the inclusion of objective or multiple measures of this variable.

We argue that an embodied cognition perspective could advance the field, as abstract concepts such as success, freedom, or, in our case, risk, are grounded in physical experiences (Klupp et al., 2023). Thus, from a bidirectionality argument in embodied cognition, speeding up movements, resulting in speeding up thinking, which may be trainable (Galhardas et al., 2025). The next stages of research investigating the influence of movement on thought speed and risk taking should address the different parts of the equation in isolation: a better understanding of the relationship between movement speed - using different types of movements (e.g., exploring reactivity and complexity), and perceived thought speed, and of movement speed (and type) and risk-taking. Understanding whether faster perceived thought speeds or racing thoughts are related to faster movement reactions will contribute to our knowledge. Indeed, reactive types of movements are a hallmark of many sports, such as soccer, which require not only fast running but split-second risky decisions. Training those decisions under high speed and under time-pressure may produce better decisions (Musculus et al., 2018).

Regardless of the future work to be done, this study is significant in opening up a new line of inquiry, and for its contribution to multiple disciplines, given the relevance for sports and movement science, general psychology, as well as mental health and clinical applications. It has the potential to contribute to understanding athlete behaviours within competition (e.g., strategies and tactics), as well as post competition (e.g., risky drinking, driving behaviours). Continued work will focus on the unique nature of movement as related to perceptions and experiences of thought and behaviour, as well as advancing our measurement and understanding of this variable.

Footnotes

ORCID iD

Clare MacMahon

Ethical Considerations

All procedures in this paper were approved by University Ethics committees.

Consent to Participate

All participants provided informed consent before participating.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data from this paper are available at https://doi.org/10.26181/30052705 (MacMahon & Raab (2026)).

Note

Author Biographies

Clare MacMahon is an Associate Professor and Discipline lead for Sport and Exercise Science at La Trobe University, in Melbourne, Australia. She is co-founder of the Australasian Skill Acquistion Network, and active internationally in Sport Psychology and Skill Acquisition conferences and research. Clare's expertise is in decision making and expertise from a cognitive perspective, with research in the areas of development and training in sports officials, and the impact of mental fatigue on physical performance. She has worked with a number of professional sporting organisations in these areas.

Markus Raab is director and professor for psychology at the Institute of Psychology at the German Sport University, Cologne. He is Editor-in-Chief of Psychology, Sport and Exercise. He is past-president of the European Federation of Sport Psychology (FEPSAC). He received a PhD in sport science as well as a PhD in psychology and was awarded by ECSS, FEPSAC, asp, DOSB, NCA, DGPs for his research. He published seven books and over 100 papers in peer-reviewed journals. His main areas he is interested in covers judgment and decision making in sports and beyond, embodiment as well as motor learning and motor control.

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