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The current experiment is considered a “Proof-of-Concept” in which we aim to establish the feasibility and rationale for an integrated theory that has been developed as part of the German Research Foundation (DFG) project “Motor heuristics and movement analogies in performance and health” (PIs: Markus Raab and Laura Voigt, RA 940/27-1 Ι VO 2789/1-1). For successful sports performance, athletes must choose between actions (movement selection) and then execute the selected action skillfully (movement execution; Raab, 2017; Voigt et al., 2022). A table tennis player who can execute a backhand stroke effectively but does not know when to use the backhand stroke will not succeed in the sport. Equally, a player who knows when to employ the backhand stroke but cannot execute it effectively is unlikely to succeed. Movement selection and execution often need to be performed when time is limited, stress is present, more than one task needs to be processed, or when fatigue or unfamiliar situations occur. Given limited ability to process multiple channels of information, performers need to find ways to manage the wealth of available information economically to make appropriate decisions and execute the chosen movement skillfully in their discipline. The idea that economic management of information is central for successful performance is supported by a common understanding of expertise in sports: During the development of expertise, the nature of the knowledge structures that support motor performance gradually changes over time, with an increasing degree of implicit (unconscious) processing and a decreasing level of explicit (conscious) processing (Fitts & Posner, 1967). Explicit processes involve the retrieval of consciously accessible (declarative) knowledge and depend on working memory. Thus, highly explicit sport performance has often been shown to be disrupted by demands resulting from performance pressure or multiple task requirements (Masters & Maxwell, 2008; Nieuwenhuys & Oudejans, 2012, 2017). In contrast, implicit processes are faster and involve sophisticated complexes of procedural knowledge that can be applied without conscious thought, with greater automaticity and fewer demands for attentional resources (e.g., Anderson, 1983; Lewicki et al., 1992; Kal et al., 2018; Masters & Maxwell, 2004; Shiffrin & Schneider, 1977; Willingham, 1998). Implicit processes are therefore less dependent on working memory, which allows the expert to economically manage multiple streams of information for movement selection and movement execution while being taxed with other demands (for a review of the theoretical architecture and function of working memory, see Baddeley, 2003). But how exactly are these streams of information for movement selection and movement execution managed? In the current study, we aim to deliver a “Proof-of-Concept” for the integration of two theories – motor heuristics (Raab, 2017) and motor analogies (Masters, 2000, 2012). We aim to investigate whether implicit learning helps athletes to manage information during movement selection and movement execution and protects performance under demanding circumstances. Motor heuristics have their theoretical basis in the simple heuristics used for decision making (Gigerenzer & Goldstein, 1996) and advise “what” movement to choose (i.e., movement selection). Motor heuristics are fast and frugal decision-making strategies (i.e., rules of thumb) that exploit information in the environment by ordering pieces of information (i.e., cues) by their validity. Decision makers judge the cues’ validity (i.e., how often in the past a particular piece of information was helpful in making a choice), and then choose the option that is favored by the cue with the highest validity. In sports, a large body of empirical findings demonstrates that superior decision making by experts is characterized by focusing on fewer (task-relevant) options and higher-quality options and decisions (Basevitch et al., 2020; Belling et al., 2015; Laborde & Raab, 2013; Musculus, 2018; Musculus et al., 2021; Raab & Johnson, 2007), especially in demanding circumstances, such as time pressure, opponent pressure, and stress (Musculus et al., 2021). For heuristics to be successful, their use needs to be matched to the environmental structures. Leuker et al. (2018, 2019) showed that statistical regularities (i.e., risk-reward structures) can be learned without explicit instructions via incidental, unsupervised learning and then utilized as heuristics for decision making under uncertainty. Implicit learning processes can thus be an important aspect in building a representation of the environment that, in turn, guides choice behavior (Hertwig et al., 2022). Importantly, the so-called description-experience gap further suggests that learning environmental structures by experience is more beneficial for performance than receiving explicit information about the environmental structures (e.g., Hertwig et al., 2004; Armstrong & Spaniol, 2017). Motor analogies have their theoretical basis in the theory of implicit motor learning (Masters, 1992, 2000) and advise ‘how’ to move (i.e., movement execution). Motor analogies leverage a concept that is already well known, such as “strike the ball while bringing the bat up the hypotenuse of a triangle” in order to convey the complex structure of the motor skill (e.g., a table tennis topspin forehand; Liao & Masters, 2001). It has been proposed that they promote economic management of information for movement control by chunking fundamental technical information (i.e., relevant pieces of information) into one well-known concept – many small “bits” of information are collapsed to into fewer larger chunks (Poolton & Masters, 2014). Although fewer chunks are processed, they contain the relevant information, meaning that information can be processed with relatively less cognitive effort and processing becomes more efficient. At the same time, motor analogies minimize accrual of conscious knowledge of the underlying rules governing the mechanics of movements (e.g., Liao & Masters, 2001). Learning movements implicitly through analogies has been shown to result in more robust motor performance under demanding conditions than learning movements by explicit step-by-step instructions (e.g., Koedijker et al., 2007, Lam et al., 2009; Liao & Masters, 2001; Schlapkohl et al., 2012; for a meta-analysis see Cabral et al., 2022), which has been attributed to the placement of fewer demands on cognitive resources than explicit motor learning. To date, motor heuristics and analogies have only been researched independently from each other. Notably, it has been shown that implicit motor learning through motor analogies did not only improve motor performance, but also decision making, potentially because implicit processing of movement execution left sufficient cognitive resources available for decision making (Lola & Tzetzis, 2021; Masters et al., 2008). Likewise, the use of heuristics (instead of a complex step-by-step planning strategy) showed combined selection and execution advantages, when participants were required to select one of two computer cursors that displace in different directions (i.e., selection) and subsequently navigate from a starting position to a goal as efficiently as possible (i.e., execution; Dundon et al., 2023). We aim to extend these findings by testing performance in demanding circumstances after promoting the economic management of information for both movement selection and execution via implicit learning of motor heuristics and motor analogies.

In this preregistration, we describe the study entitled "Identifying age from motion? – The impact of age and experience on age identification from movement stimuli " that is part of the German Research Foundation (DFG) project "Climbing to Yourself: A developmental embodied cognition perspective on the relation between the minimal self and sensorimotor and cognitive skills" (PIs: Markus Raab and Azzurra Ruggeri, SPP 2134 Ι RA 940/21-1 Ι RU 2272/1-1). It has been recently proposed that movements can be considered “kinematic fingerprints” (Coste et al., 2020, p. 1) and we are interested in whether age, being an important identifying factor, can be estimated from movement kinematics. If you think of the typical walk of a one-year-old and compare this to the walk of a school-aged child, it might indeed be possible that movements tell us something about the age of the person performing it. From a developmental embodied cognition perspective, we aim to better understand whether children, adolescents, and adults can identify age from movements and to which degree this depends on the age of the person presented in the movement stimulus as well as the participants’ own age and experience. In the present study, we will therefore present participants with kinematic recordings of human climbing movements presented as point-light display videos and test whether they can identify the age of the person climbing. The general theoretical starting point is an embodied cognition perspective which suggests that our minds and bodies, movements and thoughts are bi-directionally linked (e.g., Gentsch et al., 2015; Wilson, 2002) with the interaction changing throughout development (Musculus et al., 2021). Based on the general assumptions that cognition is for action and that off-line cognition is body-based (cf., Wilson, 2002), one can assume that the cognitive identification of how old someone is based on how they move, should be based on own motor experience. Different embodiment mechanisms have been proposed that help distinguishing own from other movements for example, ranging from common coding (e.g., Coste et al., 2020), to simulation (e.g., Hohmann et al., 2011), to internal models (e.g., Kennel et al., 2014). These have also been tested using complex movements such as basketball dribbling (Hohmann et al., 2011) or hurdling (Kennel et al., 2014). So, in general we assume an internal model mechanism according to which the age of a person moving (i.e., presented in a movement stimulus) should be identified by inferring discrepancies between how the person should move if he/she was the same age, i.e., predicted based on own bodily and motor experiences, in comparison to how the person is perceived to move. In a similar fashion, research has shown that people are better able to identify movements of people they have experienced more often (i.e., team members of a basketball team) as compared to the same movements of people (i.e., other basketball players) they have not experienced as often (Hohmann et al., 2011). This finding suggests that the experience with a specific movement also seems to play a role. However, whether and how age identification from movements is at all possible is still an open empirical question. To get an overview of the state of the art, we conducted a systematic literature search on „age from motion“ using the following keywords „age identification, age judgement, age recognition or age perception“ combined with „point light, dynamic, move or motion“ (see Appendix Figure 1 for PRISMA flowchart). This search resulted in 240 matching articles. After analyzing the titles, 56 articles remained. By reading the abstract of the remaining articles, 11 articles were deemed of interest. The full-text check of these articles showed that there is so far no published research regarding the question whether age identification from movement is possible given that not a single article addressed this question. What this literature search revealed is that there is published research on age identification from pictures presenting human faces. Regarding age identification, the studies discussed in the review form Rhodes et al. (2009) present evidence that people are able to identify age from face pictures accurately. The studies used different methods for age identification: In some the participants had to categorize pictures according to an age category (e.g., young, middle-age, older; in Anastasi & Rhodes, 2005), sort them from the youngest to oldest face presented or vice versa (Markey, 1934, Pittenger & Shaw, 1975), or discriminate between two pictures which of the faces presented was older or younger (George et al., 2000). Regarding the question when the ability to accurately identify age develops, research suggests that from about the age of 9 to 10 years and onwards, children are able to identify age in an adult-like fashion (Bell et al. 2000). This line of research indicates improvements in age identification from face pictures until late childhood. A similar developmental trajectory was portrayed in a line of research relevant to the research question at hand, in which the ability to identify biological motion was studied (see Federici et al., 2020). Starting in infancy, a recent meta-analysis (Simion et al., 2008) found that already two-day-old infants spontaneously prefer biological motion over non biological movement. Later, by the age of 3 months, infants are then able to discriminate between biological motion and their scrambled counterparts (Zhang et al., 2013). Depicting the development during childhood, the identification of biological motion improves between the ages 3 to 5 years (Pavlova et al. , 2001) and even further from age 7 to 10 years (Carter & Pelphrey , 2006). Only at the age of 14 years, adolescents seem to be as good as adults at identifying biological movement (Hadad et al. 2011). Therefore, the accurate identification of biological motion seems to undergo quite a portrayed developmental trajectory reaching adult-like levels only in adolescence. General (developmental) embodied cognition assumptions (e.g., Gentsch et al., 2015; Musculus et al., 2021; Wilson, 2002) and the integration of research on the ability of humans to identify age from human face pictures as well as on the developmental trajectories presented for the ability to identify age and biological motion accurately provide the theoretical foundation for the present study. Based thereon the present study will shed light onto the research question whether people of different age and experience are able to identify a person’s age from how they move. Therefore, the present study will uncover whether movements indeed approximate “kinematic fingerprint(s)” (Coste et al., 2020, p. 1), indeed revealing relevant identifying information such as age. In detail, we test the performance of children (6–12 year-olds), adolescents (14-16 year-olds), and adults (18-40 years old) in an age–identification task using human climbing movements, by examining to what degree such performance is dependent on a) the age of the person presented in the movement stimuli (cf., Rhodes et al., 2009), b) the participants’ age (Carter & Pelphrey , 2006; Hadad et al., 2011), and c) the participants’ experience (e.g., Hohmann et al., 2011) in climbing and with different age groups presented in the stimuli. To do so, we designed an online study, in which video stimuli of climbing kinematics presented as point-light displays and produced by the participants of previous studies will be shown to the participants. Children and adults will be asked how old the persons in the PLD stimuli are (see Figure 1 for procedure of the age identification task). Figure 1. Age identification from climbing-specific movement stimuli. Procedure and exemplary screenshots from point-light display videos.

In this pre-registration, we describe the study entitled “Identifying self from motion – The impact of the minimal self and age on self-other discrimination from movement stimuli” that is part of our DFG project "Climbing to Yourself: A developmental embodied cognition perspective on the relation between the minimal self and sensorimotor and cognitive skills" (PIs: Markus Raab and Azzurra Ruggeri, SPP 2134 Ι RA 940/21-1 Ι RU 2272/1-1). In the present study, we aim to better understand whether children and adults can identify themselves from their own movements and to which degree this depends on their minimal self. In detail, we will present participants with kinematic recordings of their own and other people’s climbing movements presented in point-light display videos and test whether they can discriminate themselves from others. In addition, we examine whether the participants’ minimal-self representation, which is defined as the phenomenological experience in the here and now, “accessible to immediate self-consciousness” (Gallagher, 2000, p. 15) as well as their age will affect their self-other discrimination performance. Our general theoretical starting point to tackle this goal is an embodied cognition perspective assuming a bidirectional link between cognition and action (e.g., Gentsch et al., 2015), which has been also specified from a developmental perspective (Musculus et al., 2021). In general, human movements are like “kinematic fingerprint(s)” (Coste et al., 2020, p. 1), clearly distinguishing ourselves from others. For self-other discrimination from movements in particular, evidence suggests that especially motor experience benefits discrimination performance (e.g., Hohmann et al., 2011; Kennel et al., 2014). Therefore, our movements and motor experience are crucial for developing distinct self-representations and should matter for self-other discrimination. To explain self-other discrimination, competing mechanisms have been proposed, from common coding (e.g., Coste et al., 2020), to simulation (e.g., Hohmann et al., 2011), to internal models (e.g., Kennel et al., 2014). Nevertheless, it is still unclear how exactly the human brain is able to do this computationally and when this ability emerges (e.g., Liu et al., 2019). With the present study, we aim to make a theoretical contribution by further scrutinizing the underlying mechanism and testing potential influencing factors. First, to shed light on how participants make the self-other discrimination, we will ask them about their strategy. Based on previous research using sport-movement stimuli (i.e., hurdling, Kennel et al., 2014), we expect that the (other) agent depicted in a movement stimulus will be identified by noting discrepancies between the predicted movement based on self-experience and the actual perceived movement of the other (cf. internal model; Kennel et al., 2014), opting for an internal-model mechanism. Given the relevance of a clear bodily and movement representation to identify own movements, a person’s so-called minimal self (Gallagher, 2000) might impact how well one is able to distinguish oneself from someone else. The minimal self is the phenomenal experience in the here and now, rooted in bodily, motor experiences, perceptions, and representations (Fotopoulou & Tsakiris, 2017; Gallagher, 2000). Further, the minimal self comprises a body ownership and agency component, which refer to the representation of whether body parts belong to oneself (or not) and whether an action such as a movement was conducted by oneself (or not; Georgie et al., 2019). In this study, we consider the minimal self as a potential influencing factor in self-other discrimination from movements. In addition, given the developmental embodied cognition perspective of our project and the lack of evidence on when the ability to identify oneself from movements emerges, we will also explore the effects of the participants’ age on self-other discrimination performance. We will enroll children and adults to test whether age-effects in self-other discrimination from movements emerge. Taken together, in the present study, we examine whether the self-other discrimination performance of children (6–12 years old) and adults (18-40 years old) from climbing movements is dependent on the participants’ minimal-self accuracy and/or age. To do so, we designed an online study, in which movement stimuli of climbing kinematics, that were previously captured in an ongoing climbing training study (see pre-registration on OSF), were presented as point-light display videos to the participants. The movement stimuli either showed the participant him/herself (self-trials) or someone else (other-trials). The children and adults participating will be asked to decide whether the movement stimuli presented showed themselves or someone else (see Figure 1 for procedure of the self-other discrimination task). Figure 1. Self-other discrimination from climbing-specific movement stimuli. Exemplary screenshots from point-light display videos.

Interoception is an often neglected but crucial aspect of the human minimal self. In this perspective, we extend the embodiment account of interoceptive inference to explain the development of the minimal self in humans. To do so, we first provide a comparative overview of the central accounts addressing the link between interoception and the minimal self. Grounding our arguments on the embodiment framework, we propose a bidirectional relationship between motor and interoceptive states, which jointly contribute to the development of the minimal self. We present empirical findings on interoception in development and discuss the role of interoception in the development of the minimal self. Moreover, we make theoretical predictions that can be tested in future experiments. Our goal is to provide a comprehensive view on the mechanisms underlying the minimal self by explaining the role of interoception in the development of the minimal self., Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe; 755