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Background and Study Aim. To study psychological features of sports coaches and physical education teachers’ professional hardiness. Material and methods. The study involved 60 participants. Among them, 24 were coaches of children's and youth sports schools, 19 coaches of national teams of Ukraine and 17 physical education teachers. English-language version of the Professional Hardiness Questionnaire (PHQ) were used. The development of the English-language version of the PHQ was based on its initial Ukrainian-language version (http://prof-diagnost.org). The accuracy of its translation into English was checked and corrected by 12 bilingual specialists with degrees in medicine and/or psychology. To identify the peculiarities of the relationship between the indicators of professional resilience in different professional groups, we conducted a Spearman correlation analysis. Results. The analysis of professional hardiness revealed the presence of significant differences between the surveyed groups of coaches and physical education teachers by various indicators. It turned out that the length of service in the respective positions of national team coaches is significantly higher than that of Youth Sports School (YSS) coaches and physical education teachers. This indicates the presence of greater professional experience among this category of coaches. Accordingly, physical education teachers and coaches of national teams have significantly higher professional experience than coaches of YSS. As a result, the majority of young specialists are among the coaching staff of the YSS. The correlation coefficient between the general level of professional hardiness and the namely professional component of 0.80 is much higher for coaches of national teams. Among physical education teachers it reaches 0.61, most likely this is because the control over the work of this group is less specific in contrast to the work of the coach. Coaches of YSS have the lowest level of the namely professional component 0.50, we explain this by the fact that the overall level of experience in the position is half as much as in the previous two groups. Conclusions. The conducted analysis made it possible to establish relationships between the general level of professional hardiness and indicators of professional hardiness in each group of coaches and teachers., Передумови та мета дослідження. Мета: дослідити психологічні особливості професійної життєстійкості спортивних тренерів та вчителів фізичної культури. Матеріал і методи. В дослідженні взяли участь 60 учасників. Серед яких 24, це тренери ДЮСШ, 19 тренери національних збірних команд України та 17 вчителів фізичної культури. При психофізіологічному тестуванні ми використовували англомовну версію опитувальника професійної життєстійкості (PHQ). Розробка англомовної версії PHQ базувалася на її початковій україномовній версії (http://prof-diagnost.org). Точність перекладу англійською мовою перевіряли та виправляли 12 двомовних фахівців із медичними та/або психологічними ступенями. Для виявлення особливостей зв’язків між показниками професійної життєстійкості в різних професійних групах нами було проведено кореляційний аналіз за Спірменом. Результати. Аналіз професійної життєстійкості виявив наявність достовірних відмінностей між обстеженими групами тренерів та вчителів фізичної культури за різними показниками. Виявилося, що стаж роботи на відповідних посадах тренерів національних збірних команд значно вищий, ніж у тренерів і та вчителів фізичної культури. Це свідчить про наявність більшого професійного досвіду у цієї категорії тренерів. Відповідно, викладачі та тренери збірних мають значно вищий професійний досвід, ніж тренери ДЮСШ. В результаті чого більшість молодих спеціалістів серед тренерського складу ДЮСШ. Коефіцієнт кореляції між загальним рівнем професійної загартованості та саме професійною складовою 0, 80 значно вищий у тренерів національних збірних команд. У вчителів фізичної культури він досягає 0, 61, швидше за все це тому, що контроль за роботою цієї групи менш специфічний на відміну від роботи тренера. Тренери ДЮСШ мають найнижчий рівень саме професійної складової 0, 50, пояснюємо це тим, що загальний рівень досвіду на посаді вдвічі менший, ніж у попередніх двох групах. Висновки. Проведений аналіз дав змогу встановити зв’язки між загальним рівнем професійної життєстійкості та показниками професійної життєстійкості в кожній групі досліджуваних.

There is an ongoing debate about whether an acute bout of physical activity has positive effects on executive functions (EF) with the latest meta-analyses revealing inconsistent results (Ishihara et al., 2021; Ludyga et al., 2016). So far, positive effects have mainly been explained on a physiological level (e.g., catecholamines, event-related potentials, cerebral oxygenation, see McMorris, 2016). Less attention has been paid to the psychological aspects of physical activity. Regarding psychological effects, Diamond and Ling (2020) have suggested that emotional investment and joy during the activity might contribute to the positive effect. In the present study, we conceptualize joy in terms of the broaden-and-build theory of positive emotions (Fredrickson, 2004). From this perspective, positive emotions, such as joy, expand one’s momentary thoughts and action repertoires to facilitate the “building” of new patterns of thought and behavior, resulting in improved cognition (for a review see Isen, 2000). Considering that physical activity below the ventilatory or lactate threshold has been shown to elicit positive affect (Ekkekakis et al., 2011), we assume that joy plays a role in the positive effect of acute exercise on EF. The mechanism behind the potential role of joy in the positive effect of physical activity on EF has, however, not been theoretically specified yet. To address this gap, we will empirically test whether joy is a moderator or mediator of the positive effect of physical acute exercise on EF (see Figure 1 and 2). In a within-subject crossover pre- and posttest comparison, 51 participants will perform an EF task battery (i.e., n-back task, flanker task, and number-letter task) during a baseline session and after a moderate cycling bout (40-59% VO2R) and seated rest in counterbalanced order on different days. Joy will be assessed by the Positive and Negative Affect extended Scale (Watson & Clarke, 1999).

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.

This project aims to measure how the cognitive processing of number magnitude affects spontaneous force production under highly controlled spatial influences and no active responses. For this, we will employ a novel method of continuous grip force measurement with high temporal and force resolution (ms/mN) during number presentations across two tasks. In the first task (1-back) we aim to measure force magnitudes during numbers from 1-9 (with a focus on 1,2,8,9). In the second task (Arithmetic task), we aim to replicate the force to number magnitude relation for the numbers 1,2,8,9. We will also explore how 1,2,8,9 affect force magnitudes following an arithmetic operation (either subtraction or addition). If there is an effect, we base our theoretical predictions based on two models: A Theory of Magnitude (ATOM, Walsh, 2003;2015) and the confidence model (Balota & Abrams, 1995). According to ATOM we would expect increasing force magnitude to increasing numbers while the confidence model would predict decreasing force magnitude to increasing numbers.

This is the second of a series of experiments on time-based expectancy for finger responses to vibrotactile stimulation. In this experiment, we induce time-based expectancy by pairing two events and two preparatory intervals. In a binary randomized response choice setting, participants respond by keypresses to vibrotactile stimulation to their fingers. Stimulus response mapping is compatible (i.e. respond with the finger that was stimulated), and stimuli are preceded by either a short or a long preparatory interval which predicts with 90% probability which finger will be stimulated next. This pairing of vibrotactile stimulation (left-right finger) and interval duration (short-long) is counterbalanced across participants. For half of the participants the short interval will predict stimulation on the left finger, and the long interval will predict stimulation on the right finger with 90% validity. For the other half, this relation will be inverted. We decide between two hypotheses: 1) Participants respond about equally fast to frequent combinations of interval duration and stimulation side than infrequent ones. Such finding would mean that participants did not form time-based expectancies to vibrotactile stimuli. 2) Participants respond faster to frequent than infrequent combinations of interval duration and stimulation side. Such finding would mean that participants build time-based expectancies to vibrotactile stimuli, and would be consistent with the time-based expectancy literature (see, Thomaschke et al., 2011; Thomaschke et al., 2015) We decide between these hypotheses by a Bayesian analogue to a paired sample t-test (Morey & Rouder, 2011). We cumulatively test participants until we reach a JZS Bayes factor over 5 for either of the hypotheses, or until we have tested 120 participants without reaching a JZS Bayes factor over 5. We collect data with the E-Prime program uploaded to this repository. We will pre-process the data as follows: We do not include participants with an overall mean RT or an overall mean error rate lying more than 2.5 SDs away from the sample mean. We do not include the first trial of each block, the first block, error trials, and trials following an error. Since we will compare two conditions observations (frequent vs. infrequent) with different number of, we will follow special recommendations to calculate the median RT. We will take the regular RT median for the infrequent condition and we will compare it with a special “average median” from the frequent condition, separately for the short and long intervals durations (for a more detailed explanation, see Miller, 1988) By frequent we mean trials with combinations of interval duration and stimulation side with 90% validity and as infrequent count trials with combinations of interval duration and stimulation side with 10% validity. We conduct the Bayesian t-test over these conditions. All further analyses will be of exploratory nature: For example, effects on error rates, effects of interval duration, etc. For following exploratory analyses, the script will be extended.