Psychology of sport and exercise最新文献

Learning a visuomotor adaptation task (VMA) is typically assessed by describing the behavioral changes during adaption (early-fast and late-slow phases) and retention (consolidation) tests. Few studies have concurrently examined behavioral and brain activity during this type of learning and therefore their time-dependent dynamics is unknown. It has been proposed that two distinct strategies can be used during such learning: a model-free and a model-based, which distinctively involved explicit and implicit learning strategies. It has also been proposed that prefrontal cortex (PFC) is more implicated when explicit processes are more relevant as it was observed in the early adaptation (Taylor & Ivry, 2014; Wolpe et al., 2020). Additionally, an explicit model-based strategy has been inferred when prefrontal (PFC) activity increases. Therefore, the study´s aims were: (1) to examine the continuous temporal dynamics of behavior and right PFC activity during adaptation and retention of a VMA, and (2) to infer the implication of explicit processes during the learning of a VMA derived from right PFC activity. Eighteen young adults (24.22 ± 3.12 years) took part in this study. Continuous measures of the performance (the initial directional error, IDE, and the root mean square error, RMSE) of a rotational visuomotor adaptation task during an adaptation (AD) and two retention sets at 1 h (RT1) and 24 h (RT24) were collected. Concurrently, measures of the right PFC activity (relative changes of the oxyhemoglobin concentration, [ΔO₂Hb]) were registered via a three-channel functional near-infrared spectroscopy device. General linear mixed models were run to explore differences across adaptation and retentions. Also, cross-correlations between performance (IDE and RMSE) and PFC activity were conducted to observe their relation during sets. The main results indicated that (1) initial fast behavioral improvement (decrease of IDE and RMSE) did not occur simultaneously with the largest increase of the [ΔO₂Hb] in the PFC during the AD, and (2) there was similar performance in the RT1 and RT24 but possibly involving the PFC differently. While in both retentions the errors improved after the first trials, in RT1, the [ΔO₂Hb] decreased from the very beginning, whereas the PFC activity initially increased in RT24. Our observations would suggest that various cooperating learning strategies, including model-free (i.e., exploratory) and model-based explicit (i.e., strategy) and implicit (i.e., sensory prediction errors), are coordinated in different timings to cooperate during the sensorimotor adaptation and consolidation processes. Furthermore, the involvement of these strategies during the retention may depend on the time elapsed from the end of the adaptation to the re-introduction of the task.

Designing representative learning tasks requires a theoretically informed sampling of key performer-environment interactions. This study sampled the constraints from basketball to create a representative environment by examining the effect of a defender on the shooting performance of skilled female basketballers. Participants performed a one-on-one defended shooting task and an undefended shooting task. Temporal variables were recorded during each shooting task, and a sample of the cohort completed a focus group to gain qualitative insights into each testing condition. Results found that the defended condition caused a decrease in shooting accuracy and shot execution time, with an increase in ball flight time and jump time compared to the undefended condition. Within-subject variability increased in the defended condition for jump time and shot execution time, suggesting that the presence of the defender prompted players to change their movements. Shooting accuracy in the defended condition showed a high correlation to the shooting accuracy exhibited in competition statistics. Qualitative data revealed three themes; affective-cognitive response, sampling to design representative practice tasks, and shooting as an adaptive skill. Introducing a defender into a shooting practice task created a more representative activity that influenced the players' shooting technique and accuracy. The players adapted to the presence of a defender by executing their shot faster, increasing jump time, and increasing the flight time of the ball. Sampling key performance constraints to create defended situations may encourage a more adaptable technique, resulting in greater skill transfer from training to in-game performance.

This study aimed to examine the relationship between physical activity levels and approach-avoidance behaviors, to promote physical activity and provide a theoretical basis for national fitness. The study examined both approach-avoidance behaviors and the influence of affective valence in the approach-avoidance task. Two experiments using approach-avoidance tasks programmed in E-prime were conducted. Experiment 1 employed a 2 (physical activity level: high, low) × 2 (image type: physical activity, sedentary) mixed design, and Experiment 2 used a 2 (physical activity level: high, low) × 2 (physical activity image types: positive, negative) mixed design. The analysis of approach tendencies revealed that: (1) individuals with high physical activity levels exhibited a stronger tendency to approach physical activity images (p < 0.05), with no significant difference observed for sedentary images (p = 0.72); (2) high physical activity individuals demonstrated a stronger tendency to approach positive images of physical activity (p < 0.05), while no significant difference was observed for negative images (p = 0.78). The analysis of approach-avoidance behaviors indicated that: (1) high physical activity individuals more quickly approached physical activity images (p < 0.001) and avoided sedentary images (p < 0.05); (2) high physical activity individuals more quickly approached positive images of physical activity (p < 0.05). The study demonstrated a clear relationship between physical activity levels and approach-avoidance behaviors, showing that individuals with high physical activity levels exhibited more positive approach tendencies and behavioral responses toward physical activity-related stimuli. These findings have important implications for designing and implementing effective physical activity promotion strategies to combat global physical inactivity.

Exercising can be theorized as the result of choosing one behavior over alternative behaviors. The Decisional Preferences in Exercising (DPEX) test is a computerized, easy-to-use, publicly available (open source Python code: https://osf.io/ahbjr/) and highly adaptive research tool based on this rationale. In the DPEX, participants are asked to choose between two images by pressing a key on the computer keyboard, one showing a physical exercise and the other showing a non-exercise behavioral alternative in a series of trials. Combinations are randomly assembled from two definable pools of stimuli trial-per-trial. The test can be scored either based on a crossed random effects model (facilitating the use of different stimulus material in different studies without compromising the comparability of test scores) or with a simple proportion score. Data from diverse study samples (N = 451) showed strong correlations of DPEX scores with past and future exercise behavior (r = 0.42 and 0.47 respectively) as well as with affective experiences with exercise (e.g., 'pleasure-displeasure': r = 0.47). DPEX test scores discriminated between exercisers and non-exercisers according to receiver operating curve (ROC) analysis. The DPEX may be used to examine research questions derived from dual process theories, the effects of psychological states on behavioral choices can be tested, or the effects of behavior change interventions can be evaluated. The DPEX helps to avoid common method bias in the assessment of exercise behavior, for example, when psychological variables are measured with questionnaires.