The motivational atmosphere in youth sport: coach, parent, and peer influences on motivation in specializing sport participants

This study qualitatively examined the motivationally relevant behaviors of key social agents in specializing sport participants. Seventy-nine participants (9-18 years old) from 26 sports participated in semi-structured focus-groups investigating how coaches, parents, and peers may influence motivation. Using a critical-realist perspective, an inductive content-analysis indicated that specializing athletes perceived a multitude of motivationally-relevant social cues. Coaches’ and parents’ influences were related to their specific roles: instruction/assessment for coaches, support-and-facilitation for parents. Peers influenced motivation through competitive behaviors, collaborative behaviors, evaluative communications, and through their social relationships. The results help to delineate different roles for social agents in influencing athletes' motivation

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

Transformational Leadership, Task-Involving Climate, and Their Implications in Male Junior Soccer Players: A Multilevel Approach

Despite the well-known positive consequences of transformational coaches in sport, there is still little research exploring the mechanisms through which coaches’ transformational leadership exerts its impact on athletes. Multilevel SEM was used to examine the relationship between coaches’ transformational leadership style, a task-involving climate, and leadership effectiveness outcome criteria (i.e., players’ extra effort, coach effectiveness, and satisfaction with their coach), separately estimating between and within effects. A representative sample of 625 Spanish male soccer players ranging from 16 to 18 years old and nested in 50 teams completed a questionnaire package tapping the variables of interest. Results confirmed that at the team level, team perceptions of transformational leadership positively predicted teams’ perceptions of task climate, which in turn positively predicted the three outcome criteria. At the individual level, players’ perceptions of transformational leadership positively predicted teams’ perceptions of task climate, which in turn positively predicted teams’ extra effort and coach effectiveness. Mediation effects appeared at the team level for all the outcome criteria, and at the individual only for extra effort. Transformational leadership is recommended to enhance task climate, in order to increase players’ extra effort, their perceptions of the effectiveness of their coach, and their satisfaction with his/her leadership style

Using Monte Carlo Search With Data Aggregation to Improve Robot Soccer Policies

RoboCup soccer competitions are considered among the most challenging multi-robot adversarial environments, due to their high dynamism and the partial observability of the environment. In this paper we introduce a method based on a combination of Monte Carlo search and data aggregation (MCSDA) to adapt discrete-action soccer policies for a defender robot to the strategy of the opponent team. By exploiting a simple representation of the domain, a supervised learning algorithm is trained over an initial collection of data consisting of several simulations of human expert policies. Monte Carlo policy rollouts are then generated and aggregated to previous data to improve the learned policy over multiple epochs and games. The proposed approach has been extensively tested both on a soccer-dedicated simulator and on real robots. Using this method, our learning robot soccer team achieves an improvement in ball interceptions, as well as a reduction in the number of opponents' goals. Together with a better performance, an overall more efficient positioning of the whole team within the field is achieved

Deep learning for video game playing

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards