Background. The main goal of the present study was to explore theta and alpha event-related desynchronization/synchronization (ERD/ERS) activity during shooting performance. We adopted the idiosyncratic framework of the multi-action plan (MAP) model to investigate different processing modes underpinning four types of performance. In particular, we were interested in examining the neural activity associated with optimal-automated (Type 1) and optimal-controlled (Type 2) performances.Methods. Ten elite shooters (6 male and 4 female) with extensive international experience participated in the study. ERD/ERS analysis was used to investigate cortical dynamics during performance. A 4 × 3 (performance types × time) repeated measures analysis of variance was performed to test the differences among the four types of performance during the three seconds preceding the shots for theta, low alpha, and high alpha frequency bands. The dependent variables were the ERD/ERS percentages in each frequency band (i.e., theta, low alpha, high alpha) for each electrode site across the scalp. This analysis was conducted on 120 shots for each participant in three different frequency bands and the individual data were then averaged.Results. We found ERS to be mainly associated with optimal-automatic performance, in agreement with the “neural efficiency hypothesis.” We also observed more ERD as related to optimal-controlled performance in conditions of “neural adaptability” and proficient use of cortical resources.Discussion. These findings are congruent with the MAP conceptualization of four performance states, in which unique psychophysiological states underlie distinct performance-related experiences. From an applied point of view, our findings suggest that the MAP model can be used as a framework to develop performance enhancement strategies based on cognitive and neurofeedback techniques.

A nomological network on team dynamics in sports consisting of a multi-framework perspective is introduced and tested. The aim was to explore the interrelationship among cohesion, team mental models (TMM), collective-efficacy (CE), and perceived performance potential (PPP). Three hundred and forty college-aged soccer players representing 17 different teams (8 female and 9 male) participated in the study. They responded to surveys on team cohesion, TMM, CE and PPP. Results are congruent with the theoretical conceptualization of a parsimonious view of team dynamics in sports. Specifically, cohesion was found to be an exogenous variable predicting both TMM and CE beliefs. TMM and CE were correlated and predicted PPP, which in turn accounted for 59% of the variance of objective performance scores as measured by teams' season record. From a theoretical standpoint, findings resulted in a parsimonious view of team dynamics, which may represent an initial step towards clarifying the epistemological roots and nomological network of various team-level properties. From an applied standpoint, results suggest that team expertise starts with the establishment of team cohesion. Following the establishment of cohesiveness, teammates are able to advance teamrelated schemas and a collective sense of confidence. Limitations and key directions for future research are outlined. not been tested yet (see Bandura 1997; Carron & Hausenblas, 1998;Eccles & Tenenbaum, 2007; 8 Klimoski & Mohammed, 1994;Mohammed et al., 2010;Salas, Sims, & Burke, 2005). The Keywords 18Team cohesion is a multidimensional phenomenon that includes both social and task 19 components at an individual and team level of analysis (Carron et al., 1985). Social cohesion 20 pertains to the notion of teammates bonding for social reasons, thus reflecting the extent that 27Of particular importance to this study is the notion that team cohesion is related to other 28 team-level constructs, such as TMM and CE (Eccles & Tenenbaum, 2007; Fiore et al., 2003). In 36TMM refer to the "collective task and team-relevant knowledge that team members bring 37 to a situation" (Cooke et al., 2003, p. 153). TMM is thought to provide a heuristic route (i.e., rule (Bandura, 1997;Myers et al., 2004;Zaccaro et al., 1995). 84From a factor analysis standpoint, the proposed model considers leading instruments 85 designed to measure cohesion, TMM and CE. Also, we aimed for a parsimonious model with 86 non-overlapping factors. Accordingly, we focused on measuring only the unique factorial 87 contributions representing cohesion, TMM, and CE. In other words, potentially overlapping 88 factors among the instruments utilized in this study were not considered. In particular, two sub- scores as measured by the Group Environment Questionnaire (see Carron et al., 1985). 95Furthermore, peer-debriefing meetings among the authors led to a unanimous agreement 96 regarding the "conceptual equivalence" of the aforementioned factors. Hence, in the proposed 97 model cohesion portrays the idea ...

1We conducted a counterbalanced repeated measure trial to investigate the effect of different 2 internal and external associative strategies on endurance performance. Seventeen college-3 aged students were randomly assigned to three experimental conditions to test the notion that 4 different attention-performance types (optimal Type 1, functional Type 2, and dysfunctional 5 Type 3) would influence endurance time on a cycling task. Specifically, Type 1 represented 6 an effortless and automatic, "flow-feeling" attentional mode. Type 2 referred to an 7 associative focus directed at core components of the task. Type 3 represented an attentional 8 focus directed at irrelevant components of the task. Participants completed three time-to-9 exhaustion-tests while reporting their perceived exertion and affective states (arousal and 10 hedonic tone). Results revealed that Type 1 and Type 2 attentional strategies, compared to 11 Type 3 strategy, exerted functional effects on performance, whereas a Type 3 strategy was 12 linked to lower performance, and lower levels of arousal and pleasantness. Applied 13 implications are discussed. 15Keywords: Attentional focus, cycling, fatigue, endurance, multi-action plan model. There is general agreement about the importance of studying how different attentional 3 strategies influence performance in sport and exercise settings (Basevitch et al., 2011; 4 Blanchfield, Hardy, de Morree, Staiano, & Marcora, 2014;Connolly & Tenenbaum, 2010; 5 Hutchinson & Razon et al., 2010; for a review, see Brick, MacIntyre, & 6 Campbell 2014). In this regard, previous research has shown that one's ability to self-regulate 7 attentional focus (e.g., attentional flexibility) is associated with the ability to sustain exertive 8 effort in endurance tasks (for a review, see Tenenbaum, 2005). To perform optimally, athletes 9 must be able to employ different attentional strategies in order to control external and internal 10 distracters, while focusing on body and task-relevant cues (Tenenbaum, 2001(Tenenbaum, , 2005. 11 Attentional Strategies for Endurance Performance 12Early research suggested that there are primarily two coping strategies that can be 13 used to enhance performance in endurance tasks (a) "association" and (b) "dissociation" 14(Weinberg, Smith, Jackson, & Gould, 1984). Association occurs when people monitor their 15 body sensations (e.g., respiration rate, body temperature, muscle pain and tightness), while 16 reminding themselves to relax and modify stride and pace to secure greater running economy. 17Indeed, elite performers monitor their body sensations more effectively than their less 18 accomplished counterparts (Raglin & Wilson, 2008 attention to improve adjustment to a physical task (Tenenbaum, 2005). This initial distinction 4 between the two broad categories of attention focus (as association and dissociation) was 5 introduced by Morgan and Pollock (1977), and has since oriented research on attentional 6 focus and physical effort (Hutchinson & Tenenbaum, 2007; ...