In basketball defence, it is impossible to keep track of all players without peripheral vision. This is the first study to investigate peripheral vision usage in an experimentally controlled setup, with sport-specific basketball stimuli from a first-person perspective, large viewing eccentricities (up to 90° to the left and right), and natural action responses. A CAVE and a motion-tracking system was used to project the scenarios and capture movement responses of high- and low-skilled basketball players, respectively. Four video conditions were created: (1) a simple reaction time task without crowding (only attackers), (2) a simple reaction time task with crowding (with attackers and defenders), (3) a choice-reaction time task where the player cutting to the basket eventually passed the ball to another player and (4) a game simulation. The results indicated eccentricity effects in all tests, a crowding effect in condition 2, and expertise differences in conditions 3 and 4 only. These findings suggest that viewing eccentricity has an impact on response times, that crowding is a limiting factor for peripheral perception in sports games, and that high-skilled but not low-skilled players can compensate for eccentricity effects in real game situations, indicating their superior positioning and perceptual strategies.
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http://dx.doi.org/10.1038/s41598-024-53706-9 | DOI Listing |
PLoS One
January 2025
CFD Research Corporation, Huntsville, AL, United States of America.
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ChemSusChem
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Dipartimento di Scienze Biologiche, Chimiche e Farmaceutiche, Università degli Studi di Palermo, Viale delle Scienze, Ed. 17, 90128, Palermo, Italia.
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Faculty of Physical Activity and Sport Sciences (INEF), Sports Department, Universidad Politécnica de Madrid (UPM), Madrid, Spain.
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View Article and Find Full Text PDFStat Med
February 2025
Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN.
The semi-competing risks data model is a special type of disease-state model that focuses on studying the association between an intermediate event and a terminal event and proves to be a useful tool in modeling disease progression. The study of the semi-competing risk data model not only allows us to evaluate whether a disease episode is related to death but also provides a toolkit to predict death, given that the episode occurred at a certain time. However, the computation of the semi-competing risk models is a numerically challenging task.
View Article and Find Full Text PDFBiol Cybern
January 2025
Institute for Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476, Potsdam, Germany.
Piecewise-deterministic Markov processes combine continuous in time dynamics with jump events, the rates of which generally depend on the continuous variables and thus are not constants. This leads to a problem in a Monte-Carlo simulation of such a system, where, at each step, one must find the time instant of the next event. The latter is determined by an integral equation and usually is rather slow in numerical implementation.
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