Elite Athletes Refine Their Internal Clocks: A Bayesian Analysis

in Motor Control
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This paper carries out a full Bayesian analysis for a data set examined in Chen & Cesari (2015). These data were collected for assessing people’s ability in evaluating short intervals of time. Chen & Cesari (2015) showed evidence of the existence of two independent internal clocks for evaluating time intervals below and above the second. We reexamine here, the same question by performing a complete statistical Bayesian analysis of the data. The Bayesian approach can be used to analyze these data thanks to the specific trial design. Data were obtained from evaluation of time ranges from two groups of individuals. More specifically, information gathered from a nontrained group (considered as baseline) allowed us to build a prior distribution for the parameter(s) of interest, and data from the trained group determined the likelihood function. This paper’s main goals are (i) showing how the Bayesian inferential method can be used in statistical analyses and (ii) showing that the Bayesian methodology gives additional support to the findings presented in Chen & Cesari (2015) regarding the existence of two internal clocks in assessing duration of time intervals.

Chen is with the Research Center for Mind, Brain and Learning, National Chengchi University, Taipei City, Taiwan. Cesari is with the Dept. of Neurological and Movement Sciences, University of Verona, Verona, Italy. Verdinelli is with the Dept. of Statistics, Carnegie Mellon University, Pittsburgh, PA.

Address author correspondence to Yin-Hua Chen at olichen@nccu.edu.tw.