From simple lab tasks to the virtual court: Bayesian integration in tennis

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Abstract
This study tests Bayesian integration in a complex sensorimotor task: returning tennis serves. We found reliability-based prior-likelihood integration on two timescales: 1) over a “match” (increasing reliability of prior information) and 2) over a single serve (increasing reliability of sensory information). More generally, this study exemplifies how leveraging extended reality technology provides a means to reduce the internal versus external validity trade-off by studying motor control under real-world task demands while ensuring rigorous experimental control.

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