Probabilistic optimization in the human perceptuo-motor system

Abstract

Despite the variability of internal and external environments, the human central nervous system (CNS) can generate precise and stable perception and motor behaviors. What mechanism enables this ability? Answering this question is one of the significant goals in the human sciences, including neuroscience, cognitive science, physical education and sports science. The Bayesian integration theory proposes that the CNS learns the prior distribution of a task and integrates it with sensory information to minimize the effect of sensory noise. In this article, we introduce psychophysical reports using motor timing and temporal order judgment (TOJ) tasks that support the Bayesian integration theory. Subsequently, we demonstrate the event-related potentials (ERPs) behind Bayesian integration that operates in somatosensory TOJ. Keywords : Bayesian, perception, motor behavior, timing, temporal order judgment, electroencephalogram, event-related potential The problem of variability in the human perceptuo-motor systemWe are exposed to variability in both our internal and external environments. Unlike electronic signals in artificial systems, the signals in our nervous system are noisy. This neural noise inevitably affects our sensory input. For example, if a baseball batter's sensory system detects a pitched ball at a certain location, the true (physical) ball location may be further upward, outward, or in other positions (Fig. 1A) due to the effect of neural noise. Our sensory information innately contains uncertainty. In addition, external events that we encounter in our daily life are also highly variable. For example, the behavior of the pitched ball is not constant, but instead fluctuates with every trial (Fig. 1B). Thus, uncertainty exists in our external as well as internal environments. To enable precise and stable perception and behavior, our central nervous system (CNS) must compensate for this internal and external variability.