Biological motion perception in the theoretical framework of perceptual decision-making: An event-related potential study

Abstract

Biological motion perception plays a critical role in various decisions in daily life for humans. Failure to decide accordingly in such a perceptual task could have life-threatening consequences. Neurophysiological studies on non-human primates and computational modeling studies suggest two processes that mediate perceptual decision-making. One of these signals is associated with the accumulation of sensory evidence and the other with response selection. Recent EEG studies with human participants have introduced an event-related potential called Centroparietal Positive Potential (CPP) as a neural marker aligned with the sensory evidence accumulation while effectively distinguishing it from motor-related event-related potential, namely lateralized readiness potential (LRP). The present study aims to investigate the neural mechanisms of biological motion perception in the theoretical framework of perceptual decision-making, which has been overlooked before. More specifically, we examine whether CPP would track the coherence of the biological motion stimuli and could be distinguished from the LRP signal. We recorded EEG from human participants while they were engaged in a direction discrimination task of a point-light walker whose coherence was manipulated by embedding it in various levels of noise. Our behavioral findings revealed shorter reaction times and reduced miss rates as the coherence of the target stimuli increased. In addition, CPP tracked the coherence of the biological motion stimuli with a tendency to reach a common level during the response, albeit with a later onset than the previously reported results in random-dot motion paradigms. Furthermore, CPP was distinguished from the LRP signal based on its temporal profile. Overall, our results suggest that the mechanisms underlying perceptual decision-making generalize to more complex and socially significant stimuli like biological motion. We discuss the implications of these findings for advancing computational models of biological motion perception and perceptual decision-making.