“Descriptive Risk-Averse Bayesian Decision-Making,” a Model for Complex Biological Motion Perception in the Human Dorsal Pathway

Abstract

Biological motion perception is integral not only to survival but also to the social life of human beings. Identifying the underlying mechanisms and their associated neurobiological substrates has been a matter of investigation and debate for some time. Although, in general, it is believed that the integration of local motion and dynamic form cues in the brain empowers the visual system to perceive/recognize biological motion stimuli, some recent studies have indicated the importance of dynamic form cues in such a process. Inspired by the previous neurophysiologically plausible biological motion perception models, a new descriptive risk-averse Bayesian simulation model, capable of discerning a ball’s direction from a set of complex biological motion soccer kick stimuli, is proposed. The model represents only the dorsal pathway as a motion information processing section of the visual system according to the two-stream theory. The stimuli used have been obtained from a previous psychophysical study on athletes in our lab. Furthermore, the acquired psychophysical data from that study have been used to re-enact human behavior using our simulation model. By adjusting the model parameters, the psychometric function of athlete subjects has been mimicked. A correlation analysis between human and simulation data shows a significant and robust correlation between angular thresholds and slopes of the psychometric functions of both groups. Although it is established that the visual system optimally integrates all available information in the decision-making process, the results conform to the speculations favoring motion cue importance over dynamic form by testing the limits in which biological motion perception only depends on motion information processing.