Abstract: Previous studies have found that the perceptual processing of various visual stimuli (e.g., orientation, speed, self-motion direction) is jointly influenced by prior knowledge and sensory noise, with these effects conforming to a Bayesian inference mechanism constrained by efficient coding. However, these findings have not been thoroughly investigated in the context of biological motion (e.g., point-light walker) direction perception. To address this gap, the present study designed two experiments in which participants were asked to report the point-light walker (PLW) direction utilizing a mouse-controlled probe on a circle.
In Experiment 1, the presentation duration of PLW stimuli was manipulated (250 ms vs. 800 ms) to modulate sensory/internal noise magnitude. Additionally, the PLW directions closer to the oblique directions (± 45°) appeared less frequently, following a hypothesized quad-modal distribution with peaks at 0°, ± 90°, and 180°. This quad-modal distribution was proposed to be consistent with the distribution of PLW directions in the natural world. The results revealed that the estimated direction of PLWs exhibited a repulsive bias away from a reference direction (e.g., 0°), and the magnitude of this reference-repulsive bias increased with increasing internal noise. In Experiment 2, the distribution of PLW directions (i.e., short-term prior) was manipulated by increasing the proportion of PLW directions closer to the oblique directions (± 45°), creating a conflict with the assumed long-term experience (the above quad-modal distribution). The findings showed that the reference-repulsive bias trend was decreased.
The above behavioral results indicates that the internal noise and prior together affect the PLW direction estimation. To uncover the computational mechanisms underlying these effects, we developed several Bayesian observer models constrained by efficient coding in which the prior used to encoding and decoding PLW directions were either single long-term or short-term, or the weighted-integration of the two priors. In addition, the perceptual estimation decoded by the model could be either proportionally scaled by an extra perception-action mapping process or not. The results showed that the efficient-coding constrained Bayesian observer model incorporating the perception-action mapping well capture the behavioral data, and the long-term prior with four peaks at 0°, ±90°, and 180° was used to encode and decode PLW directions.
In conclusion, this study combines behavioral experiments and computational modeling to elucidate how internal noise and prior knowledge shape the fine estimation of biological motion direction and its computational mechanism. The process involves three stages—efficient encoding, Bayesian decoding, and perception-action mapping—with a long-term four-peak prior critically shaping both encoding and decoding. These results establish an empirical and theoretical framework for future neurophysiological studies while highlighting open questions that warrant further exploration.

Key words: biological motion, Bayesian observer model, efficient coding