A generalized life-motion mechanism supports invariant directional coding of local biological kinematics in humans

Abstract

Humans can detect biological motion (BM) from sparse local kinematic cues, yet whether the visual system encodes directional information in a category-invariant manner remains unresolved. Here we combined a visual adaptation paradigm with computational modelling to reveal direction-sensitive neural mechanisms specialized for local biological kinematics. Adapting to a side-view scrambled point-light walker produced a robust repulsive aftereffect in the perceived direction of an intact human walker near the frontal view. Notably, this aftereffect generalized across different terrestrial vertebrates (pigeon, cat, dog) and across various actions (running, crawling, cycling), demonstrating a high degree of kinematic invariance. Crucially, the effect disappeared when biological kinematics were disrupted (inversion or removal of gravitational acceleration cues) or when the test stimulus was replaced with non-biological object motion. Individuals direction-discrimination abilities were also highly correlated across diverse local BM patterns, indicating a shared underlying mechanism. Drift-diffusion modeling further revealed that adaptation primarily altered the efficiency of sensory evidence accumulation rather than decision-level processes, and individual drift-rate changes strongly predicted the magnitude of perceptual aftereffects. These findings provide compelling evidence for a generalized, direction-sensitive neural system tuned to local biological kinematics, extending the life-motion detector theory and revealing a fundamental principle of biological motion perception. Significance StatementHumans possess an exceptional ability to detect and interpret the movements of living beings--an ability fundamental to survival, social interaction, and adaptive behavior. But how does the visual system extract "life motion" so adeptly amidst the complexity of natural scenes? Using visual adaptation and computational modelling, we provide direct evidence for direction-sensitive neural mechanisms specialized for local biological kinematics. These mechanisms operate robustly across species and actions, and remain selective for natural biological dynamics. Our findings advance the life-motion detector theory by demonstrating that the human visual system encodes directional information in a highly invariant manner, revealing a fundamental computational strategy for detecting animate agents.