Precision-Weighted Updating Explains Serial Dependence Across Sensory and Contextual Transitions

Abstract

Serial dependence is influenced by sensory uncertainty and contextual continuity, but it remains controversial whether these influences reflect separate mechanisms or different expressions of a shared updating process. Across two time reproduction experiments (N = 44), we examined how motion coherence and coherence transitions modulated the attraction of recent temporal history while controlling for central tendency effects from the current stimulus. In Experiment 1, the low coherence led to stronger serial dependence compared to the high coherence. In Experiment 2, enhanced coherence categories introduced salient contextual boundaries; serial dependence was markedly stronger on the same category transition than switch transition. A three-state Kalman filter model, comprising fast (serial dependence), slow (central tendency), and bias (decision carryover) states captured these patterns through coherence-dependent modulation of fast-state process noise and Kalman gain. Within the tested model space, this precision-weighting account was selected in both experiments; with little evidence that an explicit state reset was needed. These findings support the precision-weighted updating account in which recent history is weighted according to the reliability and stability of the current perceptual environment.