Study Finds Serial Dependence in Time Perception Follows Precision-Weighted Updating Model
Researchers conducted two time reproduction experiments showing that how much people's time estimates are influenced by recent history depends on sensory uncertainty and contextual continuity. A Kalman filter model incorporating precision-weighted updating—where recent history is weighted by environmental reliability—best explained the observed patterns. The findings suggest a unified mechanism underlying serial dependence across different types of perceptual transitions.
In two experiments with 44 participants, researchers examined how motion coherence (sensory clarity) and transitions between coherence categories affected serial dependence—the tendency for recent perceptual history to influence current judgments. Low coherence conditions produced stronger serial dependence than high coherence, and when coherence categories changed, serial dependence was stronger within the same category than across category switches. A three-state Kalman filter model best captured these patterns by modulating the weighting of recent history based on the precision (reliability) of the current perceptual environment. The model's fast state captured serial dependence, the slow state captured central tendency effects, and the bias state captured decision carryover. Notably, the precision-weighting account was superior to alternative models that posited explicit state resets, suggesting a single unified updating mechanism rather than separate processes.
Limitations & open questions
The study does not discuss potential individual differences in precision-weighted updating or whether findings generalize beyond time perception to other sensory modalities. The authors do not address whether the Kalman filter parameters vary across populations or clinical conditions.
What different sources said
- bioRxivCenter
Precision-Weighted Updating Explains Serial Dependence Across Sensory and Contextual Transitions
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