Serial dependence in duration perception reveals reliability-weighted updating of the prior

Bayesian theories of perception propose that perceptual estimates result from the integration of prior beliefs ("priors") with sensory input ("likelihood"), weighted by their reliability. While Bayesian theories assume that priors are continuously updated over time, empirical evidence for such reliability-weighted updating modulating sequential percepts remains lacking. Here, we leverage a behavioral phenomenon called serial dependence--in which perceptual judgments are attracted toward previous stimuli--to test a central prediction of Bayesian theories: that perceptual estimates are sequentially updated according to the reliability of successive stimuli. We used a duration reproduction task in which the reliability of perceived duration was manipulated via signal-to-noise ratio by embedding stimuli in dynamic white noise. Consistent with the prediction of Bayesian theories, serial dependence in perceived duration was enhanced by increased reliability of previous stimuli and attenuated by increased reliability of current stimuli. Computational modeling revealed that changes in sensory noise (i.e., the width of the likelihood) can account for the reliability-dependent modulation of serial dependence. These findings provide empirical evidence for reliability-weighted updating, supporting a central prediction of Bayesian theories that prior information and sensory input are iteratively integrated to calibrate perceptual estimates.