Probabilistic integration of preceding responses explains response bias in perceptual decision making

Expectations of sensory information change not only how well but also what we perceive. Even in an unpredictable environment, the brain is by default constantly engaged in computing probabilities between sensory events. These estimates are used to generate predictions about future sensory events. Here, we investigated the predictability of behavioral responses using three different learning models in three different one-interval two-alternative forced choice experiments with either auditory, vestibular, or visual stimuli. Results indicate that recent decisions, instead of the sequence of generative stimuli, cause serial dependence. By bridging the gap between sequence learning and perceptual decision making, we provide a novel perspective on sequential choice effects. We propose that serial biases reflect the tracking of statistical regularities of the decision variable, offering a broader understanding of this phenomenon.