Speaker
Description
The brain constantly interprets the world by combining incoming sensory information with prior knowledge, updating its beliefs as circumstances evolve. This process is captured by Bayesian inference, and its proper functioning determines whether an organism adapts flexibly to uncertainty or falls into systematic biases through faulty beliefs or maladaptive updating. Our main focus is to disentangle how the accumulated past evidence combines with the present information in making decisions. Specifically, we analyze how the history rewards and sensory evidence jointly shape sequential decision-making. In our task, the rewarded choice follows a Markov process governed by a stickiness parameter p. Optimal performance requires implicitly estimating p and adopting the appropriate win-stay/lose-shift strategy. Sixteen human participants completed a combined experiment made of three tasks: a probability task where the choices are made relying solely on reward history, a sensory task based on tactile discrimination only, and a combined condition integrating both sources of evidence. We model participants as Bayesian observers using a leaky integrator that tracks environmental stickiness according to the expectedness of the past trial outcome, and embed sensory processing within a binary classification framework that accounts for each individual's perceptual sensitivity. We incorporate choice stochasticity via a choice precision mechanism, which is integrated with a confidence mechanism to quantify how participants revise their beliefs following unrewarded trials. Fitting the model via hierarchical Bayesian inference with Hamiltonian Monte Carlo, we show that a few interpretable parameters capture a broad range of behavioral patterns. The model reproduces key features of human behavior, most notably a tendency to reduce confidence in its belief after incorrect trials and increase updating after unexpected outcomes. This pattern reflects a trade-off between exploratory updating and perseveration. Parameter estimates further expose meaningful individual differences and biases across participants.
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