Belief states and categorical-choice biases determine reward-based learning under perceptual uncertainty

In natural settings, learning and decision making often takes place under considerable perceptual uncertainty. Here we investigate the computational principles that govern reward-based learning and decision making under perceptual uncertainty about environmental states. Based on an integrated perceptual and economic decision-making task where unobservable states governed the reward contingencies, we analyzed behavioral data of 52 human participants. We formalized perceptual uncertainty with a belief state that expresses the probability of task states based on sensory information. Using several Bayesian and Q-learning agent models, we examined to which degree belief states and categorical-choice biases determine human learning and decision making under perceptual uncertainty. We found that both factors influenced participants’ behavior, which was similarly captured in Bayesian-inference and Q-learning models. Therefore, humans dynamically combine uncertain perceptual and reward information during learning and decision making, but categorical choices substantially modulate this integration. The results suggest that categorical commitments to the most likely state of the environment may generally give rise to categorical biases on learning under uncertainty.