The extent to which subjective awareness influences reward processing, and thereby affects future decisions, is currently largely unknown. In the present report, we investigated this question in a reinforcement learning framework, combining perceptual masking, computational modeling, and electroencephalographic recordings (human male and female participants). Our results indicate that degrading the visibility of the reward decreased, without completely obliterating, the ability of participants to learn from outcomes, but concurrently increased their tendency to repeat previous choices. We dissociated electrophysiological signatures evoked by the rewardbased learning processes from those elicited by the reward-independent repetition of previous choices and showed that these neural activities were significantly modulated by reward visibility. Overall, this report sheds new light on the neural computations underlying reward-based learning and decision-making and highlights that awareness is beneficial for the trial-by-trial adjustment of decisionmaking strategies.
Central to human and animal cognition is the ability to learn from feedback in order to optimize future rewards. Such a learning signal might be encoded and broadcasted by the brain’s arousal systems, including the noradrenergic locus coeruleus. Pupil responses and the positive slow wave component of event-related potentials reflect rapid changes in the arousal level of the brain. Here, we ask whether and how these variables may reflect surprise: the mismatch between one’s expectation about being correct and the outcome of a decision, when expectations fluctuate due to internal factors (e.g., engagement). We show that during an elementary decision task in the face of uncertainty both physiological markers of phasic arousal reflect surprise. We further show that pupil responses and slow wave event-related potential are unrelated to each other and that prediction error computations depend on feedback awareness. These results further advance our understanding of the role of central arousal systems in decision-making under uncertainty.
The Bayesian brain hypothesis, as formalized by the free-energy principle, is ascendant in cognitive science. But, how does the Bayesian brain obtain prior beliefs? Veissière and colleagues argue that sociocultural interaction is one important source. We offer a complementary model in which “interoceptive self-inference” guides the estimation of expected uncertainty both in ourselves and in our social conspecifics.
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