How the Level of Reward Awareness Changes the Computational and Electrophysiological Signatures of Reinforcement Learning

Abstract

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 reward-based 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 decision-making strategies.SIGNIFICANCE STATEMENT The notion of reward is strongly associated with subjective evaluation, related to conscious processes such as "pleasure," "liking," and "wanting." Here we show that degrading reward visibility in a reinforcement learning task decreases, without completely obliterating, the ability of participants to learn from outcomes, but concurrently increases subjects' tendency to repeat previous choices. Electrophysiological recordings, in combination with computational modeling, show that neural activities were significantly modulated by reward visibility. Overall, we dissociate different neural computations underlying reward-based learning and decision-making, which highlights a beneficial role of reward awareness in adjusting decision-making strategies.

Keywords: consciousness; decision-making; prediction error; reinforcement learning.