Mice adaptively generate choice variability in a deterministic task

Can our choices just be driven by chance? To investigate this question, we designed a deterministic setting in which mice reinforce non-repetitive choice sequences, and modeled it using reinforcement learning. Mice progressively increased their choice variability using a memory-free, pseudo-random selection, rather than by learning complex sequences. Our results demonstrate that a decision-making process can self-generate variability and randomness even when the rules governing reward delivery are not stochastic.