Abstract
Perceptual decisions are based on sensory information but can also be influenced by expectations built from recent experiences. Can the impact of expectations be flexibly modulated based on the outcome of previous decisions? Here, rats perform an auditory task where the probability to repeat the previous stimulus category is varied in trial-blocks. All rats capitalize on these sequence correlations by exploiting a transition bias: a tendency to repeat or alternate their previous response using an internal estimate of the sequence repeating probability. Surprisingly, this bias is null after error trials. The internal estimate however is not reset and it becomes effective again after the next correct response. This behavior is captured by a generative model, whereby a reward-driven modulatory signal gates the impact of the latent model of the environment on the current decision. These results demonstrate that, based on previous outcomes, rats flexibly modulate how expectations influence their decisions.
Highlights
Perceptual decisions are based on sensory information but can be influenced by expectations built from recent experiences
In which the statistics of the sensory information varies with time, subjects must be constantly updating their internal model by accumulating past stimuli, actions, and outcomes[4]
This expectation-based bias disappears after an error, reflecting a fast switch into an expectationfree categorization mode. This switch does not imply, the reset of the accumulated expectation, which resumes its influence on behavior as soon as the animal obtains a new reward. This ubiquitous behavior across animals is readily captured by a nonlinear dynamical model, in which previous outcomes acts as a gate for the impact of past transitions on future choices
Summary
Perceptual decisions are based on sensory information but can be influenced by expectations built from recent experiences. The internal estimate is not reset and it becomes effective again after the correct response This behavior is captured by a generative model, whereby a reward-driven modulatory signal gates the impact of the latent model of the environment on the current decision. Rats accumulate evidence over previous choice transitions, defined as repetitions or alternations of two consecutive choices, in order to predict the rewarded response This expectation-based bias disappears after an error, reflecting a fast switch into an expectationfree categorization mode. This switch does not imply, the reset of the accumulated expectation, which resumes its influence on behavior as soon as the animal obtains a new reward This ubiquitous behavior across animals is readily captured by a nonlinear dynamical model, in which previous outcomes acts as a gate for the impact of past transitions on future choices
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