Integration of sensory evidence and reward expectation in mouse perceptual decision-making task with various sensory uncertainties.

In perceptual decision-making, prior knowledge of action outcomes is essential, especially when sensory inputs are insufficient for proper choices. Signal detection theory (SDT) shows that optimal choice bias depends not only on the prior but also the sensory uncertainty; however, it is unclear how animals integrate sensory inputs with various uncertainties and reward expectations to optimize choices. We developed a tone-frequency discrimination task for head-fixed mice in which we randomly presented either a long or short sound stimulus and biased the choice outcomes. The choice was less accurate and more biased toward the large-reward side in short- than in long-stimulus trials. Analysis with SDT found that mice did not use a separate, optimal choice threshold in different sound durations. Instead, mice updated one threshold for short and long stimuli with a simple reinforcement-learning rule. Our task in head-fixed mice helps understanding how the brain integrates sensory inputs and prior.