Reinforcement learning can account for associative and perceptual learning on a visual decision task

We recently showed that improved perceptual performance on a visual motion direction-discrimination task corresponds to changes not in how sensory information is represented in the brain but rather how that information is interpreted to form a decision that guides behaviour. Here we show that these changes can be accounted for using a reinforcement learning rule to shape functional connectivity between the sensory and decision neurons. We modelled performance based on the readout of simulated responses of direction-selective sensory neurons in the middle temporal area (MT) of monkey cortex. A reward prediction error guided changes in connections between these sensory neurons and the decision process, first establishing the association between motion direction and response direction and then gradually improving perceptual sensitivity by selectively strengthening the connections from the most sensitive neurons in the sensory population. The results suggest a common, feedback-driven mechanism for some forms of associative and perceptual learning.