Reward optimization in the primate brain: a probabilistic model of decision making under uncertainty

A key problem in neuroscience is understanding how the brain makes decisions under uncertainty. Important insights have been gained using tasks such as the random dots motion discrimination task in which the subject makes decisions based on noisy stimuli. A descriptive model known as the drift diffusion model has previously been used to explain psychometric and reaction time data from such tasks but to fully explain the data, one is forced to make ad-hoc assumptions such as a time-dependent collapsing decision boundary. We show that such assumptions are unnecessary when decision making is viewed within the framework of partially observable Markov decision processes (POMDPs). We propose an alternative model for decision making based on POMDPs. We show that the motion discrimination task reduces to the problems of (1) computing beliefs (posterior distributions) over the unknown direction and motion strength from noisy observations in a bayesian manner, and (2) selecting actions based on these beliefs to maximize the expected sum of future rewards. The resulting optimal policy (belief-to-action mapping) is shown to be equivalent to a collapsing decision threshold that governs the switch from evidence accumulation to a discrimination decision. We show that the model accounts for both accuracy and reaction time as a function of stimulus strength as well as different speed-accuracy conditions in the random dots task.