Psychology and neurobiology of simple decisions

Patterns of neural firing linked to eye movement decisions show that behavioral decisions are predicted by the differential firing rates of cells coding selected and nonselected stimulus alternatives. These results can be interpreted using models developed in mathematical psychology to model behavioral decisions. Current models assume that decisions are made by accumulating noisy stimulus information until sufficient information for a response is obtained. Here, the models, and the techniques used to test them against response-time distribution and accuracy data, are described. Such models provide a quantitative link between the time-course of behavioral decisions and the growth of stimulus information in neural firing data. The question of how two-alternative decisions are made is an important one for neuroscience and psychology alike because of the pivotal role played by decision making in translating perception and cognition into action. This translation brings encoded stimulus information into contact with the behavioral intention of the decision maker to produce a goal-directed act. Psychology has a long history of decision-making research that has resulted in detailed mathematical models of underlying processes [1,2] but only recently has it become possible to observe the neural correlates of these processes directly in awake behaving monkeys. To study processes involved in simple two-choice decisions, neuroscientists have used an analog of the two-choice response-time (RT) task from psychology, in which monkeys make saccadic eye movements to indicate their decisions about visual stimuli. Recordings from cells in premotor areas of the frontal lobe and the posterior parietal cortex have shown that the time-course of activity in these cells corresponds well with that of behavioral eye movement decisions Neural correlates of simple two-choice decisions Neural activity linked to eye movement decisions has been recorded in several visual tasks The view that neural firing rate can be understood as a correlate of the behavioral decision process From neurons to sequential-sampling models The picture that emerges from these findings is strikingly consistent with statistical decision models that have bee