Accumulators, Neurons, and Response Time

The marriage of cognitive neurophysiology and mathematical psychology to understand decision-making has been exceptionally productive. This interdisciplinary area is based on the proposition that particular neurons or circuits instantiate the accumulation of evidence specified by mathematical models of sequential sampling and stochastic accumulation. This linking proposition has earned widespread endorsement. Here, a brief survey of the history of the proposition precedes a review of multiple conundrums and paradoxes concerning the accuracy, precision, and transparency of that linking proposition. Correctly establishing how abstract models of decision-making are instantiated by particular neural circuits would represent a remarkable accomplishment in mapping mind to brain. Failing would reveal challenging limits for cognitive neuroscience. This is such a vigorous area of research because so much is at stake. The collaboration between neuroscience and mathematical psychology has been highly productive. One of the anchors for this collaboration has been the focus on response time during perceptual decision-making, and the investigation of its mechanistic basis in terms of stochastic accumulation of evidence.This productivity has been powered by the belief that computational models can explain what neurons or neural circuits do, and that the properties of neurons or neural circuits can guide the selection of more accurate and effective computational models.The validity of this belief hinges on whether accumulator model parameters and neural measures can be mapped to one another. This mapping is articulated through linking propositions.This review surveys recent research that raises a variety of questions about the transparency of this mapping. Continued productivity depends on establishing valid and accurate linking propositions.