Neural dynamics implement a flexible decision bound with a fixed firing rate for choice: a model-based hypothesis.

ABSTRACT: Decisions are faster and less accurate when conditions favor speed, and are slower and more accurate when they favor accuracy. This speed-accuracy trade-off (SAT) can be explained by the principles of bounded integration, where noisy evidence is integrated until it reaches a bound. Higher bounds reduce the impact of noise by increasing integration times, supporting higher accuracy (vice versa for speed). These computations are hypothesized to be implemented by feedback inhibition between neural populations selective for the decision alternatives, each of which corresponds to an attractor in the space of network states. Since decision-correlated neural activity typically reaches a fixed rate at the time of commitment to a choice, it has been hypothesized that the neural implementation of th

SUBMITTER: Standage D 

PROVIDER: S-EPMC4204603 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature