An integration-to-bound model of decision-making that accounts for the spectral properties of neural data.

ABSTRACT: Integration-to-bound models are among the most widely used models of perceptual decision-making due to their simplicity and power in accounting for behavioral and neurophysiological data. They involve temporal integration over an input signal ("evidence") plus Gaussian white noise. However, brain data shows that noise in the brain is long-term correlated, with a spectral density of the form 1/f^α (with typically 1 < α < 2), also known as pink noise or '1/f' noise. Surprisingly, the adequacy of the spectral properties of drift-diffusion models to electrophysiological data has received little attention in the literature. Here we propose a model of accumulation of evidence for decision-making that takes into consideration the spectral properties of brain signals. We develop a genera

SUBMITTER: Guevara Erra R 

PROVIDER: S-EPMC6557846 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature