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

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 (with typically 1 < α < 2), also known as pink noise or '1/f' noise.