A Bayesian Attractor Model for Perceptual Decision Making
Fig 12
Model fit to experimental data presented in [54].
Eight different coherence levels ranged from 0% to 75%. (A) Model parameters (red: sensory uncertainty r, green: noise level s) inferred from the behavioural data. For each coherence and parameter we show an approximate posterior distribution estimated from 501 posterior samples (see Methods) where darker colours correspond to larger probability as indicated by the colour bars on the right. Both abscissa and ordinate are in log-scale. Red line: linear fit between sensory variance r2 and coherence that also exposes a linear relation between drift and coherence in the drift diffusion model. (B) Fit of mean RT of all responses. Black dots with light grey outline: behavioural data [54]. Greyscale rectangles: estimated posterior distribution over mean reaction time. (C) Fit of accuracy (fraction of correct responses). Format as in B. Black, horizontal bars for coherences greater than 9% indicate probabilities larger than 0.2 for an accuracy of 1. This means that for high coherences parameter values as indicated in A predicted an accuracy of 1.