A Bayesian Attractor Model for Perceptual Decision Making
Fig 8
Example of a decision making trial with evolution of cross-covariance and gain for parameters of point B in Fig 7.
Noisy exemplars of alternative 1 (blue) and subsequently of alternative 2 (orange) were shown with a switch at 800ms (cf. Fig 2). (A) Inferred decision state with mean state variables (lines) and two times their standard deviation (shading) indicating posterior uncertainty over decision state. State variable associated with alternative 1 shown in blue and associated with alternative 2 shown in orange. (B) Absolute cross-covariances between predicted observations and predicted decision state over time. Colours indicate cross-covariances associated with corresponding state variables as in A. Cross-covariances are large during their transition between fixed points. Once a fixed point is reached (i.e. a decision has been made) cross-covariances drop quickly. (C) Absolute gain values (elements of Kt) over time. Colouring as in B. Gain values are scaled cross-covariances, i.e., within-trial changes in gain are mostly driven by changes in cross-covariances.