Schematic comparing a pure attractor model of decision making with the Bayesian attractor model.

<p>Both plots show illustrative snapshots of the two evolving decision states while in transit towards a fixed point where a decision will be made. (A) In a pure attractor model, on the way to a fixed point, the decision state (violet) is evolving according to attractor dynamics (grey arrows). From an initial, unstable fixed point (empty, black circle) the decision state is driven by noisy evidence into one of two attracting, stable fixed points, each of which correspond to a decision alternative. (B) In the Bayesian attractor model the same attractor dynamics is used as generative model for sensory observations. The decision state effects, in a top-down fashion, both internal predictions and gain. These are in turn used together with sensory observations to compute gain-modulated prediction errors which drive updates of the decision state. The model represents uncertainty over the decision state (shaded, violet ellipse) and allows to define the decision criterion directly in terms of confidence in the decision. We show in Results that this recurrent principle stabilises the location of fixed points of the attractor dynamics while at the same time maintaining the ability to reliably switch decisions after a change in stimulus.