A Bayesian Attractor Model for Perceptual Decision Making
Fig 9
Evolution of decision state for pure attractor model (left) and Bayesian attractor model (right) for different input strengths or different uncertainty parameters, respectively.
There are two alternatives indicated by blue (alternative 1) and orange (alternative 2). Thinner lines indicate smaller stimulus strength. For the first 800ms, input reflecting alternative 1 was shown, with a switch to input caused by alternative 2 at 800ms. (A) In the pure attractor model speed and accuracy of initial and re-decisions is controlled by the input which we set to It = [ΔtI+vt,0], if alternative 1 is correct, and It = [0,ΔtI+vt], if alternative 2 is correct (vt ∼ 𝓝(0,0.22)). We varied the value of I as indicated in the plot legend. If I is large, i.e., the task is easy, initial decisions and switches are fast (thick lines). The position of the fixed point, to which the dynamics converges, depends strongly on I. (B, C): In the Bayesian attractor model timing and accuracy of initial decisions and re-decisions depend on the uncertainties in the model, but, critically, the location of the fixed points of the dynamics remain the same for different uncertainties. B and C share the same observations with noise level s = 1. In B: q = 0.5. In C: r = 1.9.