A functional theory of bistable perception based on dynamical circular inference
Fig 3
Energy landscapes of the model with and without descending loops.
(A.) Schema illustrating the relationship between wells in the energy landscape (potential = integral of the dynamic equation, in blue) and stable states. Gray and black dots represent the initial and final state from two different initial states. In the absence of external input, dots can only decrease. (B.) Schema illustrating how noise can force the state to climb an energy barrier (a hill in the energy landscape) and switch to a different stable state. (C.) Energy landscape of the model with no descending loops (dashed, aP = 0), and two increasing levels of descending loops (red: aP = 1, blue: aP = 1.3). Descending loops generate a bistable attractor, whose stable fixed points correspond to (strong beliefs about) the two interpretations (blue). In contrast, a system with no loops has only one attractor, the prior, (equal to 0 in this unbiased scenario). (D.) Energy landscape for different biases, no bias (red: ron = roff = 0.5), weak bias (magenta:, ron = 0.55, roff = 0.45) and strong bias (light green, ron = 0.6, roff = 0.4). Note that for stronger biases, the nonpreferred configuration becomes unstable.