A functional theory of bistable perception based on dynamical circular inference
Fig 1
Normative model for how 3D objects result in particular sensory inputs, and putative neural implementation of the corresponding perceptual inference.
(A.) The internal model is a simple Bayesian generative model, where 3D objects predict the 2D image, and the 2D image predicts low-level sensory inputs. The brain interprets the depth cues (basic features) as indicative of real depth. Consequently, it first reconstructs the 2D figure and from that, it infers the 3D object. Note that in reality there is one single 2D stimulus (the Necker cube drawing) containing contradictory depth cues. (B.) Close-up on the assumed “basic feature” distributions (likelihood) compared to the real input distributions. The brain interprets the depth cues as meaningful, predicting separate input distributions for the two cubes (SFA, SFB; two objects cannot occupy the same space), which corresponds to two nonoverlapping likelihood distributions in the internal model (dotted red and blue distributions). In the totally ambiguous case (cube with no extra cues), the real input is sampled from a distribution with mean 0 (black). Visual cues shift this input distribution toward mostly positive or negative values. Crucially, there is a discrepancy between the real input and the input assumed by the internal model. This, together with the loops, predicts the suboptimal inference at the heart of bistable perception. (C.) A simplified neural implementation of hierarchical perceptual inference. Reciprocal connections can combine bottom-up sensory evidence with top-down priors at all levels of the hierarchical representation. Unfortunately, this also creates redundant information loops, ascending (magenta arrow) and descending (blue arrow). (D.) The brain can cancel these loops by using inhibitory interneurons and maintaining a tight E/I balance. If this balance is impaired, however, there will be some residual loops, parameterized by aP (descending loops, amplifying prior beliefs) and aS (ascending loops, amplifying the sensory evidence). L is the log-ratio of the belief. (E.) From the Bayesian model (A.) we derived an attractor model that performs inference in the presence of loops. The model accumulates noisy evidence while descending loops add positive feedback and ascending loops increase the sensory gain.