Multi-stable perception balances stability and sensitivity - Pastukhov, Alexander; García-Rodríguez, Pedro Ernesto; Haenicke, Joachim; Guillamon, Antoni; Deco, Gustavo; Braun, Jochen (Frontiers in Computational Neuroscience, 2013)
We report that multi-stable perception operates in a consistent, dynamical regime, balancing the conflicting goals of stability and sensitivity. When a multi-stable visual display is viewed continuously, its phenomenal appearance reverses spontaneously at irregular intervals. We characterized the perceptual dynamics of individual observers in terms of four statistical measures: the distribution of dominance times (mean and variance) and the novel, subtle dependence on prior history (correlation and time-constant). The dynamics of multi-stable perception is known to reflect several stabilizing and destabilizing factors. Phenomenologically, its main aspects are captured by a simplistic computational model with competition, adaptation, and noise. We identified small parameter volumes ( 3% of the possible volume) in which the model reproduced both dominance distribution and history-dependence of each observer. For 21 of 24 data sets, the identified volumes clustered tightly ( 15% of the possible volume), revealing a consistent `operating regime' of multi-stable perception. The `operating regime' turned out to be marginally stable or, equivalently, near the brink of an oscillatory instability. The chance probability of the observed clustering was <0.02. To understand the functional significance of this empirical `operating regime', we compared it to the theoretical `sweet spot' of the model. We computed this `sweet spot' as the intersection of the parameter volumes in which the model produced stable perceptual outcomes and in which it was sensitive to input modulations. Remarkably, the empirical `operating regime' proved to be largely coextensive with the theoretical `sweet spot'. This demonstrated that perceptual dynamics was not merely consistent but also functionally optimized (in that it balances stability with sensitivity). Our results imply that multi-stable perception is not a laboratory curiosity, but reflects a functional optimization of perceptual dynamics