Frontiers in Computational Neuroscience (Mar 2013)

Multi-stable perception balances stability and sensitivity

  • Alexander ePastukhov,
  • Alexander ePastukhov,
  • Pedro Ernesto eGarcía-Rodríguez,
  • Joachim eHaenicke,
  • Joachim eHaenicke,
  • Antoni eGuillamon,
  • Gustavo eDeco,
  • Jochen eBraun,
  • Jochen eBraun

DOI
https://doi.org/10.3389/fncom.2013.00017
Journal volume & issue
Vol. 7

Abstract

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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

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