Abstract
Lissajous figures represent ambiguous structure-from-motion stimuli rotating in depth and have proven to be a versatile tool to explore the cognitive and neural mechanisms underlying bistable perception. They are generated by the intersection of two sinusoids with perpendicular axes and increasing phase-shift whose frequency determines the speed of illusory 3D rotation. Recently, we found that Lissajous figures of higher shifting frequencies elicited longer perceptual phase durations and tentatively proposed a “representational momentum” account. In this study, our aim was twofold. First, we aimed to gather more behavioral evidence related to the perceptual dynamics of the Lissajous figure by simultaneously varying its shifting frequency and size. Using a conventional analysis, we investigated the effects of our experimental manipulations on transition probability (i.e., the probability that the current percept will change at the next critical stimulus configuration). Second, we sought to test the impact of our experimental factors on the occurrence of transitions in bistable perception by means of a Bayesian approach that can be used to directly quantify the impact of contextual cues on perceptual stability. We thereby estimated the implicit prediction of perceptual stability and how it is modulated by experimental manipulations.
Highlights
In bistable vision, perception alternates between two different interpretations of a constant ambiguous sensory input
Along this line of thought, the inherently ambiguous stimulation we receive through our senses is understood to result in a clear and stable experience of our environment after taking into account what we already know about our world
The duration of self-occlusions determines the perceptual stability of the Lissajous figure
Summary
Perception alternates between two different interpretations of a constant ambiguous sensory input. Novel theories place bistable perception in the context of predictive coding [1, 2] Within this framework, perception is explained as an active process of inferring on the most likely causes of sensory input [3], which—according to Bayes’ theorem—can be implemented by combining a prior (representing “beliefs” about the world) and a likelihood (representing new sensory input) to estimate the posterior distribution, based on which perceptual decisions are formed [4]. PLOS ONE | DOI:10.1371/journal.pone.0160772 August 25, 2016
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