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Bistability and Stabilization of Human Visual Perception under Ambiguous Stimulation.

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Summary

This study models perceptual choices under intermittent visual stimuli. Longer stimulus off-times (T_off) stabilize repetitive percept sequences, while overlapping parameter regions allow for bistability.

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Area of Science:

  • Computational neuroscience
  • Perceptual psychology
  • Dynamical systems theory

Background:

  • Ambiguous visual stimuli can lead to alternating or repetitive perceptual choices.
  • The influence of stimulus presentation timing, specifically stimulus off-time (T_off) and stimulus on-time (T_on), on perceptual stability is not fully understood.
  • Previous work by Noest et al. (2007) explored these dynamics through numerical simulations.

Purpose of the Study:

  • To develop and analyze a computational model of perceptual choice stabilization under intermittent viewing.
  • To explicitly incorporate stimulus off-time (T_off) and stimulus on-time (T_on) as bifurcation parameters.
  • To elucidate the underlying bifurcations governing the switching between alternating and repetitive percept sequences.

Main Methods:

  • Direct numerical simulation of a computational model across a grid of (T_off, T_on) parameter values.
  • Mathematical analysis of an extended model incorporating T_off and T_on as bifurcation parameters.
  • Identification of limit point and period-doubling bifurcations of periodic orbits.

Main Results:

  • Both alternating and repetitive percept sequences can be stable, sometimes for identical (T_off, T_on) values.
  • Increasing T_off invariably leads to the dominance of repetitive percept sequences after initial transients.
  • Stability borders are defined by limit point and period-doubling bifurcations, with overlapping regions creating bistability.

Conclusions:

  • The computational model successfully captures the stabilization of perceptual choices under intermittent viewing.
  • Bifurcation analysis reveals the mechanisms for switching between alternating and repetitive percept dynamics.
  • Model predictions align with experimental findings, enhancing understanding of perceptual stability.