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A Two-interval Forced-choice Task for Multisensory Comparisons

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Simulating bistable perception with interrupted ambiguous stimulus using self-oscillator dynamics with percept choice

Norbert Fürstenau¹

¹Institute of Flight Guidance, German Aerospace Center, Lilienthalplatz 7, 38108, Brunswick, Germany, norbert.fuerstenau@dlr.de.

Cognitive Processing

|September 4, 2014

Summary

This study models percept reversals using a dynamical systems approach, revealing how stimulus ambiguity and attention influence cognitive processes. Findings support a unified theory for brain dynamics and psychological phenomena.

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Area of Science:

Cognitive psychology
Dynamical systems theory
Neuroscience

Background:

Perceptual ambiguity can lead to spontaneous shifts in perception (percept reversals).
Dynamical systems and coordination dynamics offer frameworks for understanding cognitive and brain processes.
Gestalt psychology provides context for perceptual organization and subjective experience.

Purpose of the Study:

To simulate and analyze percept reversals induced by ambiguous stimuli using a novel behavioral model.
To investigate the role of stimulus ambiguity and attention in perceptual dynamics.
To connect macroscopic brain dynamics (EEG) with cognitive phenomena like cognitive inertia.

Main Methods:

Developed a behavioral stochastic self-oscillator model based on coupled nonlinear equations.
Utilized a simplified field theoretical approach and phase synchronization principles.
Incorporated a stochastic Langevin force term to model attention dynamics.
Analyzed simulated time series for percept reversal rates and quantified cognitive inertia.

Main Results:

The model successfully simulates interrupted ambiguous stimulus-induced percept reversals.
A maximum percept reversal rate was predicted, aligning with existing literature.
Ambiguity control, related to EEG field coherence, influences bistable dynamics.
Attention fatigue and stimulus onset dynamics were shown to drive quasiperiodic percept reversals.

Conclusions:

The findings support a dynamical systems foundation for cognitive and psychological phenomena.
The model provides a quantitative account of cognitive inertia and attention's role in perception.
Phase synchronization and stochastic forces are crucial for explaining brain dynamics at the macroscopic EEG level.