The dynamical foundations of motion pattern formation: stability, selective adaptation, and perceptual continuity

Area of Science:

• Cognitive Neuroscience
• Computational Neuroscience
• Visual Perception

Background:

• Global motion pattern formation arises from complex interactions within neural populations.
• Understanding apparent motion perception requires models that explain phenomena like bistability and hysteresis.

Purpose of the Study:

• To present a dynamical model for global motion pattern formation.
• To demonstrate how interactive, self-stabilizing motion detectors can explain perceptual phenomena.
• To explore the role of detector interactions in the temporal persistence of percepts.

Main Methods:

• Development of a dynamical model based on concurrently activated, directionally selective motion detectors.
• Simulations of the model with various apparent motion stimuli, including bistable and monostable cases.
• Analysis of activation distributions and detector interactions to explain perceptual outcomes.

Main Results:

• The model successfully generates global motion patterns from stable activation distributions.
• The model accounts for phenomena such as bistability, spontaneous switching, hysteresis, and selective adaptation.
• Dynamical solutions for bistable stimuli were shown to be applicable to monostable stimuli.

Conclusions:

• A population of interactive, noisy, and self-stabilizing motion detectors can embody global motion pattern formation.
• Interactions among sequentially stimulated detectors are crucial for state dependence and temporal persistence of percepts.
• The model provides a unified framework for understanding diverse phenomena in apparent motion perception.