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Perceptual Resolution of Ambiguity: Can Tuned, Divisive Normalization Account for both Interocular Similarity
Jaelyn R Peiso1, Stephanie E Palmer2, Steven K Shevell3
1University of Chicago, Department of Psychology, Physics Frontier Center for Living Systems, Chicago, IL.
Biorxiv : the Preprint Server for Biology
|April 15, 2024
Summary
Neural populations compete to resolve visual ambiguity. Divisive normalization, a neural computation, explains how the brain enhances either similarity or differences among competing percepts, unifying opposite perceptual outcomes.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Visual Perception
Background:
- The visual system typically creates a single, functional representation of the world.
- Ambiguous retinal stimuli can lead to multiple interpretations, engaging neural competition for perceptual dominance.
- Contextual information influences how the visual system resolves these ambiguities.
Purpose of the Study:
- To investigate the neural mechanisms underlying the resolution of visual ambiguity.
- To determine if divisive normalization can explain opposing perceptual outcomes, such as similarity or difference enhancement.
- To propose a unified computational framework for context-dependent visual processing.
Main Methods:
- Computational modeling of neural population dynamics.
- Analysis of how divisive normalization affects neural responses to ambiguous stimuli.
- Simulations to demonstrate the capacity of divisive normalization to produce both grouping and difference enhancement.
Main Results:
- Divisive normalization, a known neural computation, can account for context-dependent sensory processing.
- This computation successfully models both enhancement of similarity (grouping) and enhancement of differences among competing percepts.
- The findings offer a unified framework for understanding seemingly opposite perceptual outcomes.
Conclusions:
- Divisive normalization provides a unified computational explanation for resolving visual ambiguity.
- This mechanism explains how the brain can either group similar percepts or enhance differences based on context.
- The study advances our understanding of neural computations governing perceptual flexibility and context dependence.
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