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Neural tuning instantiates prior expectations in the human visual system
William J Harrison1,2, Paul M Bays3, Reuben Rideaux4,5,6
1School of Psychology, The University of Queensland, St Lucia, Australia.
Nature Communications
|September 1, 2023
Summary
Human visual perception uses an active inference model, combining expectations with sensory data. This study reveals a novel visual encoding scheme that embeds environmental priors for natural images, optimizing early cortical processing.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cognitive Science
Background:
- Perception is modeled as active inference, integrating prior expectations with sensory data to understand the world.
- This framework is crucial for perception, cognition, motor control, and social interaction.
- Theoretical work suggests priors can be derived from environmental statistics, but neural implementation varies.
Purpose of the Study:
- To investigate the neural instantiation of priors in visual perception.
- To extract the brain's representation of visual orientation using a data-driven approach.
- To compare empirical findings with simulations of different sensory coding schemes.
Main Methods:
- A data-driven approach was used to analyze the brain's representation of visual orientation.
- The extracted neural representation was compared against simulations of various sensory coding schemes.
- Environmental statistics of natural images were analyzed to understand prior embedding.
Main Results:
- The human visual system's tuning is highly dependent on stimulus-specific variations, contrary to previous models.
- A novel sensory encoding scheme was identified that is conditional on these variations.
- This scheme was shown to embed environmental priors for natural image statistics.
Conclusions:
- The identified encoding scheme provides a functional architecture for optimal inference in early cortical processing.
- This finding advances our understanding of how the brain integrates prior knowledge with sensory input.
- The study highlights the adaptive nature of sensory coding in visual perception.
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