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Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
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Amodal completion instead of predictive coding can explain activity suppression of early visual cortex during
Chuyao Yan1,2, Alexis Pérez-Bellido1,3,4,5, Floris P de Lange1,6
1Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands.
Journal of Vision
|May 14, 2021
Summary
Neural adaptation, not prediction error, explains activity suppression in the early visual cortex during Kanizsa illusions. This finding challenges predictive coding models by highlighting the role of stable input in neural processing.
Area of Science:
- Neuroscience
- Visual Perception
- Cognitive Science
Background:
- Neuroimaging studies show early visual cortex activity changes with illusory shapes like the Kanizsa illusion.
- Previous research linked these modulations to predictive coding, where neural activity aligns with prediction accuracy.
Purpose of the Study:
- To investigate whether neural adaptation to stable input, rather than prediction error, explains activity suppression in the early visual cortex during Kanizsa illusions.
- To test Moors' (2015) hypothesis on neural adaptation as an alternative to predictive coding.
Main Methods:
- Manipulated the perceptual stability of the Pac-Man inducers used in the Kanizsa illusion.
- Compared neural activity patterns in the early visual cortex under conditions of stable versus unstable inducers.
Main Results:
- Replicated previous findings of enhanced activity for illusory shapes and suppressed activity for inducers when inducers were perceived as stable.
- Observed enhanced activity for illusory shapes but absent suppression for inducers when their perceptual stability was disrupted.
Conclusions:
- The findings support the neural adaptation hypothesis, suggesting that activity suppression for inducers in the Kanizsa illusion is due to adaptation to perceptually stable input.
- Challenges the interpretation of these modulations solely through the lens of reduced prediction error within the predictive coding framework.
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