Repetition suppression and its contextual determinants in predictive coding
Ryszard Auksztulewicz1, Karl Friston1
1Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.
Summary
Repetition suppression, a key brain process, is explained by predictive coding, a theory of how we perceive and learn. This model shows how minimizing prediction errors drives this suppression across different timescales.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cognitive Science
Background:
- Repetition suppression is a widely observed phenomenon in perception.
- Predictive coding offers a neurobiologically plausible framework for perceptual inference and learning.
Purpose of the Study:
- To review theoretical and empirical work on repetition suppression within the predictive coding framework.
- To explain repetition suppression as a consequence of prediction error minimization.
Main Methods:
- Review of existing theoretical and empirical literature.
- Simulations of artificial neural hierarchies to model repetition suppression.
- Analysis of contextual determinants of repetition suppression.
Main Results:
- Repetition suppression can be understood as minimizing prediction error via adaptive changes in predictions.
- Artificial neural network simulations support the predictive coding account of repetition suppression.
- Empirical studies corroborate the role of inference and learning in repetition suppression.
Conclusions:
- Predictive coding provides a unified framework for understanding repetition suppression.
- Repetition suppression reflects efficient neural processing driven by prediction error minimization.
- The theory is supported by a broad range of empirical evidence.
Keywords:
Mismatch negativityPerceptual inferencePerceptual learningPredictive codingRepetition suppressionMore Related Videos
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