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A higher order Bayesian decision theory of consciousness
1Wellcome Trust Functional Imaging Laboratory, University College London, 12 Queen Square, London, UK. hakwan@gmail.com
Progress in Brain Research
|January 2, 2008
Summary
Consciousness may not require superior information processing. A new Bayesian decision theory model suggests perception consciousness relies on learning internal signal statistics, not just computation, explaining dissociations between consciousness and performance.
Area of Science:
- Cognitive Science
- Neuroscience
- Philosophy of Mind
Background:
- Contemporary models link consciousness to advanced information processing.
- This view contrasts with philosophical intuition and empirical data.
- A strong assumption is that consciousness predicts superior performance.
Purpose of the Study:
- To present a new model of perception consciousness.
- To challenge the assumption that consciousness requires superior computation.
- To explain the dissociation between consciousness and performance.
Main Methods:
- Utilized Bayesian decision theory, a generalization of signal detection theory.
- Modeled perceptual decisions as limited by internal system noise.
- Introduced 'higher-order' learning of internal signal statistics.
Main Results:
- Consciousness is linked to optimal setting of decision criteria based on learned signal behavior.
- Failures in 'higher-order' learning can impair decision criteria, leading to dissociation.
- The model formally explains phenomena like blindsight and hallucinations.
Conclusions:
- Consciousness does not necessarily equate to superior information processing.
- Perception consciousness is critically dependent on learning and maintaining optimal decision criteria.
- The proposed model offers a new framework for understanding consciousness and its potential dissociations from performance.
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