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Consciousness and metarepresentation: a computational sketch.

Axel Cleeremans¹, Bert Timmermans, Antoine Pasquali

¹Cognitive Science Research Unit, Université Libre de Bruxelles CP 191, 50 ave. F.-D. Roosevelt, B1050 Bruxelles, Belgium. axcleer@ulb.ac.be

Neural Networks : the Official Journal of the International Neural Network Society

|October 2, 2007

Summary

This study explores computational mechanisms for consciousness, proposing that being conscious of being conscious is key. Simulations show how a second-order network observing a first-order network can enable meta-representation and understanding of mental states.

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Area of Science:

Cognitive Science
Computational Neuroscience
Philosophy of Mind

Background:

Higher-Order Thought Theory posits consciousness arises from being aware of one's own conscious states.
Connectionist networks often exhibit "knowledge in the network" rather than accessible knowledge, limiting meta-cognition.
Existing models struggle to explain how cognitive systems gain awareness of their internal knowledge states.

Purpose of the Study:

To investigate computational mechanisms underlying the Higher-Order Thought Theory of consciousness.
To explore how meta-representation can be implemented in artificial cognitive systems.
To model the emergence of awareness regarding internal knowledge and mental attitudes.

Main Methods:

Simulations involving two interacting connectionist networks.
A first-order network trained on a categorization task.
A second-order network trained to observe and either reproduce or utilize the states of the first-order network.

Main Results:

The interaction enabled a limited form of meta-representation, where the second-order network re-represented the first-order network's states.
This mechanism demonstrated the beginnings of a computational account for mental attitudes (e.g., belief, hope).
Consciousness is framed as understanding the 'geography' of one's internal representations, learned through interaction.

Conclusions:

The proposed two-network model provides a computational basis for higher-order thought and consciousness.
This framework suggests consciousness involves an agent's learned understanding of its own internal representational states.
The findings offer insights into how cognitive systems can develop awareness of their knowledge and mental states.