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Linking Neural and Symbolic Representation and Processing of Conceptual Structures
Frank van der Velde1,2, Jamie Forth3, Deniece S Nazareth1
1Department of Cognitive Psychology and Ergonomics, Faculty of Behavioural, Management and Social sciences, University of TwenteEnschede, Netherlands.
Frontiers in Psychology
|August 30, 2017
Summary
This study compares the Neural Blackboard Architecture (NBA) and Information Dynamics of Thinking (IDyOT) for representing complex conceptual structures. A combined architecture integrating both models shows promise for enhanced cognitive function representation.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Artificial Intelligence
Background:
- Existing cognitive architectures struggle with representing complex, sentence-like conceptual structures.
- The Neural Blackboard Architecture (NBA) models neural processing of combinatorial structures.
- Information Dynamics of Thinking (IDyOT) models cognitive function through statistical learning of sequential regularities.
Purpose of the Study:
- To compare and discuss representational strategies in the NBA and IDyOT.
- To explore the potential for integrating NBA and IDyOT into a unified cognitive architecture.
- To enhance the modeling of complex conceptual structure representation and processing.
Main Methods:
- Comparative analysis of representational frameworks in NBA and IDyOT.
- Identification of structural similarities between the two architectures.
- Conceptual development of a hybrid architecture combining NBA and IDyOT strengths.
Main Results:
- Significant similarities were found between composite structures in IDyOT and NBA.
- These similarities suggest a foundation for a synergistic, combined architecture.
- The proposed combined architecture aims to leverage the distinct advantages of both models.
Conclusions:
- A combined cognitive architecture integrating NBA and IDyOT offers a promising approach for modeling complex conceptual structures.
- This integration could lead to more comprehensive accounts of cognitive representation and processing.
- Future work should focus on developing and testing this hybrid model for cognitive function.
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