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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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Scalable cognitive modelling: Putting Simon's (1969) ant back on the beach.
Brendan T Johns1, Randall K Jamieson2, Michael N Jones3
1Department of Psychology, McGill University.
Summary
Scaled cognitive modeling integrates computational cognitive models with big data and machine learning. This approach aims to bridge classic cognitive theory with advanced computational methods for a more accurate representation of memory.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Artificial Intelligence
Background:
- Classic cognitive modeling struggles to accurately represent memory contents.
- Integrating process and representation remains a core challenge in cognitive theories.
- Simon's ant parable highlights the importance of environmental context in cognition.
Approach:
- Reviews standard practices in cognitive modeling.
- Examines how machine learning and big data modify these practices.
- Proposes scaled cognitive modeling as a solution for integrating process and representation.
Key Points:
- Machine learning and big data offer new avenues for cognitive modeling.
- Scaled cognitive modeling aims to improve the representation of memory.
- Bridging classic theory with computational methods is crucial for future research.
Conclusions:
- Scaled cognitive modeling provides a pathway to integrate advanced computational methods with cognitive theory.
- This integration is essential for developing more complete theories of human cognition.
- Future research should focus on leveraging big data and machine learning within established cognitive principles.
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