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The Capacity for Correlated Semantic Memories in the Cortex
Vezha Boboeva1, Romain Brasselet1, Alessandro Treves1,2
1Cognitive Neuroscience, SISSA-International School for Advanced Studies, Via Bonomea 265, 34136 Trieste, Italy.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
A new generative model explains semantic memory beyond simple trees, revealing how shared features create correlations. This model suggests human-scale cortical networks can still store millions of concepts, with partial categorization emerging spontaneously.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Psychology
Background:
- Semantic memory is often modeled as a hierarchical tree structure.
- This tree structure has limitations in explaining complex, multifactorial relationships between concepts.
Purpose of the Study:
- To propose a generative model that captures complex correlations in semantic memory.
- To overcome the limitations of traditional tree-based models of semantic representation.
Main Methods:
- Developed a generative model where items are represented by shared semantic "factors" or attributes.
- Analyzed how the number and strength of these factors influence item correlations.
- Simulated a Potts network to assess storage capacity and retrieval with correlated items.
Main Results:
- The model reproduces a spectrum of correlations, neither trivial nor strictly hierarchical.
- Correlations reduce storage capacity but human-scale networks may still store ~10^7 concepts.
- Beyond capacity, retrieval failure transitions to partial categorization due to dominant factors.
Conclusions:
- The proposed model offers a more realistic representation of semantic memory structure.
- Partial categorization emerges spontaneously, providing a potential explanation for semantic memory resilience.
- This framework may inform understanding of phenomena like Tulving's remember/know paradigms.
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