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Related Experiment Video

Updated: Apr 22, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Language networks associated with computerized semantic indices.

Serguei V S Pakhomov¹, David T Jones², David S Knopman³

¹University of Minnesota Center for Clinical and Cognitive Neuropharmacology, Minneapolis, MN, USA.

|October 16, 2014

Summary

This summary is machine-generated.

This study introduces a computational linguistic approach to analyze verbal fluency, linking semantic clustering indices to brain network connectivity. These findings enhance our understanding of how the brain organizes and retrieves lexical information.

Keywords:

Generative verbal fluency Latent semantic analysis Semantic clustering Semantic memory Semantics Task-free fMRI

Related Experiment Videos

Last Updated: Apr 22, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Neuroscience
Computational Linguistics
Cognitive Psychology

Background:

Generative semantic verbal fluency tests assess concept organization in the brain.
Previous research linked verbal fluency to semantic memory and executive control, relying on manual semantic analysis.
A gap exists in objective, computational methods for assessing semantic relatedness in verbal fluency.

Purpose of the Study:

To investigate a computational linguistic approach for measuring semantic relatedness using latent semantic analysis.
To compare computerized semantic clustering indices with brain network connectivity measures.
To explore the relationship between semantic organization, brain networks, and verbal behavior.

Main Methods:

Latent semantic analysis of word co-occurrences from an online encyclopedia to compute semantic relatedness.
Calculation of semantic clustering indices.
Comparison of these indices with task-free functional magnetic resonance imaging (fMRI) brain network connectivity in healthy and cognitively impaired individuals.

Main Results:

Semantic clustering indices showed significant associations with brain network connectivity.
Key brain regions involved included fronto-temporal, fronto-parietal, and fusiform gyrus.
The computational approach provided objective measures complementing traditional verbal fluency assessments.

Conclusions:

Computerized semantic indices offer a valuable, objective complement to traditional verbal fluency assessments.
This approach enhances the understanding of brain mechanisms underlying semantic memory and executive control.
Findings illuminate the relationship between brain network connectivity and the organization/retrieval of lexical information.