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Language and Cognition

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.

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

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Published on: December 6, 2024

Discourse- and lesion-based aphasia quotient estimation using machine learning.

Nicholas Riccardi¹, Satvik Nelakuditi², Dirk B den Ouden¹

¹Department of Communication Sciences and Disorders, University of South Carolina, United States.

Neuroimage. Clinical

|April 9, 2024

Summary

This summary is machine-generated.

Discourse analysis can estimate aphasia severity using the Western Aphasia Battery-Revised (WAB-R) Aphasia Quotient (AQ). This method offers a less resource-intensive approach to assessing linguistic ability in individuals with aphasia.

Keywords:

Aphasia Discourse Production Lesion Symptom Mapping Machine Learning Stroke

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Area of Science:

Neurolinguistics
Computational linguistics
Clinical neuroscience

Background:

Discourse production is crucial for communication and reveals linguistic capabilities.
Aphasia frequently impairs discourse production, necessitating accurate assessment.
Current comprehensive aphasia assessments, like the Western Aphasia Battery-Revised (WAB-R), are resource-intensive.

Purpose of the Study:

To determine if discourse measures can accurately estimate the WAB-R Aphasia Quotient (AQ).
To explore the ecological validity and reduced resource requirements of discourse-based aphasia assessment.
To investigate the contribution of neuroimaging lesion data to aphasia severity prediction.

Main Methods:

Extracted linguistic features from three discourse tasks (expositional, narrative, procedural) using AphasiaBank prompts.
Trained machine learning models to predict WAB-R AQ using discourse features.
Compared discourse-based models with models incorporating structural neuroimaging lesion data.

Main Results:

Discourse-based machine learning models effectively estimated WAB-R AQ.
Discourse models outperformed models based solely on lesion location.
Integrating lesion data did not significantly enhance the predictive performance of discourse models.
Analysis of informative features indicated that different discourse prompts engage distinct linguistic aspects.

Conclusions:

Discourse analysis provides a viable and less resource-intensive method for estimating aphasia severity (AQ).
Discourse-based assessments offer ecologically valid insights into linguistic function in aphasia.
Understanding linguistic demands of different discourse types can refine aphasia evaluation and intervention.