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Related Concept Videos

Language and Cognition01:27

Language and Cognition

693
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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Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Using large language models as a scalable mental status evaluation technique.

Margot Wagner1, Callum Stephenson2, Jasleen Jagayat2

  • 1Institute for Neural Computation, University of California San Diego, San Diego, CA, USA. mwagner@ucsd.edu.

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Summary

Large language models can now evaluate mental health status from text. This AI approach shows accuracy comparable to human experts in identifying symptoms of anxiety and depression.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Mental Health Technology

Background:

  • Significant gap exists between mental health care demand and service availability.
  • Objective and scalable mental health evaluation tools are crucial.
  • Spoken and written language are integral to mental health diagnostics and treatment.

Purpose of the Study:

  • To investigate the efficacy of large language models (LLMs) in mental health status evaluation.
  • To develop and fine-tune a RoBERTa-based transformer model for analyzing written language.
  • To bridge the gap in mental health service availability using AI-driven tools.

Main Methods:

  • Fine-tuning a RoBERTa-based transformer model using natural language processing.
  • Utilizing both non-clinical online forum data and clinical data from an online psychotherapy trial.
  • Expanding the text dataset through backtranslation augmentation and optimizing performance via hyperparameter tuning.

Main Results:

  • The fine-tuned LLM demonstrated the ability to analyze written language for mental health symptom identification.
  • Model accuracy in classifying symptomatic sentences was comparable to human expert evaluations.
  • The model achieved 74% prediction accuracy in identifying sentences indicative of anxiety or depression.

Conclusions:

  • LLMs show promise as scalable tools for mental health evaluation.
  • AI-powered analysis of written text can support clinical identification of mental health symptoms.
  • This approach offers a potential method to augment existing mental health diagnostic capabilities.