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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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Linguistic Markers in At-Risk Mental States Using Natural Language Processing: A Systematic Review.

Yuhan Zhang1, Alba Carrió1, Julia Sevilla-Llewellyn-Jones2

  • 1Department of Clinical and Health Psychology, Autonomous University of Barcelona, 08193 Bellaterra, Spain.

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Summary

Natural language processing (NLP) can identify linguistic markers in individuals with at-risk mental states (ARMS) to predict psychosis. These AI-driven insights aid early intervention and prevention strategies.

Keywords:
artificial intelligenceat-risk mental statelinguistic markersnatural language processingpsychosis

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Area of Science:

  • Psychiatry
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Growing focus on psychosis prevention through early intervention.
  • Linguistic markers in at-risk mental states (ARMS) are key for identifying individuals at risk.
  • Artificial intelligence (AI) and natural language processing (NLP) offer novel tools for detecting these markers.

Purpose of the Study:

  • To systematically review and synthesize evidence on linguistic markers analyzed by NLP in ARMS individuals.
  • To assess the utility of NLP-derived linguistic markers in predicting psychosis onset.

Main Methods:

  • Systematic review adhering to PRISMA 2020 guidelines.
  • Searched PubMed, PsycInfo, and Scopus databases up to October 2025.
  • Included 15 studies with 1313 participants, analyzing linguistic data from ARMS individuals.

Main Results:

  • Alterations in semantic coherence, syntactic complexity, referential cohesion, and speech/content poverty distinguish ARMS individuals from controls.
  • NLP-identified markers predicted psychosis onset with 79-100% accuracy.
  • Caution is advised due to methodological heterogeneity and sample size variations.

Conclusions:

  • NLP effectively detects language alterations in ARMS individuals.
  • These findings show promise for NLP as a complementary tool for early psychosis detection and prevention.
  • NLP aids in predicting psychosis onset, supporting clinical assessments.