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Related Concept Videos

Language and Cognition01:27

Language and Cognition

317
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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The Feasibility of Large Language Models in Verbal Comprehension Assessment: Mixed Methods Feasibility Study.

Dorit Hadar-Shoval1, Maya Lvovsky1, Kfir Asraf1

  • 1Department of Psychology, Max Stern Academic College of Emek Yezreel, Afula, Israel.

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|February 24, 2025
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Summary

Large language models (LLMs) show promise for creating accessible artificial intelligence-based verbal comprehension tests (AI-BVCTs). These AI-BVCTs demonstrated strong agreement with traditional assessments, suggesting potential for personalized cognitive evaluations.

Keywords:
AI in psychodiagnosticsWAIS-IIIWechsler Adult Intelligence Scaleartificial intelligenceethics in computerized cognitive assessmentlarge language modelspersonalized intelligence testspsychological test validityverbal comprehension assessmentverbal comprehension index

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Area of Science:

  • Cognitive Psychology
  • Artificial Intelligence
  • Psychometrics

Background:

  • Cognitive assessment is crucial in applied psychology.
  • Limited access and high costs hinder traditional evaluations.
  • Developing accessible and affordable assessment tools is essential.

Purpose of the Study:

  • To evaluate the feasibility of using large language models (LLMs) for personalized AI-based verbal comprehension tests (AI-BVCTs).
  • To compare AI-BVCT scores with traditional verbal intelligence assessments.
  • To explore AI's potential in enhancing cognitive assessment accessibility.

Main Methods:

  • A within-participants design was employed.
  • Scores from AI-BVCTs (generated by Claude) were compared to the Wechsler Adult Intelligence Scale-III (WAIS-III) verbal comprehension index (VCI).
  • Eight Hebrew-speaking participants completed both assessment types.

Main Results:

  • Strong agreement was found between AI-BVCT and VCI scores (Claude: CCC=.75; GPT-4: CCC=.73).
  • Pearson correlations indicated significant associations between VCI and AI-BVCT scores (Claude: r=.84; GPT-4: r=.77).
  • No statistically significant differences were observed between AI-BVCT and VCI scores.

Conclusions:

  • LLMs show potential for assessing verbal intelligence through AI-BVCTs.
  • AI-based cognitive tests can increase assessment accessibility and affordability.
  • Ethical considerations and further research with diverse samples are necessary to validate this approach.