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Related Concept Videos

Language and Cognition01:27

Language and Cognition

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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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Related Experiment Video

Updated: Feb 20, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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Published on: January 11, 2020

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Predicting mild cognitive impairment from spontaneous spoken utterances.

Meysam Asgari1, Jeffrey Kaye2, Hiroko Dodge2,3

  • 1Center for Spoken Language Understanding, Oregon Health & Science University (OHSU), Portland, Oregon, USA.

Alzheimer'S & Dementia (New York, N. Y.)
|October 26, 2017
PubMed
Summary

Analyzing spoken language with Linguistic Inquiry and Word Count (LIWC) effectively identified mild cognitive impairment (MCI) in older adults. This method achieved 84% accuracy, showing potential for early detection in cognitive health trials.

Keywords:
BiomarkersConversational interactionsEarly identificationMild cognitive impairment (MCI)Social markersSpeech characteristics

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Area of Science:

  • Neurology
  • Psycholinguistics
  • Computational Linguistics

Background:

  • Alzheimer's disease research increasingly targets prevention in asymptomatic individuals.
  • Mild cognitive impairment (MCI) may manifest subtle linguistic changes in spoken language.
  • Identifying early indicators of cognitive decline is crucial for timely intervention.

Purpose of the Study:

  • To investigate if linguistic features of spoken language can differentiate individuals with MCI from cognitively intact older adults.
  • To assess the utility of linguistic analysis in identifying early signs of cognitive impairment.

Main Methods:

  • Linguistic analysis of spoken word data from 14 MCI and 27 cognitively intact participants in a clinical trial.
  • Utilized the Linguistic Inquiry and Word Count (LIWC) system to categorize words into 68 subcategories.
  • Employed support vector machine and random forest classifiers to distinguish between cognitive groups.

Main Results:

  • Linguistic features derived from LIWC analysis achieved an 84% classification accuracy in distinguishing MCI from cognitively intact participants.
  • This accuracy significantly surpassed the chance level of 60%.

Conclusions:

  • Spoken language analysis, using LIWC, shows significant potential as a non-invasive tool for identifying mild cognitive impairment.
  • Further research is warranted to explore the use of these linguistic measures for tracking cognitive changes in clinical trials.