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Related Concept Videos

Language and Cognition01:27

Language and Cognition

874
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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Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
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Related Experiment Video

Updated: Apr 28, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Explainable Patient-Level Cognitive Impairment Screening via Temporal, Semantic, and Psycholinguistic Multimodal AI.

Abdullah1,2, Zulaikha Fatima3, Miguel Jesús Torres Ruiz1

  • 1Center for Computing Research, Instituto Politécnico Nacional, Mexico City 07738, Mexico.

Journal of Intelligence
|April 27, 2026
PubMed
Summary

This study introduces a novel AI framework for early detection of cognitive decline, like mild cognitive impairment and Alzheimer's disease, using language patterns in health records. The system accurately identifies patient states, offering hope for timely interventions.

Keywords:
Alzheimer’s diseasecognitive impairmentexplainable AIlongitudinal clinical notesmild cognitive impairmentpsycholinguistic biomarkerssemantic graph reasoningtemporal progression

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Area of Science:

  • Artificial Intelligence in Medicine
  • Computational Linguistics
  • Neuroscience

Background:

  • Early diagnosis of cognitive decline is crucial for effective treatment of mild cognitive impairment (MCI) and Alzheimer's disease (AD).
  • Standard clinical assessments often fail to detect subtle, longitudinal changes in patient language.
  • Electronic Health Records (EHRs) contain valuable linguistic data for tracking cognitive changes over time.

Purpose of the Study:

  • To develop and validate a hierarchical hybrid intelligence framework for classifying patient cognitive states (Normal, MCI, AD).
  • To leverage longitudinal EHR data, integrating advanced AI techniques for enhanced diagnostic accuracy.
  • To identify reliable linguistic biomarkers indicative of cognitive decline.

Main Methods:

  • A hybrid AI framework combining long-context language modeling, temporal progression analysis, semantic graph reasoning (using UMLS concepts via GraphSAGE), and psycholinguistic feature extraction.
  • Utilized BioClinicalBERT embeddings and Bi-LSTM for feature representation and temporal encoding.
  • Trained and validated the model on large-scale datasets (MIMIC-III and MIMIC-IV) with rigorous cross-validation and feature ablation studies.

Main Results:

  • Achieved exceptionally high performance on the MIMIC-III dataset, including 99.999% accuracy and 0.999 macro F1-score.
  • Demonstrated strong generalization capabilities on the MIMIC-IV dataset with minimal performance degradation in zero-shot testing.
  • Feature ablation confirmed the significant contributions of temporal, semantic, and psycholinguistic modules, outperforming text-only models.

Conclusions:

  • The proposed framework offers a scalable, non-invasive method for early cognitive decline screening using EHR data.
  • Identified specific linguistic markers, such as pronoun overuse and syntactic simplification, as key predictors of cognitive decline.
  • The model's explainability features (SHAP, attention maps) provide clinical interpretability, supporting its adoption in diverse healthcare settings.