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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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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.
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Interference and Decay01:16

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Forgetting is a complex cognitive phenomenon influenced by several factors, among which interference and decay are particularly prominent. These processes explain why individuals often struggle to retrieve specific information from memory, leading to lapses in recall that can be observed in everyday situations.
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Cognitive Development During Adulthood01:30

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Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
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Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
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Cognitive enhancers, also known as "smart drugs," are substances used to enhance memory, mental alertness, and concentration. These can be natural or synthetic and improve cognition in conditions like Alzheimer's disease (AD) and other neurodegenerative diseases. Some common examples include caffeine, amphetamines, methylphenidate, modafinil, arecoline, donepezil, vortioxetine, and piracetam. These enhancers work on the principle of synaptic plasticity and altered circuit function.
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Related Experiment Video

Updated: Jul 6, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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An explainable machine learning model of cognitive decline derived from speech.

Chelsea Chandler1, Catherine Diaz-Asper2, Raymond S Turner3

  • 1Institute of Cognitive Science University of Colorado Boulder Colorado USA.

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|December 29, 2023
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Summary

Artificial intelligence (AI) models analyzing speech data can detect cognitive decline, including Alzheimer's disease (AD) and mild cognitive impairment (MCI), with 75% accuracy. This multimodal approach offers a more sensitive and timely screening method than traditional tools.

Keywords:
Alzheimer's diseaseMCINLPcognitive screeningmultimodal machine learning

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Traditional screening for Alzheimer's disease (AD) and mild cognitive impairment (MCI) often lacks the sensitivity and timeliness needed for early detection of cognitive decline.
  • Multimodal artificial intelligence (AI) leveraging speech data presents a promising avenue for enhancing the detection of neurodegenerative disorders.

Purpose of the Study:

  • To develop and evaluate an AI-driven approach for improved screening of cognitive decline using only speech data.
  • To assess the accuracy of a multimodal AI model in distinguishing between cognitively healthy individuals, those with AD, and those with amnestic MCI (aMCI).

Main Methods:

  • Speech data were collected via telephone from 91 participants: 29 cognitively healthy, 30 with AD, and 32 with aMCI.
  • Multimodal natural language and speech processing methods were employed.
  • An explainable ensemble decision tree classifier was developed for multiclass prediction of cognitive decline.

Main Results:

  • The AI model achieved an overall accuracy of 75% in classifying cognitive status.
  • This accuracy represents an improvement over traditional speech-based screening and unimodal language-based models.
  • A dashboard was developed for examining results and interpreting data in novel ways.

Conclusions:

  • The developed AI methodology provides a foundation for significant advancements in clinical practice for cognitive decline screening.
  • Core considerations for clinical translation, scalability, and user-friendliness were integrated into the study's design.
  • Remote assessments via AI models can increase accessibility to cognitive decline screening, with significant differences in speech patterns observed across diagnostic groups.