Related Concept Videos
01:27Language 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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01:30Cognitive Development During Adulthood
749
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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01:21Cognitive Learning
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
975
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Automatic Detection of Early Cognitive Decline Using Multimodal Feature Fusion and Transfer Learning on Real-World
IEEE Journal of Biomedical and Health Informatics
|December 8, 2025
Summary
CognoMemory detects cognitive decline using speech analysis. This system shows high accuracy in identifying dementia and mild cognitive impairment (MCI), outperforming other models and demonstrating strong generalizability.
Area of Science:
- Neurology
- Computational Linguistics
- Artificial Intelligence
Background:
- Early detection of cognitive decline, including dementia and mild cognitive impairment (MCI), is crucial for timely intervention.
- Conversational speech contains subtle linguistic and acoustic markers indicative of cognitive changes.
- Existing methods for cognitive decline detection often lack the accuracy and efficiency needed for widespread application.
Purpose of the Study:
- To develop and evaluate CognoMemory, a novel system for detecting cognitive decline through speech analysis.
- To compare the performance of a multimodal feature fusion and transfer learning approach against established Large Language Model (LLM) based methods.
- To assess the generalizability and stability of the CognoMemory model on an independent dataset.
Main Methods:
- Collected 307 hours of real-world conversational speech from 1,639 participants using a virtual agent and 14 memory-probing questions.
- Extracted acoustic, linguistic features, and LLM embeddings from speech data.
- Employed a CNN/Bi-LSTM-based transfer learning architecture with multimodal feature fusion, pre-trained on a subset and fine-tuned on dementia, MCI, and healthy participant groups.
Main Results:
- The CognoMemory system achieved high F1-scores (0.83 for 2-way, 0.54 for 3-way classification) using only the initial 'motivation' question.
- The proposed approach outperformed several LLM-based models (BART, DistilBERT, RoBERTa, HuBERT) in accuracy.
- Transfer learning enhanced performance by 3% and increased speed by 38%; the model achieved an F1-score of 0.89 on the DementiaBank dataset, showing strong generalizability.
Conclusions:
- CognoMemory offers a highly accurate and efficient method for detecting cognitive decline from conversational speech.
- The multimodal feature fusion and CNN/Bi-LSTM transfer learning architecture demonstrate superior performance and robustness compared to LLM-only approaches.
- The system's ability to generalize across datasets highlights its potential for real-world clinical application in early cognitive decline screening.