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Introduction to Cognitive Psychology

Introduction to Cognitive Psychology

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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Updated: Jan 7, 2026

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

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Published on: February 14, 2014

Using Machine Learning Methods to Predict Cognitive Age from Psychophysiological Tests.

Daria D Tyurina¹, Sergey V Stasenko^1,2, Konstantin V Lushnikov¹

¹Institute of Biology and Biomedicine, Lobachevsky State University of Nizhniy Novgorod, Gagarin Avenue 23, 603022 Nizhny Novgorod, Russia.

Healthcare (Basel, Switzerland)

|December 30, 2025

Summary

This summary is machine-generated.

Machine learning models can predict chronological age using psychophysiological tests, with RidgeCV achieving the best performance. Key predictors include Stroop timing measures and task-related metrics, highlighting cognitive processes linked to aging.

Keywords:

cognitive test data analysis human age machine learning algorithms

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Last Updated: Jan 7, 2026

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Published on: January 11, 2020

Area of Science:

Neuroscience
Cognitive Psychology
Machine Learning

Background:

Predicting chronological age from psychophysiological data is an emerging area of research.
Understanding age-related cognitive changes is crucial for various applications.

Purpose of the Study:

To predict chronological age using machine learning regressors based on psychophysiological test results.
To identify the most effective feature selection methods and regression models for age prediction.

Main Methods:

Collected data from 99 subjects (68% male) on cognitive functions including reaction time, memory, and perception.
Generated 43 features and employed SHAP and Permutation Importance for optimal feature selection (10 features).
Evaluated regression models (Random Forest, RidgeCV, etc.) using MAE, R2, and cross-validation, with bootstrapping for uncertainty assessment.

Main Results:

RidgeCV with winsorization and standardization achieved the best performance (MAE: 5.7 years, R2: 0.60).
Stroop timing measures (stroop_time_color, stroop_var_attempt_time) were identified as the strongest predictors of age.
Stronger age-associated effects were observed in men compared to women.

Conclusions:

Psychophysiological tests, particularly Stroop timing and task-related metrics, can effectively predict cognitive age.
Feature selection and interpretable models are valuable for analyzing psychophysiological data.
Future research should incorporate longitudinal studies and biological markers for enhanced clinical relevance.