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Related Experiment Video

Updated: Oct 4, 2025

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

A tutorial on cognitive modeling for cognitive aging research.

Nathaniel R Greene¹, Stephen Rhodes²

¹Department of Psychological Sciences, University of Missouri.

Psychology and Aging

|February 3, 2022

Summary

This summary is machine-generated.

Cognitive modeling offers a robust method for understanding how aging affects cognitive processes. This approach enhances the analysis of cognitive aging research, improving replicability and identifying specific age-related cognitive changes.

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

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

Published on: February 14, 2014

Area of Science:

Cognitive psychology
Neuroscience
Gerontology

Background:

Standard analyses like ANOVA often fail to fully capture the complex link between latent cognitive processes and observed behavior in aging.
Cognitive models provide a formal framework to elucidate these relationships, offering greater insight into age-related cognitive changes.

Purpose of the Study:

To provide a tutorial on fitting and interpreting cognitive models for measuring age differences in cognitive processes.
To demonstrate the application of cognitive modeling in aging research using a two-choice discrimination task.

Main Methods:

Utilizing the Stan software for flexible cognitive model fitting.
Implementing hierarchical modeling to simultaneously estimate group and individual effects.
Employing a Bayesian statistical framework to quantify evidence, including for null effects.

Main Results:

The tutorial demonstrates a practical workflow for applying cognitive modeling to aging data.
Hierarchical Bayesian modeling allows for nuanced estimation of individual and group-level cognitive aging effects.
The Bayesian approach facilitates the quantification of evidence supporting or refuting specific hypotheses about cognitive aging.

Conclusions:

Cognitive modeling is a powerful tool for aging researchers to precisely identify cognitive processes affected by age.
Widespread adoption of cognitive modeling can help address nonreplicability issues in cognitive aging research.
This methodology offers a more rigorous approach to understanding the specific cognitive mechanisms underlying aging.