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Working Memory

Working Memory

Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...

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Related Experiment Video

Updated: Jul 16, 2025

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment

Published on: September 20, 2020

Unicorn, Hare, or Tortoise? Using Machine Learning to Predict Working Memory Training Performance.

Yi Feng¹, Anja Pahor^2,3,4, Aaron R Seitz^2,3

¹University of California, Irvine, School of Education, School of Social Sciences (Department of Cognitive Sciences), Irvine, California, USA.

Journal of Cognition

|September 11, 2023

Summary

This summary is machine-generated.

Individual differences in working memory (WM) training outcomes can be predicted. Pre-existing WM capacity and openness are key factors for high performance, while openness and gaming experience predict persistence in low performers.

Keywords:

Individual differences Machine learning Working memory

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Last Updated: Jul 16, 2025

Working Memory Training for Older Participants: A Control Group Training Regimen and Initial Intellectual Functioning Assessment

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Published on: September 20, 2020

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Cognitive Psychology
Neuroscience
Human Factors

Background:

Individual differences significantly impact working memory (WM) training benefits.
Understanding the combination of factors predicting training trajectories is crucial for personalized interventions.

Purpose of the Study:

To identify individual difference variables that predict working memory training patterns.
To develop a predictive model for classifying training performance.

Main Methods:

568 undergraduates completed N-back training variants over two weeks.
Machine learning algorithms, including a binary tree model, were used for prediction.
Individual differences assessed included cognitive abilities, personality, motivation, and background factors.

Main Results:

A predictive model demonstrated good accuracy in distinguishing high from low performers.
Pre-existing working memory capacity and openness were the strongest predictors of high performance.
For low performers, openness and video game experience predicted learning persistence.

Conclusions:

Individual characteristics can predict working memory training performance.
Findings support the development of personalized cognitive training interventions based on individual profiles.