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Updated: May 13, 2026

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

Controlling working memory with learned instructions.

J C Sylvester¹, J A Reggia, S A Weems

¹Department of Computer Science, University of Maryland, A.V. Williams Building, College Park, MD 20742, USA. jsylvest@umd.edu

Neural Networks : the Official Journal of the International Neural Network Society

|March 8, 2013

Summary

This summary is machine-generated.

We developed GALIS, a new framework for autonomous cognitive control in neural networks. GALIS models learn tasks and control mechanisms, showing human-like performance on the n-Back task.

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Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Current neural network models of cognition require significant human input for control.
Developing autonomous cognitive control mechanisms for these models is challenging.

Purpose of the Study:

To present GALIS, a general-purpose framework for creating autonomous cognitive control models.
To demonstrate GALIS's capability in learning both task content and control procedures.

Main Methods:

GALIS models consist of interacting attractor networks ('regions') inspired by primate cerebral cortex.
Regions learn temporal sequences, exchange information, and gate each other's functions.
A GALIS-based n-Back task model was trained on five task variations.

Main Results:

The GALIS n-Back model autonomously controlled learning and memory item management.
Model performance (accuracy, response time) strongly correlated with human subjects.
The model made testable predictions about human performance under varying task demands.

Conclusions:

GALIS provides an effective framework for developing autonomous cognitive control in neural networks.
This approach advances neurocomputational methods for modeling cognition.
GALIS facilitates the creation of more sophisticated and self-regulating cognitive models.