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Association Areas of the Cortex

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...

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Related Experiment Video

Updated: Jan 16, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Brain Cortical Area Characterization and Machine Learning-Based Measure of Rasmussen's S-R-K Model.

Daniele Amore¹, Daniele Germano¹, Gianluca Di Flumeri^1,2

¹Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy.

|September 27, 2025

Summary

This summary is machine-generated.

This study objectively measures human cognitive control using brain activity. Electroencephalography (EEG) data helps differentiate Skill, Rule, and Knowledge behaviors for better performance analysis.

Keywords:

Brodmann areas EEG KNN S-R-K model brain cortex cognitive control behaviour decision-making machine learning neurophysiological characterization sLORETA

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Area of Science:

Neuroscience
Cognitive Psychology
Human Factors Engineering

Background:

The Skill, Rule, and Knowledge (S-R-K) model categorizes human behavior based on cognitive control needs.
Current S-R-K model lacks quantifiable metrics, hindering objective performance measurement.
This study addresses the need for neurophysiological characterization of the S-R-K model.

Purpose of the Study:

To neurophysiologically characterize the S-R-K model by analyzing operator cerebral cortical activity.
To develop a machine learning model for estimating cognitive control behaviors.
To establish objective metrics for differentiating Skill, Rule, and Knowledge levels.

Main Methods:

Participants performed tasks simulating Skill (tracking), Rule (navigation), and Knowledge (unfamiliar) conditions.
Electroencephalogram (EEG) was recorded during task execution.
Global Field Power (GFP) in EEG frequency bands and Brodmann areas (BAs) were analyzed.

Main Results:

Distinct S-R-K patterns were identified in cerebral cortical activity.
Machine learning models were built using EEG features and BAs.
The models successfully estimated participants' cognitive control behaviors.

Conclusions:

Objective measurement of S, R, and K levels is possible through brain activation analysis.
Findings align with existing literature on cognitive functions at different control levels.
This research provides a neurophysiological basis for the S-R-K model.