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

Amore, Daniele; Germano, Daniele; Di Flumeri, Gianluca; Aricò, Pietro; Ronca, Vincenzo; Giorgi, Andrea; Vozzi, Alessia; Capotorto, Rossella; Bonelli, Stefano; Drogoul, Fabrice; Imbert, Jean-Paul; Granger, Géraud; Babiloni, Fabio; Borghini, Gianluca

doi:10.3390/brainsci15090981

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Open AccessArticle

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

by

Daniele Amore

¹

,

Daniele Germano

¹

,

Gianluca Di Flumeri

^1,2

,

Pietro Aricò

^2,3

,

Vincenzo Ronca

^2,3

,

Andrea Giorgi

^2,4

,

Alessia Vozzi

^2,4

,

Rossella Capotorto

^2,4

,

Stefano Bonelli

⁵

,

Fabrice Drogoul

⁶,

Jean-Paul Imbert

⁷

,

Géraud Granger

⁷

,

Fabio Babiloni

^2,8,9

and

Gianluca Borghini

^1,2,*

¹

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

²

BrainSigns SRL, via Sesto Celere, 00152 Rome, Italy

³

Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy

⁴

Department of Anatomical, Histological, Forensic & Orthopedic Sciences, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy

⁵

DeepBlue srl, Piazza Buenos Aires 20, 00185 Rome, Italy

⁶

EUROCONTROL, Rue de la Fusée 96, 1130 Brussels, Belgium

⁷

École Nationale de l’Aviation Civile, 7 Avenue Edouard Belin, 31000 Toulouse, France

⁸

Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy

⁹

College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China

^*

Author to whom correspondence should be addressed.

Brain Sci. 2025, 15(9), 981; https://doi.org/10.3390/brainsci15090981

Submission received: 11 August 2025 / Revised: 5 September 2025 / Accepted: 9 September 2025 / Published: 12 September 2025

(This article belongs to the Special Issue Computational Intelligence and Brain Plasticity)

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Abstract

Background: the Skill, Rule, and Knowledge (S-R-K) model is a framework used to describe and analyze human behaviour and decision-making in complex environments based on the nature of the task and kind of cognitive control required. The S-R-K model is particularly useful in fields like human factor engineering, system design, and safety-critical industries because it helps to understand human errors and how they relate to different levels of cognitive control. However, the S-R-K model is still qualitative and lacks specific and quantifiable metrics for determining what kind of cognitive control a person is using at any given time. This aspect makes difficult to directly measure and compare performance across the three levels. This study aimed therefore to characterize the S-R-K model from a neurophysiological perspective by analyzing the operator’s cerebral cortical activity. Methods: in this study, participants carried out experimental tasks able to replicate the Skill (tracking task), Rule (rule-based navigation) and Knowledge conditions (unfamiliar situations). Results: participants’ Electroencephalogram (EEG) was recorded during tasks execution and then Global Field Power (GFP) was estimated in the different EEG frequency bands. Brodmann areas (BAs) and EEG features were then used to characterize the S-R-K pattern over the cerebral cortex and as inputs to build up the machine learning-based model to estimate participants’ cognitive control behaviours while dealing with tasks. Conclusions: the results demonstrate the possibility of objectively measuring the different S, R and K levels in terms of brain activations. Furthermore, such evidence is consistent with the scientific literature in terms of cognitive functions corresponding to the different levels of cognitive control.

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

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MDPI and ACS Style

Amore, D.; Germano, D.; Di Flumeri, G.; Aricò, P.; Ronca, V.; Giorgi, A.; Vozzi, A.; Capotorto, R.; Bonelli, S.; Drogoul, F.; et al. Brain Cortical Area Characterization and Machine Learning-Based Measure of Rasmussen’s S-R-K Model. Brain Sci. 2025, 15, 981. https://doi.org/10.3390/brainsci15090981

AMA Style

Amore D, Germano D, Di Flumeri G, Aricò P, Ronca V, Giorgi A, Vozzi A, Capotorto R, Bonelli S, Drogoul F, et al. Brain Cortical Area Characterization and Machine Learning-Based Measure of Rasmussen’s S-R-K Model. Brain Sciences. 2025; 15(9):981. https://doi.org/10.3390/brainsci15090981

Chicago/Turabian Style

Amore, Daniele, Daniele Germano, Gianluca Di Flumeri, Pietro Aricò, Vincenzo Ronca, Andrea Giorgi, Alessia Vozzi, Rossella Capotorto, Stefano Bonelli, Fabrice Drogoul, and et al. 2025. "Brain Cortical Area Characterization and Machine Learning-Based Measure of Rasmussen’s S-R-K Model" Brain Sciences 15, no. 9: 981. https://doi.org/10.3390/brainsci15090981

APA Style

Amore, D., Germano, D., Di Flumeri, G., Aricò, P., Ronca, V., Giorgi, A., Vozzi, A., Capotorto, R., Bonelli, S., Drogoul, F., Imbert, J.-P., Granger, G., Babiloni, F., & Borghini, G. (2025). Brain Cortical Area Characterization and Machine Learning-Based Measure of Rasmussen’s S-R-K Model. Brain Sciences, 15(9), 981. https://doi.org/10.3390/brainsci15090981

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

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