Detection of atypical attentional behaviors in young subjects

• Published in: Journal of neuroscience methods Vol. 407; p. 110141
• Main Authors: Rekik, Wafa, Le Hégarat-Mascle, Sylvie, Ezzedini, Souhir, de Marco, Giovanni
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2024; Elsevier
• Subjects: Adult; Algorithms; Attention; Attention - physiology; Cluster Analysis; Clustering; Cognitive science; Female; Force production; Humans; Inlier; Machine Learning; Male; Neuroscience; Outlier; Precision; Psychomotor Performance - physiology; RANSAC; Reaction time; Reaction Time - physiology; Vigilance; Young Adult; Force production; RANSAC; Outlier; Vigilance; Precision; Attention; Machine learning; Reaction time; Clustering; Inlier; vigilance; inlier; force production; precision; outlier; reaction time; attention; clustering; machine learning
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2024.110141

Vigilance ability refers to the accuracy and speed with which a person performs a cognitive-motor task, either voluntarily (endogenous mode) or following a warning stimulus (exogenous mode). In the context of a force production task, our study focuses on the impact of the states of vigilance by proposing an original approach that allows distinguishing between good (inlier) and poor (outlier) participants. We assume that the use of an external signal and duration of the temporal preparation (foreperiod) increase the speed and the precision of motor responses. Our objective is particularly challenging in the context of a limited dataset with a high level of noise. Our original methodological approach consists of coupling the RANSAC (RANdom SAmple Consensus) algorithm with a statistical machine learning algorithm to handle noise. Our clustering approach, based on the coupling of RANSAC methodology with ensemble classifiers, overcomes the limitations of conventional supervised algorithms that are either not robust to outliers (such as K-Nearest Neighbors) and/or not adapted to few-shot learning (such as Support Vector Machines and Artificial Neural Networks). The clustering results were validated in terms of reaction time distributions and force error distributions with respect to participant groups. We show that the use of an external signal and duration of the temporal preparation (foreperiod) increase the speed and the precision of motor responses. Our study has allowed us to detect atypical attentional patterns and succeeds in separating the inliers from the outliers. •Atypical attentional patterns are detected in young subjects.•Score disentangles high (inliers) and low (outliers) attention performance.•Score combines RANSAC and classifiers for outlier handling in few-shot learning.•Score-based subject clustering clarifies RT and force accuracy duality.