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CLUSTERING OF USER COGNITIVE LOAD ZONES BASED ON OCULOMOTOR DATA FROM GRAPHICAL STRUCTURE VIEWING SESSIONS

Goryachkin Boris Sergeyevich  ( candidate of technical Sciences, associate Professor, Moscow State Technical University names Bauman)

Savelyev Alexey Alexandrovich  (postgraduate, Moscow State Technical University names Bauman)

Fonin Maxim Alekseevich  (Moscow State Technical University names Bauman)

Lobanov Dmitriy Sergeyevich  (Moscow State Technical University names Bauman)

A non-invasive approach to identifying areas of elevated cognitive load based on eye-tracking data was investigated in this paper. Gaze fixation patterns during interaction with graphical structures were analyzed. The DBSCAN density clustering method was applied to automatically detect areas of interest (AOI) without prior labeling. The Cognitive Load Index (CLI), quantifying the intensity of attention within the identified clusters, was calculated. Experiments on visual scenes of varying complexity were conducted, involving the variation of the neighborhood radius and minimum points parameters. The sensitivity of the algorithm to clusterization parameters was evaluated. The effectiveness of the method for the spatial interpretation of mental effort distribution without the use of specialized equipment was confirmed.

Keywords:cognitive load, eye tracking, fixations, attention zones, clustering, visualization.

 

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Citation link:
Goryachkin B. S., Savelyev A. A., Fonin M. A., Lobanov D. S. CLUSTERING OF USER COGNITIVE LOAD ZONES BASED ON OCULOMOTOR DATA FROM GRAPHICAL STRUCTURE VIEWING SESSIONS // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2026. -№01. -С. 70-77 DOI 10.37882/2223-2966.2026.01.12
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