Abstract
The aim of the present study is to investigate the separation abilities of three statistical parameters for grouping participants in the visual-motor experiment by their age and gender. These parameters represent different characteristics of the decision-making process and were determined by applying the hierarchical drift diffusion model to the response time and accuracy of the experimental data [1]. The objective function cluster analysis was applied to explore distinct data spaces formed by the parameters’ data. The ability for grouping is assessed and interpreted according to the differences in the subjects’ capabilities to perform the visuo-motor task. The study compares the conclusions based by drift-diffusion model using Bayesian parameter estimation with those based on the cluster analysis in terms of ability to distinguish the performance of different age groups. The investigation of gender effects are uniquely investigated by cluster analysis technique.
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Acknowledgement
This work was supported by the National Science Fund under Grant DN02/3/2016 “Modelling of voluntary saccadic eye movements during decision making” and by the Science Fund of Sofia University “St. Kliment Ohridski” under project no. 80-10-61/13.04.2020.
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Georgieva, O., Bocheva, N., Genova, B., Stefanova, M. (2020). Eye Movement Data Analysis. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_36
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