267 | Neural Patterns in the Hybrid Search Task on Natural Scenes: Concurrent EEG and Eye-tracking Study

Theoretical and Computational Neuroscience

Author: Damian Ariel Care | Email: damianos.care@gmail.com

Damian Ariel Care , Guadalupe Rodriguez Ferrante , Joaquin E Gonzalez , Anthony J Ries , Matias J Ison , Juan Esteban Kamienkowski

1° Laboratorio de Inteligencia Artificial Aplicada, Instituto de Ciencias de Computación, Facultad de Ciencias Exactas y Naturales (UBA-CONICET)
2° Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires
3° DEVCOM, ARL, United States
4° School of Psychology, University of Nottingham, United Kingdom

In everyday behaviors, often arises the need to locate a single instance from a range of possible targets in a display containing distractor items. For instance, imagine we are inspecting a supermarket aisle to find a specific cookie from a list of our preferred ones. The cognitive constrains and the concurrent memory load may play crucial roles in elucidating how ecologically relevant parameters collectively impact neural activity. Through a concurrent EEG and eye-tracking experiment, we investigated the influences of task-related variables on fixation-related potentials (FRPs) during a hybrid search paradigm, where participants sought any of multiple memorized targets, with varying memory set sizes (MSS). We explored the contributions of different task components on the fixation evoked-responses, including task progression, target presence, and the MSS, using linear model-based analysis. This approach effectively handled the temporal overlap inherent in natural viewing responses. Additionally, we implemented a specialized tool for conducting this type of analysis in Python, enabling us to explore solvers other than ordinary least squares (OLS) that are more aligned with the characteristics of actual data. Altogether, we showed how combining empirical and analytical approaches allows us to distinguish interacting neural processes while preserving the genuine traits found in real-world tasks.