Theoretical and Computational Neuroscience
Author: Damian Ariel Care | Email: damianos.care@gmail.com
Damian A. Care1°, Joaquin E. Gonzalez1°, Anthony J. Ries3°, Matias J. Ison4°, Juan E. Kamienkowski1°2°
1° Laboratorio de Inteligencia Artificial Aplicada ( ICC, CONICET-UBA, Argentina)
2° Departamento de Computación , FCEyN-UBA
3° ARL(United States)
4° Scholl of Psychology (University of Nottingham, UK)
In everyday scenarios, we frequently encounter the task of pinpointing a specific item amid distractions. For instance, imagine navigating a supermarket aisle to find any cereal from a list of preferred options. The demands of this hybrid visual and memory search task, where search is coupled with the simultaneous need to access and recall items from memory, represent significant cognitive challenges. To explore the dynamics underlying hybrid search, we conducted a study using concurrent EEG and eye-tracking measurements, focusing on fixation-related potentials (FRPs). Across two sites, 42 participants were asked to identify any of multiple memorized targets, with varying memory set sizes (MSS). Our analysis investigated how different task components—such as task progression, target presence, and memory load—affect FRPs using linear model-based techniques. This approach effectively managed the temporal overlap inherent in natural viewing responses, allowing for a clearer disentanglement of the effects we sought to study. Additionally, we developed a specialized analysis tool in Python that facilitated exploration of alternative solvers beyond ordinary least squares, improving estimation accuracy and addressing collinearity issues. Ultimately, our findings demonstrate how integrating empirical and analytical approaches enables the differentiation of interacting neural processes while faithfully capturing the intricacies of real-world tasks.