Time–space signatures of hybrid search resolution using EEG and eye movements concurrent recordings

Studying visual and memory processes during hybrid searchi requires disentangling overlapping neural signals arising from the interaction between attention and memory. Conventional event-related methods fail in these ecologically valid settings. We employed a deconvolution-based approach to coregistered EEG and eye-tracking data during a hybrid visual and memory search task with unrestricted eye movements. This methodology successfully isolated fine-grained activation patterns in temporal response functions (TRFs) associated with both task-related main effects and their interactions. Starting with hypothesis-driven models, we replicated established neural components for visual processing and target detection. By extending to hierarchically larger data-driven models, we explored interactions between effects typically studied in isolation. Our results show that TRF estimates remain stable as model complexity increases, supported by improved Pearson’s correlation coefficients and controlled variance inflation factors (VIF). We identified a late activation consistent with the P300 component for target detection and found that missed targets elicited similar but weaker responses, suggesting a nuanced neural role beyond binary detection. These findings demonstrate that deconvolution methods, paired with robust performance measures, can uncover the dynamic interplay of attention and memory underlying free-viewing behavior.