Complex trade-offs in a dual-target visual search task are indexed by lateralised ERP components

Henare, Dion; Tünnermann, Jan; Wagner, Ilja; Schütz, Alexander C.; Schubö, Anna · 2024 · OpenAlex-citations

DOI: 10.1038/s41598-024-72811-3

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Summary

This study investigates the neural mechanisms underlying complex decision-making in dual-target visual search tasks, specifically examining how selective attention influences choice behavior. In everyday scenarios, individuals must constantly trade off conflicting factors, such as search effort and perceptual difficulty, to optimize performance. While previous behavioral research has established that humans flexibly adjust their strategies to balance these trade-offs, the role of early attentional processes in determining final choices remains debated. Some models suggest attention actively drives choice, while others posit it plays a passive role in information extraction. To resolve this, the authors employed electroencephalography (EEG) to measure lateralized event-related potential (ERP) components, which index the dynamics of visual attention, during a task requiring participants to choose between two targets with differing search costs and discrimination difficulties. The experimental design involved 25 participants who performed a visual search task where they could respond to either of two targets, each color-coded to indicate gap discrimination difficulty (easy vs. difficult). Orthogonally, the number of distractors matching each target’s color was manipulated to modulate search costs. This setup forced participants to trade off the ease of finding a target against the ease of discriminating its gap. EEG data were recorded to capture the N2pc component, reflecting early spatial attention selection, and the SPCN component, associated with working memory maintenance. Bayesian generalized linear mixed models were used to analyze behavioral data and predict target choice based on set size and ERP amplitudes. Behavioral results confirmed that participants successfully optimized their performance by flexibly trading off search costs and discrimination difficulty. As the number of distractors for the easy target increased, preference for that target decreased, yet overall response speed and accuracy remained stable. Crucially, the study found that lateralized ERP components reliably predicted the specific choice participants made on each trial. Model comparison revealed that the best predictive model included both the set size of easy-colored objects and the amplitudes of the N2pc and SPCN components. Specifically, higher N2pc and SPCN amplitudes were associated with a decreased probability of choosing the easy target, indicating that these neural signatures reflect the attentional prioritization of the chosen target. These findings demonstrate that initial attentional processes are not merely passive but play a central, active role in determining choice behavior in complex, open-ended tasks. The ability of N2pc and SPCN to predict trial-by-trial choices suggests that the allocation of spatial attention and the maintenance of target information in working memory are integral to the decision-making process. This highlights the importance of considering early attentional dynamics when modeling how humans optimize performance under conflicting constraints, supporting theories that view attention as a key driver of downstream behavioral choices rather than just a mechanism for information extraction.

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• visual search