No abstract

Visual perceptual decision-making involves multiple components including visual encoding, attention, accumulation of evidence, and motor execution. Recent research suggests that EEG oscillations can identify the time of encoding and the onset of evidence accumulation during perceptual decision-making. Although scientists show that spatial attention improves participant performance in decision making, little is known about how spatial attention influences the individual cognitive components that give rise to that improvement in performance. We found evidence in this work that both visual encoding time (VET) before evidence accumulation and other non-decision time processes after or during evidence accumulation are influenced by spatial top-down attention, but not evidence accumulation itself. Specifically, we used an open-source data set in which participants were informed about the location of a target stimulus in the visual field on some trials during a face-car perceptual decision-making task. Fitting neural drift-diffusion models to response time, accuracy, and single-trial N200 latencies (~ 125 to 225 ms post-stimulus) of EEG allowed us to separate the processes of visual encoding and the decision process from other non-decision time processes such as motor execution. These models were fit in a single step in a hierarchical Bayesian framework. Model selection criteria and comparison to model simulations show that spatial attention manipulates both VET and other non-decision time processes. We discuss why spatial attention may affect other non-evidence accumulation processes, such as motor execution time (MET), and why this may seem unexpected given the literature. We make recommendations for future work on this topic.

Model-based cognitive neuroscience consolidates the cognitive processes and neurophysiological oscillations which are reflections of behavioral performance (e.g., reaction times and accuracy). Here, based on one of the well-known sequential sampling models (SSMs), named the diffusion decision model, and the nested model comparison, we explore the underlying latent process of spatial prioritization in perceptual decision processes, so that for estimating the model parameters (i.e. the drift rate, the boundary separation, and the non-decision time), a Bayesian hierarchical approach is considered, which allows inferences to be done simultaneously in the group and individual level. Moreover, well-established neural components of spatial attention which contributed to the latent process and behavioral performance in a visual face-car perceptual decision are detected based on the event-related potential (ERP) analysis. Our cognitive modeling analysis revealed that the non-decision time parameter provides a better fit to the top-down attention with the measures of two powerful weapons, i.e. the deviance information criterion called DIC score and R-square. Also, using multiple regression analysis on the contralateral minus neutral N2 sub-component (N2nc) at central electrodes and contralateral minus neutral alpha power (Anc) at posterior-occipital electrodes in the voluntary attention, it can be concluded that poststimulus N2nc can predict reaction time (RT) and non-decision time parameter relating to spatial prioritization. Whereas, the poststimulus Anc only can predict the RT and not the non-decision time relating to spatial prioritization. The result suggested that the difference of contralateral minus neutral oscillations was more important to reflect the modulation of the top-down spatial attention mechanism in comparison with the difference of ipsilateral minus neutral oscillations.