ABSTRACT
Model-based cognitive neuroscience elucidates the cognitive processes and neurophysiological oscillations that lead to behavioral performance in cognitive tasks (e.g., response times and accuracy). In this paper we explore the underlying latent process of spatial prioritization in perceptual decision processes, based on one well-known sequential sampling model (SSM), the drift-diffusion model (DDM), and subsequent nested model comparison. Neural components of spatial attention which contributed to the latent process and behavioral performance in a visual face-car perceptual decision were detected based on both time-frequency decomposition and event-related potential (ERP) analysis. For estimating DDM 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 performed simultaneously on the group and individual level. Our cognitive modeling analysis revealed that spatial attention changed the non-decision time parameter across experimental conditions, such that a model with a changing non-decision time parameter provides a better fit to the data than other model parameters, quantified using the deviance information criterion (DIC) score and R-squared. Using multiple regression analysis on the contralateral minus neutral N2 sub-component (N2nc) at central electrodes, it can be concluded that poststimulus N2nc can predict mean response times (RTs) and non-decision time parameters related to spatial prioritization. However the contralateral minus neutral alpha power (Anc) at parieto-occipital electrodes can only predict the mean RTs and not the non-decision time relating to spatial prioritization. It was also found that the difference of contralateral minus neutral neural oscillations were more reflective of the modulation of the top-down spatial attention in comparison to the difference of ipsilateral minus neutral neural oscillations. These results suggest that individual differences in spatial attention are encoded by contralateral (and not ipsilateral) N2 oscillations and non-decision times. This work highlights how model-based Cognitive Neuroscience can further reveal the role of EEG in spatial attention during perceptual decision making.
Competing Interest Statement
The authors have declared no competing interest.
