How spatial attention affects the decision process: looking through the lens of Bayesian hierarchical diffusion model & EEG analysis

Abstract

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.

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

“…These methods include popular EEG-band limited analyses (e.g. calculating 8 to 13 Hz alpha power over posterior electrodes in Ghaderi-Kangavari et al, 2021) and event-related potential (ERP)…”

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“…Target for the higher cortical hierarchies is to find a single interpretation that explains the sum of signals best. Although being a well‐established model it neglects the proven bi‐directionality of sensory signaling (feed‐back loops, bi‐ or multistable decisions, and parallel multi‐signaling) and our ability to reestablish sensory perceptions without actually receiving them (imagination of sensory signals) (Ghaderi‐Kangavari et al, 2023; Jones et al, 2006; Linares et al, 2019).…”

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

“…This is typically performed outside of joint modeling, but future researchers may be able to model these methods explicitly to retain sources of noise in the model. These methods include popular EEG-band limited analyses (e.g., calculating 8 to 13 Hz alpha power over posterior electrodes in Ghaderi-Kangavari et al, 2023a) and event-related potential (ERP) analyses that mitigate artifactual components through averaging across trials (e.g., N200 latencies in Nunez et al, 2019a).…”

“…If out-of-sample prediction is not available, then often penalizing by model complexity after in-sample prediction is used. This is often why information criteria measures are used (e.g., Ghaderi-Kangavari et al, 2023a;Ghaderi-Kangavari et al, 2022). Essentially, these measures are in-sample prediction measures that penalize for model complexity.…”