Contralateral oscillations related to modulation of top-down attention in perceptual decision making: a Bayesian hierarchical diffusion model and EEG analysis
The neural mechanisms underlying attention-based perceptual decisions are of vital importance to a comprehensive understanding of behavior and cognition. Recent work has suggested that attention may play a key role in perceptual decision making. However, the exact cognitive components involved as well as the biomarkers of attention to predict behavioral performance in perceptual decisions have not yet been determined. To accomplish this, based on the Bayesian hierarchical diffusion model we have explored the underlying latent process of spatial attention in perceptual decision processes simultaneously at the group and individual level. The model’s parameters discovery showed that non-decision time (encoding plus motor execution) received the smallest deviance information criterion (DIC) and largest R-square relating to prioritized and non-prioritized top-down spatial attention. Moreover, based on the event-related potential (ERP) analysis and multiple linear regression model, N2 sub-component contralateral amplitude at central electrodes and alpha power band at parietal-occipital can predict very well response time (RT) relating to to-down spatial prioritization. But, the non-decision time parameter was predicted by only the contralateral N2 sub-component and not contralateral alpha power. Conversely, ipsilateral N2 sub-component and alpha power could not interpret the modulation of spatial prioritization in the decision process. In order to verify the convergence of the Markov chain Monte Carlo (MCMC) sampling, the R-hat Gelman-Rubin statistic was under 1.0001 which appears that the best scenario of the diffusion model was superior convergence and the same stationary distribution.