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

Amin Ghaderi-Kangavari, View ORCID ProfileKourosh Parand, View ORCID ProfileReza Ebrahimpour, Michael D. Nunez, View ORCID ProfileJamal Amani Rad
doi: https://doi.org/10.1101/2021.05.12.443763
Amin Ghaderi-Kangavari
1Department of Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
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Kourosh Parand
2Faculty of Mathematical Sciences, Department of Data and Computer Sciences, Shahid Beheshti University, Tehran, Iran
3Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
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Reza Ebrahimpour
4Faculty of Computer Engineering, Shahid Rajaie Teacher Training University, Tehran, Iran
5Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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Michael D. Nunez
6Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands
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Jamal Amani Rad
1Department of Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
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  • For correspondence: j_amanirad@sbu.ac.ir j.amanirad@gmail.com
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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.

Footnotes

  • https://github.com/AGhaderi/hDDM_attention

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 24, 2021.
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How spatial attention affects the decision process: looking through the lens of Bayesian hierarchical diffusion model & EEG analysis
Amin Ghaderi-Kangavari, Kourosh Parand, Reza Ebrahimpour, Michael D. Nunez, Jamal Amani Rad
bioRxiv 2021.05.12.443763; doi: https://doi.org/10.1101/2021.05.12.443763
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How spatial attention affects the decision process: looking through the lens of Bayesian hierarchical diffusion model & EEG analysis
Amin Ghaderi-Kangavari, Kourosh Parand, Reza Ebrahimpour, Michael D. Nunez, Jamal Amani Rad
bioRxiv 2021.05.12.443763; doi: https://doi.org/10.1101/2021.05.12.443763

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