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EEG-based Identification of Evidence Accumulation Stages in Decision-Making.

Hermine S Berberyan¹, Leendert van Maanen^2,3, Hedderik van Rijn¹

¹University of Groningen.

Journal of Cognitive Neuroscience

|December 16, 2020

Summary

A new method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA), successfully identifies cognitive processing stages directly from EEG data. This technique offers a more detailed understanding of cognitive stages compared to traditional reaction-time methods.

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Area of Science:

Cognitive Science
Neuroscience
Computational Neuroscience

Background:

The study of cognitive processing stages has a long history in cognitive science.
Traditional methods often rely on reaction times, which may not fully capture the nuances of cognitive processing.

Purpose of the Study:

To validate a novel method, hidden semi-Markov model multivariate pattern analysis (HsMM-MVPA), for discovering cognitive processing stages directly from electroencephalography (EEG) data.
To compare the efficacy of HsMM-MVPA with established reaction-time-based methods.

Main Methods:

Application of HsMM-MVPA to visual discrimination tasks using EEG data.
Manipulation of perceptual processing and decision difficulty.
Utilizing evidence accumulation models (EAMs) for comparative analysis.

Main Results:

HsMM-MVPA identified five distinct cognitive processing stages.
Brain activation in one stage depended on perceptual processing.
Brain activation and duration of two stages varied with decision difficulty.
HsMM-MVPA results showed high correlation with EAMs, providing a more detailed stage description.

Conclusions:

HsMM-MVPA is a valid and robust method for inferring cognitive stages directly from EEG data.
The method provides a more granular understanding of cognitive processing compared to traditional approaches.
This technique advances the ability to study cognitive architecture using neurophysiological data.