Sequence models for by-trial decoding of cognitive strategies from neural data

TL;DR

This paper introduces a novel machine learning approach combining Hidden Multivariate Pattern analysis with a Structured State Space Sequence model to decode trial-level cognitive strategies from EEG data, revealing dynamic variability in decision-making processes.

Contribution

It presents a new method for decoding cognitive strategies at the trial level, uncovering previously unobserved cognitive operations and variability in decision-making.

Findings

Identification of a new cognitive operation, Confirmation, associated with accuracy.

Modeling shows Confirmation correlates with correct responses and changes of mind.

Demonstrates trial-level variability challenges homogeneous process assumptions.

Abstract

Understanding the sequence of cognitive operations that underlie decision-making is a fundamental challenge in cognitive neuroscience. Traditional approaches often rely on group-level statistics, which obscure trial-by-trial variations in cognitive strategies. In this study, we introduce a novel machine learning method that combines Hidden Multivariate Pattern analysis with a Structured State Space Sequence model to decode cognitive strategies from electroencephalography data at the trial level. We apply this method to a decision-making task, where participants were instructed to prioritize either speed or accuracy in their responses. Our results reveal an additional cognitive operation, labeled Confirmation, which seems to occur predominantly in the accuracy condition but also frequently in the speed condition. The modeled probability that this operation occurs is associated with higher probability of responding correctly as well as changes of mind, as indexed by electromyography data. By successfully modeling cognitive operations at the trial level, we provide empirical evidence for dynamic variability in decision strategies, challenging the assumption of homogeneous cognitive processes within experimental conditions. Our approach shows the potential of sequence modeling in cognitive neuroscience to capture trial-level variability that is obscured by aggregate analyses. The introduced method offers a new way to detect and understand cognitive strategies in a data-driven manner, with implications for both theoretical research and practical applications in many fields.