Quantifying the time for accurate EEG decoding of single value-based decisions.

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Value-based decision-making is increasingly linked to evidence accumulation mechanisms.
• Previous research has not explicitly determined the precise timing of single decisions within this framework.

Purpose of the Study:

• To introduce and validate a novel electroencephalography (EEG) decoding technique for analyzing the temporal dynamics of value-based decision-making.
• To investigate the time-course of individual decisions by accumulating evidence from EEG signals.

Main Methods:

• Developed a novel EEG decoding technique based on accumulating probabilities of prototypical voltage topographies.
• Applied this method to predict decisions in a task involving reward vs. loss comparisons for offer acceptance/rejection.
• Utilized a diffusion model for independent behavioral-level evaluation of drift rates.

Main Results:

• The novel EEG decoding method accurately predicted decisions for a majority of subjects.
• Decision timing was modulated by task difficulty, with easier decisions decoded around 500 ms and harder decisions around 700 ms.
• Identified decision latencies significantly preceding the behavioral response time.
• Decision timing correlated with independently evaluated drift rates from a diffusion model.

Conclusions:

• The developed algorithm outperforms logistic regression and support vector machine in decoding decisions for a larger number of subjects.
• Presents a novel single-trial approach using topographic EEG activity patterns to study the timing of value-based decision-making.