Single-trial EEG dissociates motivation and conflict processes during decision-making under risk

Publication date: Available online 14 December 2018

Source: NeuroImage

Author(s): Narun Pornpattananangkul, Shannon Grogans, Rongjun Yu, Robin Nusslock

Abstract

In making decisions under risk (i.e., choosing whether to gamble when the outcome probabilities are known), two aspects of decision are of particular concern. The first, if gambling, is how large are potential gains compared to losses? The subjectively larger, the more rewarding to gamble. Thus, this aspect of decision-making, quantified through expected utility (EU), is motivation-related. The second concern is how easy is it to reach the decision? When subjective desirability between gambling and not-gambling is clearly different from each other (regardless of the direction), it is easier to decide. This aspect, quantified through utility distance (UD), is conflict-related. It is unclear how the brain simultaneously processes these two aspects of decision-making. Forty-five participants decided whether to gamble during electroencephalogram (EEG) recording. To compute trial-by-trial variability in EU and UD, we fit participants' choices to models inspired by Expected-Utility and Prospect theories using hierarchical-Bayesian modeling. To examine unique influences of EU and UD, we conducted model-based single-trial EEG analyses with EU and UD as simultaneous regressors. While both EU and UD were positively associated with P3-like activity and delta-band power, the contribution of EU was around 200 ms earlier. Thus, during decision-making under risk, people may allocate their attention to motivation-related aspects before conflict-related aspects. Next, following learning the options and before reporting their decision, higher EU was associated with stronger alpha and beta suppression, while higher UD was associated with a stronger contingent-negativity-variation-like activity. This suggests distinct roles of EU and UD on anticipation-related processes. Overall, we identified time and frequency characteristics of EEG signals that differentially traced motivation-related and conflict-related information during decision-making under risk.

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