Dissociable feedback valence effects on frontal midline theta during reward gain versus threat avoidance learning

Stolz, Christopher; Pickering, Alan and Mueller, Erik M.. 2023. Dissociable feedback valence effects on frontal midline theta during reward gain versus threat avoidance learning. Psychophysiology, 60(5), e14235. ISSN 0048-5772 [Article]

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Abstract or Description

While frontal midline theta (FMθ) has been associated with threat processing, with cognitive control in the context of anxiety, and with reinforcement learning, most reinforcement learning studies on FMθ have used reward rather than threat-related stimuli as reinforcer. Accordingly, the role of FMθ in threat-related reinforcement learning is largely unknown. Here, n = 23 human participants underwent one reward-, and one punishment-, based reversal learning task, which differed only with regard to the kind of reinforcers that feedback was tied to (i.e., monetary gain vs. loud noise burst, respectively). In addition to single-trial EEG, we assessed single-trial feedback expectations based on both a reinforcement learning computational model and trial-by-trial subjective feedback expectation ratings. While participants' performance and feedback expectations were comparable between the reward and punishment tasks, FMθ was more reliably amplified to negative vs. positive feedback in the reward vs. punishment task. Regressions with feedback valence, computationally derived, and self-reported expectations as predictors and FMθ as criterion further revealed that trial-by-trial variations in FMθ specifically relate to reward-related feedback-valence and not to threat-related feedback or to violated expectations/prediction errors. These findings suggest that FMθ as measured in reinforcement learning tasks may be less sensitive to the processing of events with direct relevance for fear and anxiety.

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Funding information: The present study was supported by the Deutsche Forschungsgemeinschaft (DFG; Project-ID 290878970–RTG 2271 and Project-ID 422744262–TRR 289). Open Access funding enabled and organized by Projekt DEAL.

Data Availability Statement: https://openneuro.org/datasets/ds004295/


computational model, EEG, prediction error, punishment avoidance, reinforcement learning, theta

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17 November 2022Accepted
18 December 2022Published Online
May 2023Published

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Date Deposited:

10 Jan 2023 13:18

Last Modified:

17 Apr 2023 10:25

Peer Reviewed:

Yes, this version has been peer-reviewed.



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