Valence‐dependent brain potentials of processing augmented feedback in learning a complex arm movement sequence

Abstract

ERPs in the EEG were scrutinized in learning a complex arm movement sequence with the aim to examine valence effects on processing augmented feedback during practice. Twenty‐four healthy subjects practiced one session with 192 feedback trials according to an adaptive bandwidth feedback approach with a high informational level of feedback information (i.e., amplitude and direction of errors). The bandwidth for successful performance (increase of a score for a monetary competition) was manipulated to yield a success rate (positive feedback frequency) of approximately 50% adaptive to the current performance level. This allowed a variation of feedback valence unconfounded by success rate. In line with our hypotheses, the EEG data showed a valence‐dependent feedback‐related negativity (FRN) and a later fronto‐central component at the FCz electrode as well as a P300 component at the Pz electrode. Moreover, the P300 and amplitudes in the FRN time window reduced in the second half of practice but were still dependent on feedback valence. Behavioral adjustments were larger after feedback with negative valence and were predicted by the late fronto‐central component. The data support the assumption of feedback valence‐dependent modulation of attentional cognitive involvement in motor control and learning.

The reward positivity reflects the integrated value of temporally threefold‐layered decision outcomes

“…did not relate to adjustments in trial-to-trial choice. With that, our results are in line with recent studies that used similar levels of feedback information content following single or multi-step choices (e.g., Bellebaum et al, 2010;Cockburn & Holroyd, 2018;Frömer et al, 2016;Luft et al, 2014;Osinsky et al, 2018). They also underscore current theoretical assumptions about the role of the aMCC within a hierarchical reinforcement learning system (Holroyd & Umemoto, 2016;Holroyd & Yeung, 2012), which is able to produce and learn from prediction errors that stem from the processing of complex outcome information.…”

“…These tasks revealed independent reward prediction errors for subgoal-and overall goal-related actions in the aMCC, providing support for the assumption that the aMCC responds to information from multiple levels of abstraction. In this regard, an increasingly recognized question is the modulation of the RewP by the level of feedback information content as well as the subjective weighing of different information features in single-and multi-step contexts (e.g., Cockburn & Holroyd, 2018). In particular, we recently introduced an adaptation of the so-called doors task, in which each decision led to a singular outcome event with two interlaced layers of independent temporal consequences, that is, an immediate monetary consequence and a more delayed monetary consequence (Osinsky et al, 2018).…”

Electrophysiological evidence for the effects of pain on the different stages of reward evaluation under a purchasing situation

“…According to the previous study, enhanced FRN amplitude after negative feedback may index error-provoked attentional control (Krause et al, 2020). Besides, the discrepancy of the FRN amplitude between gain and loss (named as d-FRN) is also used to index the reward processing, and the larger d-FRN can reflect the increased attentional resources devoted to the reward processing (Krause et al, 2020).…”

Frontal theta reveals further information about neural valence‐dependent processing of augmented feedback in extensive motor practice—A secondary analysis

“…Of particular interest were the feedback-related negativity (FRN) and the late fronto-central positivity (LFCP). The FRN was discussed in association with prediction errors in reinforcement learning (e.g., Glazer et al, 2018;Walsh & Anderson, 2011), whereas the LFCP was associated with more complex feedback processing and supervised learning (e.g., Arbel et al, 2013;Krause et al, 2020). As an ERP only carries the information of an EEG signal that is time-and phase-locked to the stimulus (e.g., feedback onset; FBO), the underlying cognitive processes might be more distinctly represented in the frequency domain, which can also reflect non-phase-locked neural activity (e.g., Cohen, 2014;Luck, 2014).…”

“…The neural correlates of processing valence‐dependent feedback in the motor domain (e.g., Joch et al, 2018; Krause et al, 2020; Lohse et al, 2014; Margraf et al, 2022a; Reuter et al, 2020) and long‐term learning (i.e., retention and automatization) (Margraf et al, 2022b) have been recently revealed by analysing ERPs of the human electroencephalogram (EEG). Of particular interest were the feedback‐related negativity (FRN) and the late fronto‐central positivity (LFCP).…”