Factors mediating performance monitoring in humans—from context to personality

Thoma, Patrizia; Bellebaum, Christian

doi:10.3389/fnhum.2013.00023

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…perspective taking) and affective (affective sharing and personal affective responses) empathy. This might be based on overlapping neural circuits, with a key role for the ACC (see Thoma & Bellebaum, 2012 for a review). Positive associations between trait empathy and the ERN were reported for active responding (Larson, Fair, Good, & Baldwin, 2010;Santesso & Segalowitz, 2009), increasing in strength during the observation of another person's behavior (e.g.…”

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confidence: 99%

Performance monitoring and empathy during active and observational learning in patients with major depression

Thoma

Norra

Juckel

et al. 2015

Biological Psychology

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confidence: 99%

Performance monitoring and empathy during active and observational learning in patients with major depression

Thoma

Norra

Juckel

et al. 2015

Biological Psychology

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“…Moreover, our analyses focus on conflict monitoring (N2 component) and feedback processing (FN component) of correctly performed trials, while leaving analysis of ERN/Ne aside because of too few incorrect responses per task block. However, other learning studies investigated performance monitoring by means of ERN/Ne (Thoma & Bellebaum, 2013). Participants demonstrated a strong learning curve, providing evidence of high motivation to perform the task and high compliance with task instructions.…”

Section: Discussionmentioning

confidence: 99%

Individual differences of conflict monitoring and feedback processing during reinforcement learning in a mock forensic context

Leue¹,

Nieden²,

Scheuble

et al. 2020

Cogn Affect Behav Neurosci

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This study investigated individual differences of conflict monitoring (N2 component), feedback processing (feedback negativity component), and reinforcement learning in a discrimination learning task using a mock (fictitious) forensic scenario to set participants in a semantic task context. We investigated individual differences of anxiety-related, impulsivity-related traits and reasoning ability during trial-and-error learning of mock suspect and nonsuspect faces. Thereby, we asked how the differential investment of cognitive-motivational processes facilitates learning in a mock forensic context. As learning can be studied by means of time-on-task effects (i.e., variations of cognitive processes across task blocks), we investigated the differential investment of cognitive-motivational processes block-wise in N = 100 participants. By performing structural equation modeling, we demonstrate that conflict monitoring decreased across task blocks, whereas the percentage of correct responses increased across task blocks. Individuals with higher reasoning scores and higher impulsivity-related traits relied rather on feedback processing (i.e., external indicators) during reinforcement learning. Individuals with higher anxiety-related traits intensified their conflict monitoring throughout the task to learn successfully. Observation by relevant others intensified conflict monitoring more than nonobservation. Our data highlight that individual differences and social context modulate the intensity of information processing in a discrimination learning task using a mock forensic task scenario. We discuss our data with regard to recent cognitivemotivational approaches and in terms of reinforcement learning.

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Factors mediating performance monitoring in humans—from context to personality

Cited by 2 publications

References 13 publications

Performance monitoring and empathy during active and observational learning in patients with major depression

Performance monitoring and empathy during active and observational learning in patients with major depression

Individual differences of conflict monitoring and feedback processing during reinforcement learning in a mock forensic context

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