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Perfectionism-related variations in error processing in a task with increased response selection complexity.

ABSTRACT: Perfectionists strive for a flawless performance because they are intrinsically motivated to set and achieve high goals (personal standards perfectionism; PSP) and/or because they are afraid to be negatively evaluated by others (evaluative concern perfectionism; ECP). We investigated the differential relationships of these perfectionism dimensions with performance, post-response adaptation, error processing (reflected by two components of the event-related potential: error/correct negativity - Ne/c; error/correct positivity - Pe/c) and error detection. In contrast to previous studies, we employed a task with increased response selection complexity providing more room for perfectionistic dispositions to manifest themselves. Although ECP was related to indicators of increased preoccupation with errors, high-EC perfectionists made more errors than low-EC perfectionists. This observation may be explained by insufficient early error processing as indicated by a reduced Ne/c effect and a lack of post-response adaptation. PSP had a moderating effect on the relationship between ECP and early error processing. Our results provide evidence that pure-EC perfectionists may spend many of their cognitive resources on error-related contents and worrying, leaving less capacity for cognitive control and thus producing a structural lack of error processing.

SUBMITTER: Mattes A

PROVIDER: S-EPMC9880962 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Perfectionism-related variations in error processing in a task with increased response selection complexity.

Mattes André A Mück Markus M Stahl Jutta J

Personality neuroscience 20230111

Perfectionists strive for a flawless performance because they are intrinsically motivated to set and achieve high goals (personal standards perfectionism; PSP) and/or because they are afraid to be negatively evaluated by others (evaluative concern perfectionism; ECP). We investigated the differential relationships of these perfectionism dimensions with performance, post-response adaptation, error processing (reflected by two components of the event-related potential: error/correct negativity - N ...[more]

PMID: 36721395

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Project description:Trial-and-error learning is a universal strategy for establishing which actions are beneficial or harmful in new environments. However, learning stimulus-response associations solely via trial-and-error is often suboptimal, as in many settings dependencies among stimuli and responses can be exploited to increase learning efficiency. Previous studies have shown that in settings featuring such dependencies, humans typically engage high-level cognitive processes and employ advanced learning strategies to improve their learning efficiency. Here we analyze in detail the initial learning phase of a sample of human subjects (N = 85) performing a trial-and-error learning task with deterministic feedback and hidden stimulus-response dependencies. Using computational modeling, we find that the standard Q-learning model cannot sufficiently explain human learning strategies in this setting. Instead, newly introduced deterministic response models, which are theoretically optimal and transform stimulus sequences unambiguously into response sequences, provide the best explanation for 50.6% of the subjects. Most of the remaining subjects either show a tendency towards generic optimal learning (21.2%) or at least partially exploit stimulus-response dependencies (22.3%), while a few subjects (5.9%) show no clear preference for any of the employed models. After the initial learning phase, asymptotic learning performance during the subsequent practice phase is best explained by the standard Q-learning model. Our results show that human learning strategies in the presented trial-and-error learning task go beyond merely associating stimuli and responses via incremental reinforcement. Specifically during initial learning, high-level cognitive processes support sophisticated learning strategies that increase learning efficiency while keeping memory demands and computational efforts bounded. The good asymptotic fit of the Q-learning model indicates that these cognitive processes are successively replaced by the formation of stimulus-response associations over the course of learning.

Dataset Information

Perfectionism-related variations in error processing in a task with increased response selection complexity.

Publications

Perfectionism-related variations in error processing in a task with increased response selection complexity.

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