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非临床样本中情感和动机个体差异与错误监测的网络分析。

A network analysis of affective and motivational individual differences and error monitoring in a non-clinical sample.

机构信息

Doctoral School in the Social Sciences, Jagiellonian University, Main Square 34, 31-110 Krakow, Poland.

Centre for Cognitive Science, Jagiellonian University, Ingardena 3, 30-060 Krakow, Poland.

出版信息

Cereb Cortex. 2024 Oct 3;34(10). doi: 10.1093/cercor/bhae397.

DOI:10.1093/cercor/bhae397
PMID:39462813
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11513196/
Abstract

Error monitoring, which plays a crucial role in shaping adaptive behavior, is influenced by a complex interplay of affective and motivational factors. Understanding these associations often proves challenging due to the intricate nature of these variables. With the aim of addressing previous inconsistencies and methodological gaps, in this study, we utilized network analysis to investigate the relationship between affective and motivational individual differences and error monitoring. We employed six Gaussian Graphical Models on a non-clinical population ($N$ = 236) to examine the conditional dependence between the amplitude of response-related potentials (error-related negativity; correct-related negativity) and 29 self-report measures related to anxiety, depression, obsessive thoughts, compulsive behavior, and motivation while adjusting for covariates: age, handedness, and latency of error-related negativity and correct-related negativity. We then validated our results on an independent sample of 107 participants. Our findings revealed unique associations between error-related negativity amplitudes and specific traits. Notably, more pronounced error-related negativity amplitudes were associated with increased rumination and obsessing, and decreased reward sensitivity. Importantly, in our non-clinical sample, error-related negativity was not directly associated with trait anxiety. These results underscore the nuanced effects of affective and motivational traits on error processing in healthy population.

摘要

错误监控在塑造适应性行为方面起着至关重要的作用,它受到情感和动机因素的复杂相互作用的影响。由于这些变量的复杂性质,理解这些关联通常具有挑战性。为了解决之前的不一致和方法上的差距,在这项研究中,我们使用网络分析来研究情感和动机个体差异与错误监控之间的关系。我们在非临床人群($N$=236)中使用了六个高斯图形模型,以检查与焦虑、抑郁、强迫思维、强迫行为和动机相关的 29 个自我报告测量与反应相关电位(错误相关负波;正确相关负波)之间的条件依赖关系,同时调整了协变量:年龄、手性和错误相关负波和正确相关负波的潜伏期。然后,我们在 107 名独立参与者的样本上验证了我们的结果。我们的研究结果揭示了错误相关负波振幅与特定特征之间的独特关联。值得注意的是,更明显的错误相关负波振幅与更多的沉思和强迫思维以及较低的奖励敏感性有关。重要的是,在我们的非临床样本中,错误相关负波与特质焦虑之间没有直接关联。这些结果强调了情感和动机特征对健康人群中错误处理的细微影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/95a60928a1d4/bhae397f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/1a110bb13eee/bhae397f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/a607384f34d9/bhae397f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/693784e5c2a8/bhae397f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/24fd5ba43ff0/bhae397f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/2489ad53913b/bhae397f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/95a60928a1d4/bhae397f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/1a110bb13eee/bhae397f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/a607384f34d9/bhae397f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/693784e5c2a8/bhae397f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/24fd5ba43ff0/bhae397f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/2489ad53913b/bhae397f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cb2/11513196/95a60928a1d4/bhae397f6.jpg

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本文引用的文献

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Machine learning reveals differential effects of depression and anxiety on reward and punishment processing.机器学习揭示了抑郁和焦虑对奖惩处理的差异影响。
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A Machine Learning Study of Anxiety-related Symptoms and Error-related Brain Activity.
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Temporal unpredictability increases error monitoring as revealed by EEG-EMG investigation.时间的不可预测性增加了错误监测,这一点通过 EEG-EMG 研究得到了揭示。
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