学习训练作为认知重组干预。

Agnes Norbury, Quentin Dercon, Tobias U Hauser, Raymond J Dolan, Quentin J M Huys
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引用次数: 0

摘要

背景:低情绪认知疗法的核心部分是学会识别和挑战消极信念。然而,目前尚不清楚,识别这些信念的能力的提高,以及对可能维持这些信念的事件的有偏见的解释,是否是治疗期间症状改变的机制。方法:我们调查了完成学习任务(训练识别和选择自我增强的事件解释)和短暂的认知重组干预(如何探索事件的替代解释可能导致情绪改善)对因果归因倾向的影响。研究采用随机对照实验设计在线进行(N=200 &N=164),并使用分层贝叶斯模型对数据进行分析。结果:学习训练和重组干预均降低了非帮助归因倾向,增加了自我强化归因倾向。在两项研究中,归因倾向的变化与学习训练期间更高的学习率有关,这是一种特定于学习不同类型事件归因的效应。与预期相反,我们发现没有证据表明更快的学习与认知重组后归因倾向的变化有特别的联系。由于具有较高学习率估计的参与者也提供了更接近基本事实的事件原因的明确评级和自由文本描述,我们将其解释为代表学习训练对能够更好地理解任务状态空间的个人有更大的好处。结论:我们建议个性化训练,结合基于可解释的计算模型输出的反馈,可能在治疗期间提供一种有用的增强形式或学习支持工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning training as a cognitive restructuring intervention.

Background: A core part of cognitive therapy for low mood is learning to identify and challenge negative beliefs. However, it is currently unclear whether improved ability to recognise such beliefs, and the biased interpretations of events which may maintain them, is a mechanism of symptom change during treatment.

Methods: We investigated the effects of completing a learning task (training to identify and select self-enhancing interpretations of events) and a brief cognitive restructuring intervention (how exploring alternative explanations of events may result in improved mood) on causal attribution tendencies. Studies were conducted online using randomized-controlled experimental designs (N=200 &N=164), and data were analysed using hierarchical Bayesian models.

Results: We found that both learning training and the restructuring intervention decreased tendencies to make unhelpful attributions and increased tendencies to make self-enhancing attributions. Across two studies, changes in attribution tendencies were associated with higher learning rates during learning training, an effect specific to learning about different kinds of event attribution. Contrary to expectation, we found no evidence that faster learning was associated specifically to changes in attribution tendencies following cognitive restructuring. Since participants with higher learning rate estimates also provided explicit ratings and free-text descriptions of event causes which were closer to the ground truth, we interpret this as representing a greater benefit of learning training in individuals who were better able to understand the task state space.

Conclusions: We suggest that personalized training, in conjunction with feedback based on interpretable computational model output, may provide a useful form of augmentation or learning support tool during therapy.

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