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Learning Training as a Cognitive Restructuring Intervention
Agnes Norbury1, Quentin Dercon1, Tobias U Hauser2
1Applied Computational Psychiatry Laboratory, Max Planck Centre for Computational Psychiatry and Ageing Research, Division of Psychiatry, Queen Square Institute of Neurology and Mental Health Neuroscience Department, University College London, London, United Kingdom.
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 recognize such beliefs, and the biased interpretations of events that 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 and N = 164), and data were analyzed 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 2 studies, changes in attribution tendencies were associated with higher learning rates during learning training, an effect specific to learning about different kinds of event attributions. Contrary to expectation, we found no evidence that faster learning was associated specifically with changes in attribution tendencies following cognitive restructuring. Because participants with higher learning rate estimates also provided explicit ratings and free-text descriptions of event causes that 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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