Article 2021

A Comparison of "Pruning" During Multi-Step Planning in Depressed and Healthy Individuals

{Background Real-life decisions are often Background Real-life decisions are often complex because they involve making sequential choices that constrain future options. We have previously shown that to render such multi-step decisions manageable, people \textquoteleftprune\textquoteright (i.e. selectively disregard) branches of decision trees that contain negative outcomes. We have theorized that sub-optimal pruning contributes to depression by promoting an oversampling of branches that result in unsavoury outcomes, which results in a negatively-biased valuation of the world. However, no study has tested this theory in depressed individuals. Methods Thirty unmedicated depressed and 31 healthy participants were administered a sequential reinforcement-based decision-making task to determine pruning behaviours, and completed measures of depression and anxiety. Computational, Bayesian and frequentist analyses examined group differences in task performance and relationships between pruning and depressive symptoms. Results Consistent with prior findings, participants robustly pruned branches of decision trees that began with large losses, regardless of the potential utility of those branches. However, there was no group difference in pruning behaviours. Further, there was no relationship between pruning and levels of depression/anxiety. Conclusions We found no evidence that sub-optimal pruning is evident in depression. Future research could determine whether maladaptive pruning behaviours are observable in specific sub-groups of depressed patients (e.g. in treatment-resistant individuals), or whether misuse of other heuristics may contribute to depression.}

Author(s): Faulkner, P and Huys, QJM and Renz, D and Eshel, N and Pilling, S and Dayan, P and Roiser, JP
Journal: {Psychological Medicine}
Volume: Epub ahead
Year: 2021
Publisher: Cambridge University Press
Bibtex Type: Article (article)
DOI: 10.1017/S0033291721000799
Address: Cambridge, England
Electronic Archiving: grant_archive

BibTex

@article{item_3231464,
  title = {{A Comparison of "Pruning" During Multi-Step Planning in Depressed and Healthy Individuals}},
  journal = {{Psychological Medicine}},
  abstract = {{Background Real-life decisions are often Background Real-life decisions are often complex because they involve making sequential choices that constrain future options. We have previously shown that to render such multi-step decisions manageable, people \textquoteleftprune\textquoteright (i.e. selectively disregard) branches of decision trees that contain negative outcomes. We have theorized that sub-optimal pruning contributes to depression by promoting an oversampling of branches that result in unsavoury outcomes, which results in a negatively-biased valuation of the world. However, no study has tested this theory in depressed individuals. Methods Thirty unmedicated depressed and 31 healthy participants were administered a sequential reinforcement-based decision-making task to determine pruning behaviours, and completed measures of depression and anxiety. Computational, Bayesian and frequentist analyses examined group differences in task performance and relationships between pruning and depressive symptoms. Results Consistent with prior findings, participants robustly pruned branches of decision trees that began with large losses, regardless of the potential utility of those branches. However, there was no group difference in pruning behaviours. Further, there was no relationship between pruning and levels of depression/anxiety. Conclusions We found no evidence that sub-optimal pruning is evident in depression. Future research could determine whether maladaptive pruning behaviours are observable in specific sub-groups of depressed patients (e.g. in treatment-resistant individuals), or whether misuse of other heuristics may contribute to depression.}},
  volume = {Epub ahead},
  publisher = {Cambridge University Press},
  address = {Cambridge, England},
  year = {2021},
  slug = {item_3231464},
  author = {Faulkner, P and Huys, QJM and Renz, D and Eshel, N and Pilling, S and Dayan, P and Roiser, JP}
}