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  Increased decision thresholds enhance information gathering performance in juvenile Obsessive-Compulsive Disorder (OCD)

Hauser, T., Moutoussis, M., Iannaccone, R., Brem, S., Walitza, S., Drechsler, R., et al. (2017). Increased decision thresholds enhance information gathering performance in juvenile Obsessive-Compulsive Disorder (OCD). PLoS Computational Biology, 13(4), 1-19. doi:10.1371/journal.pcbi.1005440.

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Hauser, TU, Author
Moutoussis, M, Author
Iannaccone, R, Author
Brem, S, Author
Walitza, S, Author
Drechsler, R, Author
Dayan, P1, Author           
Dolan, RJ, Author
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1External Organizations, ou_persistent22              

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 Abstract: Patients with obsessive-compulsive disorder (OCD) can be described as cautious and hesitant, manifesting an excessive indecisiveness that hinders efficient decision making. However, excess caution in decision making may also lead to better performance in specific situations where the cost of extended deliberation is small. We compared 16 juvenile OCD patients with 16 matched healthy controls whilst they performed a sequential information gathering task under different external cost conditions. We found that patients with OCD outperformed healthy controls, winning significantly more points. The groups also differed in the number of draws required prior to committing to a decision, but not in decision accuracy. A novel Bayesian computational model revealed that subjective sampling costs arose as a non-linear function of sampling, closely resembling an escalating urgency signal. Group difference in performance was best explained by a later emergence of these subjective costs in the OCD group, also evident in an increased decision threshold. Our findings present a novel computational model and suggest that enhanced information gathering in OCD can be accounted for by a higher decision threshold arising out of an altered perception of costs that, in some specific contexts, may be advantageous.

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Language(s): eng - English
 Dates: 2017-04
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1005440
eDoc: e1005440
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 13 (4) Sequence Number: - Start / End Page: 1 - 19 Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1