Skip to main content Accessibility help
×
Home
Hostname: page-component-78dcdb465f-bmnx5 Total loading time: 5.304 Render date: 2021-04-17T12:54:34.929Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false, "newCitedByModal": true }

A comparison of ‘pruning’ during multi-step planning in depressed and healthy individuals

Published online by Cambridge University Press:  12 March 2021

Paul Faulkner
Affiliation:
Department of Psychology, University of Roehampton, London, UK
Quentin J. M. Huys
Affiliation:
Division of Psychiatry, University College London, London, UK Max Planck Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
Daniel Renz
Affiliation:
Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
Neir Eshel
Affiliation:
Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California, USA
Stephen Pilling
Affiliation:
Division of Psychology and Language Sciences, University College London, London, UK
Peter Dayan
Affiliation:
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
Jonathan P. Roiser
Affiliation:
Institute of Cognitive Neuroscience, University College London, London, UK
Corresponding

Abstract

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 ‘prune’ (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.

Type
Original Article
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below.

References

Anderson, I. M. (2000). Selective serotonin reuptake inhibitors versus tricyclic antidepressants: A meta-analysis of efficacy and tolerability. Journal of Affective Disorders, 58, 1936.CrossRefGoogle ScholarPubMed
Baek, K., Kwon, J. H., Chae, J.-H., Chung, Y. A., Kralik, J. D., Min, J.-A., … Jeong, J. (2017). Heightened loss aversion to risk and loss in depressed patients with a suicide attempt history. Scientific Reports, 7, 1122811241.CrossRefGoogle ScholarPubMed
Beck, A. T., Steer, R., & Brown, G. (1996). Manual for the Beck Depression Inventory-II. San Antonio, TX: Psychological Corporation.Google Scholar
Boureau, Y.-L., & Dayan, P. (2010). Opponency revisited: Competition and cooperation between dopamine and serotonin. Neuropscyhopharmacology, 36, 7497.CrossRefGoogle ScholarPubMed
Cannon, D. M., Ichise, M., Rollis, D., Klaver, J. M., Gandhi, S. K., Charney, D. S., … Drevents, W. C. (2007). Elevated serotonin transporter binding in major depressive disorder assessed using positron emission tomography and [11C]DASB; comparison with bipolar disorder. Biological Psychiatry, 62(8), 870877.CrossRefGoogle Scholar
Charpentier, C. J., Aylward, J., Roiser, J. P., & Robinson, O. J. (2017). Enhanced risk aversion, but not loss aversion, in unmedicated pathological anxiety. Biological Psychiatry, 81(12), 10141022.CrossRefGoogle Scholar
Clark, G., Hops, H., Lewinsohn, P. M., Andrews, J., Seeley, J. R., & Williams, J. (1992). Cognitive-behavioural group treatment of adolescent depression: Prediction of outcome. Behavior Therapy, 23(3), 341354.CrossRefGoogle Scholar
Clifford, P. I., & Hemsley, D. R. (1987). The influence of depression on the processing of personal attributes. The British Journal of Psychiatry, 150(1), 98103.CrossRefGoogle ScholarPubMed
Cools, R., Nakamura, K., & Daw, N. D. (2011). Serotonin and dopamine: Unifying affective, activational, and decision functions. Neuropsychopharmacology, 36, 98113.CrossRefGoogle ScholarPubMed
Crockett, M. J., Clark, L., & Robbins, T. W. (2009). Reconciling the role of serotonin in behavioural inhibition and aversion: Acute tryptophan depletion abolishes punishment-induced inhibition in humans. Journal of Neuroscience, 29(38), 1199311999.CrossRefGoogle ScholarPubMed
Daw, N. D., Kakade, S., & Dayan, P. (2002). Opponent interactions between serotonin and dopamine. Neural Networks, 15(4–6), 603616.CrossRefGoogle ScholarPubMed
Dayan, P., & Huys, Q. J. M. (2008). Serotonin, inhibition and negative mood. PLoS Computational Biology, 4(2), 111.CrossRefGoogle ScholarPubMed
Dayan, P., & Huys, Q. J. M. (2009). Serotonin in affective control. Annual Review of Neuroscience, 32, 95126.CrossRefGoogle ScholarPubMed
Drevets, W. C., Price, J. L., Simpson, J. R. Jr., Todd, R. D., Reich, T., Vannier, M., & Raichle, M. E. (1997). Subgenual prefrontal cortex abnormalities in mood disorders. Nature, 386, 824827.CrossRefGoogle ScholarPubMed
Drevets, W. C., Savitz, J., & Trimble, M. (2008). The subgenual anterior cingulate cortex in mood disorders. CNS Spectrums, 13, 663681.CrossRefGoogle ScholarPubMed
Eshel, N., & Roiser, J. P. (2010). Reward and punishment processing in depression. Biological Psychiatry, 68(2), 118124.CrossRefGoogle ScholarPubMed
Garrett, N., Sharot, T., Faulkner, P., Korn, C. W., Roiser, J. P., & Dolan, R. J. (2014). Losing the rose tinted glasses: Neural substrates of unbiased belief updating in depression. Frontiers in Human Neuroscience, 8(639), 19.CrossRefGoogle ScholarPubMed
Hebart, M. N., & Gläscher, J. (2014). Serotonin and dopamine differentially affect appetitive and aversive general Pavlovian-to-instrumental transfer. Psychopharmacology, 232(2), 437451.CrossRefGoogle ScholarPubMed
Husain, M., & Roiser, J. P. (2018). Neuroscience of apathy and anhedonia: A transdiagnostic approach. Nature Reviews Neuroscience, 19(8), 470484.CrossRefGoogle ScholarPubMed
Huys, Q. J. M., Daw, N. D., & Dayan, P. (2015a). Depression: A decision-theoretic analysis. Annual Reviews of Neuroscience, 38, 123.CrossRefGoogle Scholar
Huys, Q. J. M., Eshel, N., O'Nions, E., Sheridan, L., Dayan, P., & Roiser, J. P. (2012). Bonsai trees in your head: How the Pavlovian system sculpts goal-directed choices by pruning decision trees. PLoS Computational Biology, 8(3), 113.CrossRefGoogle ScholarPubMed
Huys, Q. J. M., Lally, N., Faulkner, P., Eshel, N., Seifritz, E., Gershman, S. J., … Roiser, J. P. (2015b). Interplay of approximate planning strategies. PNAS, 112(10), 30983103.CrossRefGoogle Scholar
Jeffreys, H. (1961). Theory of probability (3rd ed.). Oxford: Oxford University Press, Clarendon Press.Google Scholar
Joormann, J., & Gotlib, I. H. (2008). Updating the contents of working memory in depression: Interference from irrelevant negative material. Journal of Abnormal Psychology, 117(1), 182192.CrossRefGoogle ScholarPubMed
Joormann, J., Hertel, P. T., Brozovich, F., & Gotlib, I. H. (2005). Remembering the good, forgetting the bad: Intentional forgetting of emotional material in depression. Journal of Abnormal Psychology, 114(4), 640648.CrossRefGoogle ScholarPubMed
Kumar, P., Goer, F., Murray, L., Dillon, D. G., Beltzer, M. L., Cohen, A. L., … Pizzagalli, D. A. (2018). Impaired reward prediction error encoding and striatal-midbrain connectivity in depression. Neuropsychopharmacology, 43, 15811588.CrossRefGoogle ScholarPubMed
Lally, N., Huys, Q. J. M., Eshel, N., Faulkner, P., Dayan, P., & Roiser, J. P. (2017). The neural basis of aversive Pavlovian guidance during planning. Journal of Neuroscience, 37(42), 1021510229.CrossRefGoogle ScholarPubMed
Mayberg, H. S., Brannan, S. K., Mahurin, R. K., Jerabek, P. A., Brickman, J. S., Tekell, J. L., … Fox, P. T. (1997). Cingulate function in depression: A potential predictor of treatment response. Neuroreport, 3(8), 10571061.CrossRefGoogle Scholar
McFarland, B. R., & Klein, D. N. (2009). Emotional reactivity in depression: Diminished responsiveness to anticipated reward but not to anticipated punishment or to nonreward or avoidance. Depression and Anxiety, 26(2), 117122.CrossRefGoogle ScholarPubMed
Moses-Kolko, E. L., Wsiner, K. L., Price, J. C., Berga, S. L., Drevets, W. C., Hanusa, B. H., … Meltzer, C. C. (2008). Serotonin 1A receptor reductions in postpartum depression: A positron emission tomography study. Fertility and Sterility, 89(3), 685692.CrossRefGoogle ScholarPubMed
Nestler, E. J., & Carlezon, W. A. (2006). The mesolimbic dopamine reward circuit in depression. Biological Psychiatry, 59(12), 11511159.CrossRefGoogle ScholarPubMed
Parsey, R. V., Oquendo, M. A., Ogden, R. T., Olvet, D. M., Simpson, N., Huang, Y.-Y., … Mann, J. J. (2003). Altered serotonin 1A binding in major depression: A [carbonyl-C-11]WAY100635 positron emission tomography. Biological Psychiatry, 59(2), 106113.CrossRefGoogle Scholar
Pulcu, E., Thomas, E. J., Trotter, P. D., & McFarquhar, M. (2015). Social-economic decision making in current and remitted major depression. Psychological Medicine, 45(6), 13011313.CrossRefGoogle Scholar
Roiser, J. P., Elliott, R., & Sahakian, B. J. (2012). Cognitive mechanisms of treatment in depression. Neuropsychopharmacology, 37, 117136.CrossRefGoogle ScholarPubMed
Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., … Dunbar, G. C. (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry, 59(20), 3457.Google ScholarPubMed
Speilberger, C. D., Gorsuch, R. L., Lushene, P. R., Vagg, P. R., & Jacobs, G. A. (1983). State–trait anxiety inventory for adults (STAIS-AD) manual. Palo Alto, CA: Consulting Psychologists Press.Google Scholar
Ubl, B., Kuehner, C., Kirsch, P., Ruttorf, M., Diener, C., & Flor, H. (2015). Altered reward and loss processing and prediction error signalling in depression. Social Cognitive and Affective Neuroscience, 10(8), 11021112.CrossRefGoogle ScholarPubMed
Wechsler, D. (2001). Wechsler text of adult reading. San Antonio, TX: Harcourt Assessment.Google Scholar
Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., & Wagenmakers, E.-J. (2011). Statistical evidence in experimental psychology: An empirical comparison using 855 t tests. Perspectives in Psychological Science, 6(3), 291298.CrossRefGoogle ScholarPubMed
World Health Organization (2017). Depression and other common mental disorders: Global Health Estimates. Geneva. Licence: CC BY-NC-SA 3.0 IGO.Google Scholar

Faulkner et al. supplementary material

Faulkner et al. supplementary material

File 9 MB

Altmetric attention score

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 47
Total number of PDF views: 20 *
View data table for this chart

* Views captured on Cambridge Core between 12th March 2021 - 17th April 2021. This data will be updated every 24 hours.

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

A comparison of ‘pruning’ during multi-step planning in depressed and healthy individuals
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

A comparison of ‘pruning’ during multi-step planning in depressed and healthy individuals
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

A comparison of ‘pruning’ during multi-step planning in depressed and healthy individuals
Available formats
×
×

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *