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Depression is associated with blunted affective responses to naturalistic reward prediction errors

Published online by Cambridge University Press:  02 February 2024

William J Villano  and
Aaron S Heller Open the ORCID record for Aaron S Heller [Opens in a new window]
William J Villano
Affiliation:
Department of Psychology, University of Miami, Coral Gables, FL, USA
Aaron S Heller*
Affiliation:
Department of Psychology, University of Miami, Coral Gables, FL, USA
*
Corresponding author: Aaron S Heller; Email: aheller@miami.edu
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Abstract

Background

Depression is characterized by abnormalities in emotional processing, but the specific drivers of such emotional abnormalities are unknown. Computational work indicates that both surprising outcomes (prediction errors; PEs) and outcomes (values) themselves drive emotional responses, but neither has been consistently linked to affective disturbances in depression. As a result, the computational mechanisms driving emotional abnormalities in depression remain unknown.

Methods

Here, in 687 individuals, one-third of whom qualify as depressed via a standard self-report measure (the PHQ-9), we use high-stakes, naturalistic events – the reveal of midterm exam grades – to test whether individuals with heightened depression display a specific reduction in emotional response to positive PEs.

Results

Using Bayesian mixed effects models, we find that individuals with heightened depression do not affectively benefit from surprising, good outcomes – that is, they display reduced affective responses to positive PEs. These results were highly specific: effects were not observed to negative PEs, value signals (grades), and were not related to generalized anxiety. This suggests that the computational drivers of abnormalities in emotion in depression may be specifically due to positive PE-based emotional responding.

Conclusions

Affective abnormalities are core depression symptoms, but the computational mechanisms underlying such differences are unknown. This work suggests that blunted affective reactions to positive PEs are likely mechanistic drivers of emotional dysregulation in depression.

Keywords

computational psychiatrydepressionEMAemotionevent-triggered EMAexperience samplingnaturalisticprediction error
Type
Original Article
Information
Psychological Medicine , First View , pp. 1 - 9
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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References

Abramson, L. Y., Metalsky, G. I., & Alloy, L. B. (1989). Hopelessness depression: A theory-based subtype of depression. Psychological review, 96(2), 358.CrossRefGoogle Scholar
Atanasova, B., El-Hage, W., Chabanet, C., Gaillard, P., Belzung, C., & Camus, V. (2010). Olfactory anhedonia and negative olfactory alliesthesia in depressed patients. Psychiatry Research, 176(2–3), 190–196.CrossRefGoogle ScholarPubMed
Bürkner, P.-C. (2021). Bayesian item response modeling in R with brms and Stan. Journal of Statistical Software, 100(5), 1–54. doi:10.18637/jss.v100.i05.CrossRefGoogle Scholar
Dichter, G. S., Smoski, M. J., Kampov-Polevoy, A. B., Gallop, R., & Garbutt, J. C. (2010). Unipolar depression does not moderate responses to the sweet taste test. Depression and Anxiety, 27(9), 859–863.CrossRefGoogle ScholarPubMed
Eldar, E., & Niv, Y. (2015). Interaction between emotional state and learning underlies mood instability. Nature Communications, 6(1), 6149. doi:10.1038/ncomms7149.CrossRefGoogle ScholarPubMed
Eldar, E., Rutledge, R. B., Dolan, R. J., & Niv, Y. (2016). Mood as representation of momentum. Trends in Cognitive Sciences, 20(1), 15–24. doi:10.1016/j.tics.2015.07.010.CrossRefGoogle ScholarPubMed
Ferrante, M., Redish, A. D., Oquendo, M. A., Averbeck, B. B., Kinnane, M. E., & Gordon, J. A. (2019). Computational psychiatry: A report from the 2017 NIMH workshop on opportunities and challenges. Molecular Psychiatry, 24(4), 479–483. doi:10.1038/s41380-018-0063-z.CrossRefGoogle ScholarPubMed
Ferrari, A. J., Charlson, F. J., Norman, R. E., Patten, S. B., Freedman, G., Murray, C. J., … Whiteford, H. A. (2013). Burden of depressive disorders by country, sex, age, and year: Findings from the global burden of disease study 2010. PLoS Medicine, 10(11), e1001547.CrossRefGoogle ScholarPubMed
Frijda, N. H., Mesquita, B., Sonnemans, J., & van Goozen, S. (1991). The duration of affective phenomena or emotions, sentiments and passions. In International review of studies on emotion (pp. 187–225). Chichester: John Wiley & Sons.Google Scholar
Huys, Q. J. M., Daw, N. D., & Dayan, P. (2015). Depression: A decision-theoretic analysis. Annual Review of Neuroscience, 38(1), 1–23. doi:10.1146/annurev-neuro-071714-033928.CrossRefGoogle ScholarPubMed
Jaso, B. A., Kraus, N. I., & Heller, A. S. (2021). Identification of careless responding in ecological momentary assessment research: From posthoc analyses to real-time data monitoring. Psychological Methods, 27(6), 958–981. doi:10.1037/met0000312.Google ScholarPubMed
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., & Walters, E. E. (2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey replication. Archives of General Psychiatry, 62(6), 593–602. doi:10.1001/archpsyc.62.6.593.CrossRefGoogle ScholarPubMed
Kube, T., Schwarting, R., Rozenkrantz, L., Glombiewski, J. A., & Rief, W. (2020). Distorted cognitive processes in major depression: A predictive processing perspective. Biological Psychiatry, 87(5), 388–398.CrossRefGoogle ScholarPubMed
Maia, T. V., Huys, Q. J. M., & Frank, M. J. (2017). Theory-based computational psychiatry. Biological Psychiatry, 82(6), 382–384. doi:10.1016/j.biopsych.2017.07.016.CrossRefGoogle ScholarPubMed
Mayer, J. D., Gaschke, Y. N., Braverman, D. L., & Evans, T. W. (1992). Mood-congruent judgment is a general effect. Journal of Personality and Social Psychology, 63(1), 119.CrossRefGoogle Scholar
Mobbs, D., Trimmer, P. C., Blumstein, D. T., & Dayan, P. (2018). Foraging for foundations in decision neuroscience: Insights from ethology. Nature Reviews Neuroscience, 19(7), 419–427. doi:10.1038/s41583-018-0010-7.CrossRefGoogle ScholarPubMed
Otto, A. R., & Eichstaedt, J. C. (2018). Real-world unexpected outcomes predict city-level mood states and risk-taking behavior. PLOS ONE, 13(11), e0206923. doi:10.1371/journal.pone.0206923.CrossRefGoogle ScholarPubMed
Pause, B. M., Miranda, A., Göder, R., Aldenhoff, J. B., & Ferstl, R. (2001). Reduced olfactory performance in patients with major depression. Journal of Psychiatric Research, 35(5), 271–277.CrossRefGoogle ScholarPubMed
Pulcu, E., & Browning, M. (2019). The misestimation of uncertainty in affective disorders. Trends in Cognitive Sciences, 23(10), 865–875. doi:10.1016/j.tics.2019.07.007.CrossRefGoogle ScholarPubMed
R Core Team. (2020). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/.Google Scholar
Rutledge, R. B., Moutoussis, M., Smittenaar, P., Zeidman, P., Taylor, T., Hrynkiewicz, L., … Dolan, R. J. (2017). Association of neural and emotional impacts of reward prediction errors with major depression. JAMA Psychiatry, 74(8), 790–797. doi:10.1001/jamapsychiatry.2017.1713.CrossRefGoogle ScholarPubMed
Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014). A computational and neural model of momentary subjective well-being. Proceedings of the National Academy of Sciences of the United States of America, 111(33), 12252–12257. doi:10.1073/pnas.1407535111.CrossRefGoogle ScholarPubMed
Scholl, J., & Klein-Flügge, M. (2018). Understanding psychiatric disorder by capturing ecologically relevant features of learning and decision-making. Behavioural Brain Research, 355, 56–75. doi:10.1016/j.bbr.2017.09.050.CrossRefGoogle ScholarPubMed
Seligman, M. E., Abramson, L. Y., Semmel, A., & von Baeyer, C. (1979). Depressive attributional style. Journal of Abnormal Psychology, 88(3), 242–247. doi:10.1037/0021-843X.88.3.242.CrossRefGoogle ScholarPubMed
Spitzer, R. L., Kroenke, K., & Williams, J. B. W., & and the Patient Health Questionnaire Primary Care Study Group. (1999). Validation and utility of a self-report version of PRIME-MDThe PHQ primary care study. JAMA, 282(18), 1737–1744. doi:10.1001/jama.282.18.1737.CrossRefGoogle ScholarPubMed
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092–1097. doi:10.1001/archinte.166.10.1092.CrossRefGoogle ScholarPubMed
Treadway, M. T., & Zald, D. H. (2013). Parsing anhedonia: Translational models of reward-processing deficits in psychopathology. Current Directions in Psychological Science, 22(3), 244–249. doi:10.1177/0963721412474460.CrossRefGoogle ScholarPubMed
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Bürkner, P.-C. (2021). Rank-normalization, folding, and localization: An improved R^ for assessing convergence of MCMC (with discussion). Bayesian Analysis, 16(2), 667–718. doi:10.1214/20-BA1221.CrossRefGoogle Scholar
Villano, W. J., Kraus, N. I., Reneau, T. R., Jaso, B. A., Otto, A. R., & Heller, A. S. (2023). Individual differences in naturalistic learning link negative emotionality to the development of anxiety. Science Advances, 9(1), eadd2976. doi:10.1126/sciadv.add2976.CrossRefGoogle Scholar
Villano, W. J., Otto, A. R., Ezie, C. E. C., Gillis, R., & Heller, A. S. (2020). Temporal dynamics of real-world emotion are more strongly linked to prediction error than outcome. Journal of Experimental Psychology: General, 149(9), 1755–1766. (2020-09746-001). doi:10.1037/xge0000740.CrossRefGoogle ScholarPubMed
Watson, D., & Clark, L. A. (1999). The PANAS-X: Manual for the Positive and Negative Affect Schedule - Expanded Form [Other]. doi:10.17077/48vt-m4t2.CrossRefGoogle Scholar
Watson, R., Harvey, K., McCabe, C., & Reynolds, S. (2020). Understanding anhedonia: A qualitative study exploring loss of interest and pleasure in adolescent depression. European Child & Adolescent Psychiatry, 29(4), 489–499.CrossRefGoogle ScholarPubMed
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