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Cognitive effort-based decision-making in major depressive disorder

Published online by Cambridge University Press:  25 April 2022

Yuen-Siang Ang Open the ORCID record for Yuen-Siang Ang [Opens in a new window] ,
Steven E. Gelda  and
Diego A. Pizzagalli
Yuen-Siang Ang*
Affiliation:
McLean Hospital, Belmont MA, USA Department of Psychiatry, Harvard Medical School, Boston MA, USA Social and Cognitive Computing Department, Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
Steven E. Gelda
Affiliation:
McLean Hospital, Belmont MA, USA
Diego A. Pizzagalli
Affiliation:
McLean Hospital, Belmont MA, USA Department of Psychiatry, Harvard Medical School, Boston MA, USA
*
Author for correspondence: Yuen-Siang Ang, E-mail: angys@ihpc.a-star.edu.sg
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Abstract

Background

The association between major depressive disorder and motivation to invest cognitive effort for rewards is unclear. One reason might be that prior tasks of cognitive effort-based decision-making are limited by potential confounds such as physical effort and temporal delay discounting.

Methods

To address these interpretive challenges, we developed a new task – the Cognitive Effort Motivation Task – to assess one's willingness to exert cognitive effort for rewards. Cognitive effort was manipulated by varying the number of items (1, 2, 3, 4, 5) kept in spatial working memory. Twenty-six depressed patients and 44 healthy controls went through an extensive learning session where they experienced each possible effort level 10 times. They were then asked to make a series of choices between performing a fixed low-effort-low-reward or variable higher-effort-higher-reward option during the task.

Results

Both groups found the task more cognitively (but not physically) effortful when effort level increased, but they still achieved ⩾80% accuracy on each effort level during training and >95% overall accuracy during the actual task. Computational modelling revealed that a parabolic model best accounted for subjects' data, indicating that higher-effort levels had a greater impact on devaluing rewards than lower levels. These procedures also revealed that MDD patients discounted rewards more steeply by effort and were less willing to exert cognitive effort for rewards compared to healthy participants.

Conclusions

These findings provide empirical evidence to show, without confounds of other variables, that depressed patients have impaired cognitive effort motivation compared to the general population.

Keywords

Cognitive effort-based decision-makingcomputational modellingdepressionmotivation
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 9 , July 2023 , pp. 4228 - 4235
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Ang, Y.-S., Lockwood, P., Apps, M. A. J., Muhammed, K., & Husain, M. (2017). Distinct subtypes of apathy revealed by the apathy motivation index. PLoS ONE, 12(1), e0169938. https://doi.org/10.1371/journal.pone.0169938.CrossRefGoogle ScholarPubMed
Beck, A., Steer, R., & Brown, G. (1996). Manual for the Beck depression inventory-II. San Antonio, TX: Psychological Corporation.Google Scholar
Callicott, J. H. (1999). Physiological characteristics of capacity constraints in working memory as revealed by functional MRI. Cerebral Cortex, 9(1), 20–26. https://doi.org/10.1093/cercor/9.1.20.CrossRefGoogle ScholarPubMed
Chong, T. T.-J., Apps, M., Giehl, K., Sillence, A., Grima, L. L., & Husain, M. (2017). Neurocomputational mechanisms underlying subjective valuation of effort costs. PLoS Biology, 15(2), e1002598. https://doi.org/10.1371/journal.pbio.1002598.CrossRefGoogle ScholarPubMed
Chong, T. T.-J., Bonnelle, V., & Husain, M. (2016). Quantifying motivation with effort-based decision-making paradigms in health and disease. Progress in Brain Research, 229, 71–100. https://doi.org/10.1016/bs.pbr.2016.05.002.CrossRefGoogle ScholarPubMed
Cohen, R., Lohr, I., Paul, R., & Boland, R. (2001). Impairments of attention and effort among patients with major affective disorders. The Journal of Neuropsychiatry and Clinical Neurosciences, 13(3), 385–395. https://doi.org/10.1176/jnp.13.3.385.CrossRefGoogle ScholarPubMed
Culbreth, A., Westbrook, A., & Barch, D. (2016). Negative symptoms are associated with an increased subjective cost of cognitive effort. Journal of Abnormal Psychology, 125(4), 528–536. https://doi.org/10.1037/abn0000153.CrossRefGoogle ScholarPubMed
Ellis, H. A., & Ashbrook, P. W. (1988). Resource allocation model of the effects of depressed mood states on memory. In K. Fielder and J. Forgas (Eds.), Affect, cognition, and social behavior (pp. 25–43). Lewiston, NY: Hogrefe.Google Scholar
First, M., Williams, J., Karg, R., & Spitzer, R. (2015). Structured clinical interview for DSM-5 – research version (SCID-5 for DSM-5, research version; SCID-5-RV). Arlington, VA: American Psychiatric Association.Google Scholar
Hammar, A., & Ardal, G. (2009). Cognitive functioning in major depression – a summary. Frontiers in Human Neuroscience, 3, 26. https://doi.org/10.3389/neuro.09.026.2009.CrossRefGoogle ScholarPubMed
Hartlage, S., Alloy, L. B., Vázquez, C., & Dykman, B. (1993). Automatic and effortful processing in depression. Psychological Bulletin, 113(2), 247–278.CrossRefGoogle ScholarPubMed
Hasher, L., & Zacks, R. T. (1979). Automatic and effortful processes in memory. Journal of Experimental Psychology: General, 108(3), 356–388. https://doi.org/10.1037/0096-3445.108.3.356.CrossRefGoogle Scholar
Horne, S. J., Topp, T. E., & Quigley, L. (2021). Depression and the willingness to expend cognitive and physical effort for rewards: A systematic review. Clinical Psychology Review, 88, 102065. https://doi.org/10.1016/j.cpr.2021.102065.CrossRefGoogle ScholarPubMed
Husain, M., & Roiser, J. P. (2018). Neuroscience of apathy and anhedonia: A transdiagnostic approach. Nature Reviews Neuroscience, 19(4), 470–484. https://doi.org/10.1038/s41583-018-0029-9.CrossRefGoogle ScholarPubMed
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), e1002410. https://doi.org/10.1371/journal.pcbi.1002410.CrossRefGoogle ScholarPubMed
Khazanov, G. K., Ruscio, A. M., & Forbes, C. N. (2020). The positive valence systems scale: Development and validation. Assessment, 27(5), 1045–1069. https://doi.org/10.1177/1073191119869836.CrossRefGoogle ScholarPubMed
Massar, S. A. A., Libedinsky, C., Weiyan, C., Huettel, S. A., & Chee, M. W. L. (2015). Separate and overlapping brain areas encode subjective value during delay and effort discounting. NeuroImage, 120, 104–113. https://doi.org/10.1016/j.neuroimage.2015.06.080.CrossRefGoogle ScholarPubMed
Owen, A. M., McMillan, K. M., Laird, A. R., & Bullmore, E. (2005). N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Human Brain Mapping, 25(1), 46–59. https://doi.org/10.1002/hbm.20131.CrossRefGoogle ScholarPubMed
Salamone, J. D., & Correa, M. (2012). The mysterious motivational functions of mesolimbic dopamine. Neuron, 76(3), 470–485. https://doi.org/10.1016/j.neuron.2012.10.021.CrossRefGoogle ScholarPubMed
Salamone, J. D., Correa, M., Yang, J.-H., Rotolo, R., & Presby, R. (2018). Dopamine, effort-based choice, and behavioral economics: Basic and translational research. Frontiers in Behavioral Neuroscience, 12, 52. https://doi.org/10.3389/fnbeh.2018.00052.CrossRefGoogle ScholarPubMed
Snaith, R. P., Hamilton, M., Morley, S., Humayan, A., Hargreaves, D., & Trigwell, P. (1995). A scale for the assessment of hedonic tone the Snaith–Hamilton pleasure scale. British Journal of Psychiatry, 167(01), 99–103. https://doi.org/10.1192/bjp.167.1.99.CrossRefGoogle ScholarPubMed
Soutschek, A., Kang, P., Ruff, C. C., Hare, T. A., & Tobler, P. N. (2018). Brain stimulation over the frontopolar cortex enhances motivation to exert effort for reward. Biological Psychiatry, 84(1), 38–45. https://doi.org/10.1016/j.biopsych.2017.11.007.CrossRefGoogle ScholarPubMed
Suthaharan, P., Corlett, P. R., & Ang, Y.-S. (2021). Computational modeling of behavioral tasks: An illustration on a classic reinforcement learning paradigm. The Quantitative Methods for Psychology, 17(2), 105–104.CrossRefGoogle Scholar
Tran, T., Hagen, A. E. F., Hollenstein, T., & Bowie, C. R. (2021). Physical- and cognitive-effort-based decision-making in depression: Relationships to symptoms and functioning. Clinical Psychological Science, 9(1), 53–67. https://doi.org/10.1177/2167702620949236.CrossRefGoogle Scholar
Treadway, M. T., Bossaller, N. A., Shelton, R. C., & Zald, D. H. (2012). Effort-based decision-making in major depressive disorder: A translational model of motivational anhedonia. Journal of Abnormal Psychology, 121(3), 553–558. https://doi.org/10.1037/a0028813.CrossRefGoogle ScholarPubMed
Treadway, M. T., Buckholtz, J. W., Schwartzman, A. N., Lambert, W. E., & Zald, D. H. (2009). Worth the ‘EEfRT’? The effort expenditure for rewards task as an objective measure of motivation and anhedonia. PLoS ONE, 4(8), e6598. https://doi.org/10.1371/journal.pone.0006598.CrossRefGoogle ScholarPubMed
Treadway, M. T., & Zald, D. H. (2011). Reconsidering anhedonia in depression: Lessons from translational neuroscience. Neuroscience and Biobehavioral Reviews, 35(3), 537–555. https://doi.org/10.1016/j.neubiorev.2010.06.006.CrossRefGoogle ScholarPubMed
Walton, M. E., Kennerley, S. W., Bannerman, D. M., Phillips, P. E. M., & Rushworth, M. F. S. (2006). Weighing up the benefits of work: Behavioral and neural analyses of effort-related decision making. Neural Networks, 19(8), 1302–1314. https://doi.org/10.1016/j.neunet.2006.03.005.CrossRefGoogle ScholarPubMed
Westbrook, A., Kester, D., & Braver, T. S. (2013). What is the subjective cost of cognitive effort? Load, trait, and aging effects revealed by economic preference. PLoS ONE, 8(7), e68210. https://doi.org/10.1371/journal.pone.0068210.CrossRefGoogle ScholarPubMed
Zakzanis, K. K., Leach, L., & Kaplan, E. (1998). On the nature and pattern of neurocognitive function in major depressive disorder. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 11(3), 111–119.Google ScholarPubMed
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