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Dissecting the impact of depression on decision-making

Published online by Cambridge University Press:  08 July 2019

Victoria M. Lawlor ,
Christian A. Webb ,
Thomas V. Wiecki ,
Michael J. Frank ,
Madhukar Trivedi ,
Diego A. Pizzagalli  and
Daniel G. Dillon Open the ORCID record for Daniel G. Dillon [Opens in a new window]
Victoria M. Lawlor
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital/Harvard Medical School, Belmont, Massachusetts, USA Emory University, Atlanta, Georgia, USA
Christian A. Webb
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital/Harvard Medical School, Belmont, Massachusetts, USA
Thomas V. Wiecki
Affiliation:
Quantopian, Inc, Boston, Massachusetts, USA
Michael J. Frank
Affiliation:
Brown University, Providence, Rhode Island, USA
Madhukar Trivedi
Affiliation:
UT Southwestern Medical Center, Dallas, Texas, USA
Diego A. Pizzagalli
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital/Harvard Medical School, Belmont, Massachusetts, USA
Daniel G. Dillon*
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital/Harvard Medical School, Belmont, Massachusetts, USA
*
Author for correspondence: Daniel G. Dillon, E-mail: [email protected]
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Abstract

Background

Cognitive deficits in depressed adults may reflect impaired decision-making. To investigate this possibility, we analyzed data from unmedicated adults with Major Depressive Disorder (MDD) and healthy controls as they performed a probabilistic reward task. The Hierarchical Drift Diffusion Model (HDDM) was used to quantify decision-making mechanisms recruited by the task, to determine if any such mechanism was disrupted by depression.

Methods

Data came from two samples (Study 1: 258 MDD, 36 controls; Study 2: 23 MDD, 25 controls). On each trial, participants indicated which of two similar stimuli was presented; correct identifications were rewarded. Quantile-probability plots and the HDDM quantified the impact of MDD on response times (RT), speed of evidence accumulation (drift rate), and the width of decision thresholds, among other parameters.

Results

RTs were more positively skewed in depressed v. healthy adults, and the HDDM revealed that drift rates were reduced—and decision thresholds were wider—in the MDD groups. This pattern suggests that depressed adults accumulated the evidence needed to make decisions more slowly than controls did.

Conclusions

Depressed adults responded slower than controls in both studies, and poorer performance led the MDD group to receive fewer rewards than controls in Study 1. These results did not reflect a sensorimotor deficit but were instead due to sluggish evidence accumulation. Thus, slowed decision-making—not slowed perception or response execution—caused the performance deficit in MDD. If these results generalize to other tasks, they may help explain the broad cognitive deficits seen in depression.

Keywords

Computational modelingdecision-makingdepressiondrift diffusion modelreward
Type
Original Articles
Information
Psychological Medicine , Volume 50 , Issue 10 , July 2020 , pp. 1613 - 1622
Copyright
Copyright © Cambridge University Press 2019

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