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[Preprint]. 2025 Apr 2:2024.06.17.599286.
doi: 10.1101/2024.06.17.599286.

A computational approach to understanding effort-based decision-making in depression

Vincent Valton  1 , Anahit Mkrtchian  2 , Madeleine Moses-Payne  3 , Alan Gray  1 , Karel Kieslich  1 , Samantha VanUrk  1 , Veronika Samborska  1 , Don Chamith Halahakoon  1 , Sanjay G Manohar  4 , Peter Dayan  5 , Masud Husain  4 , Jonathan P Roiser  1
Affiliations

A computational approach to understanding effort-based decision-making in depression

Vincent Valton et al. bioRxiv. .

Abstract

Objective: Motivational dysfunction is a core feature of depression, and can have debilitating effects on everyday function. However, it is unclear which disrupted cognitive processes underlie impaired motivation, and whether impairments persist following remission. Decision-making concerning exerting effort to obtain rewards offers a promising framework for understanding motivation, especially when examined with computational tools which can offer precise quantification of latent processes.

Methods: Effort-based decision-making was assessed using the Apple Gathering Task, in which participants decide whether to exert effort via a grip-force device to obtain varying levels of reward; effort levels were individually calibrated and varied parametrically. We present a comprehensive computational analysis of decision-making, initially validating our model in healthy volunteers (N=67), before applying it in a case-control study including current (N=41) and remitted (N=46) unmedicated depressed individuals, and healthy volunteers with (N=36) and without (N=57) a family history of depression.

Results: Four fundamental computational mechanisms that drive patterns of effort-based decisions, which replicated across samples, were identified: overall bias to accept effort challenges; reward sensitivity; and linear and quadratic effort sensitivity. Traditional model-agnostic analyses showed that both depressed groups showed lower willingness to exert effort. In contrast with previous findings, computational analysis revealed that this difference was primarily driven by lower effort acceptance bias, but not altered effort or reward sensitivity.

Conclusions: This work provides insight into the computational mechanisms underlying motivational dysfunction in depression. Lower willingness to exert effort could represent a trait-like factor contributing to symptoms, and might represent a fruitful target for treatment and prevention.

Keywords: Anhedonia; Computational Psychiatry; Depression; Effort-based decision-making; Motivation.

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Conflict of interest statement

Disclosures The authors report no biomedical financial interests or potential conflicts of interest.

Figures

Figure 1:
Figure 1:. Apple Gathering Task (AGT) and acceptance rates.
(A) On each trial, participants are given a different offer comprising a number of apples (3, 6, 9, or 12 apples) for a given effort cost (20%, 40%, 60% or 80% of their maximum grip strength). Participants can either accept the offer or refuse the offer. If the offer is accepted, participants need to squeeze the gripper to the required effort level (or above) for 3 seconds in order to win the apples on this trial. For refused offers, ‘no response required’ was displayed, followed by the next decision. (B) Average acceptance rates as a function of reward level (number of points) and effort level (% MVC) for the Pilot study. (C) Distribution of the number of accepted offers (out of 80) in the Pilot. (D) Average acceptance rates as a function of reward level and effort level across all groups in the Case-control study. (E) Distribution of the number of accepted offers (out of 80) across all groups in the Case-control study. Continuous lines denote the kernel density estimate from the data, dashed lines denote the mean, and dotted lines represent ± 1 standard deviation from the mean.
Figure 2:
Figure 2:. Acceptance rates for the Case-control study.
(A) Average acceptance rate as a function of reward level (points) and effort level (% MVC) for the control (CTR), first degree relatives (REL), patients with current depression (MDD), and remitted depression (REM) group. (B) Distribution of the number of accepted offers for each group. Continuous lines denote the kernel density estimate from the data, dashed lines denote the mean, and dotted lines represent ± 1 standard deviation from the mean. (C) Overall probability to accept offers for each group. Black dots represent the mean and error bars represent the standard error of the mean. Note that raw data is presented but analyses were conducted on arcsine transformed data.
Figure 3:
Figure 3:. Estimated model parameters for the Pilot (A-D) and Case-control (E-G) study.
Figures are showing violin and boxplots as well as the mean (plus sign) and median (notch) for (A) estimated intercept/bias (K), (B) reward sensitivity (LinR), (C) linear effort (LinE), and (D) quadratic effort sensitivity (E2) parameter values from the winning model in the pilot study. Data are shown for estimated (E) intercept/bias (K), (F) reward sensitivity (LinR), and (G) quadratic effort (E2) sensitivity parameter values from the winning model in the Case-control study. CTR: Control group; REL: First-degree relative group, MDD: Current depression group; REM: Remitted depression group. *Denotes significance at p<0.05.
Figure 4:
Figure 4:. Correlations between computational parameters and symptom factors.
(A) Correlation between the Low Mood factor and the linear reward sensitivity (LinR) parameter in the pilot study. (B) Correlation between the Low Mood factor and the quadratic effort sensitivity (E2) parameter in the pilot study. (C) Correlation between the Hedonia factor and the quadratic effort sensitivity (E2) parameter in the pilot study. (D) Correlation between the Low Mood factor and the linear reward sensitivity (LinR) parameter in the case-control study for the CTR+REL group only.
See this image and copyright information in PMC

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