Hostname: page-component-54dcc4c588-br6xx Total loading time: 0 Render date: 2025-10-12T23:59:01.027Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  08 October 2025

Vincent Valton Open the ORCID record for Vincent Valton [Opens in a new window] ,
Anahit Mkrtchian Open the ORCID record for Anahit Mkrtchian [Opens in a new window] ,
Madeleine Moses-Payne Open the ORCID record for Madeleine Moses-Payne [Opens in a new window] ,
Alan Gray ,
Karel Kieslich Open the ORCID record for Karel Kieslich [Opens in a new window] ,
Samantha VanUrk ,
Veronika Samborska Open the ORCID record for Veronika Samborska [Opens in a new window] ,
Don Chamith Halahakoon ,
Sanjay G. Manohar Open the ORCID record for Sanjay G. Manohar [Opens in a new window]  and
Peter Dayan Open the ORCID record for Peter Dayan [Opens in a new window]
...Show all authors
Vincent Valton
Affiliation:
Institute of Cognitive Neuroscience, University College London, London, UK
Anahit Mkrtchian*
Affiliation:
Division of Psychiatry and Max Planck Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology, University College London, London, UK
Madeleine Moses-Payne
Affiliation:
Department of Clinical, Educational and Health Psychology, University College London, London, UK
Alan Gray
Affiliation:
Institute of Cognitive Neuroscience, University College London, London, UK
Karel Kieslich
Affiliation:
Institute of Cognitive Neuroscience, University College London, London, UK
Samantha VanUrk
Affiliation:
Institute of Cognitive Neuroscience, University College London, London, UK
Veronika Samborska
Affiliation:
Institute of Cognitive Neuroscience, University College London, London, UK
Don Chamith Halahakoon
Affiliation:
Institute of Cognitive Neuroscience, University College London, London, UK
Sanjay G. Manohar
Affiliation:
Nuffield Department of Clinical Neurosciences and Department of Experimental Psychology, Oxford University, Oxford, UK
Peter Dayan
Affiliation:
Max Planck Institute for Biological Cybernetics, University of Tübingen , Tübingen, Germany
Masud Husain
Affiliation:
Nuffield Department of Clinical Neurosciences and Department of Experimental Psychology, Oxford University, Oxford, UK
Jonathan P. Roiser
Affiliation:
Institute of Cognitive Neuroscience, University College London, London, UK
*
Corresponding author: Anahit Mkrtchian; Email: a.mkrtchian@ucl.ac.uk
Rights & Permissions [Opens in a new window]

Abstract

Background

Motivational dysfunction is a core feature of depression and can have debilitating effects on everyday function. However, it is unclear which 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.

Methods

Effort-based decision-making was assessed using the Apple Gathering Task, where 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 a fruitful target for treatment and prevention.

Keywords

anhedoniacomputational psychiatrydepressioneffort-based decision-makingmotivation

Information

Type
Original Article
Information
Psychological Medicine , Volume 55 , 2025 , e302
Check for updates
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Introduction

Motivational impairment is common in depression (Fervaha, Foussias, Takeuchi, Agid, & Remington, Reference Fervaha, Foussias, Takeuchi, Agid and Remington2016; Pelizza & Ferrari, Reference Pelizza and Ferrari2009; Yuen et al., Reference Yuen, Bhutani, Lucas, Gunning, AbdelMalak, Seirup and Alexopoulos2015), closely linked to anhedonia, decision-making difficulties and fatigue, which together predict poorer treatment outcome (Uher et al., Reference Uher, Perlis, Henigsberg, Zobel, Rietschel, Mors and McGuffin2012) and lower quality of life (Spijker, Bijl, De Graaf, & Nolen, Reference Spijker, Bijl, De Graaf and Nolen2001). Although anhedonia – the loss of interest or pleasure in previously enjoyable activities – is a cardinal symptom of depression, in-the-moment pleasurable experience appears to be relatively preserved in depressed individuals (Amsterdam, Settle, Doty, Abelman, & Winokur, Reference Amsterdam, Settle, Doty, Abelman and Winokur1987; Beevers, Strong, Meyer, Pilkonis, & Miller, Reference Beevers, Strong, Meyer, Pilkonis and Miller2007; Bylsma, Taylor-Clift, & Rottenberg, Reference Bylsma, Taylor-Clift and Rottenberg2011; Chentsova-Dutton & Hanley, Reference Chentsova-Dutton and Hanley2010; Dichter, Smoski, Kampov-Polevoy, Gallop, & Garbutt, Reference Dichter, Smoski, Kampov-Polevoy, Gallop and Garbutt2010; Peeters, Nicolson, Berkhof, Delespaul, & deVries, Reference Peeters, Nicolson, Berkhof, Delespaul and deVries2003); by contrast, reward learning and decision-making have repeatedly been shown to be impaired in depression (Halahakoon et al., Reference Halahakoon, Kieslich, O’Driscoll, Nair, Lewis and Roiser2020), consistent with disrupted motivation.

A consistent theme emerging from this body of work is that effort-based decision-making for reward offers a promising lens through which motivational dysfunction can be understood. A ubiquitous finding is that the willingness to engage in effort (physical or mental) depends on both the perceived reward magnitude and the discounting of that reward by the effort required to obtain it (Chong et al., Reference Chong, Apps, Giehl, Sillence, Grima and Husain2017). While numerous studies have examined effort discounting in healthy individuals (Bonnelle, Manohar, Behrens, & Husain, Reference Bonnelle, Manohar, Behrens and Husain2016; Chong et al., Reference Chong, Apps, Giehl, Sillence, Grima and Husain2017; Croxson, Walton, O’Reilly, Behrens, & Rushworth, Reference Croxson, Walton, O’Reilly, Behrens and Rushworth2009; Klein-Flügge, Kennerley, Friston, & Bestmann, Reference Klein-Flügge, Kennerley, Friston and Bestmann2016; Kurniawan, Guitart-Masip, Dayan, & Dolan, Reference Kurniawan, Guitart-Masip, Dayan and Dolan2013; Pessiglione, Vinckier, Bouret, Daunizeau, & Le Bouc, Reference Pessiglione, Vinckier, Bouret, Daunizeau and Le Bouc2018), and contemporary neurocognitive models of depression suggest that lower willingness to exert effort drives depressive symptoms related to motivation (Eshel & Roiser, Reference Eshel and Roiser2010; Pizzagalli, Reference Pizzagalli2022; Roiser, Elliott, & Sahakian, Reference Roiser, Elliott and Sahakian2012), few studies have investigated these processes in patients experiencing motivational symptoms.

The first systematic attempt to examine effort-based decision-making in depression (i.e. measuring explicit choices to exert effort, as opposed to the degree of exertion; Cléry-Melin et al., Reference Cléry-Melin, Schmidt, Lafargue, Baup, Fossati and Pessiglione2011) was reported by Treadway, Bossaller, Shelton, and Zald (Reference Treadway, Bossaller, Shelton and Zald2012), who compared performance on the Effort Expenditure for Rewards Task (EEfRT; Treadway, Buckholtz, Schwartzman, Lambert, & Zald, Reference Treadway, Buckholtz, Schwartzman, Lambert and Zald2009) between currently depressed individuals and healthy volunteers. Drawing on an extensive literature in rodents (Salamone, Yohn, López-Cruz, San Miguel, & Correa, Reference Salamone, Yohn, López-Cruz, San Miguel and Correa2016), the EEfRT requires participants to choose between a ‘hard’ versus an ‘easy’ task (quick versus slow button pressing), with more reward delivered for hard choices, with varying levels of probabilities. Treadway and colleagues reported that depressed participants were less willing to choose the hard task and less sensitive to both reward and probability. Since this initial study, several others have reported comparable findings in currently depressed individuals (Yang et al., Reference Yang, Huang, Zhu, Wang, Cheung, Chan and Xie2014; Yang et al., Reference Yang, Huang, Lan, Zhu, Liu, Wang and Chan2016; Zou et al., Reference Zou, Ni, Wang, Yu, Lui, Zhou and Chan2020), although with discrepant results for remitted depression (Berwian et al., Reference Berwian, Wenzel, Collins, Seifritz, Stephan, Walter and Huys2020; Yang et al., Reference Yang, Huang, Zhu, Wang, Cheung, Chan and Xie2014).

Although these studies provided an important foundation for advancing our understanding of disrupted motivation in depression, several design features complicate their interpretation. First, most studies, including those examining remitted depression, recruited individuals taking antidepressant medication, which represents a potentially important confound given that antidepressants are known to blunt neural reward processing (McCabe, Mishor, Cowen, & Harmer, Reference McCabe, Mishor, Cowen and Harmer2010). Second, the exertion level required to obtain a reward was typically not calibrated individually, making the hard task easier for some participants than others. Third, the inclusion of different probability conditions (with no deterministic condition) raises the possibility of an interaction between probability and effort discounting. Fourth, effort levels are not varied parametrically in the EEfRT, and no high-reward/low-effort or low-reward/high-effort options are included. Consequently, individual differences in choices of the high-reward/high-effort option could be driven by sensitivity to either reward or effort.

Another effort-based decision task, the Apple Gathering Task (AGT; Bonnelle et al., Reference Bonnelle, Veromann, Burnett Heyes, Lo Sterzo, Manohar and Husain2015), offers several key advantages: a grip-strength device is used for exertion (Cléry-Melin et al., Reference Cléry-Melin, Schmidt, Lafargue, Baup, Fossati and Pessiglione2011; Schmidt et al., Reference Schmidt, d’Arc, Lafargue, Galanaud, Czernecki, Grabli and Pessiglione2008), with force level calibrated to each participant; reward and effort are varied independently and parametrically, and outcomes are deterministic. Studies using the AGT have reported that patients with Parkinson’s disease (PD; in which depression is very common; Costello, Husain, & Roiser, Reference Costello, Husain and Roiser2024)) were less willing to exert effort for low rewards (Chong et al., Reference Chong, Bonnelle, Manohar, Veromann, Muhammed, Tofaris and Husain2015), especially those with pronounced apathy (Le Heron et al., Reference Le Heron, Plant, Manohar, Ang, Jackson, Lennox and Husain2018). Other studies found that experimentally induced inflammation increased sensitivity to effort in healthy volunteers (Draper et al., Reference Draper, Koch, van der Meer, Aj Apps, Pickkers, Husain and van der Schaaf2018), and that, relative to matched controls, patients with treatment-resistant schizophrenia were both less willing to exert effort overall and less sensitive to reward (Saleh et al., Reference Saleh, Jarratt-Barnham, Petitet, Fernandez-Egea, Manohar and Husain2023). However, no study to date has reported behavior on the AGT in depression.

Over the past decade, computational modelling has provided important insights into the cognitive processes underlying disrupted motivation (Adams, Huys, & Roiser, Reference Adams, Huys and Roiser2016). A computational approach systematically evaluates competing models to identify the latent cognitive processes governing behavior. Computational modelling enables the precise measurement of cognitive processes by estimating specific parameters, offering deeper insights than traditional (model-agnostic) measures of performance, often with superior psychometric properties (Karvelis, Paulus, & Diaconescu, Reference Karvelis, Paulus and Diaconescu2023; Mkrtchian, Valton, & Roiser, Reference Mkrtchian, Valton and Roiser2023).

Therefore, in the present study, we performed a computational analysis of AGT performance to provide insights into the cognitive processes underlying effort-based decision-making in depression. To address the question of whether altered effort-based decision-making is merely a consequence of ongoing depressive symptoms, or if it plays a causal role in their development, we studied four groups, all unmedicated: currently depressed individuals (MDD); individuals remitted from depression (REM); healthy individuals with a depressed close relative, but no personal history (REL, who are known to be at elevated risk); and healthy individuals without any personal or family history of psychiatric diagnosis (CTR).

Drawing on contemporary neurocognitive models of depression (Eshel & Roiser, Reference Eshel and Roiser2010; Pizzagalli, Reference Pizzagalli2022; Pringle, McCabe, Cowen, & Harmer, Reference Pringle, McCabe, Cowen and Harmer2013; Roiser et al., Reference Roiser, Elliott and Sahakian2012), we hypothesized that lower willingness to exert effort drives depressive symptoms, especially those related to motivation. We predicted that the MDD group would accept fewer offers to exert effort relative to the CTR group and that this would be driven by lower sensitivity to reward and greater sensitivity to effort. Including the REL and REM groups allowed us to examine whether effort-based decision-making represents a risk factor for depression. If indeed it is a trait marker and risk factor, any motivational impairment observed in currently depressed individuals should also be present in the REM group, and possibly also the REL group (albeit they are at lower risk of developing a future depressive episode than the REM group). Given this, we predicted that a similar pattern of behavior would be observed in the REM group as in the MDD group and also conducted comparisons including the REL group.

Methods

Participants

Two studies were conducted, an initial pilot with healthy volunteers (HVs; ‘Pilot’), and a study including four groups (‘Case–control’). All participants were aged 18–60 years, unmedicated and native English speakers. They were recruited through local advertisements, institutional participant databases and local outpatient psychological treatment services, and all provided written informed consent. The study received ethical approval from the UCL Research Ethics Committee (fMRI/2013/005) and the London Queen Square NHS Research Ethics Committee (for depressed individuals: 10/H0716/2).

Pilot study

Only HVs (N = 102) completed this study. The final sample consisted of 67 participants (see Supplemental Information [SI] for exclusions).

Case–control study

Sixty-two HVs without a family history of depression (CTR; independent from the Pilot study), 38 HVs with a depressed first-degree relative (REL), 50 remitted depressed participants (REM), and 51 currently depressed participants (MDD) completed the Case–control study. The final sample included 57 CTR, 36 REL, 46 REM, and 41 MDD participants (see SI for exclusions). With this sample size, at α = 0.05, we had 80% power to detect effect sizes of at least f = 0.25 (medium effect size).

Experimental procedure

For the Pilot study eligibility was assessed using a structured telephone interview. For the Case–control study, participants attended a screening session, completing the Mini International Neuropsychiatric Interview (Sheehan et al., Reference Sheehan, Lecrubier, Harnett Sheehan, Janavs, Weiller, Keskiner and Dunbar1997), the Family Interview for Genetic Studies (Maxwell, Reference Maxwell1992), and symptom questionnaires before returning for cognitive testing in a separate session.

In both studies, participants completed the digit span forward and backward (Wechsler, Reference Wechsler1997), the Wechsler Test of Adult Reading (WTAR; Holdnack, Reference Holdnack2001), the Apathy Evaluation Scale (AES; Marin, Biedrzycki, & Firinciogullari, Reference Marin, Biedrzycki and Firinciogullari1991), the Beck Depression Inventory (BDI-II; Beck, Steer, & Brown, Reference Beck, Steer and Brown1996), the Dysfunctional Attitudes Scale (DAS-SF1-2; Beevers et al., Reference Beevers, Strong, Meyer, Pilkonis and Miller2007), the Life Orientation Test-Revisited (LOTR; Scheier, Carver, & Bridges, Reference Scheier, Carver and Bridges1994), the Snaith-Hamilton Pleasure Scale (SHAPS; Snaith et al., Reference Snaith, Hamilton, Morley, Humayan, Hargreaves and Trigwell1995), the State–Trait Anxiety Inventory for Adults (STAI; Spielberger, Gorsuch, & Lushene, Reference Spielberger, Gorsuch and Lushene1970), and the Temporal Experience of Pleasure Scale (TEPS; Gard, Gard, Kring, & John, Reference Gard, Gard, Kring and John2006). In the Pilot study, participants completed the Chapman Physical Anhedonia Scale (CPAS; Chapman, Chapman, & Raulin, Reference Chapman, Chapman and Raulin1976). In the Case–control study, participants completed the Hamilton Depression Rating Scale (HAM-D; Hamilton, Reference Hamilton1967).

Apple gathering task (AGT)

The AGT measures willingness to exert physical effort for reward (Chong et al., Reference Chong, Bonnelle, Manohar, Veromann, Muhammed, Tofaris and Husain2015). Before the main task, participants performed a six-trial calibration phase, squeezing a hand-dynamometer as hard as possible with their non-dominant hand to fill an on-screen gauge. The peak force from the final three trials determined their maximum voluntary contraction (MVC), setting comparable effort levels across subjects. Participants then attempted four effort levels (20%, 40%, 60%, and 80% of MVC).

On each trial in the main task, participants saw a tree containing a number of apples representing the available reward, and a bar on the tree trunk representing the force required to obtain them (Figure 1a; see SI). There were four levels of reward (3, 6, 9, or 12 apples) and effort (20%, 40%, 60%, and 80%). Each reward/effort combination was repeated five times, resulting in a total of 80 trials. Participants could accept or refuse offers. For refused offers, ‘no response required’ was displayed, followed by the next decision. An accepted offer required squeezing the hand-dynamometer at or above the effort level to win points, followed by feedback. The next trial started immediately after the feedback. To mitigate fatigue, the exertion phase was omitted and ‘No response required’ appeared on 25% of accepted trials.

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 study. (d) Overall probability to accept offers for all Pilot study participants. Black dots represent the mean and error bars represent the standard error of the mean. (e) Average acceptance rates as a function of reward level and effort level across all groups in the Case–control study. (f) Distribution of the number of accepted offers (out of 80) across all groups in the Case–control study. (g) Overall probability to accept offers for all Case–control participants. 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.

Statistical analysis

Repeated-measures analysis of variance (ANOVA) was used to examine how acceptance rates varied as a function of reward and effort. For the Case–control study, group was added as a between-subjects measure. Greenhouse–Geisser correction was applied when sphericity violations occurred. Acceptance rates were arcsine transformed prior to analysis to satisfy Gaussian assumptions. Decision reaction times, success rates, and fatigue effects were also analyzed (see SI). Covariates in analyses (age, see below) were mean corrected. To examine whether altered effort-based decision making is a trait marker of depression, as opposed to a consequence of experiencing current symptoms, we conducted two planned comparisons following the initial ANOVA: (1) MDD + REM versus REL + CTR and (2) MDD + REM + REL versus CTR.

Questionnaire data were synthesized using exploratory factor analysis on the total score of each questionnaire using Promax rotation (see SI).

Computational analysis

Computational analysis was performed to compare competing hypotheses of reward and effort contributions to decision-making, and to estimate participant-level parameters. All models were implemented using hierarchical Bayesian estimation in Stan (Carpenter et al., Reference Carpenter, Gelman, Hoffman, Lee, Goodrich, Betancourt and Riddell2017). Participants in the Pilot study were fit under the same group-level priors. For the Case–control study, all participants were fit under the same prior for parameters of no interest and separate group-level priors were used for the parameter of interest (one parameter at a time; see SI; Supplementary Figure S1). Sensitivity analyses with separate group priors did not materially impact the estimated parameters or group comparison results (Supplementary Figure S2).

Winning model

Seventy models of varying complexity were compared to select the winning model (see SI). The winning model in both studies was a four-parameter model including two effort sensitivity terms (linear: LinE – more negative = greater subjective effort; and quadratic: E 2 – effort cost increases disproportionately with increasing effort when E 2 < 0), a linear reward sensitivity term (LinR – more positive = greater subjective reward), and an acceptance bias term representing the overall tendency to accept offers independent of reward or effort (K – higher = more likely to accept; Supplementary Figure S1). Unlike the reward and effort sensitivity parameters, which capture how participants evaluate the components of a given offer (e.g. how much value they assign to increasing reward, or how aversive they evaluate increasing effort to be), the K parameter captures an overall motivation to exert effort, irrespective of the potential reward or exertion required (Supplementary Figure S1d). In the Case–control study, owing to high trade-off between the LinE and E 2 parameters and limited LinE range, the LinE term was constrained to that of the average value estimated from the Pilot study (ConstE = −15; see SI). Together, these parameters could accurately recapitulate both the group-level pattern of choices and individual differences in acceptance rates (Supplementary Figure S3; Pilot: r = 0.997; Case–control: r = 0.999). All parameters showed high recoverability (Supplementary Figure S4).

Results

Participant characteristics are presented in Table 1.

Table 1. Participant characteristics

Note: The DAS short-form scales were used in the pilot. Brackets represent standard deviations. In the Pilot study, there was missing data for the digit span forward (N = 2), digit span backward (N = 3) and IQ (N = 4). IQ data were missing for four Case–control participants (REM N = 2, REL N = 2). Note that no statistical test was conducted for the symptom scales in the Case–control study as mental health symptoms were part of the patient recruitment and definition; therefore, statistical comparisons would be inappropriate, since the null hypothesis is known to be false.

Abbreviations: HV, healthy volunteers (in the Pilot study); CTR, control participants; REL, participants with a first-degree relative with depression; REM, remitted depressed participants; MDD, currently depressed participants; WTAR, Wechsler Test of Adult Reading; HAM-D, Hamilton Depression Rating Scale; BDI-II, Beck Depression Inventory; STAI, State-Trait Anxiety Inventory; SHAPS, Snaith-Hamilton Pleasure Scale; TEPS-A, Temporal Experience of Pleasure Scale – anticipatory subscale; TEPS-C, Temporal Experience of Pleasure Scale – consummatory subscale; AES, Apathy Evaluation Scale; DAS, Dysfunctional Attitudes Scale; LOTR, Life Orientation Test-Revised.

Model-agnostic AGT analysis

In preliminary analyses we found that older individuals accepted more offers overall (Pearson r = 0.20, p = 0.014), and therefore age was included as a covariate in all Case–control analyses. We observed wide variability in overall acceptance rates, ranging from 35%–100% in the Pilot and 40–100% in the Case–control study (Figure 1c, e). As expected, acceptance rates decreased significantly as effort increased (Pilot: F(1.57, 103.77) = 120.19, p < 0.001; Case–control: F(1.75, 306.31) = 351.79, p < 0.001) and increased significantly as reward increased (Pilot: F(1.63,107.57) = 92.02, p < 0.001; Case–control: F(1.65, 288.06) = 325.45, p < 0.001). There was a significant reward-by-effort interaction in both studies, driven by particularly high acceptance rates for high-reward/low-effort trials (Pilot: F(4.25, 280.88) = 16.42, p < 0.001; Case–control: F(4.95, 866.79) = 90.13, p < 0.001; Figure 1b, d).

In the Case–control study, we detected a significant main effect of group (F(3,175) = 3.26, p = 0.023; Figure 2), but all interactions with group were non-significant. Post-hoc tests revealed significantly lower acceptance rates for the REM and MDD groups compared to REL (REM vs. REL: Cohen’s d = 0.63, p = 0.007; MDD vs. REL: d = 0.41, p = 0.015; reported effect sizes exclude covariates). Planned comparisons revealed that the combined MDD + REM group accepted significantly lower acceptance rates than the combined REL + CTR group (d = 0.38, p = 0.004), but the difference between the combined MDD + REM + REL group and the CTR group was non-significant (d = 0.17, p = 0.22). Reported effect sizes exclude covariates.

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 relative (REL), patients with current depression (MDD), and remitted depression (REM) groups. (b) Distribution of the number of accepted offers for each group. (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.

There were no significant group differences in success rates, decision RTs, MVCs, or either immediate or cumulative fatigue (all p > 0.05, see SI; Supplementary Figure S5 and S6). Importantly, success rate at the highest effort level was above 80% across all groups.

Questionnaire factor analysis

Factor analysis was performed on questionnaire measures for both studies. Despite minor differences in measures (CPAS and the short form of the DAS were only included in the Pilot; HAM-D was only included in the Case–control study), a similar four-factor solution was obtained in both studies (Supplementary Table S1). The factors were (named according to the scales displaying the highest loadings [>0.3]): ‘Low-mood’, with loadings for BDI-II, HAM-D, LOTR, and STAI; ‘Apathy’, mostly comprising the AES sub-scales; ‘Hedonia’, including the SHAPS and TEPS (owing to scoring conventions these load with opposite signs), and (in the Pilot) the CPAS; and ‘Dysfunctional Attitudes’, comprising the two DAS sub-scales.

Computational AGT analysis

Model comparison

The winning model in both studies was a derivative of a four-parameter model, including an acceptance bias parameter (K), linear reward (LinR), and effort (LinE) sensitivity parameters and a quadratic effort parameter (E 2; Figure 3; Supplementary Figures S7 and S8). In the Case–control study, the LinE term was set at ConstE = −15 (see Materials and Methods and SI).

Figure 3. Estimated model parameters for the Pilot (a–d) and Case–control (e–g) study. Figures show violin and boxplots as well as the mean (plus sign) and median (notch) for estimated (a) intercept/acceptance bias (K), (b) reward sensitivity (LinR), (c) linear effort sensitivity (LinE), and (d) quadratic effort sensitivity (E 2) parameter values from the winning model in the Pilot study. Similar figures show estimated (e) intercept/acceptance bias (K), (f) reward sensitivity (LinR), and (g) quadratic effort sensitivity (E 2) parameter values from the winning model in the Case–control study. Note: CTR, control group; REL, first-degree relative group; MDD, current depression group; REM, remitted depression group. *Denotes significance at p < 0.05.

Case–control comparisons

Participant-level parameters were compared between the groups including age as a covariate (Figure 3). The K (acceptance bias) parameter differed significantly between the groups (F(3,175) = 3.19, p = 0.025). Post-hoc tests revealed that this was driven by the REM (d = 0.61, p = 0.006) and MDD (d = 0.41, p = 0.032) groups having a lower acceptance bias than the REL group and the REM group having a lower acceptance bias than the HC group (d = 0.41, p = 0.04). Planned comparisons revealed that the combined REM + MDD group had a lower acceptance bias than the combined REL + CTR group (d = 0.39, F(1,177) = 8.26, p = 0.005). However, the combined REL + REM + MDD group did not differ from the CTR group (d = 0.16, p = 0.276). No other parameter differed significantly between groups (LinR: F(3,175) = 0.33, p = 0.806; E 2: F(3,175) = 0.13, p = 0.942). Reported effect sizes exclude covariates.

The above analysis suggests that lower acceptance rates in the depression groups were driven by a lower overall willingness to exert effort, not alterations in reward or effort sensitivity.

Associations between parameters and symptoms

Pilot study

In the Pilot study, the LinR parameter correlated negatively with Low-mood (r = −0.344, p = 0.004; Figure 4), suggesting that participants with greater anxiety/depression perceived increasing rewards as less valuable. The E 2 parameter correlated negatively with Low-mood (r = −0.25, p = 0.04) and positively with Hedonia (r = 0.248, p = 0.043), suggesting that participants with greater anxiety/depression and anhedonia perceived increasing levels of effort as disproportionately costly.

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 (E 2) parameter in the Pilot study. (c) Correlation between the Hedonia factor and the quadratic effort sensitivity (E 2) 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 combined CTR + REL group only.

Case–control study

In the Case–control study, no significant associations between symptom factors and computational parameters were observed, either in the combined CTR + REL + REM sample or in the MDD group alone. In the combined CTR + REL group, surprisingly (and in contrast to the corresponding result in the Pilot study) the LinR parameter correlated with Low-mood (r = 0.248, p = 0.016; Figure 4).

Discussion

Impaired motivation is a hallmark of depression, but the underlying cognitive and computational processes are poorly understood. Consistent with our hypotheses, we found that depression is characterized by a lower willingness to engage in effort. Our computational analyses suggest that this difference was not driven by altered effort or reward sensitivity but rather by a lower overall tendency to avoid exerting effort, reflected in the acceptance bias parameter. The presence of lower acceptance bias in both the remitted and currently depressed groups suggests that this decision-making process could represent a trait-like feature, as proposed by neurocognitive models of depression (Eshel & Roiser, Reference Eshel and Roiser2010; Pizzagalli, Reference Pizzagalli2022; Pringle et al., Reference Pringle, McCabe, Cowen and Harmer2013; Roiser et al., Reference Roiser, Elliott and Sahakian2012), or possibly a ‘scar’ effect of having previously experienced depression (Rohde, Lewinsohn, & Seeley, Reference Rohde, Lewinsohn and Seeley1990).

These findings align with previous work demonstrating lower willingness to exert effort for reward in depression (Treadway et al., Reference Treadway, Buckholtz, Schwartzman, Lambert and Zald2009; Treadway et al., Reference Treadway, Bossaller, Shelton and Zald2012; Yang et al., Reference Yang, Huang, Zhu, Wang, Cheung, Chan and Xie2014; Yang et al., Reference Yang, Huang, Lan, Zhu, Liu, Wang and Chan2016; Zou et al., Reference Zou, Ni, Wang, Yu, Lui, Zhou and Chan2020) and following recovery (Kuhn et al., Reference Kuhn, Palermo, Pagnier, Blank, Steinberger, Long and Pizzagalli2025). Note that this last study also identified a counter-intuitive greater sensitivity to high rewards in remitted patients which we did not observe here. Interestingly, our findings challenge a key assumption that mis-calibrated reward or effort valuation drives lower willingness to exert effort (Husain & Roiser, Reference Husain and Roiser2018; Pessiglione et al., Reference Pessiglione, Vinckier, Bouret, Daunizeau and Le Bouc2018; Treadway & Zald, Reference Treadway and Zald2011). By examining a large space of computational models, we instead suggest that a different process is at play: acceptance bias. While surprising and not entirely straightforward to interpret, this result clarifies prior findings from tasks that were unable to disambiguate between these factors. One possibility is that lower acceptance bias could be driven by lower confidence in being able to achieve the required effort, despite individual calibration. This would indicate that metacognitive processes might play a role in motivational impairment. Indeed, previous studies have identified that transdiagnostic symptoms of apathy, poor self-esteem and low mood are associated with low confidence in perceptual decisions (Moses-Payne, Rollwage, Fleming, & Roiser, Reference Moses-Payne, Rollwage, Fleming and Roiser2019; Rouault, Seow, Gillan, & Fleming, Reference Rouault, Seow, Gillan and Fleming2018), independent of accuracy, mirroring our findings. This could plausibly be related to low global expectations of success in depression (Huys, Daw, Nathaniel, & Dayan, Reference Huys, Daw, Nathaniel and Dayan2015; Seow, Rouault, Marion, Claire, & Fleming, Reference Seow, Rouault, Marion, Claire and Fleming2021; Stephan et al., Reference Stephan, Manjaly, Mathys, Weber, Paliwal, Gard and Petzschner2016). It is also possible that fatigue may influence acceptance bias, although we did not detect any group differences in sensitivity analyses examining task-related fatigue.

An important goal of neuroscientific research in depression is to determine whether cognitive disruptions are simply a consequence of depressive symptoms or whether they contribute causally to their development (Halahakoon, Lewis, & Roiser, Reference Halahakoon, Lewis and Roiser2019). We observed a similar pattern of effort-based decisions for reward in both current and remitted depressed groups. This suggests that a general bias against exerting effort might represent a core feature of depression. However, we did not observe a similar pattern in never-depressed first-degree relatives. Therefore, an alternative interpretation might be that lower willingness to exert effort is a consequence rather than a cause of depression, which does not recover after symptoms remit (akin to a ‘scar’ effect). A third possibility is that the first-degree relatives we tested were not at elevated risk for depression but were instead actually resilient: depression often presents early in life and our REL sample was older (mean age ~ 26) than the peak onset of depressive symptoms (Lewinsohn, Clarke, Seeley, & Rohde, Reference Lewinsohn, Clarke, Seeley and Rohde1994; Solmi et al., Reference Solmi, Radua, Olivola, Croce, Soardo, de Pablo and Fusar-Poli2022). Thus, many individuals in this group might never go on to develop depression. Future studies should recruit younger samples and test these predictions longitudinally.

If confirmed as a risk factor for depression, lower overall willingness to exert effort may represent a fruitful target for intervention. Such targets are sorely needed for motivational symptoms, which are particularly difficult to treat and constitute an area of unmet clinical need (McMakin et al., Reference McMakin, Olino, Porta, Dietz, Emslie, Clarke and Birmaher2012; Uher et al., Reference Uher, Perlis, Henigsberg, Zobel, Rietschel, Mors and McGuffin2012). Treatments that can boost engagement in activities through altering effort-acceptance bias could be particularly effective in treating or even preventing depression. Elements of behavioral activation therapy, such as activity scheduling, might play such a role, as these require acting on pre-planned commitments rather than internal states (Martell, Dimidjian, & Herman-Dunn, Reference Martell, Dimidjian and Herman-Dunn2013). Interestingly, this component has recently been shown to alter effort processing in healthy participants, offering proof of concept of this idea (Norbury, Hauser, Fleming, Dolan, & Huys, Reference Norbury, Hauser, Fleming, Dolan and Huys2024).

Limitations

While our model of effort-based decision-making replicated across studies, relationships with specific symptom factors were inconsistent, potentially owing to the limited sample size. Care should also be taken in interpreting these associations as they were exploratory and would not survive correction for multiple comparisons. Larger studies are needed to confirm these associations. An important strength of the effort task we employed is its individually tailored effort levels, which resulted in >80% average success rates at all effort levels. Nonetheless, it remains possible that occasional failures to obtain reward following exertion could have influenced decisions in some participants. Importantly, however, there was no group difference in success rates – or in effort sensitivity – making it unlikely that the lower effort-acceptance bias we observed in depression was influenced by success rates. Additionally, 14 participants had to be excluded as they were unable to achieve the highest effort level, likely due to issues with calibrating the grip squeeze device. Finally, we interpreted differences in the acceptance bias parameter as reflecting an overall lower willingness to exert effort. However, it is possible that this difference is not specific to effort costs but instead reflects a broader disruption in cost–benefit decision-making – we cannot test this possibility directly here, as our study design only included decisions relating to effort. That said, previous research has not identified differences in other types of reward discounting in depression, for example, willingness to take risks (probability discounting; Charpentier, Aylward, Roiser, & Robinson, Reference Charpentier, Aylward, Roiser and Robinson2017), although there is some evidence of elevated delay aversion (temporal discounting), at least in currently symptomatic individuals (Pulcu et al., Reference Pulcu, Trotter, Thomas, McFarquhar, Juhasz, Sahakian and Elliott2014).

Conclusion

This study advances our understanding of motivational impairment in depression by identifying low effort-acceptance bias, independent of reward or effort sensitivity, as a computational feature of both current and remitted depression. These results raise the possibility that interventions that boost this bias could improve treatment outcomes.

Supplementary material

The supplementary material for this article can be found at http://doi.org/10.1017/S0033291725101967.

Acknowledgments

This research was funded in whole, or in part, by a Wellcome Trust grant (101798/Z/13/Z) to JPR.

Funding statement

This research was funded in whole, or in part, by a Wellcome Trust grant (101798/Z/13/Z) to JPR. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. PD was funded by the Max Planck Society and the Humboldt Foundation, SGM was supported by an MRC Clinician Scientist Fellowship MR/P00878/X and by the National Institute of Healthcare Research (NIHR) Oxford Biomedical Research Centre (BRC), MH was supported by a Wellcome Trust grant (226645/Z/22/Z) and by the NIHR Oxford Health BRC. VV is currently an employee of Oura Health Oy. His contributions to this work, as well as statements and opinions expressed in this work, are solely the responsibility of the author and does not represent the official views of Oura Health Oy. The authors report no biomedical financial interests or potential conflicts of interest.

Ethical standard

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Footnotes

V.V. and A.M. contributed equally to this work.

References

Adams, R. A., Huys, Q. J., & Roiser, J. P. (2016). Computational psychiatry: Towards a mathematically informed understanding of mental illness. Journal of Neurology, Neurosurgery, and Psychiatry, 87(1), 53–63. https://doi.org/10.1136/jnnp-2015-310737.Google ScholarPubMed
Amsterdam, J. D., Settle, R. G., Doty, R. L., Abelman, E., & Winokur, A. (1987). Taste and smell perception in depression. Biological Psychiatry, 22(12), 1481–1485. https://doi.org/10.1016/0006-3223(87)90108-9.CrossRefGoogle ScholarPubMed
Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Manual for the Beck depression inventory-II. San Antonio, TX: Psychological Corporation.Google Scholar
Beevers, C. G., Strong, D. R., Meyer, B., Pilkonis, P. A., & Miller, I. R. (2007). Efficiently assessing negative cognition in depression: An item response theory analysis of the dysfunctional attitude scale. Psychological Assessment, 19(2), 199–209. https://doi.org/10.1037/1040-3590.19.2.199.CrossRefGoogle ScholarPubMed
Berwian, I. M., Wenzel, J. G., Collins, A. G. E., Seifritz, E., Stephan, K. E., Walter, H., & Huys, Q. J. M. (2020). Computational mechanisms of effort and reward decisions in patients with depression and their association with relapse after antidepressant discontinuation. JAMA Psychiatry, 77(5), 513–522. https://doi.org/10.1001/jamapsychiatry.2019.4971.CrossRefGoogle ScholarPubMed
Bonnelle, V., Manohar, S., Behrens, T., & Husain, M. (2016). Individual differences in premotor brain systems underlie behavioral apathy. Cerebral Cortex, 26(2), 807–819. https://doi.org/10.1093/cercor/bhv247.Google ScholarPubMed
Bonnelle, V., Veromann, K. R., Burnett Heyes, S., Lo Sterzo, E., Manohar, S., & Husain, M. (2015). Characterization of reward and effort mechanisms in apathy. Journal of Physiology, Paris, 109(1–3), 16–26. https://doi.org/10.1016/j.jphysparis.2014.04.002.CrossRefGoogle ScholarPubMed
Bylsma, L. M., Taylor-Clift, A., & Rottenberg, J. (2011). Emotional reactivity to daily events in major and minor depression. Journal of Abnormal Psychology, 120(1), 155–167. https://doi.org/10.1037/a0021662.CrossRefGoogle ScholarPubMed
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76. https://doi.org/10.18637/jss.v076.i01.CrossRefGoogle Scholar
Chapman, L. J., Chapman, J. P., & Raulin, M. L. (1976). Scales for physical and social anhedonia. Journal of Abnormal Psychology, 85(4), 374–382. https://doi.org/10.1037/0021-843x.85.4.374.CrossRefGoogle ScholarPubMed
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), 1014–1022. https://doi.org/10.1016/j.biopsych.2016.12.010.CrossRefGoogle Scholar
Chentsova-Dutton, Y., & Hanley, K. (2010). The effects of anhedonia and depression on hedonic responses. Psychiatry Research, 179(2), 176–180. https://doi.org/10.1016/j.psychres.2009.06.013.CrossRefGoogle ScholarPubMed
Chong, T. T., 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., Bonnelle, V., Manohar, S., Veromann, K. R., Muhammed, K., Tofaris, G. K., & Husain, M. (2015). Dopamine enhances willingness to exert effort for reward in Parkinson’s disease. Cortex, 69, 40–46. https://doi.org/10.1016/j.cortex.2015.04.003.CrossRefGoogle ScholarPubMed
Cléry-Melin, M. L., Schmidt, L., Lafargue, G., Baup, N., Fossati, P., & Pessiglione, M. (2011). Why don’t you try harder? An investigation of effort production in major depression. PLoS One, 6(8), e23178. https://doi.org/10.1371/journal.pone.0023178.CrossRefGoogle ScholarPubMed
Costello, H., Husain, M., & Roiser, J. P. (2024). Apathy and motivation: Biological basis and drug treatment. Annual Review of Pharmacology and Toxicology, 64, 313–338. https://doi.org/10.1146/annurev-pharmtox-022423-014645.CrossRefGoogle ScholarPubMed
Croxson, P. L., Walton, M. E., O’Reilly, J. X., Behrens, T. E., & Rushworth, M. F. (2009). Effort-based cost-benefit valuation and the human brain. The Journal of Neuroscience, 29(14), 4531–4541. https://doi.org/10.1523/jneurosci.4515-08.2009.CrossRefGoogle ScholarPubMed
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. https://doi.org/10.1002/da.20690.CrossRefGoogle ScholarPubMed
Draper, A., Koch, R. M., van der Meer, J. W., Aj Apps, M., Pickkers, P., Husain, M., & van der Schaaf, M. E. (2018). Effort but not reward sensitivity is altered by acute sickness induced by experimental endotoxemia in humans. Neuropsychopharmacology, 43(5), 1107–1118. https://doi.org/10.1038/npp.2017.231.CrossRefGoogle Scholar
Eshel, N., & Roiser, J. P. (2010). Reward and punishment processing in depression. Biological Psychiatry, 68(2), 118–124. https://doi.org/10.1016/j.biopsych.2010.01.027.CrossRefGoogle ScholarPubMed
Fervaha, G., Foussias, G., Takeuchi, H., Agid, O., & Remington, G. (2016). Motivational deficits in major depressive disorder: Cross-sectional and longitudinal relationships with functional impairment and subjective well-being. Comprehensive Psychiatry, 66, 31–38. https://doi.org/10.1016/j.comppsych.2015.12.004.CrossRefGoogle ScholarPubMed
Gard, D. E., Gard, M. G., Kring, A. M., & John, O. P. (2006). Anticipatory and consummatory components of the experience of pleasure: A scale development study. Journal of Research in Personality, 40(6), 1086–1102.10.1016/j.jrp.2005.11.001CrossRefGoogle Scholar
Halahakoon, D. C., Kieslich, K., O’Driscoll, C., Nair, A., Lewis, G., & Roiser, J. P. (2020). Reward-processing behavior in depressed participants relative to healthy volunteers: A systematic review and meta-analysis. JAMA Psychiatry, 77(12), 1286–1295. https://doi.org/10.1001/jamapsychiatry.2020.2139.CrossRefGoogle ScholarPubMed
Halahakoon, D. C., Lewis, G., & Roiser, J. P. (2019). Cognitive impairment and depression-cause, consequence, or coincidence? JAMA Psychiatry, 76(3), 239–240. https://doi.org/10.1001/jamapsychiatry.2018.3631.CrossRefGoogle ScholarPubMed
Hamilton, M. (1967). Development of a rating scale for primary depressive illness. The British Journal of Social and Clinical Psychology, 6(4), 278–296. https://doi.org/10.1111/j.2044-8260.1967.tb00530.x.CrossRefGoogle ScholarPubMed
Holdnack, H. A. (2001). Wechsler test of adult reading: WTAR. San Antonio, TX: The Psychological Corporation.Google Scholar
Husain, M., & Roiser, J. P. (2018). Neuroscience of apathy and anhedonia: A transdiagnostic approach. Nature Reviews. Neuroscience, 19(8), 470–484. https://doi.org/10.1038/s41583-018-0029-9.CrossRefGoogle ScholarPubMed
Huys, Q., Daw, J. M., Nathaniel, D., & Dayan, P. (2015)). Depression: A decision-theoretic analysis. Annual Review of Neuroscience, 38(15), 1–23. https://doi.org/10.1146/annurev-neuro-071714-033928.CrossRefGoogle ScholarPubMed
Karvelis, P., Paulus, M. P., & Diaconescu, A. O. (2023). Individual differences in computational psychiatry: A review of current challenges. Neuroscience & Biobehavioral Reviews, 148, 105137. https://doi.org/10.1016/j.neubiorev.2023.105137.CrossRefGoogle ScholarPubMed
Klein-Flügge, M. C., Kennerley, S. W., Friston, K., & Bestmann, S. (2016). Neural signatures of value comparison in human cingulate cortex during decisions requiring an effort-reward trade-off. The Journal of Neuroscience, 36(39), 10002–10015. https://doi.org/10.1523/jneurosci.0292-16.2016.CrossRefGoogle Scholar
Kuhn, M., Palermo, E. H., Pagnier, G., Blank, J. M., Steinberger, D. C., Long, Y., & Pizzagalli, D. A. (2025). Computational phenotyping of effort-based decision making in unmedicated adults with remitted depression. Biological psychiatry Cognitive neuroscience and neuroimaging, 10(6), 607–615. https://doi.org/10.1016/j.bpsc.2025.02.006.CrossRefGoogle ScholarPubMed
Kurniawan, I. T., Guitart-Masip, M., Dayan, P., & Dolan, R. J. (2013). Effort and valuation in the brain: The effects of anticipation and execution. The Journal of Neuroscience, 33(14), 6160–6169. https://doi.org/10.1523/jneurosci.4777-12.2013.CrossRefGoogle ScholarPubMed
Le Heron, Campbell, Plant, Olivia, Manohar, Sanjay, Ang, Yuen-Siang, Jackson, Matthew, Lennox, Graham, … Husain, Masud. (2018). Distinct effects of apathy and dopamine on effort-based decision-making in Parkinson’s disease. Brain, 141(5), 1455–1469. https://doi.org/10.1093/brain/awy110CrossRefGoogle Scholar
Lewinsohn, P. M., Clarke, G. N., Seeley, J. R., & Rohde, P. (1994). Major depression in community adolescents: Age at onset, episode duration, and time to recurrence. Journal of the American Academy of Child & Adolescent Psychiatry, 33(6), 809–818. https://doi.org/10.1097/00004583-199407000-00006.Google ScholarPubMed
Marin, R. S., Biedrzycki, R. C., & Firinciogullari, S. (1991). Reliability and validity of the apathy evaluation scale. Psychiatry Research, 38(2), 143–162. https://doi.org/10.1016/0165-1781(91)90040-v.CrossRefGoogle ScholarPubMed
Martell, C. R., Dimidjian, S., & Herman-Dunn, R. (2013). Behavioral activation for depression: A clinician’s. Guide: Guilford Press.Google Scholar
Maxwell, M. E. (1992). Family interview for genetic studies (FIGS): A manual for FIGS. Bethesda, MD: Clinical Neurogenetics Branch, Intramural Research Program, National Institute of Mental Health.Google Scholar
McCabe, C., Mishor, Z., Cowen, P. J., & Harmer, C. J. (2010). Diminished neural processing of aversive and rewarding stimuli during selective serotonin reuptake inhibitor treatment. Biological Psychiatry, 67(5), 439–445. https://doi.org/10.1016/j.biopsych.2009.11.001.CrossRefGoogle ScholarPubMed
McMakin, D. L., Olino, T. M., Porta, G., Dietz, L. J., Emslie, G., Clarke, G., & Birmaher, B. (2012). Anhedonia predicts poorer recovery among youth with selective serotonin reuptake inhibitor treatment–resistant depression. Journal of the American Academy of Child & Adolescent Psychiatry, 51(4), 404–411.Google ScholarPubMed
Mkrtchian, A., Valton, V., & Roiser, J. P. (2023). Reliability of decision-making and reinforcement learning computational parameters. Computational Psychiatry. https://doi.org/10.5334/cpsy.86.CrossRefGoogle ScholarPubMed
Moses-Payne, M. E., Rollwage, M., Fleming, S. M., & Roiser, J. P. (2019). Postdecision evidence integration and depressive symptoms. Frontiers in Psychiatry, 10, 639. https://doi.org/10.3389/fpsyt.2019.00639.CrossRefGoogle ScholarPubMed
Norbury, A., Hauser, T. U., Fleming, S. M., Dolan, R. J., & Huys, Q. J. M. (2024). Different components of cognitive-behavioral therapy affect specific cognitive mechanisms. Science Advances, 10(13), eadk3222. https://doi.org/10.1126/sciadv.adk3222.CrossRefGoogle ScholarPubMed
Peeters, F., Nicolson, N. A., Berkhof, J., Delespaul, P., & deVries, M. (2003). Effects of daily events on mood states in major depressive disorder. Journal of Abnormal Psychology, 112, 203–211.10.1037/0021-843X.112.2.203CrossRefGoogle ScholarPubMed
Pelizza, L., & Ferrari, A. (2009). Anhedonia in schizophrenia and major depression: State or trait? Annals of General Psychiatry, 8, 22. https://doi.org/10.1186/1744-859x-8-22.CrossRefGoogle ScholarPubMed
Pessiglione, M., Vinckier, F., Bouret, S., Daunizeau, J., & Le Bouc, R. (2018). Why not try harder? Computational approach to motivation deficits in neuro-psychiatric diseases. Brain, 141(3), 629–650. https://doi.org/10.1093/brain/awx278.CrossRefGoogle Scholar
Pizzagalli, D. A. (2022). Toward a better understanding of the mechanisms and pathophysiology of anhedonia: Are we ready for translation? The American Journal of Psychiatry, 179(7), 458–469. https://doi.org/10.1176/appi.ajp.20220423.CrossRefGoogle Scholar
Pringle, A., McCabe, C., Cowen, P. J., & Harmer, C. J. (2013). Antidepressant treatment and emotional processing: Can we dissociate the roles of serotonin and noradrenaline? Journal of Psychopharmacology, 27(8), 719–731. https://doi.org/10.1177/0269881112474523.CrossRefGoogle ScholarPubMed
Pulcu, E., Trotter, P. D., Thomas, E. J., McFarquhar, M., Juhasz, G., Sahakian, B. J., & Elliott, R. (2014). Temporal discounting in major depressive disorder. Psychological Medicine, 44(9), 1825–1834. https://doi.org/10.1017/s0033291713002584.CrossRefGoogle ScholarPubMed
Rohde, P., Lewinsohn, P. M., & Seeley, J. R. (1990). Are people changed by the experience of having an episode of depression? A further test of the scar hypothesis. Journal of Abnormal Psychology, 99(3), 264–271. https://doi.org/10.1037/0021-843x.99.3.264.CrossRefGoogle ScholarPubMed
Roiser, J. P., Elliott, R., & Sahakian, B. J. (2012). Cognitive mechanisms of treatment in depression. Neuropsychopharmacology, 37(1), 117–136. https://doi.org/10.1038/npp.2011.183.CrossRefGoogle ScholarPubMed
Rouault, M., Seow, T., Gillan, C. M., & Fleming, S. M. (2018). Psychiatric symptom dimensions are associated with dissociable shifts in metacognition but not task performance. Biological Psychiatry, 84(6), 443–451. https://doi.org/10.1016/j.biopsych.2017.12.017.CrossRefGoogle Scholar
Salamone, J. D., Yohn, S. E., López-Cruz, L., San Miguel, N., & Correa, M. (2016). Activational and effort-related aspects of motivation: Neural mechanisms and implications for psychopathology. Brain, 139(Pt 5), 1325–1347. https://doi.org/10.1093/brain/aww050CrossRefGoogle ScholarPubMed
Saleh, Y., Jarratt-Barnham, I., Petitet, P., Fernandez-Egea, E., Manohar, S. G., & Husain, M. (2023). Negative symptoms and cognitive impairment are associated with distinct motivational deficits in treatment resistant schizophrenia. Molecular Psychiatry. https://doi.org/10.1038/s41380-023-02232-7.CrossRefGoogle ScholarPubMed
Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): A reevaluation of the life orientation test. Journal of Personality and Social Psychology, 67(6), 1063–1078. https://doi.org/10.1037/0022-3514.67.6.1063.CrossRefGoogle ScholarPubMed
Schmidt, L., d’Arc, B. F., Lafargue, G., Galanaud, D., Czernecki, V., Grabli, D., … Pessiglione, M. (2008). Disconnecting force from money: Effects of basal ganglia damage on incentive motivation. Brain, 131(Pt 5), 1303–1310. https://doi.org/10.1093/brain/awn045CrossRefGoogle ScholarPubMed
Seow, T., Rouault, X. F., Marion, G., Claire, M., & Fleming, S. M. (2021). How local and global metacognition shape mental health. Biological Psychiatry, 90(7), 436–446. https://doi.org/10.1016/j.biopsych.2021.05.013.CrossRefGoogle ScholarPubMed
Sheehan, D. V., Lecrubier, Y., Harnett Sheehan, K., Janavs, J., Weiller, E., Keskiner, A., & Dunbar, G. C. (1997). The validity of the MINI international neuropsychiatric interview (MINI) according to the SCID-P and its reliability. European Psychiatry, 12(5), 232–241. https://doi.org/10.1016/S0924-9338(97)83297-X.CrossRefGoogle Scholar
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. The British Journal of Psychiatry, 167(1), 99–103. https://doi.org/10.1192/bjp.167.1.99.CrossRefGoogle ScholarPubMed
Solmi, M., Radua, J., Olivola, M., Croce, E., Soardo, L., de Pablo, G. S., & Fusar-Poli, P. (2022). Age at onset of mental disorders worldwide: Large-scale meta-analysis of 192 epidemiological studies. Molecular Psychiatry, 27(1), 281–295. https://doi.org/10.1038/s41380-021-01161-7.CrossRefGoogle ScholarPubMed
Spielberger, C. D., Gorsuch, R. L., & Lushene, R. E. (1970). Manual for the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press.Google Scholar
Spijker, J., Bijl, R. V., De Graaf, R., & Nolen, W. A. (2001). Determinants of poor 1-year outcome of DSM-III-R major depression in the general population: Results of the Netherlands mental health survey and incidence study (NEMESIS). Acta Psychiatrica Scandinavica, 2, 122–130. https://doi.org/10.1034/j.1600-0447.2001.103002122.x.CrossRefGoogle Scholar
Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A., Paliwal, S., Gard, T., & Petzschner, F. H. (2016). Allostatic self-efficacy: A metacognitive theory of Dyshomeostasis-induced fatigue and depression. Frontiers in Human Neuroscience, 10, 550. https://doi.org/10.3389/fnhum.2016.00550.CrossRefGoogle ScholarPubMed
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 Scholar
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
Uher, R., Perlis, R. H., Henigsberg, N., Zobel, A., Rietschel, M., Mors, O., & McGuffin, P. (2012). Depression symptom dimensions as predictors of antidepressant treatment outcome: Replicable evidence for interest-activity symptoms. Psychological Medicine, 42(5), 967–980. https://doi.org/10.1017/S0033291711001905.CrossRefGoogle ScholarPubMed
Wechsler, D. (1997). WMS-III Administration and scoring manual. San Antonio, TX: The Psychological Corporation. Harcourt Brace & Co.Google Scholar
Yang, X. H., Huang, J., Lan, Y., Zhu, C. Y., Liu, X. Q., Wang, Y. F., & Chan, R. C. (2016). Diminished caudate and superior temporal gyrus responses to effort-based decision making in patients with first-episode major depressive disorder. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 64, 52–59. https://doi.org/10.1016/j.pnpbp.2015.07.006.CrossRefGoogle ScholarPubMed
Yang, X. H., Huang, J., Zhu, C. Y., Wang, Y. F., Cheung, E. F., Chan, R. C., & Xie, G. R. (2014). Motivational deficits in effort-based decision making in individuals with subsyndromal depression, first-episode and remitted depression patients. Psychiatry Research, 220(3), 874–882. https://doi.org/10.1016/j.psychres.2014.08.056.CrossRefGoogle ScholarPubMed
Yuen, G. S., Bhutani, S., Lucas, B. J., Gunning, F. M., AbdelMalak, B., Seirup, J. K., & Alexopoulos, G. S. (2015). Apathy in late-life depression: Common, persistent, and disabling. The American Journal of Geriatric Psychiatry, 23(5), 488–494. https://doi.org/10.1016/j.jagp.2014.06.005.CrossRefGoogle ScholarPubMed
Zou, Y. M., Ni, K., Wang, Y. Y., Yu, E. Q., Lui, S. S. Y., Zhou, F. C., & Chan, R. C. K. (2020). Effort-cost computation in a transdiagnostic psychiatric sample: Differences among patients with schizophrenia, bipolar disorder, and major depressive disorder. Psych J, 9(2), 210–222. https://doi.org/10.1002/pchj.316.CrossRefGoogle Scholar
Figure 0

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 study. (d) Overall probability to accept offers for all Pilot study participants. Black dots represent the mean and error bars represent the standard error of the mean. (e) Average acceptance rates as a function of reward level and effort level across all groups in the Case–control study. (f) Distribution of the number of accepted offers (out of 80) across all groups in the Case–control study. (g) Overall probability to accept offers for all Case–control participants. 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 1

Table 1. Participant characteristics

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 relative (REL), patients with current depression (MDD), and remitted depression (REM) groups. (b) Distribution of the number of accepted offers for each group. (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 show violin and boxplots as well as the mean (plus sign) and median (notch) for estimated (a) intercept/acceptance bias (K), (b) reward sensitivity (LinR), (c) linear effort sensitivity (LinE), and (d) quadratic effort sensitivity (E2) parameter values from the winning model in the Pilot study. Similar figures show estimated (e) intercept/acceptance bias (K), (f) reward sensitivity (LinR), and (g) quadratic effort sensitivity (E2) parameter values from the winning model in the Case–control study. Note: 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 combined CTR + REL group only.

Supplementary material: File

Valton et al. supplementary material

Valton et al. supplementary material
Download Valton et al. supplementary material(File)
File 4.1 MB