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. Author manuscript; available in PMC: 2026 Mar 19.
Published in final edited form as: Cogn Affect Behav Neurosci. 2026 Jan 24;26(3):1283–1295. doi: 10.3758/s13415-026-01400-w

Examining effects of effort and reward magnitude on effort-cost decision-making in people with major depressive disorder

Alice R Klein 1, Soobin Hong 2,3, Aram Kim 2,3,4, Vikram S Chib 2,3,5, Sanjay G Manohar 6, Masud Husain 6, James M Gold 1, Adam J Culbreth 1
PMCID: PMC12997560  NIHMSID: NIHMS2151984  PMID: 41578111

Abstract

Motivational impairment is a primary feature of major depressive disorder (MDD); yet the cognitive mechanisms underlying this deficit remain unclear. Effort-cost decision making is a popular framework for understanding motivation in MDD. This study examined the relative contributions of reward and effort to decision-making in an effort-cost decision-making (ECDM) task. A combined sample (remitted and current) of medicated and nonmedicated individuals with MDD (N = 32) and healthy controls (HC, N = 30) completed the Apple Gathering Task. In this task, reward and effort magnitudes are parametrically manipulated to independently estimate effects of effort and reward on choice. Choice data were analyzed by using conventional analyses, as well as computational models of behavior. Compared with HC, individuals with MDD were less willing to accept offers characterized by low reward and high effort amounts. Computational analyses using hierarchical drift diffusion modeling mirrored these findings, suggesting that evidence accumulation in MDD compared with HC was 1) faster when rejecting high effort options, and 2) faster when accepting high reward options. Additionally, higher fatigue and presence of a current depressive episode was associated with reduced ECDM in those with MDD, for low reward and high effort offers. Our results highlight influences of both reward and effort on reduced ECDM in MDD. Furthermore, our results point to critical symptom (fatigue) and state-based associations (current vs. remitted status).

Keywords: Major depressive disorder, Effort-cost decision-making, Motivation, Fatigue, Reward

Introduction

Reductions in motivation are a core feature of major depressive disorder (MDD), impacting functioning and quality of life (Fervaha et al., 2016). Despite functional consequences, reduced motivation in MDD remains inadequately addressed by current interventions. Poor treatment efficacy may stem from incomplete understanding of factors that contribute to motivation in MDD, underscoring the need to more completely understand underlying mechanisms. A recent approach to understanding reduced motivation in MDD has been to consider it within the framework of effort-cost decision-making (ECDM) (Culbreth et al., 2018; Husain & Roiser, 2018; Pessiglione et al., 2018; Whitton et al., 2015). In this conceptualization, individuals vary in their subjective valuation of whether an outcome is worth the effort required to obtain it. Prior studies have found that people with MDD demonstrate reduced willingness to exert effort to gain reward, in many, but not all, studies (Barch et al., 2023; Clery-Melin et al., 2011; Culbreth et al., 2024a, b; Hershenberg et al., 2016; Moran et al., 2023; Treadway et al., 2012; Yang et al., 2014). However, it remains unclear whether this reduction in ECDM is driven by 1) increased contributions of effort to decision-making, 2) reduced contributions of reward to decision-making, or 3) a combination of both creating a general tendency for people with MDD not to choose to pursue options characterized by higher effort and lower reward. Therefore, our primary goal was to delineate the contributions of effort and reward to ECDM in MDD. Furthermore, there are several different features of depression that may contribute to reduced ECDM, including 1) fatigue, 2) anhedonia/motivation, and 3) phase of illness (current episode vs. remission). While prior work has shown that individuals with remitted MDD retain deficits in reward responsivity and ECDM despite improvements in mood, suggesting a trait-based impairment (Kuhn et al., 2025; Pechtel et al., 2013; Whitton et al., 2015), others have shown that ECDM deficits suggest state-based features (Yang et al., 2014). Therefore, we sought to determine whether effects on ECDM in MDD were associated with these symptoms and present across different phases of illness (i.e., remitted and current) in a medicated sample.

Effort-cost decision-making is typically assessed through paradigms that require participants to choose between hard or easy task options to gain larger or smaller rewards, respectively (Culbreth et al., 2018; Gold et al., 2015; Husain & Roiser, 2018; Treadway et al., 2009; Westbrook et al., 2013). For example, in the Effort Expenditure for Rewards Task (EefRT), participants choose between completing ~20 button presses with their dominant index finger or completing ~100 button presses with their nondominant pinky finger to gain monetary rewards (Treadway et al., 2009). In their seminal manuscript, Treadway and colleagues used the EefRT and found that MDD patients were less willing to expend effort for rewards than controls (Treadway et al., 2012). This finding has since been replicated across multiple studies (Treadway et al., 2009; Yang et al., 2014; 2016; Zou et al., 2020). Moreover, many of these studies report that greater depressive symptom severity and higher levels of anhedonia are associated with reduced ECDM (Treadway et al., 2009; Yang et al., 2014; Tran et al., 2021). At a broad level, this paradigm has been instrumental in identifying reduced ECDM in MDD. However, a core limitation is that the magnitudes of effort and reward for offers are not manipulated independently. This makes it difficult to determine whether decision-making is driven by reduced contributions of reward, heightened contributions of effort, or a combination of both. Assessing the individual contributions of effort and reward to choice behavior is highly important, because it could have implications for treatment. For example, if reduced effect of reward is found to drive aberrant ECDM in MDD, then interventions using behavioral or neuromodulatory approaches to boost effects of reward would be beneficial. In contrast, if increased effects of effort are found to drive aberrant ECDM, then effective interventions should target perceptions of effort.

We used the Apple Gathering Task (AGT), a task previously developed to assess ECDM in several different clinical populations (Chong et al., 2015; Culbreth et al., 2024a, b; Le Heron et al., 2018). This task requires participants to accept/reject offers of varying combinations of reward and effort levels, allowing for independent estimation of effort and reward effects (Fig. 1). Recently, Valton et al. (2025) used the AGT to assess ECDM in MDD. Through computational modeling, they concluded that individuals with MDD exhibited a bias avoiding effort compared with controls, irrespective of reward or effort level. Here, we administer a similar task to determine whether these results replicated and extend these findings to better understand individual variability in symptom expression. We did not have a specific hypothesis about whether altered contributions of reward or effort to decision-making would solely drive behavior. Instead, we aimed to use a well-validated task and associated computational analyses to provide a comprehensive assessment of ECDM in MDD. In particular, the drift diffusion model that we implement allows us to not only assess relative contributions of effort and reward to ECDM but also provides insights into which specific decision-making processes (e.g., bias, evidence accumulation) may be most relevant to ECDM in MDD (Berwian et al., 2020, Saleh et al., 2023, Kuhn et al., 2025).

Fig. 1.

Fig. 1

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Apple Gathering Task Trial. (Top) On each trial, participants are presented with an offer of a certain amount of apples to be won for a certain amount of work. If they choose to accept the offer, they must complete the required effort to obtain the reward. If they choose not to accept the offer, there is a brief pause and then the next trial is presented. (Bottom) Trials vary in terms of the number of apples (n = 1, 3, 6, 9, 12, 15) and amount of effort (10%, 24%, 38%, 52%, 66%, 88% of the participant’s maximum voluntary contraction).

Method

Participants

Study participants included 32 individuals meeting Diagnostic and Statistical Manual of Mental Disorder, fifth edition (DSM-5) criteria for MDD (15 in remission, and 17 in current episode) and 30 comparison control participants (HC) with no history of a mood or psychotic disorder. Participants were recruited through online advertisements from the Baltimore, Maryland, community. Exclusion criteria included 1) DSM-5 diagnosis of alcohol or substance use disorder in the past year; 2) past head injury with loss of consciousness > 10 min; 3) Wechsler Test of Adult Reading (WTAR; Weschler, 2001) Estimated Full Scale IQ < 70; and 4) diagnosis of bipolar disorder, schizophrenia, or schizoaffective disorder. Additional exclusion criteria for MDD patients included medication changes in the past month. There were no significant group differences in terms of age, biological sex, racial identity, or parental education (Table 1). Participants provided informed consent for a protocol approved by the University of Maryland School of Medicine Institutional Review Board. Participants were compensated at a rate of $20 per hour.

Table 1.

Demographic, medication, and symptom descriptions

HC group
(n = 30)		 MDD group
(n = 32)		 rMDD
(n = 15)		 cMDD
(n = 17)		 Statistics
(HC, MDD)		 Statistics
(rMDD, cMDD)
Demographic information
Age, mean (SD) 39.9 (11.6) 35.5 (10.8) 38.8 (11.7) 33.94(9.4) t = 1.570, p = 0.122 t = 1.297, p = 0.204
Education, mean (SD)
 Participant 15.7 (2.1) 15.9 (2.6) 16.2 (2.6) 15.29 (2.4) t = −0.232, p = 0.818 t = 1.006, p = 0.323
 Parental (Maternal) 14.6 (3.9) 15 (3.1) 14.67 (2.8) 14.69 (2.8) t = −0.288, p = 0.775 t = −0.019, p = 0.985
 Parental (Paternal) 14.8 (3.2) 15.4 (2.6) 14.77 (2.5) 15.67 (2.8) t = −0.621, p = 0.537 t = −0.877, p = 0.389
Sex, no. = −0.001, p = .974 = −1.771, p = 0.087
 Male 17 18 6 12
 Female 13 14 9 5
Race/ethnicity, no. = 1.663, p = 0.645 = 0.039, p = 0.969
 Asian 2 4 1 3
 White 19 19 9 10
 Black 9 8 4 4
 Mixed Race 0 1 1 0
Current medications
 Antidepressants no. 22 12 10
 Mood stabilizers no. 1 0 1
 Anxiolytic no. 4 2 2
 Anticholinergic no. 0 0 0
Symptom data
Individual differences (SD)
 AES 27.6 (7.8) 38.9 (9.6) 33.6 (9.3) 44.18 (7.4) t = −5.032, p <.001 t = −3.586, p =0.001
 BDI 4.6 (6.3) 21.7 (11.9) 17.47 (12.8) 25.76 (9.6) t = −6.902, p <.001 t = −2.081, p = 0.046
 PROMIS 15.3 (4.7) 26.8 (4.9) 25.93 (5.3) 27.53 (4.7) t = −9.328, p <.001 t = −0.905, p = 0.373
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HC healthy control; MDD major depressive disorder; SD standard deviation; AES Apathy Evaluation Scale: Self-Report; BDI Beck Depression Inventory; PROMIS Patient-Reported Outcomes Measurement Information System, Fatigue Subscale)

Diagnosis of MDD in the MDD group and the absence of current psychiatric diagnoses in HC were confirmed using the Structured Clinical Interview for DSM-5 (First et al., 2015). We included the Apathy Evaluation Scale-Self Report (AES) as a dimensional measure of motivation. The Beck Depression Inventory (BDI) was used to measure depressive symptoms. The Patient-Reported Outcomes Measurement Information System (PROMIS) Fatigue scale was used to assess fatigue. On all measures, higher scores indicate greater impairment.

Equipment

Participants were seated in front of a computer running PsychoPy (Peirce et al., 2019). A hand dynamometer (TSD121B-MRI, BIOPAC Systems) was used to record handgrip force during effortful exertions. The hand dynamometer was calibrated to the strength of the participant’s dominant hand. Each participant’s maximal voluntary contraction (MVC) was calculated as the greatest force value recorded over three separate contractions. Effort levels presented in each task were normalized to the subject’s MVC.

Apple Gathering Task

Participants completed the Apple Gathering Task, originally developed by Husain and colleagues (Heron et al., 2018). On each trial, an offer to perform effort, a handgrip exertion, for a reward was presented (Fig. 1). The offer was presented on a cartoon apple tree. The reward level was indicated by the number of apples on the tree (n = 1, 3, 6, 9, 12, or 15). The effort level was indicated by the height of a yellow bar on the trunk of the tree (10%, 24%, 38%, 52%, 66%, or 80% of MVC). Each effort level was presented with each reward level creating 36 unique trial types (Fig. 1). Each trial type was presented six times (216 trials total). Offers were presented in a pseudorandomized manner that was identical across subjects.

Participants were instructed to determine whether the offer was “worth it.” They were instructed to either accept (by pressing the one key on a computer keyboard) or reject (by pressing the zero key on a computer keyboard) the offer, prior to squeezing the handgrip. If they accepted the offer, they were required to squeeze the handgrip at or above the required force for at least 1 s (during a 5-s window) to obtain the reward. During the squeeze, participants were provided visual feedback on their exertion in relation to the threshold. If they rejected the offer, participants waited for an equivalent time window (5 s) before proceeding to the next trial. Prior to decision-making, participants experienced each of the six effort levels to familiarize themselves with the effort required. They were informed that they would be paid a bonus based on the number of apples they collected. In actuality, all participants received a $5 bonus at the end of the testing session.

Data analysis

Model-free analysis

Apple Gathering Task data was analyzed using a series of mixed model repeated measures ANOVAs, similar to previous reports using the Apple Gathering Task (Chong et al., 2015; Le Heron et al., 2018; Culbreth et al., 2024a, b) and other similar ECDM paradigms (Gold et al., 2013; Chong et al., 2017; Barch et al., 2023; Moran et al., 2023). The ANOVA included Group (HC,MDD) as a between-subject factor. Reward Level (6 levels) and Effort Level (6 levels) were within-subject factors.

Model-based analysis

To compliment traditional analysis methods and investigate latent cognitive processes relevant to ECDM, we employed the Hierarchical Sequential Sampling Modeling (HSSM) toolbox (https://lnccbrown.github.io/HSSM/), a Python-based framework for Bayesian inference of drift diffusion models (DDMs). We implemented a DDM as the DDM is a common computational model to apply to ECDM task (Berwian et al., 2020; Kuhn et al., 2025) including the AGT specifically (Saleh et al., 2021). However, for direct comparison to the Valton paper, we also provide modeling results from the best fitting subjective utility model in that particular study (using code provided to us by those authors; see Supplemental Materials S7). Finally, we have made the current dataset openly available (OSF dataset) so that other groups can fit additional models to these data in the future.

DDM

These models have been used to capture dynamic aspects of decision-making that are often inaccessible through conventional analytical approaches (Ging-Jehli et al., 2024). A classical DDM comprises four parameters: v (drift rate), z (a priori decision bias), t (nondecision time), and a (boundary separation). Drift diffusion models simultaneously take in reaction time (RT) and choice data as inputs to infer the parameter values (v,z,t, and a) that best explain observed behavior. In the context of the ECDM and AGT, the upper decision boundary corresponds to offer acceptance. The decision terminates when evidence accumulation that starts from initial point z at nondecision time t reaches either of the boundaries (either 0 or a) at a drift rate of v (Kuhn et al., 2025).

We first excluded participants whose made monotonic choices, defined as selecting one option in more than 95% of trials. We then excluded trials with extreme RTs (<200 ms or >10 s). These exclusion criteria resulted in a remaining sample of 25 HCs and 31 MDD (model-free analyses in this subset of participants were largely similar to the full sample with the exception of exploratory analyses between remitted and current depression; see supplemental materials S5). Prior to model fitting, we z-scored the regressor variables (i.e., effort and reward level) to facilitate parameter interpretability and comparability. To address two primary research questions, we compared five classical DDMs (Table 2.):

Table 2.

Computational models of behavior

Model name Formula Description
Model 1 v ~ 1 + (1|participant), z ~ 1 General Drift Rate and Bias (no influence of effort nor reward)
Model 2 v ~ 1 + effort + reward + (1|participant), z ~ 1 Main Effects of Effort and Reward Separately for Drift Rate with a General Bias Parameter (no influence of effort nor reward)
Model 3 v ~ 1 + effort + reward + effort:reward + (1|participant), z ~ 1 Main Effects and Interaction of Effort and Reward for Drift Rate with a General Bias Parameter (no influence of effort nor reward)
Model 4 v ~ 1 + effort + reward + (1|participant), z ~ 1 + effort Main Effects of Effort and Reward Separately for Drift Rate with Main Effect of Effort on the Bias Parameter
Model 5 v ~ 1 + effort + reward + effort:reward + (1|participant), z ~ 1 + effort Main Effects and Interaction of Effort and Reward for Drift Rate with Main Effect of Effort on the Bias Parameter
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  1. Do individuals with MDD demonstrate reduced/heightened effects of reward/effort on drift rate compared with HCs?

  2. Do individuals with MDD show a bias toward the low effort vs. high effort option compared with HCs?

For fixed effects of drift rate (v) and starting-point bias (z), we used weakly informative priors to facilitate model convergence. Priors included: Intercept ~ (0,1., Effort ~ (−11, Reward ~ (−11, Intercept ~ (0.5,, which was constrained to 0 and 1. The additional parameters (Effort X Rewar and Effor) used hssm’s default priors. The group-level parameter for Interce t also used hssm’s default prior. For fixed effects of nondecision time (t) and boundary separation (a), we used highly informative priors for t and a to mitigate the risk of model divergence (Intercept ~ (0.15 0.01 and Intercept ~ (1,0 01). The prior for t was selected as (minimum RT cutoff) −50 ms, and the prior for a was selected as non-collapsing boundary of 1.

Model convergence was examined by visually inspecting parameter traces and ensuring the Gelman-Rubin (R-hat statistic) to be below 1.05 as an acceptable convergence (see Supplemental Materials S8 for R-hat statistics for each model conducted). All parameters achieved R-hat < 1.05. Each model was run with four Markov chain Monte Carlo (MCMC) chains, with a total of 2,000 iterations per chain (1,000 for burn-in and 1000 for sampling: Supplemental Materials S1). To evaluate model fit, we first computed loglikelihood for each posterior sample. Then we used arviz. compare function, which estimates the Bayesian leave-one-out cross-validation estimate of the expected log pointwise predictive density (elpd_loo) and ranks the models with a degree of certainty. Higher elpd_loo indicates a better fitting model.

We fit separate DDMs for each group, allowing the drift rate to vary as a function of both effort and reward magnitude on a trial-by-trial basis. Separate DDMs for each group were conducted as models including group as a factor in a single model failed to converge. However, the approach of modeling groups separately has received some criticism as it has the potential to inflate group differences. Thus, our DDM results need to be considered with these interpretative limitations in mind. Model validation involved inspecting posterior predictive checks, including 1) generating simulated data using winning DDM parameters; 2) determining whether the simulated data can recapitulate the qualitative patterns seen in observed data; and 3) performing parameter recovery analyses by refitting the model to simulated data to confirm estimation robustness.

To determine whether there were credible group differences in drift rates, prior decision biases, boundaries, or nondecision times, we computed the mean and 95% highest density interval (HDI) of the difference between posterior distributions for each parameter, comparing the two groups (HCs – MDD). If the 95%HDI of the posterior difference excluded zero, we concluded that there was a significant group difference for the parameter.

Model comparison

We excluded model 4 from model comparison as it diverged and considered the remaining models (1, 2, 3, and 5) for model comparison. In both groups, model 5 had the highest elpd_loo values (Supplemental Materials S2). Therefore, we chose model 5 to be the best fitting model. The winning DDM captured qualitative patterns in both groups well (Supplemental Materials S3).

Individual difference analyses

Individual differences in general depressive symptoms, fatigue, and anhedonia were analyzed using a series of mixed-model repeated measures ANOVA within the MDD group. We implemented a Bonferroni correction across these symptom measures to correct for multiple comparisons. Specifically, we divided the p-value by the number of symptom measures (0.05/3 = 0.016). Within each ANOVA, each measure was used as a covariate. Reward level (1–6) and effort level (1–6) were within-subject factors. Furthermore, each measure was analyzed with a mixed-model repeated measures ANOVA with the difference between reward and effort as a within-subjects factor, similar to the group analyses described above.

Results

Group differences

There were significant main effects of reward (F(5,300) = 169.09, p < 0.001, η2 = 0.18) and effort (F(5,300) = 171.57, p < 0.001, η2 = 0.31), such that participants were more willing to accept offers as reward increased and less willing as effort increased (Fig. 2). We did not observe a significant main effect of group (F(1,60) = 2.98, p = 0.090, η2 = 0.01). However, there was a significant group X reward interaction, such that the effect of increasing reward on offer acceptance was significantly greater in MDD compared with HC (F(5,300) = 5.22, p < 0.001, η2 = 0.01). While post-hoc Tukey tests were non-significant (potentially due to the large number of cells), visual inspection of Fig. 2 suggests that the elevated reward effect seen in MDD participants was driven by reduced acceptance rates at the smallest reward levels (Fig. 2). In contrast, the group X effort interaction was not significant (F(5,300) = 1.11, p = 0.353, η2 < .001). There was a trend-level three-way interaction between effort, reward, and group (F(25,1500) = 1.51, p = 0.051, η2 =.003), which was characterized by reduced acceptance rates at options values with the lowest reward and highest effort in MDD.

Fig. 2. Group differences in effort-cost decision-making (ECDM).

Fig. 2

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As expected, offer acceptance decreased as the effort level increased and increased as the reward level increased. Differences between healthy controls and people with major depressive disorder were most notable at the lowest reward levels and offers characterized by low reward and high effort (as indicated by cooler colors on the heat map).

Hierarchical Bayesian Drift Diffusion Modeling

Drift rate increased with reward and decreased with effort levels in both groups. Notably, both reward (mean and 95% HDI of difference: 0.046 [0.0008, 0.09]) and effort level (mean and 95% HDI of difference: −0.099 [−0.133, −0.067]) parameters describing the drift rate were significantly different between the HC and MDD groups (Fig. 3). Specifically, individuals with MDD accumulated evidence faster towards rejecting the offer as effort magnitude increased. They also accumulated evidence faster towards acceptance as reward magnitude increased. No significant group differences were found for the drift rate intercept reflecting the propensity to accept the offer and the effort-reward interaction terms. Moreover, the group non-decision time parameter also showed significant differences between groups (mean and 95% HDI difference: −1.07 [−0.129, −0.085]). The MDD group showed a longer nondecision time, indicating that they required a longer time to process the task and execute motor commands to indicate their decisions. All the other parameters, including a priori decision-bias z and decision-boundary a, did not show significant group level differences.

Fig. 3. Drift diffusion model analysis of effort-cost decision-making.

Fig. 3

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(Top row) As expected, drift rate was negatively modulated by effort level. Furthermore, individuals with MDD accumulated evidence faster toward not accepting the offer as effort magnitude increased. (Middle row) As expected, drift rate was positively modulated by reward level. Further, individuals with MDD accumulated evidence slower towards accepting the offer as reward magnitude increased. (Bottom row) Individuals with MDD were characterized by a longer non-decision time.

Individual differences

We conducted a mixed-model repeated measures ANOVA within the MDD group, with PROMIS fatigue scores as a between-subjects covariate, and effort and reward level as within-subject factors (Fig. 4). There were no significant main effects of reward (F(5, 150) = 1.79, p = 0.117, η2 = 0.01) or effort level (F(5,150) = 0.38, p = 0.859, η2 = 0.00). We did not observe a significant main effect of PROMIS fatigue scores (F(1,30) = 3.67, p = 0.065). However, there was a significant effort level x PROMIS interaction (F(5,150) = 3.25, p = 0.008, η2 = 0.02) such that individuals with higher fatigue were less likely to accept offers as effort increased. In contrast, the reward x PROMIS interaction was not significant (F(5,150 = 0.73, p = 0.604, η2 <.001). There was a significant three-way interaction between effort, reward, and PROMIS scores (F(25,750) = 2.29, p < 0.001, η2 = 0.02) such that individuals with higher PROMIS scores were less likely to accept high effort and low reward offers.

Fig. 4. State-based and symptom severity effects in major depressive disorder (MDD).

Fig. 4

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(Top row) Differences between individuals with current and remitted MDD were most notable at the lowest reward levels. (Bottom row) Differences between individuals with high vs. low severity of fatigue (median split: 26) were most notable at the highest effort levels.

Contrary to our hypotheses, we did not observe significant interactions between general depressive symptoms (BDI) or self-reported apathy (AES) and task variables (Supplemental Materials S4).

Hierarchical Bayesian Drift Diffusion Modeling

Given convergence issues, we were unable to fit our DDMs on an individual subject basis to observe effects of symptom severity on computational modeling parameters.

Remitted vs. Current MDD

We conducted exploratory analyses to provide preliminary evidence for state-based effects of MDD. Specifically, we conducted the same two ANOVA analyses mentioned above but included Group as a three-level between-subjects factor (i.e., HC, MDD current, MDD remitted). In the first ANOVA, we observed a significant interaction between reward and group (F(20,590) = 2.80, p < 0.003, η2 = 0.01). While post-hoc Tukey tests were nonsignificant (potentially owing to the large number of cells), examination of marginal means (Fig. 4) illustrated a pattern, wherein participants with current MDD responded with the lowest frequency to low reward options, followed by those with remitted MDD, and finally HCs. Although clearly preliminary, the current analyses provide evidence for state-based effects on ECDM in those with MDD.

Discussion

Our primary goal was to determine whether ECDM in MDD is driven by 1) increased contributions of effort to decision-making, 2) reduced contributions of reward to decision-making, or 3) a combination of both. We found that individuals with MDD were less willing to accept offers characterized by both 1) low reward and 2) offers characterized by low reward and high effort in comparison to HC. Drift diffusion modeling results mirrored these model-free findings and extended them to clarify which component processes (and their relative modulation by reward and effort) may be most critical for ECDM deficits in MDD. Specifically, we found 1) stronger rate of evidence accumulation as effort increased, and 2) weaker rate of evidence accumulation as reward increased in MDD vs. HC. While bias and other components of the DDM model did not differ between groups. In MDD, higher fatigue, motivational impairment, and clinical state (current depressive episode) was associated with reduced ECDM, particularly for offers characterized by 1) higher effort and 2) low reward/high effort.

Our results align with previous research showing reduced ECDM in MDD (Clery-Melin et al., 2011; Hershenberg et al., 2016; Treadway et al., 2012; Valton et al., 2025; Yang et al., 2014). However, this literature is inconsistent (Barch et al., 2023; Culbreth et al., 2024a, b; Sherdell et al., 2012; Moran et al., 2023). Our ability to detect significant effects may be attributed to several factors—most notably, we manipulated reward and effort levels orthogonally across trials, allowing for a more precise estimation of each effect. Additionally, phase of illness is an important consideration. Although underpowered and exploratory, our findings showed that individuals with current MDD showed reduced ECDM compared to remitted individuals, suggesting that phase of illness may influence the relative contributions of reward and effort to ECDM. Finally, our results closely align with those of Valton et al. (2025), who also used the AGT to examine ECDM in MDD. However, whereas they attributed ECDM deficits to a general tendency or bias to avoid exerting effort irrespective of the effort and reward effects, our findings suggest reductions in ECDM between MDD and HC for specific types of trials (i.e., those with lower reward and higher effort). Furthermore, we observed significant relationships between fatigue severity and ECDM, whereas they did not measure fatigue. However, several design differences between the two studies may account for divergent results. First, Valton et al. (2025) included 80 trials, whereas the current study used 216, consistent with the original AGT design, to maximize the reliability of task effects. Second, our design included six reward and six effort levels, allowing for potentially more refined estimation of the contribution of reward and effort to ECDM. Third, most participants in our MDD sample were currently prescribed antidepressant medications (Table 1). Finally, our task required participants to squeeze on every trial, whereas Valton et al. (2025) allowed participants to not squeeze on 25% of trials, potentially introducing fatigue throughout the task administration.

Our finding of reduced ECDM for low reward-value options in MDD aligns with prior work, highlighting deficits in reward processing in MDD. Previous research has used non-ECDM paradigms and reported blunted aspects of reward processing in MDD (Olino et al., 2014; Pizzagalli et al., 2008a, b; 2009; Smoski et al., 2009; Stringaris et al., 2015; Vrieze et al., 2013). Neuroimaging studies have demonstrated that individuals with MDD exhibit reduced activation in reward-related brain regions (e.g., ventromedial prefrontal cortex, ventral striatum) following monetary gain (Pizzagalli et al., 2009). Furthermore, behavioral studies have shown that individuals with MDD exhibit reduced reward responsivity (Pizzagalli et al., 2008a, b). These findings support reward processing as having an integral role in motivational processes in MDD (Admon & Pizzagalli, 2015; Eshel & Roiser, 2010; Ng et al., 2019; Treadway & Zald, 2011). It may be useful for future studies to collect tasks assessing reward sensitivity or bias (Pizzagalli et al., 2005) and the AGT in the same participants to determine shared variability in reward effects across task paradigms.

Prior evidence also suggests higher subjective experience of effort in MDD. For example, Clery-Melin et al. (2011) found reduced ECDM in MDD compared with control, particularly when effort requirements are perceived as high. Furthermore, they showed that although individuals with MDD did not objectively exert more effort than controls, they reported greater subjective experience of effort. Similarly, Vinckier and colleagues used a series of behavioral tasks to assess effort sensitivity and found that individuals with MDD demonstrated elevated sensitivity to effort costs compared with controls (Vinckier et al., 2022).

Reduced acceptance of offers characterized by low reward and high effort in people with MDD found in our model-free analyses are further clarified by the DDM analyses. Specifically, evidence accumulation in MDD vs. HC was 1) faster when rejecting high effort options, and 2) faster when accepting high reward options. Furthermore, the MDD group showed a longer nondecision time compared to the HC group, replicating prior research (Berwian et al., 2020).

We found relationships between individual differences in fatigue, motivation, and ECDM in MDD. Prior research has shown that fatigue directly contributes to ECDM (Hogan et al., 2020; Iodice et al., 2017; Muller & Apps, 2019; Steward et al., 2025). For example, Hogan et al. (2020) demonstrated that physical fatigue increases the subjective value of effort, making individuals less likely to choose high-effort options. Similarly, Iodice et al. (2017) found that participants who experienced fatigue induced by cycling were more likely to choose low-effort options, whereas nonfatigued participants preferred higher-effort choices that offered greater rewards. These findings suggest that fatigue heightens the relative contribution of effort to ECDM. This is supported by the current findings in which individuals with higher self-reported trait fatigue exhibited a reduced willingness to accept offers low in reward and high in effort. However, in the current study, we did not link choice behavior to the experience of fatigue experienced in the task itself. Therefore, future research should examine how fluctuations in fatigue directly manipulated in an experimental context would impact ECDM in MDD.

Contrary to our hypotheses, dimensional measures of general depressive symptoms (BDI-II) were not significantly related to ECDM in the current study. Prior work examining associations between depressive symptom severity and ECDM has produced mixed results. Some studies have found significant relationships between depressive symptoms and reduced willingness to exert effort (Sherdell et al., 2012), while others have reported null effects (Clery-Melin et al., 2011; Hershenberg et al., 2016; Treadway et al., 2012; Yang et al., 2014). These inconsistencies may reflect variability in sample characteristics, task paradigm, or phase of illness.

Limitations

Our sample was small, particularly for examining state-based effects. State-based effects should be interpreted as preliminary. Second, medications could have influenced ECDM. However, analyses between medicated and nonmedicated individuals with MDD would be difficult given the small number of non-medicated individuals. Furthermore, as shown in Table 1, medication status was roughly equivalent across remitted vs. current MDD groups, suggesting that medication differences are not wholly accountable for state-based differences. While there has been evidence that serotonin influences ECDM (Meyniel et al., 2016), our inclusion of medicated individuals enhances the generalizability of the current findings. However, future research should aim to examine the effect of medication on ECDM. Third, symptom severity in our MDD sample was relatively mild (~50% scored below the BDI-moderate depression cutoff). Third, convergence issues precluded our ability to model all subjects within one larger DDM model, potentially inflating group differences as groups were modeled separately.

Summary

This study aimed to examine ECDM in MDD. We found evidence for reduced ECDM in MDD, particularly for low reward offers and offers characterized by low reward and high effort. Furthermore, we found that higher fatigue and amotivation severity was associated with lower offer acceptance, particularly for offers characterized by low reward and high effort. Our findings add to the growing body of evidence that individuals with MDD show reduced ECDM and identifies contributions of fatigue and motivational symptoms in shaping these impairments.

Supplementary Material

supplementalmaterials

Supplementary information The online version contains supplementary material available at https://doi.org/10.3758/s13415-026-01400-w.

Acknowledgment

The current work was supported by 1) a NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation to AJC; 2) K23 MH126986 to AJC, and 3) MH was supported by a Wellcome Trust grant (226645/Z/22/Z) and by the NIHR Oxford Health BRC 4) SM was supported by the Medical Research Council (MRC UK) and the National Institute for Healthcare Research (NIHR) Oxford Health Biomedical Research Council (BRC).

Footnotes

Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Board of University of Maryland, Baltimore.

Consent to participate Informed consent was obtained from all individual participants included in the study.

Consent for publication The authors affirm that human research participants provided informed consent for publication of this articles data.

Open practice statement Data or materials for the experiments are available upon request, and none of the experiment was preregistered.

Competing interests The authors have no competing interest to declare that are relevant to the content of this article.

Availability of data and materials

Data or materials for the experiments are available upon request.

Code availability

Code is available upon request.

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Supplementary Materials

supplementalmaterials
NIHMS2151984-supplement-supplementalmaterials.docx (3.4MB, docx)

Data Availability Statement

Data or materials for the experiments are available upon request.

Code is available upon request.

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