S119. A DIMENSIONAL APPROACH TO UNDERSTANDING COST-BENEFIT DECISION-MAKING IN SCHIZOPHRENIA AND DEPRESSION
Bibliographic record
Abstract
Abstract Background Reductions in motivation figure prominently in the clinical presentation of schizophrenia (SZ) and major depressive disorder (MDD). One critical nexus in the motivation system that drives real-world behaviour is effort-based decision-making (EBDM), which refers to the cost-benefit calculations involved in computing the amount of effort one is willing to expend in order to obtain a desired reward. Important individual differences are associated with these processes, and impairments in motivation can arise if any relevant cost-benefit information is not properly computed, appraised, or integrated. Thus, in order to better understand the computations guiding choice behaviour, the present study sought to utilize a more person-centric approach to characterize individual differences in the effort-cost computations that underlie cost-benefit decision-making in individuals with SZ and MDD. Methods A sample of 51 individuals with SZ, 43 individuals with MDD, and 51 healthy control (HC) participants underwent a comprehensive clinical and cognitive characterization, and completed the Effort Expenditure for Rewards Task (EEfRT) as a measure of EBDM. Random effects modelling was conducted to estimate the subject-specific predictors of reward magnitude, probability, and perceived cost on choice behaviour. Cluster analysis was subsequently applied to these predictors in order to identify subtypes of impairments within the entire sample, irrespective of diagnostic status. Results Data-driven cluster analysis identified unique subgroups of individuals with distinct patterns of utilizing cost-benefit information to guide effort-based decision-making. Analyses of variance revealed significant differences between clusters with respect to their utilization of reward (F (3, 133) = 51.58, p < .001), probability (F (3, 133) = 48.71, p < .001), and cost (F (3, 133) = 45.24, p < .001). The first cluster was characterized by an indifference to all cost-benefit information, the second cluster was more influenced by perceived cost, the third cluster demonstrated a preference for reward-based information, and the fourth cluster mainly utilized probability to guide their decision-making. While the clusters did not differ in their severity of clinical amotivation (p = .11), there was a significant effect for cognition, specifically with impairments in clusters 1 and 2. All diagnostic groups were represented in each cluster, but the distribution of SZ, MDD, and HC participants was significantly different (X2 (6, N = 137) = 16.18, p = .013). Discussion The emergence of four distinct subgroups in our sample suggests that there are individual differences amongst SZ, MDD, and HC participants in their utilization of cost-benefit information to guide choice behaviour. Moreover, with elevated levels of clinical amotivation present in all four clusters, it is possible that these unique cost-benefit decision-making patterns represent different underlying motivational impairments, the nature of which depending on how reward magnitude, probability, and perceived cost are weighed. Thus, by characterizing the specific mechanisms underlying EBDM in SZ and MDD, the results of this work may be able to help guide the identification of more precise targets for the effective treatment of motivation deficits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".