An Examination of the Multi-Faceted Motivation System in Healthy Young Adults
Bibliographic record
Abstract
Background: Amotivation is a prevalent symptom in schizophrenia (SZ) and depression (MDD), and is linked to poor functional outcomes in affected individuals. Conceptualizations of motivation have outlined a multi-faceted construct comprised of reward responsiveness, reward expectancy, reward valuation, effort valuation, and action selection/preference-based decision making. To date, findings from studies utilizing variable-centered approaches to examining isolated facets of motivation in SZ and MDD have been inconsistent. Thus, the present study adopted a person-centered approach, and comprehensively examined the reward system in a non-clinical sample in an attempt to explore potential subtypes of motivation impairments, while minimizing the effects of illness-related confounds. Methods: Ninety-six healthy undergraduate students were evaluated for amotivation, schizotypal traits, depressive symptoms, and cognition, and administered objective computerized tasks to measure the different facets of motivation. Cluster analysis was performed to explore subgroups of individuals based on similar motivation task performance. Additionally, correlational analyses were conducted in order to examine inter-relationships between motivation facets, and relations between clinical measures and facets of motivation. Results: Cluster analysis identified two subgroups of individuals with differential motivation performance profiles. Correlational analyses revealed that reward responsiveness was associated with amotivation, depressive symptoms, and negative schizotypy. Further, significant inter-correlations were found between reward responsiveness and reward expectancy, as well as between reward valuation and effort valuation. Conclusions: Our results mark important steps forward in understanding motivation in a non-clinical sample, and guide future dimensional and comprehensive analyses of the multi-faceted reward system. It remains to be seen whether these patterns of results will be similar in clinical populations such as SZ and MDD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".