The influence of effort expenditure on the neurophysiological substrates of reward processing in early adolescence
Bibliographic record
Abstract
Reward and motivation are two processes that drive goal-directed behaviors and are closely linked to the development of psychopathology. One behavioral measure of motivation is effort expenditure-the amount of effort exerted to attain a desirable outcome. In adults, higher effort expenditure has been associated with heightened neural responses to reward cues but diminished responses to reward anticipation and feedback. However, it is unclear whether similar patterns exist in early adolescence, a critical period for the development of reward- and motivation-related processes. Applying event-related potentials to a novel Effort-Doors task, we explored the effort-reward relationship in a community sample of 92 10-to-13-year-olds (53 females, mean/SD of age = 12.06/1.2 years). Behaviorally, youths were slower to respond in high-effort trials; they were also more likely to switch their choices following higher effort and reward losses in previous trials. At the neural level, greater effort expenditure increased youths' attention to reward cues (indexed by a larger cue-P3) and decreased the overall value associated with the reward trial (indexed by a smaller RewP). Effort did not influence feedback-elicited P3 or the late positive potential (LPP). Our work provided important preliminary evidence on the distinct effects of effort on different stages of reward processing in early adolescents. Future work is needed to better understand the underlying mechanisms of effort-based reward processes, their development across adolescence, and how they relate to behavior.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".