P.001 The neural correlates of effort-reward decision-making in older adults
Bibliographic record
Abstract
Background: Effort mobilization is important in older adults to stay healthy, notably for decision-making. The process of decreasing subjective value of a reward as required effort increases is called effort discounting. By identifying underlying neural correlates related to effort discounting, we can better understand factors affecting normal cognitive aging. Methods: We acquired resting-state functional magnetic resonance images from 19 cognitively normal older adults (10 males; 66±6 years). Participants completed a computerized cognitive task—called Effort Expenditure for Rewards Task—capturing the willingness to expend effort for rewards through binary choices between high-reward-high-effort or low-reward-low-effort option to obtain varying monetary rewards. We modelled subjective value to assess the k parameter, effort discounting. A functional connectivity analysis examined the involvement of regions associated to the salience network. Results: The seed-to-voxel analysis revealed increased connectivity within the precuneus cortex and to clusters in the right temporal and posterior cingulate gyri, with increased k-value or decreased willingness to expend effort. There was also decreased connectivity between the anterior cingulate and right lateral occipital cortex, and between the left anterior insula to the cerebellum and precuneus cortex. Conclusions: The process of effort discounting is correlated to functional connectivity changes involving the precuneus, anterior cingulate, and left anterior insula in healthy older adults.
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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 | 0.001 |
| 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) | 0.004 | 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".