Cost-benefit analysis in physical effort expenditure: An electrophysiological registered report
Bibliographic record
Abstract
Navigating through everyday life requires us to make series of choices involving effort: Isit worth the effort for what I want to accomplish? Effort-based decision making depends on evaluating the value of effort-related costs against potential rewards, and only when the rewards outweigh their effort costs do effortful behaviors tend to get carried out. Despite a surge of research on this topic, what effortful control and reward processes are involved in such decisions and whether electrophysiological measures of control and reward processes could better elucidate these processes remain unclear. Here, we will parametrically manipulate effort and reward levels to investigate their effects on different decision processes (i.e., choice evaluation,choice itself, subsequent physical effort production, reward feedback valuation). To assess these decision processes, we will examine two electrophysiological indices: frontal midline theta power and reward positivity amplitude; further, we will investigate whether these indices trackcost-benefit integration, which will be reflected in subjective values derived from behavioralmodelling of choices. Our goal is to understand how effort and reward affect different aspects of decision making and effort production, and how the electrophysiological and behavioral measures of these processes relate to each other.
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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 | 0.002 |
| 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) | 0.002 | 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".