Electrophysiological measures of conflict and reward processing are associated with decisions to engage in physical effort
Bibliographic record
Abstract
Anterior cingulate cortex (ACC), a key brain region involved in cognitive control and decision making, is suggested to mediate effort- and value-based decision making, but the specific role of ACC in this process remains debated. Here we used frontal midline theta (FMT) and the reward positivity (RewP) to examine ACC function in a value-based decision making task requiring physical effort. We investigated whether (1) FMT power is sensitive to the difficulty of the decision or to selecting effortful actions, and (2) RewP is sensitive to the subjective value of reward outcomes as a function of effort investment. On each trial, participants chose to execute a low-effort or a high-effort behavior (that required squeezing a hand-dynamometer) to obtain smaller or larger rewards, respectively, while their brainwaves were recorded. We replicated prior findings that tonic FMT increased over the course of the hour-long task, which suggests increased application of control in the face of growing fatigue. RewP amplitude also increased following execution of high-effort compared to low-effort behavior, consistent with increased valuation of reward outcomes by ACC. Although neither phasic nor tonic FMT were associated with decision difficulty or effort selection per se, an exploratory analysis revealed that the interaction of phasic FMT and expected value of choice predicted effort choice. This interaction suggests that phasic FMT increases specifically under situations of decision difficulty when participants ultimately select a high-effort choice. These results point to a unique role for ACC in motivating and persisting at effortful behavior when decision conflict is high.
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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 | 0.003 |
| 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".