The Average Reward Rate Modulates Behavioral and Neural Indices of Effortful Control Allocation
Bibliographic record
Abstract
People tend to avoid exerting cognitive effort, and findings from recent behavioral studies suggest that effort allocation is in part determined by the opportunity cost of slothful responding-operationalized as the average reward rate per unit time. When the average rate of reward is high, individuals make more errors in cognitive control tasks, presumably owing to a withdrawal of costly cognitive processing. An open question remains whether the presumed modulations of cognitively effortful control processes are observable at the neural level. Here, we measured EEG while participants completed the Simon task, a well-known response conflict task, while the experienced average reward rate fluctuated across trials. We examined neural activity associated with the opportunity cost of time by applying generalized eigendecomposition, a hypothesis-driven source separation technique, to identify a midfrontal component associated with the average reward rate. Fluctuations in average reward rate modulated not only component amplitude but also, most importantly, component theta power (4-8 Hz). Higher average reward rate was associated with reduced theta power, suggesting that the opportunity of time modulates effort allocation. These neural results provide evidence for the idea that people strategically modulate the amount of cognitive effort they exert based on the opportunity cost of time.
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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".