It’s all relative: Reward-induced cognitive control modulation depends on context.
Bibliographic record
Abstract
Although people seek to avoid expenditure of cognitive effort, reward incentives can increase investment of processing resources in challenging situations that require cognitive control, resulting in improved performance. At the same time, subjective value is relative, rather than absolute: The value of a reward is increased if the local context is reward-poor versus reward-rich. Although this notion is supported by work in economics and psychology, we propose that reward relativity should also play a critical role in the cost-benefit computations that inform cognitive effort allocation. Here we demonstrate that reward-induced cognitive effort allocation in a task-switching paradigm is sensitive to reward context, consistent with the notion of relative value. Informed by predictions of a computational model of divisive reward normalization, we demonstrate that reward-induced switch cost reductions depend critically upon reward context, such that the same reward amount engenders greater control allocation in impoverished versus rich reward context. Succinctly, these results confirm that reward relativity factors into the value computation driving effort allocation, revealing that motivated cognitive control, like choice, is all relative. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".