The influence of large and small reward associations on Stroop performance in rewarded and nonrewarded contexts
Bibliographic record
Abstract
Reward-associated but task-irrelevant information can interfere with our ability to focus on goal-relevant stimuli. In previous studies using a rewarded-Stroop task, font color naming responses were slower when the meaning of the color word matched the reward-associated color but the font color was a nonrewarded color (e.g., the word RED in blue font when red was a potential reward color and blue was not). This impairment has been referred to as the modulation of interference by reward associations (MIRA). It was proposed that MIRA occurs due to the transfer of reward association to the task-irrelevant dimension which then impacts attention. In this study, we associated one of four colors with a large reward and another with a small reward to determine whether the larger reward association would lead to greater MIRA when the word meaning was presented. Participants also completed a second block in which they completed the same task but fast and accurate responses to previously rewarded colors were no longer rewarded. Overall, responses were faster and more accurate when the font color signaled a large versus small reward, especially for a subsample of participants who were explicitly informed of the color-reward associations. However, MIRA was similar in the rewarded block for the small and large value-associated color word meanings, regardless of whether participants were informed or not. In the subsequent nonrewarded block, only the large value-associated word interfered with ink naming performance. The persistence of this behavioral response for highly rewarded colors coincides with previous findings in that reward-associated stimuli can influence behavior after reward is discontinued. These results suggest that the persistence of MIRA following the discontinuation of reward is dependent on the magnitude of the reward association but not awareness of the color-reward associations, and that larger reward associations might be more immune to extinction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".