An examination of how reward associations differentially facilitate and impair Stroop performance
Bibliographic record
Abstract
Behavioral performance is improved when the color of a Stroop stimulus is tied to a potential reward but is impaired when the irrelevant word meaning is reward related. The facilitation (reward responsiveness) and impairment (modulation of interference of reward association; MIRA) are different cognitive processes. In this study, we explored whether reward responsiveness and MIRA were subject to: 1) the reward magnitude, 2) whether stimulus-reward color instructions were provided, 3) the certainty of the stimulus reward contingency, 4) whether the influence of reward on Stroop performance continued following reward discontinuation, and 5) the dispositional approach motivation of the participant. Results from a high-powered online study (N = 205) suggest that large, versus small, reward colors increased reward responsiveness before and after reward discontinuation but relatively greater impairment by a large reward related word was only observed following reward discontinuation. Stimulus-reward colorinformation allowed participants to respond faster to obtain larger reward, especially when the stimulus-reward contingency was uncertain, but did not influence the impairment by reward related words. Individuals who were highly reward responsive, as indicated by their behavioral activation system (BAS) scale scores, were faster to respond to obtain a large reward when provided stimulus reward color instructions but were equally impaired by large reward related words as low reward responsive individuals. Results suggest that reward responsiveness was influenced by motivation whereby stimulus-reward information and higher BAS was associated with greater reward responsiveness. In contrast, MIRA, like other forms of reward-based distraction, was not influenced by motivation or reward responsiveness.
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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 | 0.001 |
| 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".