Stimulus devaluation by backward inhibition exceeds any emotional impact of cognitive conflict: Evidence from task switching
Bibliographic record
Abstract
Ignoring or withholding a response from a stimulus causes it to become affectively devalued. Leading accounts posit that this is due to negative affect elicited by neurocognitive inhibition when it is applied to resolve conflict from distracting or otherwise inappropriate stimulus/response representations. Other research, however, suggests that stimulus/response conflict may itself elicit negative affect and devalue stimuli, raising questions about whether effects previously attributed to inhibition may instead reflect the emotional impact of conflict, per se. To address this, we measured affective ratings of art-like patterns that previously appeared on critical trials of a task-switching paradigm (ABA vs. CBA task sequences) known for its capacity to distinguish behavioural effects of inhibition and conflict. Stimuli from the ABA-sequence experimental condition showing behavioural evidence of backward inhibition (n-2 repetition costs) received more negative ratings than those from the CBA-sequence control condition. This stimulus-devaluation effect was not impacted by the level of conflict associated with high uncertainty or low uncertainty about upcoming task order. Moreover, the response-time index of inhibition was larger on ABA trials in which the associated stimuli later received negative ratings than on trials preceding relatively positive ratings. Inhibition therefore appears to have negative affective consequences that exceed any emotional impact of conflict, with fluctuations in inhibition linked to subsequent stimulus evaluations.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".