The affective consequences of cognitive inhibition: Devaluation or neutralization?
Bibliographic record
Abstract
Affective evaluations of previously ignored visual stimuli are more negative than those of novel items or prior targets of attention or response. This has been taken as evidence that inhibition has negative affective consequences. But inhibition could act instead to attenuate or "neutralize" preexisting affective salience, predicting opposite effects for stimuli that were initially positive or negative in valence. We tested this hypothesis by presenting trustworthy and untrustworthy faces (Experiment 1), strongly positive and negative photographs (Experiment 2), and monetary gain- and loss-associated patterns (Experiment 3) in a Go/No-Go task and assessing subsequent affective ratings. Evaluations of prior No-Go (inhibited) stimuli were more negative than of prior Go (noninhibited) stimuli, regardless of a priori affective valence. Ratings of No-Go stimuli also became increasingly negative (vs. increasingly neutral) when preexisting salience was increased via stimulus repetition (Experiment 4). Our results suggest inhibition leads to affective devaluation, not affective neutralization.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".