Bibliographic record
Abstract
Evaluative conditioning (EC) refers to the change in the valence of a conditioned stimulus (CS) due to its pairing with a positive or negative unconditioned stimulus (US). To the extent that core affect can be characterised by the two dimensions of valence and arousal, EC has important implications for the origin of affective responses. However, the distinction between valence and arousal is rarely considered in research on EC or conditioned responses more generally. Measuring the subjective feelings elicited by a CS, the results from two experiments showed that (1) repeated pairings of a CS with a positive or negative US of either high or low arousal led to corresponding changes in both CS valence and CS arousal, (2) changes in CS arousal, but not changes in CS valence, were significantly related to recollective memory for CS-US pairings, (3) subsequent presentations of the CS without the US reduced the conditioned valence of the CS, with conditioned arousal being less susceptible to extinction and (4) EC effects were stronger for high arousal than low arousal USs. The results indicate that the conditioning of affective responses can occur simultaneously along two independent dimensions, supporting evidence in related areas that calls for a consideration of both valence and arousal. Implications for research on EC and the acquisition of emotional dispositions are discussed.
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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) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".