Is evaluative conditioning really resistant to extinction? Evidence for changes in evaluative judgements without changes in evaluative representations
Bibliographic record
Abstract
Evaluative conditioning (EC) is defined as the change in the evaluation of a conditioned stimulus (CS) due to its pairing with a positive or negative unconditioned stimulus (US). Although several individual studies suggest that EC is unaffected by unreinforced presentations of the CS without the US, a recent meta-analysis indicates that EC effects are less pronounced for post-extinction measurements than post-acquisition measurements. The disparity in research findings suggests that extinction of EC may depend on yet unidentified conditions. In an attempt to uncover these conditions, three experiments (N = 784) investigated the influence of unreinforced post-acquisition CS presentations on EC effects resulting from simultaneous versus sequential pairings and pairings with single versus multiple USs. For all four types of CS-US pairings, EC effects on self-reported evaluations were reduced by unreinforced CS presentations, but only when the CSs had been rated after the initial presentation of CS-US pairings. EC effects on an evaluative priming measure remained unaffected by unreinforced CS presentations regardless of whether the CSs had been rated after acquisition. The results suggest that reduced EC effects resulting from unreinforced CS presentations are due to judgement-related processes during the verbal expression of CS evaluations rather than genuine changes in the underlying evaluative representations.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".