Can the Formation of Conditioned Attitudes Be Intentionally Controlled?
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 valenced unconditioned stimulus (US). Counter to views that EC is the product of automatic learning processes, recent research has revealed various characteristics of nonautomatic processing in EC. The current research investigated whether the formation of conditioned attitudes can be intentionally controlled. Whereas EC effects on self-reported evaluations were reduced (enhanced) when participants were instructed to prevent (promote) the influence of CS-US pairings, EC effects on an evaluative priming measure remained unaffected by control instructions. Moreover, although EC effects on self-reported evaluations varied as a function of evaluative priming effects and recollective memory for CS-US pairings, motivation to control the influence of CS-US pairings qualified only the predictive relation of recollective memory. The results highlight functionally distinct contributions of uncontrollable encoding-related processes and controllable expression-related processes to EC effects.
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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 | 0.006 |
| 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 | 0.000 |
| 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".