Evidence for the role of cognitive resources in flavour–flavour evaluative conditioning
Bibliographic record
Abstract
One way that dis/likes are formed is through evaluative conditioning (EC). In two experiments we investigated the role of cognitive resources in flavour-flavour conditioning. Both experiments employed an EC procedure in which three novel flavoured conditioned stimuli (CSs) were consumed. One was consumed with a pleasant unconditioned stimulus (US; CS+ sugar), one with an aversive US (CS+ saline), and a third with plain water (CS-). Half of participants in each experiment performed a cognitive load task during conditioning. We measured EC using self-reported measures of liking (Experiments 1 and 2) and an indirect measure of liking: drink pick-up latency (Experiment 2). In both experiments, differential EC was observed in the no cognitive load condition but not in the cognitive load condition. This pattern of results was observed in self-reported measures of liking as well as in the drink pick-up latency data. Results from both experiments show that EC occurs only when there are sufficient cognitive resources available. The fact that this was observed using both self-reported and indirect measures suggests that insufficient cognitive resources affect learning itself rather than merely obstructing reporting.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".