In the real world, people prefer their last whisky when tasting options in a long sequence
Bibliographic record
Abstract
When people in laboratory studies sample products in a sequence, they tend to prefer options presented first and last. To what extent do these primacy and recency effects carry over to real-world settings where numerous sources of information determine preferences? To investigate this question, we coded archival data from 136 actual whisky tastings each featuring seven whiskies. We analyzed people's ratings of whiskies featured at different serial positions in the tastings. We found a recency effect: people gave their highest rating to whiskies in the last position, and voted the last whisky as their favorite more frequently. This recency effect persisted when we controlled for the counter explanation that whiskies with higher alcohol content tended to occupy later serial positions. The recency effect also persisted when we controlled for the age of the whiskies. Taken together, our findings suggest that the order of presentation matters in real-world settings, closely resembling what happens in laboratory settings with longer sequences of options.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".