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 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".