Inducing preference reversals in aesthetic choices for paintings: Introducing the contrast paradigm
Why this work is in the frame
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Bibliographic record
Abstract
Understanding what leads people to reverse their choices is important in many domains. We introduce a contrast paradigm for studying reversals in choices-here between pairs of abstract paintings-implemented in both within-subject (Experiment 1; N = 320) and between-subject (Experiment 2; N = 384) designs. On each trial, participants chose between a pair of paintings. A critical pair of average-beauty paintings was presented before and after either a reversal or control block. In the reversal block, we made efforts to bias preference away from the chosen average-beauty painting (by pairing it with more-beautiful paintings) and toward the non-chosen average-beauty painting (by pairing it with less-beautiful paintings). Meta-analysis revealed more reversals after reversal blocks than after control blocks, though only when the biasing manipulations succeeded. A second meta-analysis revealed that reversals were generally more likely for participants who later misidentified their initial choice, demonstrating that memory for initial choices influences later choices. Thus, the contrast paradigm has utility both for inducing choice reversals and identifying their causes.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it