Characterization of a food image stimulus set for the study of multi-attribute decision-making
Why this work is in the frame
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Bibliographic record
Abstract
<ns4:p>Everyday decisions are generally made between options that vary on multiple different attributes. These might vary from basic biological attributes (e.g. caloric density of a food) to higher-order attributes like healthiness or aesthetic appeal. There is a long tradition of studying the processes involved in explicitly multi-attribute decisions, with information presented in a table, for example. However, most naturalistic choices require attribute information to be identified from the stimulus during evaluation or value comparison. Well-characterized stimulus sets are needed to support behavioral and neuroscience research on this topic. Here we present a set of 200 food images suited to the study of multi-attribute value-based decision-making. The set includes food items likely to appeal to those accustomed to North American and European diets, varying widely on the subjective attributes of visual-aesthetic appeal (“beauty”), tastiness and healthiness, as rated by healthy young Canadian participants (N=30-67). The images have also been characterized on objective characteristics relevant to food decision-making, including caloric density, macronutrient content and visual salience. We provide all attribute data by image and show the extent to which attributes are correlated across the stimulus set. We hope this stimulus set will accelerate progress in the study of naturalistic, value-based decision-making.</ns4:p>
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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