Sequential dependency for affective appraisal of food images
Bibliographic record
Abstract
Abstract How we perceive the world is not solely determined by our experiences at a given moment in time, but also by what we have experienced in our immediate past. Here, we investigated whether such sequential effects influence the affective appraisal of food images. Participants from 16 different countries (N = 1278) watched a randomly presented sequence of 60 different food images and reported their affective appraisal of each image in terms of valence and arousal. For both measures, we conducted an inter-trial analysis, based on whether the rating on the preceding trial(s) was low or high. The analyses showed that valence and arousal ratings for a given food image are both assimilated towards the ratings on the previous trial (i.e., a positive serial dependence). For a given trial, the arousal rating depends on the arousal ratings up to three trials back. For valence, we observed a positive dependence for the immediately preceding trial only, while a negative (repulsive) dependence was present up to four trials back. These inter-trial effects were larger for males than for females, but independent of the participants’ BMI, age, and cultural background. The results of this exploratory study may be relevant for the design of websites of food delivery services and restaurant menus.
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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 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".