Categorization of continuously changing ambiguous scenes
Bibliographic record
Abstract
Perceptual categorization is a fundamental task in the human visual system. Although categorical judgements made by humans usually achieve high accuracy, many studies have suggested that human perception is dependent on perceptual history such that categorical judgements are influenced and not consistent in some situations. Here we are interested in this phenomenon by studying the categorization of ambiguous scene images in continuous transitions between scene categories. We created synthesized in-door scene images based on three categories: bedroom, living room and dining room. Participants were shown sequences of scene images that smoothly changed from one category to another and were asked to respond when they perceived a change in category. Each sequence was shown in two directions such that both transitions from category A to B and B to A were presented, and the differences in the categorical responses in the two directions were compared. Participants were also shown sequences of abrupt changes with sudden shifts between categories, and these responses were used to estimate response time. We predicted the perception of the scene category to be biased toward the starting category in a transition. Our results confirm this prediction: participants’ perception in the two directions for the same transition was different such that the perceived category change was delayed, and the ambiguous scene images tended to be categorized as the starting category of a transition, thus giving rise to a perceptual hysteresis. This study found evidence that the same stimuli can be interpreted in different ways for categorical judgement. During dynamic changes of stimuli, the human visual system tends to persist in the current interpretation when resolving ambiguities. This implies that in scene perception, instead of a tendency to shift the perception, a stable perception is preferred by the human visual system in dynamically changing environments.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".