Crossing category boundaries: Perceptual hysteresis for scenes even with endpoint preview
Bibliographic record
Abstract
In dynamically changing environments, how does our visual system make quick perceptual decisions based on information that is actively changing? The hysteresis effect suggests that when resolving perceptual ambiguity, people tend to stick with their current interpretation of sensory information and find it difficult to change their perception until there is a noticeable and significant change in input. We here demonstrate this effect for dynamically changing scenes. We further explored how prior exposure to target scene would affect the hysteresis effect. We used indoor scene images generated with a Generative Adversarial Network to create smooth yet realistic transitions between scene categories. Participants were asked to report when they perceived a shift in category during these transitions. Before each trial, participants were provided with information about the scene category at the end of the transition, either in the form of words, images, or both. Each transition was repeated in both directions (A to B and B to A), and the differences in responses between the two opposite directions were analyzed. Our findings indicated that exposure to words or images had no effect on perceptual hysteresis. Even when participants had knowledge of the target category, their perception of the image categories was still biased towards the initial category. This result suggests that neither semantic knowledge nor visual representation of the future can influence the hysteresis effect. The top-down knowledge of the future direction does not impact or eliminate the conservatism of the visual system. This has important implications, indicating that hysteresis is an innate characteristic of the visual system and is not easily influenced by higher-level control. Thus, perceptual conservatism is likely instrumental for the apparent stability of visual perception in most real-world settings.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".