Perceptual suppression of predicted natural images
Bibliographic record
Abstract
Perception is shaped not only by current sensory inputs but also by expectations generated from past sensory experience. Humans viewing ambiguous stimuli in a stable visual environment are generally more likely to see the perceptual interpretation that matches their expectations, but it is less clear how expectations affect perception when the environment is changing predictably. We used statistical learning to teach observers arbitrary sequences of natural images and employed binocular rivalry to measure perceptual selection as a function of predictive context. In contrast to previous demonstrations of preferential selection of predicted images for conscious awareness, we found that recently acquired sequence predictions biased perceptual selection toward unexpected natural images and image categories. These perceptual biases were not associated with explicit recall of the learned image sequences. Our results show that exposure to arbitrary sequential structure in the environment impacts subsequent visual perceptual selection and awareness. Specifically, for natural image sequences, the visual system prioritizes what is surprising, or statistically informative, over what is expected, or statistically likely.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.001 | 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 teacher head, 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".