Time to wave good-bye to phase scrambling: Creating controlled scrambled images using diffeomorphic transformations
Bibliographic record
Abstract
To isolate the neural mechanisms associated with recognizing objects from those processing basic visual properties, control stimuli are required that contain the same perceptual properties as the objects but are unrecognizable. We demonstrate that conventional methods for generating control stimuli (phase scrambling, box scrambling, texture scrambling) yield poor controls because they dramatically distort the basic visual properties (e.g., spatial frequency, perceptual organization) to which even the earliest stages of visual processing are sensitive. We developed a new scrambling method, using a diffeomorphic transformation that preserves the basic perceptual properties of the image while removing meaning. We acquired perceptual ratings to determine the least amount of scrambling necessary to remove recognition. We hypothesized that our "diffeomorphic" images would produce neural activity at the earliest stages of the visual system that more closely matched activity in response to intact images relative to the other scrambling methods. To test this hypothesis, we used the HMAX computational model of object recognition and compared the simulated neural activity at the earliest stages of the visual system (layers S1, C1, and S2) between a set of 149 images scrambled using each distortion method to their intact version. We found that scrambled "diffeomorphed" images were indistinguishable to intact images in each layer of the model, but all of the other distortion methods yielded quite different patterns. Our results indicate that "diffeomorphed" images serve as more appropriate control stimuli in neuroimaging studies that aim to disentangle the representations of perceptual and semantic object properties.
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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 | 0.001 |
| 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 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".