Generate visual metamers using fMRI and deep learning to assess the specificity of human visual processing and encoding
Bibliographic record
Abstract
This research proposes a methodological approach allowing to study the representation and encoding of the different levels of visual processing by generating metameric visual stimuli – i.e., stimuli recruiting different populations of neurons at certain levels of processing but not others. To that end, we used fMRI datasets from natural pictures and developed an encoding model predicting fMRI activation for an image, based on the activation for the same image in a robust Resnet-50 pre-trained on millions of images. Using the encoding model, we then predicted the fMRI activations associated with an image X and find the image X’ (representing a metamer of X) using an Adam optimizer. The loss function minimized the distance between X and X' in some parts of the visual cortex (IT to V2) but maximized the distance in other parts of visual processing (V1). So, for the image X, we changed the loss function and calculated the images X', X'' and X''' which represent gradual metamers of image X for each part of the visual system, where X' is different from X in V1, X'' 'is different from X in V1 and V2, X''' in V1, V2 and V4. This approach allows a better understanding of the role of each level of perceptual processing by constructing a mapping of activated brain areas in a more interpretable space - that of stimuli - and make possible the development of more precise experimental protocols in visual neuroscience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".