Metamer generation 2.0: using fMRI and deep learning to assess the specificity of human visual processing and encoding
Bibliographic record
Abstract
This methodological research validates the most efficient approach to generate metameric stimuli i.e., stimuli recruiting different populations of neurons in different brain regions. We first trained different encoding models to predict linearly the fMRI activation for an image in each visual ROI, based on the activation of each layer in deep convolutional neural networks (DCNN). To find the most accurate models, we then compared multiple DCNN trained to classify object categories on millions of images, and different fMRI datasets of natural images. Using the most accurate encoding models, we predicted the fMRI activation associated with an image X and iteratively found the image X’ (representing a metamer of X) with an Adam optimizer function. We compared different loss function to minimize the distance between X and X' in some parts of the visual cortex (IT to V2) but maximize the distance in other parts of visual processing (V1). For the image X, we changed the loss function parameter 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 different levels in the visual ventral stream by mapping the 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".