High-level visual cortex representations are organized along visual rather than abstract principles
Bibliographic record
Abstract
Abstract A fundamental question dominating the study of human visual cortex is whether it is organized along visual or semantic information. This question is unresolved, and the controversy has been rekindled by the recent report that surprisingly, revealed that the representations of textual description of images by linguistic artificial networks’ successfully predict the response of high-level visual cortex to visual images. These findings appear to support a linguistic, abstract, organizing principle of human visual cortex. Here, using iEEG recordings from high level visual cortex in patients, we contributed to this debate, by testing the hypothesis that this linguistic alignment is restricted to textual descriptions of the visual content of the images (visual text) and does not extend to abstract textual descriptions (abstract text). We selected images that depict familiar faces and places, as these images allow for the best dissociation between these two types of text and generated their visual and abstract (e.g., name and biography of a person) textual descriptions. We then predicted the relational structures of the iEEG response to the images using their textual representations based on a large language model and the image representation based on a convolutional neural network. Neural relational-structures in high-level visual cortex were similarly predicted by images and visual-text but not abstract-text representations. Abstract text best predicted responses of the fronto-parietal cortex to the images. These results demonstrate that visual-language alignment in high-level visual cortex is limited to visually grounded language.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".