Unsupervised object learning explains face but not animate category structure in human visual cortex
Bibliographic record
Abstract
Deep convolutional neural networks (DCNNs) are currently the best computational models of human vision. However, DCNNs cannot fully explain the representation of natural object categories in high-level human visual cortex. DCNNs are classically trained to recognize objects using supervised learning, while humans rely heavily on unsupervised learning. Here, we test whether unsupervised learning yields an object representation that more strongly emphasizes natural categories and better explains human brain activity than supervised learning. We trained ResNet50 on the ImageNet database, using both supervised and contrastive unsupervised learning. For both types of learning, we characterized the network’s internal representation of 96 real-world object images over the course of 200 training epochs. We fitted a category model to the resulting learning trajectories using ordinary least squares to measure the strength of category clustering for faces, animate objects and inanimate objects. We then compared the networks’ learning trajectories and clustering strengths with the object representation in high-level visual cortex, measured with fMRI in human adult observers. We focused our analysis on the deepest convolutional layer and used bootstrap resampling for statistical inference. We found that the unsupervised network better explains the human object representation than the supervised network (FDR corrected p<0.05 for 80 percent of epochs). This difference emerges relatively early in training and increases as learning progresses. Better performance of the unsupervised network is partly driven by its ability to discover natural face category structure in the input images. Importantly, both supervised and unsupervised models fall short of predicting category clusters of animate and inanimate objects in the human brain data (FDR corrected p<0.05 for all epochs), suggesting that these categories are difficult to learn from static images alone. Our findings suggest that the natural category structure in the human high-level visual cortex may arise from unsupervised learning during development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".