Bridging visual developmental neuroscience and deep learning: challenges and future directions
Bibliographic record
Abstract
I will synthesize the work presented in this symposium and provide an outlook for the steps ahead in bridging visual developmental neuroscience and deep learning. I will first paint a picture of the emerging understanding of how categorical object representations in visual cortex arise over the course of development. The answer to this question can be considered to lie on a continuum, with one extreme suggesting that we are born with category-selective cortical modules, and the other extreme suggesting that categorical object representations in visual cortex arise from the structure of visual experience alone. Emerging evidence from both experimental and computational work suggests that the answer lies in between: categorical object representations may arise from an interplay between visual experience and constraints imposed by behavioral pressures as well as inductive biases built into our visual system. This interplay may yield the categorical object representations we see in adults, which emphasize natural categories of ecological relevance such as faces and animals. Deep learning provides a powerful computational framework for putting this hypothesis to the test. For example, unsupervised learning objectives may provide an upper bound on what can be learnt from the structure of visual experience alone. Furthermore, within the deep learning framework, we can selectively turn on constraints during the learning process and examine effects on the learnt object representations. I will end by highlighting challenges and opportunities in realizing the full potential of deep learning as a modeling framework for the development of categorical object representations.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.023 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".