Exploiting large-scale neuroimaging datasets to reveal novel insights in vision science
Bibliographic record
Abstract
Building quantitative models of neural activity in the visual system is a long-standing goal in neuroscience. Though this research program is fundamentally limited by the small scale and low signal-to-noise of most existing datasets, with the advent of large-scale datasets it has become possible to build, test, and discriminate increasingly expressive competing models of neural representation. In this talk I will describe how the scale of the 7T fMRI Natural Scenes Dataset (NSD) has made possible novel insights into the mechanisms underlying scene perception. We harnessed recent advancements in linguistic artificial intelligence to construct models that capture progressively richer semantic information, ranging from object categories to word embeddings to scene captions. Our findings reveal a positive correlation between a model's capacity to capture semantic information and its ability to predict NSD data, a feature then replicated with recurrent convolutional networks trained to predict sentence embeddings from visual inputs. This collective evidence suggests that the visual system, as a whole, is better characterized by an aim to extract rich semantic information rather than merely cataloging object inventories from visual inputs. Considering the substantial power of NSD, collecting additional neuroimaging and behavioral data using the same image set becomes highly appealing. We are expanding NSD through the development of two innovative datasets: an electroencephalography dataset called NSD-EEG, and a mental imagery vividness ratings dataset called NSD-Vividness. Datasets like NSD not only provide fresh insights into the visual system but also inspire the development of new datasets in the field.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".