From Tasks to Topology: Dorsal and Ventral Streams Emerge in Optimized Neural Networks
Bibliographic record
Abstract
Abstract The primate visual system is organized into dorsal and ventral pathways, classically linked to visuomotor control and perception. A long-standing question is whether this division reflects intrinsic architectural priors or emerges from task demands. We trained a single convolutional network to perform classification and grasp prediction of 3D objects, without imposing modular structure. Dual-stream topology - functionally distinct visuomotor and perceptual pathways - emerged spontaneously with rich cross-communication. Shapley value analyses revealed that action- and perception-selective features developed progressively across depth, reflecting task-driven hierarchical specialization. Time-resolved EEG showed that model activity mapped onto dissociable temporal components in human cortex: ventral-aligned signals emerged early and late, where dorsal- and ventral-aligned responses coincided in the intervening interval. These results demonstrate that task optimization alone can explain core features of dorsal-ventral organization, and that distinct temporal roles for perception and action arise naturally atop a shared feedforward scaffold, without requiring architectural hard-coding or recurrence.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".