Adaptive recruitment of cortex-wide recurrence for visual object recognition
Bibliographic record
Abstract
Abstract Theories of the neural mechanism underpinning rapid recognition debate whether it relies solely on a feedforward sweep through the ventral stream or instead requires recurrent processing, possibly engaging additional brain regions. Here we directly tested the “adaptive recurrence hypothesis”, that attempts to unify these disparate views by proposing that additional recurrent cortical resources beyond the visual stream are recruited when feedforward processing alone is insufficient to solve object recognition. To investigate this hypothesis, we contrasted functional MRI (fMRI) and electroencephalography (EEG) responses to compare neural responses to images that are equally well recognized by humans, but that differ in whether they could be solved by a feedforward deep neural network; a computational proxy for ventral stream feedforward processing. We found that when feedforward processing in the ventral visual stream is insufficient, additional parieto-frontal networks are rapidly and transiently recruited, representationally reconfiguring the ventral visual stream. Our results reveal that object recognition flexibly adapts through fast, cortex-wide recurrence, providing a unifying framework for competing theories of visual recognition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".