Recurrent Processing in the Formation of Shape Percepts
Bibliographic record
Abstract
The human visual system must extract reliable object information from cluttered visual scenes several times per second, and this temporal constraint has been taken as evidence that the underlying cortical processing must be strictly feedforward. Here we use a novel rapid reinforcement paradigm to probe the temporal dynamics of the neural circuit underlying rapid object shape perception and thus test this feedforward assumption. Our results show that two shape stimuli are optimally reinforcing when separated in time by ∼60 ms, suggesting an underlying recurrent circuit with a time constant (feedforward + feedback) of 60 ms. A control experiment demonstrates that this is not an attentional cueing effect. Instead, it appears to reflect the time course of feedback processing underlying the rapid perceptual organization of shape. SIGNIFICANCE STATEMENT: Human and nonhuman primates can spot an animal shape in complex natural scenes with striking speed, and this has been taken as evidence that the underlying cortical mechanisms are strictly feedforward. Using a novel paradigm to probe the dynamics of shape perception, we find that two shape stimuli are optimally reinforcing when separated in time by 60 ms, suggesting a fast but recurrent neural circuit. This work (1) introduces a novel method for probing the temporal dynamics of cortical circuits underlying perception, (2) provides direct evidence against the feedforward assumption for rapid shape perception, and (3) yields insight into the role of feedback connections in the object pathway.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".