Cortical magnification analysis of shape adaptation reveals early curvature coding mechanisms
Bibliographic record
Abstract
Background. Sustained stimulation of the retina with a circular stimulus produces the impression of a polygon (Khuu et al 2002, Ito 2012). What could explain this surprising phenomenon? A low-level account posits an opponency between localized curvature-tuned neurons in early visual cortex, while a high-level account assumes an opponency between neurons in higher visual areas coding whole shapes. To test these competing accounts, we measure how the illusion varies with the size and eccentricity of the stimulus. While a high-level account predicts invariance to size and position, a low-level account predicts that the order of the perceived polygon will increase with the size and decrease with the eccentricity of the stimulus, due to cortical magnification. Method. We employed the method of Sakurai (2014) to rapidly induce the circle-polygon illusion. The stimulus was a static dark outline circle augmented with a 2 Hz pulsed luminance gradient around the inner border. Both the radius and eccentricity of the stimulus were varied in a crossed design over 1-8 deg. Observers indicated their percept by selecting from a palette of shapes that included a circle and polygons of orders 3-10, and also reported the strength of the polygon percept on a 0-10 scale. Results. As previously reported, the strength of the percept increased with eccentricity. Importantly, the mean order of the perceived polygon systematically increased with stimulus size and decreased with eccentricity. To relate this more clearly to the underlying neural substrate, we computed the circumference of each stimulus in both retinal and cortical coordinates, taking cortical magnification into account. Linear regression analysis reveals that the cortical size of the stimulus is a significantly better predictor of perceived polygon order. Implications. These results suggest that the circle-polygon illusion arises from and reveals the architecture of an early visual opponent mechanism for curvature coding.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".