Multiplication of 1st-stage inputs to curvature detectors
Bibliographic record
Abstract
Aim. How are 1st-stage inputs to the mechanisms that encode contour curvature combined? One possibility is that they are multiplied, which would increase the mechanism's selectivity to curvature (e.g. Poirier & Wilson, Vis. Res. 2006, 46, 2443–2455). Here we demonstrate a method for revealing multiplication in curvature coding. Suppose a curvature-tuned mechanism receives a small number (say anywhere between 2 and 6) of 1st-stage inputs whose receptive fields are positioned along a curve and whose responses are multiplied. Consider how such a mechanism would respond to a matched curved line, but one broken into segments of equal length, with gap length equal to segment length. Simulations reveal a pronounced dip in the response of the mechanism to intermediate segment lengths, a dip that appears to only happen when the 1st-stage inputs are multiplied (or combined by an operation equivalent to multiplication). Methods. We tested for such a dip in two shape after-effects, the shape-frequency and shape-amplitude after-effects; these are the shifts one obtains in respectively the apparent shape-frequency and apparent shape-amplitude of a sinusoidal-shaped test contour following adaptation to a contour of a slightly different shape-frequency/amplitude. We have recently shown that both after-effects are mediated by mechanisms that encode curvature (Gheorghiu & Kingdom, 2007, Vis. Res., in press). Results. Using segmented adaptors of various segment lengths we found that both after-effects show a pronounced dip at similar intermediate segment lengths. Conclusion. 1st-stage inputs to curvature-tuned mechanisms are multiplied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".