Size and shape-frequency after-effects: same or different mechanism?
Bibliographic record
Abstract
Aim. The size, or luminance spatial frequency after-effect (LFAE) is the phenomenon in which adaptation to a luminance grating of given spatial frequency causes a shift in the perceived spatial frequency of a grating away from that of the adapting grating (Blakemore & Sutton, 1969, Science, 166, 245–7).The analogous shape-frequency after-effect (or SFAE) is the phenomenon in which adaptation to a sine-wave-shaped contour causes a shift in the apparent shape-frequency of a test contour away from that of the adapting stimulus (Gheorghiu & Kingdom, 2007, Vis.Res., 47, 834–44). It is widely believed that the LFAE is mediated by luminance-spatial-frequency-selective channels, while it has been suggested that the SFAE is mediated by curvature-selective channels. However it is possible that the SFAE is mediated by the same mechanism as underlies the LFAE, in spite of the fact that the stimuli involved have little Fourier energy in common. Methods. We measured both SFAEs and LFAEs using a conventional staircase procedure. The contour-shape stimuli were sine-wave-shaped contours and edges; luminance stimuli were sine-wave, square-wave and line luminance gratings. The rationale was that if the after-effects were reduced when adaptor and test stimuli were of a different class (shape versus luminance), this suggested that the SFAE and LFAE were mediated by different mechanisms. Results. While similar-sized after-effects were found for same-class adaptor-and-test stimuli (either shape or luminance), the after-effects were greatly reduced for different-class adaptor-and-test stimuli (shape adaptors and luminance tests, or vice-versa). Conclusion. SFAEs are mediated by different mechanisms to the LFAE.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".