Bibliographic record
Abstract
Adaptation to a sinusoidally modulated contour produces a shift in the apparent shape frequency of a subsequently presented test contour, in a direction away from that of the adaptation stimulus. The phenomenon has been termed the ‘shape-frequency after-effect’ or SFAE (Kingdom & Prins, 2005, JOV, 5, 464). We describe an even larger after-effect of contour-shape amplitude, which we term the ‘shape-amplitude after-effect’ or SAAE. What underlies these after-effects? They occur even when the shape-phase of the contour is randomly changed every half second during the adaptation period, which would tend to lead one to reject the idea that local tilt (or orientation) after-effects (TAEs) are the underlying cause. However we show that even with adaptation contour phase-randomization, the geometrical relationships between adaptor and test are such that the TAE is difficult to rule out. We provide evidence against the TAE: sizeable after-effects are obtained for adaptors that are sine-wave-shaped and tests that are square-wave-shaped. In addition we test, and reject, three other candidates besides local orientation: global average curvature, local signed curvature and global spatial frequency. We suggest that contour shape after-effects result from adaptation to the sizes of local, partly-bounded regions defined by the contour's shape. This in turn implies that contour shape is processed by mechanisms that encode the sizes of partly-bounded regions of the stimulus.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".