Evaluating curvature aftereffects with radial frequency contours
Bibliographic record
Abstract
Psychophysical and physiological evidence demonstrates that global shape coding depends on mechanisms located at intermediate levels of visual processing. Evidence also suggests that these mechanisms are vulnerable to adaptation techniques historically used to probe mechanisms underlying performance in lower-level visual tasks. We explored the nature of these global shape aftereffects using radial frequency (RF) patterns, where stimuli are defined in terms of deformations from a circular pattern. On each trial, subjects adapted to a RF pattern with a high (x15 threshold) amplitude for 5 seconds, followed by a brief (53 ms) test RF that was either in-phase or anti-phase to the adapted RF. Subjects identified the phase of the test RF pattern using a 2AFC paradigm. Performance was evaluated by determining the RF amplitude at which subjects equally classified the test stimulus as the in-phase or anti-phase pattern (Point of Subjective Equality (PSE)). With no adaptation, subjects were exquisitely accurate when classifying RF patterns (PSE=1.0arcsec). After adaptation, the PSE shifted towards the pattern that was in-phase with the adapted RF (Peq=52.3arcsec), demonstrating that subjects were more likely to classify the test RF as the anti-phase pattern. This perceived shift is equivalent to a stimulus that is modulated 2–3x above threshold under these conditions. When subjects adapted to a RF pattern with a larger number of cycles, on the other hand, the PSE did not change. Preliminary results suggest that the strength of the shape-specific aftereffect is similar when adapting to either a high (90%) or low (10%) contrast RF pattern. Together, these results suggest that the mechanisms adapted by RF patterns code information that is specific to the geometry of the stimulus, and are located beyond those responsible for contrast gain control.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".