Motion processing: the most sensitive detectors differ in temporally localized and extended noise
Bibliographic record
Abstract
Contrast thresholds for discriminating orientation and direction of a drifting, oriented grating are usually similar to contrast detection thresholds, which suggest that the most sensitive detectors are labeled for both orientation and direction (Watson and Robson, 1981). This was found to be true in noiseless condition, but Arena et al. (2013) recently found that this was not true in localized noise (i.e., noise having the same spatiotemporal window as the target) as thresholds for discriminating direction were higher than for discriminating orientation. They suggested that this could be explained by the fact that there are more neurons selective to orientation than direction. Another possible interpretation is that, unlike contrast thresholds in absence of noise, the most sensitive detectors in localized noise were labeled for orientation, but not for direction. This hypothesis is supported by recent findings showing different processes operating in localized and extended noise (i.e., full-screen, continuously displayed noise, Allard and Cavanagh, 2011). In the current study, we evaluated contrast thresholds for orientation and direction discrimination tasks in noiseless conditions, and in noise that was either spatially localized or extended, and temporally localized or extended. We found similar orientation and direction thresholds in absence of noise and in temporally extended noise, but greater direction thresholds in temporally localized noise. This suggests that in noiseless and temporally extended noise the most sensitive detectors were labeled for both orientation and direction (e.g., direction-selective complex cells), whereas in temporally localized noise the most sensitive detectors were labeled for orientation but not direction (e.g., simple cells). We conclude that to avoid violating the noise-invariant processing assumption, external noise paradigms investigating motion processing should use noise that is temporally extended, not localized.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".