Evidence for adjustable bandwidth orientation channels
Bibliographic record
Abstract
The standard model of early vision claims that orientation and spatial frequency are encoded with multiple, quasi-independent channels that have fixed spatial frequency and orientation bandwidths. The standard model was developed using detection and discrimination data collected from experiments that used deterministic patterns such as Gabor patches and gratings used as stimuli. However, detection data from experiments using noise as a stimulus suggests that the visual system may use adjustable-bandwidth, rather than fixed-bandwidth, channels. In our previous work, we used classification images as a key piece of evidence against the hypothesis that pattern detection is based on the responses of channels with an adjustable spatial frequency bandwidth. Here we tested the hypothesis that channels with adjustable orientation bandwidths are used to detect two-dimensional, filtered noise targets that varied in orientation bandwidth and were presented in white noise. Consistent with our previous work that examined spatial frequency bandwidth, we found that detection thresholds were consistent with the hypothesis that observers sum information across a broad range of orientations nearly optimally: absolute efficiency for stimulus detection was 20-30% and approximately constant across a wide range of orientation bandwidths. Unlike what we found with spatial frequency bandwidth, the results of our classification image experiment were consistent with the hypothesis that the orientation bandwidth of internal filters were adjustable. Thus, for orientation summation, both detection thresholds and classification images support the adjustable channels hypothesis. Classification images also revealed hallmarks of inhibition or suppression from uninformative spatial frequencies and/or orientations. This work highlights the limitations of the standard model of summation for orientation. The standard model of orientation summation and tuning was chiefly developed with narrow-band stimuli that were not presented in noise, stimuli that are arguably less naturalistic than the variable bandwidth stimuli presented in noise used in our experiments. Finally, the disagreement between the results from our experiments on spatial frequency summation with the data presented in this paper suggests that orientation may be encoded more flexibly than spatial frequency channels.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".