A role for spatial alignments in early vision
Bibliographic record
Abstract
Purpose: We set out to investigate the interaction between suprathreshold, spatially broadband stimuli in early vision. We wondered whether Foley's divisive inhobition model, derived for stimuli of different orientation, when applied to stimuli of different spatial frequency could predict the different masking effects from a sinewave versus a squarewave stimulus. Methods: Odd man out paradigm was used to measure the contrast discrimination threshold of sinewave(S) for three types of contrast maskers; a sinewave (S), a squarewave(Q) and a missing fundamental waveform(M). We wondered if we could predict the response to a squarewave from a knowledge of the responses to a sinewave and missing fundamental. Results: For S-S condition, a typical TvC function was found. For the S-Q condition, the TvC function exhibited less facilitation and more inhibition. For S-M configuration, no facilitatory effect was found at low pedestal contrasts and only limited inhibition at high pedestal contrasts. The effects of the squarewave masker could not be predicted, using Foley's model, from the responses of the sinewave and missing fundamental results; the squarewave masker exhibited more than the predicted amount of inhibition at high pedestal contrasts. However, when we phase-scrambled the squarewave and missing fundamental waveforms, Foley's model was able to predict the phase-scrambled squarewave result on the basis of the other two. Conclusion: Our data concerning spatial frequency broadband stimuli, can be described by Foley's divisive model, but only for phase-scrambled stimuli. We conclude that the phase-alignment of spatial frequency components does not go unrecognized by early visual mechanisms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".