No pointwise nonlinearity in shape discrimination
Bibliographic record
Abstract
Purpose: Human observers are often modelled as linear discriminators (e.g., as noisy cross-correlators) in shape discrimination tasks. One prediction of such models is that the influence of a stimulus pixel on an observer's response is proportional to the contrast at that pixel. We used a new variant of the reverse correlation technique to test this prediction. Methods: Observers performed several two-alternative identification tasks in external white noise: dot detection, orientation discrimination, and face discrimination, as well as shape discriminations involving illusory contours and occluded contours. We computed classification images to determine what regions of the stimuli observers used to perform the task, and within these regions we computed the correlation between the contrast level at each pixel and the observer's responses. Results: We confirmed the prediction of the linear discriminator model: the influence of each pixel on the observer's decision was linearly related to the contrast at that pixel. This was true even when observers used illusory and occluded contours to perform the task. Conclusions: These results have several implications. (1) Either there is no early transduction nonlinearity, or any such nonlinearity is compensated for and effectively undone during shape discrimination. This is consistent with Chubb and Nam's (2000) findings for judgements of texture luminance and texture variance. (2) The visual system is optimized for an approximately Gaussian noise distribution in the external world. (3) Illusory and occluded contours are used in the same way as luminance-defined contours in threshold shape discrimination tasks. (4) Observers are linear discriminators in threshold shape discrimination tasks.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".