Contrast dependence of spatial summation revealed by classification image analysis
Bibliographic record
Abstract
Detection of low-contrast luminance-defined stimuli can involve spatial summation over a large portion of the visual field. However prior psychophysical results suggest that the summation region may shrink substantially in the presence of high-contrast masking gratings or noise (Legge & Foley, 1980; Kersten, 1984). This may be related to recent findings that the extent of spatial summation in V1 neurons depends upon contrast (Sceniak et al., 1999). Here we use a classification image technique to directly test whether the psychophysical receptive field for a simple stimulus (a vertical edge in noise) is dependent upon contrast. Classification images for yes/no edge detection and 2IFC contrast discrimination were estimated at signal (edge) contrasts ranging from 0.6%–50% and noise contrasts ranging from 4%–50%. Estimated receptive fields were found to be well approximated by elongated 2D Gaussian derivative or Gabor filters, and were much longer than V1 receptive fields. Both signal contrast and external noise contrast were found to strongly affect the extent of summation: a good predictor for receptive field size and shape was found to be signal-to-noise ratio (SNR). As SNR was increased from as low as 0.07 to as high as 2.0, receptive fields were found to shrink dramatically, by a factor of more than 5 in width and by roughly 60% in length. The dependence of summation on SNR may be due to ‘late’ noise sources and/or to nonlinear summation mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".