Size dependent increase in sensitivity to the slope of the amplitude spectrum is not solely dependent on the increased low spatial frequency representation of larger stimuli.
Bibliographic record
Abstract
We have previously investigated if threshold discrepancies between studies that have measured sensitivity to the slope of the amplitude spectrum may be due to differences between stimulus size (Richard et al., VSS 2012). We found that slope discrimination thresholds decreased as stimulus size increased, and concluded that the decrease in thresholds might be due to either the greater spatial summation of contrast in the larger stimuli, or the additional lower spatial frequencies present in larger stimuli. Here, we investigated if the latter can explain the size dependent increase in sensitivity to an amplitude spectrum slope. We used the largest stimulus size from the previous study (11.3°) containing the largest range of spatial frequencies (0.09 – 13.4 cpd), and filtered it with an ideal high-pass filter to mimic the spatial frequency content of smaller stimuli. The stimulus covered approximately 7 octaves (0.09-0.18, 0.18-0.35, 0.35-0.71, 0.71-1.42, 1.42-2.84, 2.84-5.68,5.68-11.36 cpd), and we used 6 high-pass filters (cut-off frequencies 0.12, 0.25, 0.50, 1.00, 2.00, 4.02 cpd). Thresholds did increase as higher spatial frequencies were removed from the stimulus, which indicates lower thresholds are somewhat dependent on the lower spatial frequencies present within larger stimuli. Yet, this increase did not match the decrease in threshold found in the previous study. Our results show that a minimum of three octaves - which represent a stimulus size of about 2° - was required for thresholds to begin increasing. When more than three octaves were present, thresholds remained relatively stable. Slope discrimination is influenced by spatial frequency content of the stimuli, but this does not completely explain the size effect found in the previous study. Therefore, we conclude that spatial summation of contrast may also contribute to the decrease in thresholds found when stimulus size is increased. Meeting abstract presented at VSS 2013
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".