Size matters: Increasing stimulus size reduces thresholds in an amplitude spectrum discrimination task.
Bibliographic record
Abstract
The relation of spatial content to spatial scales falls at a 1/fα in natural scenes (Field, 1987). Previous studies have demonstrated that the primate visual system is sensitive to this regularity by investigating peak alpha discrimination sensitivity. The findings surrounding human sensitivity have shown to vary considerably, ranging from peaks in sensitivity of alphas (i.e., amplitude spectrum slope) between 1.0-1.3, or peaks across the entire range of alphas – excluding 0.8 (Knill et al., 1990; Tadmore & Tolhurst, 1994; Hansen & Hess, 2006), while still, some have found no peak sensitivity across all alphas (Thompson & Foster, 1997; Johnson et al., 2011). One possible account of these differences may lie in the size of stimuli previously presented to participants, which has ranged widely from 0.75° to 10° of visual angle. Although the 1/fα relation has long been considered scale invariant (Field, 1987), it is likely that alpha discrimination may be benefited at coarser scales, therefore reducing thresholds and affecting peak sensitivity. We presented 7 different stimulus sizes (ranging from 1° to 8° in steps of 1.84) at the fovea and measured alpha discrimination sensitivity of 1/f noise stimuli. Results indicate that sensitivity to select alphas disappeared when stimulus size increased and surpasses 2° of visual angle, while smaller sizes exhibited similar peak sensitivity (1.0 – 1.3) as was found in more recent studies (e.g. Hansen & Hess, 2006). In addition, overall alpha discrimination thresholds decreased as a function of stimulus size and demonstrated a size tuning to the amplitude spectrum slope, which explains part of the variability between measured thresholds of studies that presented small stimuli (Knill et al., 1991) as opposed to larger stimuli (Johnson et al., 2011; Thompson & Foster, 1997). Meeting abstract presented at VSS 2012
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".