Evaluating Temporal Interactions Between Pairs of Shapes
Bibliographic record
Abstract
Previous work evaluating temporal interactions between shapes defined by radial frequency (RF) contours has demonstrated that thresholds for detecting curvature along a target shape increase in the presence of a forward or backward mask, though backward masks presented at stimulus onset asynchronies (SOAs) between 80-100ms result in the most dramatic elevation in thresholds (Habak et al., 2006). If a pair of masks is used, where the first mask is presented concurrently with the target, and the second mask is presented at the peak backward SOA, the two shapes exert the same magnitude of masking as is observed when a single mask appears at the same SOA onset as the first mask shown in sequence (Habak et al., 2006). The current study aimed to extend these previous finding by examining how the effect of masking changes when the second mask is presented at both positive and negative SOAs, as forward masking using pairs of masks has yet to be explored. We measured detection thresholds for an RF5 contour in the presence of a surrounding RF5 mask presented at the same time as the target, along with a second RF5 mask presented at one of five different SOAs (-100ms, -50ms, 0ms, +50ms, +100ms). Consistent with previous findings, the strength of the pair of masks remains approximately the same between the zero and +100ms SOA condition. However, two out of the three observers show a significant increase in the effect of masking when the second mask is presented at negative SOAs, where the effect of masking is strongest at a -100ms SOA. Overall, these results suggest that there exist important differences in the dynamic interactions that occur between isolated versus pairs of shapes across time. Meeting abstract presented at VSS 2016
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".