Bibliographic record
Abstract
Purpose. It is well established that the detection of a luminance-defined Gabor is improved if measured in the presence of two high contrast aligned flanking Gabors and this is termed collinear facilitation. Here the temporal properties in collinear facilitation were investigated in order to better the understanding of its underlying mechanism. Methods and Results. Collinear facilitation was measured at different onset times of the target (2 cpd, 1 octave bandwidth, 80ms presenting time) when the contrast of the flanks was modulated at 1 Hz (1 sec) and the results showed that facilitation occurred in the spatially out-of-phase condition, suggesting a long-lasting, sustained facilitatory effect. In experiment 2, the order between target and flanks in collinear facilitation was investigated by varying the ISI between target and flanks, both of which were presented for 50ms. Results were collected for 3 different target-flank distances (2, 3, 6 λ). The results showed that the amount of facilitation decreased with the time lag between target and flanks and the peak was shifted with the target-flanks distance. However, we also found the peak facilitatory effect occurred when the target preceded the flanks. The results showed that maximal facilitation occurs at or before (not after) flank presentation, suggesting fast dynamics. Conclusion. The dynamics of collinear facilitation are complex. Facilitation occurs rapidly (tens of milliseconds) lowering thresholds at and sometimes before flank presentation but its effects are sustained (hundreds of milliseconds).
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".