A Temporal Window of Facilitation in the Formation of Shape Percepts
Bibliographic record
Abstract
The human visual system must extract reliable shape information from cluttered visual scenes several times per second, yet the nature of the underlying computation remains poorly understood. Here we probe the dynamics of this process to estimate time constants that might provide clues to the underlying neural circuit. We employed a repetitive-presentation shape discrimination paradigm. On each trial, one or two instances of a single-frame (10msec) stimulus presentation were embedded in a continuous dynamic noise sequence consisting of randomly positioned and oriented line segments. Each stimulus frame consisted of a target contour, also embedded in random line segment noise. With 50% probability the target contourwas either a) an animal shape or b) a "metamer" shape. Animal shapes were line segment sequences approximating the boundaries of animal models. Metamer contours were line segment sequences with the same first-order statistics as the animal shapes, but random higher-order statistics. In the two-stimulus-frame condition, the same shape was used in both stimulus frames. The inter-stimulus interval (ISI) was varied, ranging from 0 msec to 100 msec. QUEST was used to measure the threshold number of distractor elements in each frame, for 75% correct shape discrimination. We found a significant facilitation of shape discrimination for two stimulus presentations compared to a single stimulus presentation. Interestingly, discrimination performance varied systematically and significantly as function of ISI (for 4 of 5 subjects), peaking at roughly 50 msec delay between the two stimulus frames. These results suggest a narrow temporal "window of opportunity" in which shape processing can be optimally reinforced. The fact that facilitation is not monotonic as a function of time excludes both iconic memory decay and probability summation as simple explanations for our results. The timing of the facilitation may instead reflect the time course of the recurrent processing underling rapid visual shape computation. 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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".