Effects of onset-transients on the perception of visual motion
Bibliographic record
Abstract
The sudden onset of a visual stimulus causes a short burst of activity in motion-sensitive areas of the brain. Because such onset-transients are by definition non-selective for motion direction, they are likely to disrupt motion perception during the period immediately following the appearance of a novel visual stimulus. We examined the effects of onset-transients on visual perception and on neuronal responses in MT. Human subjects discriminated the direction of a single motion step for Gabor patches of varying sizes (5.3–18.5 degrees) and contrasts (98% or 1.5%). We also varied the time between the appearance of the stimulus and its motion (the motion onset delay). When the motion onset delay was short (35ms), subjects showed increasing discrimination thresholds for larger sizes of high-contrast gratings (similar to Tadin et al. 2003). However, for longer motion onset delays ([[gt]]120ms), discrimination thresholds were lower and independent of stimulus size. When the motion onset delay period was interrupted by a short gap (12–188ms) which ended 35ms before the motion onset, the size-dependence of the thresholds returned. To examine the neuronal basis for these findings, we recorded the responses of MT neurons to moving gratings of different size, contrast and motion onset delay. In general the sudden onset of a moving stimulus caused a short period (~30ms) of non-direction-selective activity that was strongest for large, high-contrast gratings. These onset-transients effectively masked the earliest portions of the directional MT responses. When the motion onset delay was increased, the early MT responses were again direction-selective. Our results suggest that onset-transients temporarily disrupt motion processing. This may help to explain psychophysical phenomena such as the flash-lag illusion, the Frohlich effect, and the surprising decrease in motion sensitivity for large high-contrast stimuli reported recently by others (Tadin et al. 2003).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".