Stereoacuity for physically moving targets is unaffected by retinal motion
Bibliographic record
Abstract
Westheimer and McKee (1978, Journal of the Optical Society of America, 68(4), 450-455) reported that stereoacuity is unaffected by the speed of moving vertical line targets by up to 2 deg/s. Subsequent studies found that thresholds rise exponentially at higher velocities (Ramamurthy, Patel & Bedell, 2005, Vision Research, 45(6), 789-799). This decrease in sensitivity has been attributed to retinal motion smearing; however, these experiments have not taken into account the additional effects of display persistence. Here we reassess the effects of lateral velocity on stereoacuity in the absence of display persistence, using physically moving stimuli. Luminous vertical line targets were mounted on computer-controlled motion stages. This purpose-built system permitted precise control of target position and movement, in three dimensions. In a 1IFC paradigm with 120ms viewing duration, observers fixated a stationary point and discriminated the relative depth of the two moving lines. The velocity of the line pair ranged from 0 (stationary) to 16 deg/s; each speed was tested in a separate block of trials. Our results confirm the resilience of stereoacuity to lateral retinal motion at velocities less than 2 deg/s. At higher speeds, for all observers thresholds increased marginally with speed. The rate of increase was 0.6 arc seconds per deg/s which was approximately 10 times smaller than reported by Ramamurthy et al. (2005). It is clear that stereoacuity is more robust to lateral motion than previously believed; we argue that the threshold elevation reported previously is largely due to display persistence. Meeting abstract presented at VSS 2015
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".