Estimating time-to-collision: Further investigations of an animal model.
Bibliographic record
Abstract
When Mongolian gerbils are trained to run down a dark corridor to a visual target, kinematic analyses show that their velocity profiles are influenced by the dynamic properties of the target. Increasing or decreasing target size in synchrony with the animals' movements lead to early or late braking, respectively [1] Experiments in our laboratory [2] have shown that visual modulation of braking is dependent on a small region of visual cortex lateral to VI. The present experiments are aimed at refining our understanding of the manner in which vision modulates braking, in order to better understand the role of this region of visual cortex in motion processing. We report several new findings: 1. There is a strong negative correlation between peak running velocity and time of braking. Such a correlation is normally taken as evidence for the use of tau — a perceptual invariant that can be used to predict time-to-collision. 2. There is a linear relationship between the rate at which the target expands or contracts and the magnitude of retarded or accelerated braking, respectively, but the size of this effect is always considerably smaller than that predicted by the hypothesis that the gerbils are using tau alone. Collectively, these results provide some evidence that gerbils are using tau to estimate time-to-collision, but they also suggest that gerbils are combining these estimates with other information to compute a stable, long-term estimate of target location. (1) SunH-JCareyDPGoodaleMA(1992) A mammalian model of optic-flow utilization in the control of locomotion, Exp Brain Res, 91: 171–175. (2) ShankarSEllardC(2000) Visually guided locomotion and computation of time-to-collision in the Mongolian gerbil (Meriones unguiculatus): the effects of frontal and visual cortical lesions. Behav Brain Res, 108( 1): 21–37
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".