Cue dynamics underlying rapid detection of animals in natural scenes
Why this work is in the frame
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Bibliographic record
Abstract
Humans are known to be good at rapidly detecting animals in natural scenes. Evoked potential studies indicate that the corresponding neural signals can emerge in the brain within 150 msec of stimulus onset (S. Thorpe, D. Fize, & C. Marlot, 1996) and eye movements toward animal targets can be initiated in roughly the same timeframe (H. Kirchner & S. J. Thorpe, 2006). Given the speed of this discrimination, it has been suggested that the underlying visual mechanisms must be relatively simple and feedforward, but in fact little is known about these mechanisms. A key step is to understand the visual cues upon which these mechanisms rely. Here we investigate the role and dynamics of four potential cues: two-dimensional boundary shape, texture, luminance, and color. Results suggest that the fastest mechanisms underlying animal detection in natural scenes use shape as a principal discriminative cue, while somewhat slower mechanisms integrate these rapidly computed shape cues with image texture cues. Consistent with prior studies, we find little role for luminance and color cues throughout the time course of visual processing, even though information relevant to the task is available in these signals.
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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 | 0.000 | 0.000 |
| 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 it