Free viewing of dynamic stimuli by humans and monkeys
Bibliographic record
Abstract
Due to extensive homologies, monkeys provide a sophisticated animal model of human visual attention. However, for electrophysiological recording in behaving animals simplified stimuli and controlled eye position are traditionally used. To validate monkeys as a model for human attention during realistic free viewing, we contrasted human (n = 5) and monkey (n = 5) gaze behavior using 115 natural and artificial video clips. Monkeys exhibited broader ranges of saccadic endpoints and amplitudes and showed differences in fixation and intersaccadic intervals. We compared tendencies of both species to gaze toward scene elements with similar low-level visual attributes using two computational models--luminance contrast and saliency. Saliency was more predictive of both human and monkey gaze, predicting human saccades better than monkey saccades overall. Quantifying interobserver gaze consistency revealed that while humans were highly consistent, monkeys were more heterogeneous and were best predicted by the saliency model. To address these discrepancies, we further analyzed high-interest gaze targets--those locations simultaneously chosen by at least two monkeys. These were on average very similar to human gaze targets, both in terms of specific locations and saliency values. Although substantial quantitative differences were revealed, strong similarities existed between both species, especially when focusing analysis onto high-interest targets.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".