Free viewing of dynamic stimuli by humans and monkeys
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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