Macaque monkeys follow gaze cues of human avatars
Bibliographic record
Abstract
Joint attention is a fundamental ability of humans and other social primates. Gaze direction could be informative of the behavioral relevance of objects in the environment. Paradigms to explore joint attention usually involve measurements of gaze in at least two subjects making it difficult to conduct with non-human primates. Here we show a novel paradigm using avatars presented in a virtual environment while exploring gaze behavior and joystick responses of monkeys in an experimental setup. We trained two rhesus monkeys to respond to a human avatar's attention by moving a joystick towards the gazed-at object. We designed our social cues by applying natural eye and head movements on a highly realistic human avatar (released by Reallusion). Each trial commenced with the avatar gazing at the animal, while four identical objects were presented at the screen’s corners. After 500 ms, the avatar randomly shifted gaze towards one object, cueing the animal to move the joystick toward that object to obtain a juice reward. In 10% of the trials, the avatar gaze moved in between two objects. Eye positions were measured using EyeLink (SR Research). The animals followed the avatar’s gaze, achieving 90% correct trials. We trained a classifier to identify the animals' choice from their eye positions during the cue period (350 ms), achieving an 80% accuracy. The classifier’s accuracy decreased to 53% in catch trials when the avatar directed gaze to intermediate positions between objects. Here, the animals chose one of the two objects closest to the avatar’s gaze final position, further indicating the animals followed the avatar gaze cues. Our results demonstrate the use of human avatars in experimental setups to explore joint attention in macaque monkeys. It also demonstrates a degree of cognitive flexibility and extrapolation of human gaze cues in macaques.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".