Head-Mounted Mobile Eye-Tracking in the Domestic Dog: A New Method
Bibliographic record
Abstract
Humans rely on dogs for countless tasks, ranging from companionship to highly specialized detection work. In their daily lives, dogs must navigate a human-built visual world, yet comparatively little is known about what dogs visually attend to as they move through their environment. Real-world eye-tracking, or head-mounted eye-tracking, allows participants to freely move through their environment, providing more naturalistic results about visual attention while interacting with objects and agents. In dogs, real-world eye-tracking has the potential to inform our understanding of cross-species cognitive abilities as well as working dog training, however a robust and easily deployed head-mounted eye-tracking method for dogs has not previously been developed and tested. We present a novel method for real-world eye-tracking in dogs, using a simple head-mounted mobile apparatus mounted onto goggles designed for dogs. This new method, adapted from systems that are widely used in humans, allows for eye-tracking during more naturalistic behaviors, namely walking around and interacting with real-world stimuli, as well as reduced training time as compared to traditional stationary eye-tracking methods. We found that while completing a simple forced-choice treat-finding task, dogs look primarily to the treat, and we demonstrated the accuracy of this method using alternative gaze-tracking methods. Additionally, eye-tracking revealed more fine-grained time course information and individual differences in looking patterns.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| opus | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".