Ontogeny of dogs’ sensitivity to the human’s attentional state: Do the eyes have it?
Bibliographic record
Abstract
Dogs have been shown to differentiate attentional states in humans in competitive situations over food or when they are told to obey a command. Here we test to what extent dogs' attention to a human's attentional state might be learnt ontogenetically. We exposed Labrador puppies (N=90) of different ages (6 weeks, N=18; 8 weeks, N=19; 10 weeks, N=18; 12 weeks, N=17; 16 weeks, N=18 and adult Labradors (between 1 and 11 years old), N=25) to a social interaction with a human experimenter during which the attention of the experimenter systematically varies (she either has her eyes open, eyes closed, is facing away or has her back turned). Dogs were free to roam throughout the whole trial, no food or communicative directives were given, and we recorded and analysed dogs unrestricted behavioural responses throughout the trials. Dogs of all ages oriented and reached towards the humans face more when the face was visible than when it was not visible. Interestingly, varying the status of the eyes (eyes open versus eye closed) did not seem to affect the dog’s response. Here we discuss that this might be because of the more neutral setting of the current study, which changes dogs’ perception of the relevance of human attention.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".