Man's best furiend: The direct and averted gaze cues of humans and dogs are processed similarly
Bibliographic record
Abstract
Humans engage in frequent interactions amongst and between different species of animals to complete a variety of tasks. During these interactions, such as playing catch or fetch, visual gaze acts as an important cue to facilitate the completion of this task. The objective of the present study was to examine how humans process the visual gaze of non-human animals, and in particular whether or not humans are sensitive to the direct and averted visual gaze of dogs. A visual search experiment was conducted where participants were required to indicate if a target was present or absent in a search array of distractor items (targets present on 50% of trials). Participants performed the visual search task with human and dog gaze cues in a blocked fashion. Reaction times for the dog stimuli were significantly shorter than those for human stimuli. As well, participants detected averted visual gaze targets faster than direct visual gaze targets. Importantly, the averted visual gaze advantage was observed for both human and dog stimuli suggesting that the strategies used for the human targets were not different from the pattern of strategies used to detect the dog targets. Overall, the facilitation effect observed for the averted visual gaze and the dog stimuli are contradictory to previous findings in the literature. This discrepancy may have resulted from participants using the low-level features of the stimuli, such as the amount and distribution of white/black regions, to guide their visual search instead of higher-order processes related to gaze direction.Acknowledgments: Joel Sartore Photography Inc.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".