Friend or foe? Using eye-tracking technology to investigate the visual discrimination ability of giant pandas
Bibliographic record
Abstract
Abstract The role that visual discriminative ability plays among giant pandas in social communication and individual discrimination has received less attention than olfactory and auditory modalities. Here, we used an eye-tracker technology to investigate pupil fixation patterns for 8 captive male giant pandas Ailuropoda melanoleuca. We paired images (N = 26) of conspecifics against: 1) sympatric predators (gray wolves and tigers), and non-threatening sympatric species (golden pheasant, golden snub-nosed monkey, takin, and red panda), 2) conspecifics with atypical fur coloration (albino and brown), and 3) zookeepers/non-zookeepers wearing either work uniform or plain clothing. For each session, we tracked the panda’s pupil movements and measured pupil first fixation point (FFP), fixation latency, total fixation count (TFC), and duration (TFD) of attention to each image. Overall, pandas exhibited similar attention (FFPs and TFCs) to images of predators and non-threatening sympatric species. Images of golden pheasant, snub-nosed monkey, and tiger received less attention (TFD) than images of conspecifics, whereas images of takin and red panda received more attention, suggesting a greater alertness to habitat or food competitors than to potential predators. Pandas’ TFCs were greater for images of black-white conspecifics than for albino or brown phenotypes, implying that familiar color elicited more interest. Pandas reacted differently to images of men versus women. For images of women only, pandas gave more attention (TFC) to familiar combinations (uniformed zookeepers and plain-clothed non-zookeepers), consistent with the familiarity hypothesis. That pandas can use visual perception to discriminate intra-specifically and inter-specifically, including details of human appearance, has applications for panda conservation and captive husbandry.
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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 | 0.000 |
| 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) | 0.002 | 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".