Call Usage Learning by a Beluga (Delphinapterus leucas) in a Categorical Matching Task
Bibliographic record
Abstract
The ability to modify the structure and context of vocalizations through learning plays a key role in the social interactions of many species. The investigation of categorical matching, an aspect of contextual vocal learning, is the first step toward determining how contextual learning plays a role in the use, comprehension, and categorization of sounds in the wild. To this end, we conducted a study at the Vancouver Aquarium to test the ability of a juvenile female beluga, Qila, to respond to playbacks of two types of in-air beluga calls with vocalizations that matched the category of call played (a scream, which is a vocalization type shaped over time with reinforcement and not part of this species'natural repertoire, and a pulse-train, a natural call category). We first tested Qila with random sequences of the same version of the two vocalizations with which she had been trained. Her overall success in matching all playback stimuli was above chance but not statistically so (66%). She had more difficulty matching screams (54% success) than pulse trains (80% success). We next played random sequences of six novel pulse-trains and seven novel screams, which Qila had not been trained with. She responded correctly to the set of novel stimuli of both call types in 64% of the trials, a success rate that did not differ statistically from chance. Again, she had more difficulty matching screams (55% success), relative to pulse trains (74% success). These results indicate that Qila successfully matched only pulse trains, the class that is part of this species’ natural repertoire. Her poor performance on matching screams might be partly explained by a difficulty to perceive categorically a signal that lacks a function in the natural repertoire of belugas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".