Individual Discrimination Within, but Not Between, Two Vocalization Types of the Black‐Capped Chickadee
Bibliographic record
Abstract
ABSTRACT Many songbird species use individual vocal recognition in their social behaviors. Researchers commonly use individual discrimination tasks, such as operant conditioning Go/No‐go tasks, to assess individual vocal recognition. Several black‐capped chickadee ( Poecile atricapillus ) vocalizations contain individually distinct features which may be used for individual discrimination. However, not all such vocalizations have been tested for individual recognition with live birds. Additionally, cross vocalization generalization of learned individual discrimination has not been tested. Such generalizability would be advantageous for chickadees, as chickadees often communicate outside of visual contact and use vocal communication to guide their social interactions. Here we test whether black‐capped chickadees can discern individual identity of callers in black‐capped chickadee chick‐a‐dee calls. We also aim to answer whether chickadees can generalize learned individual discrimination using chick‐a‐dee calls to fee‐bee songs, and vice versa. Black‐capped chickadees were trained to discriminate several chick‐a‐dee calls or fee‐bee songs produced by one male and one female chickadee from those produced by a different male and female in an operant conditioning Go/No‐go experiment. We then tested for generalization across vocalization types by presenting birds with recordings from the same four individuals, this time of the opposing vocalization type. Chickadees were able to discriminate between individuals using either chick‐a‐dee calls or fee‐bee songs but were unable to generalize this learning to the opposing vocalization type. While our findings suggest that chickadees can employ individual discrimination within at least two vocalization types, the mechanism by which songs and calls are recognized as belonging to the same individual remains unclear. External contextual cues may play an important role in bridging identity information across those vocalization types.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".