Mechanisms of call note-type perception in black-capped chickadees (Poecile atricapillus): Peak shift in a note-type continuum.
Bibliographic record
Abstract
We report on operant conditioning and artificial neural network (ANN) simulations aimed at further elucidating mechanisms of black-capped chickadee chick-a-dee call note category perception. Specifically, we tested for differences in the speed of acquisition among different discrimination tasks and, in two selected discrimination groups, searched for evidence of peak shift. Earlier, unreported ANN data were instrumental in providing the motivation for the current set of studies with chickadees and are provided here. The ANNs revealed differences in the speed of learning among note-type discrimination groups that is related to the degree of perceptual similarity among the three note types tested (i.e., A, B, and C notes). In many respects, bird and network results were in agreement (i.e., in the observation of peak shift in the same group), but they also differed in important ways (i.e., all discrimination groups showed differences in speed of learning in simulations but not in chickadees). We suggest that the start, peak and end frequency of the chick-a portion of chick-a-dee call notes, which form a graded but overlapping continuum, may drive the peak shift observed.
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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 | 0.000 | 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) | 0.001 | 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".