European starlings (<i>sturnus vulgaris</i>) discriminate rhythms by rate, not temporal patterns
Bibliographic record
Abstract
Humans can perceive a regular psychological pulse in music known as the beat. The evolutionary origins and neural mechanisms underlying this ability are hypothetically linked to imitative vocal learning, a rare trait found only in some species of mammals and birds. Beat perception has been demonstrated in vocal learning parrots but not in songbirds. We trained European starlings (Sturnus vulgaris) on two sound discriminations to investigate their perception of the beat and temporal structure in rhythmic patterns. First, we trained birds on a two-choice discrimination between rhythmic patterns of tones that contain or lack a regular beat. Despite receiving extensive feedback, the starlings were unable to distinguish the first two patterns. Next, we probed the temporal cues that starlings use for discriminating rhythms in general. We trained birds to discriminate a baseline set of isochronous and triplet tone sequences. On occasional probe trials, we presented transformations of the baseline patterns. The starlings' responses to the probes suggest they relied on absolute temporal features to sort the sounds into "fast" and "slow" and otherwise ignored patterns that were present. Our results support that starlings attend to local features in rhythms and are less sensitive to the global temporal organization.
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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".