Brain response to birdsongs in bird experts
Bibliographic record
Abstract
Auditory expertise has mostly been studied in relation to musical processing, but expert auditory processing can also involve nonmusical auditory stimuli, such as birdsongs in bird experts. In this study, the neural correlates of bird expertise were investigated by using electroencephalography to measure auditory-evoked potentials in bird experts and novices. Auditory stimuli in three categories (birdsongs, environmental sounds and voices) were presented in a pseudo-random order while participants performed a simple target detection task (pure tone). We observed similar amplitudes and distributions of the N100-component in bird experts and novices. In contrast, the amplitude of the P200 component was significantly smaller in bird experts at the Pz and Cz electrodes, reflecting a more frontal topography of this positivity. Notably, this group difference was observed not only for birdsongs, but also for voices and environmental sounds, suggesting a general processing difference in bird experts, not restricted to the category of expertise.
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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 | 0.002 |
| 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".