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Record W2021496169 · doi:10.1097/wnr.0b013e328013cea9

Brain response to birdsongs in bird experts

2007· article· en· W2021496169 on OpenAlexaff
Jean‐Pierre Chartrand, Sarah Filion-Bilodeau, Pascal Belin

Bibliographic record

VenueNeuroreport · 2007
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsInternational Laboratory for Brain, Music and Sound ResearchMcGill UniversityUniversité de Montréal
Fundersnot available
KeywordsN100ElectroencephalographyAudiologyPsychologyContrast (vision)Event-related potentialCognitive psychologyNeural correlates of consciousnessTone (literature)Auditory perceptionTask (project management)PerceptionAuditory stimuliCognitionCommunicationNeuroscienceComputer scienceArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.301
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2007
Admission routes1
Has abstractyes

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