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Record W2174065847 · doi:10.1152/jn.00688.2015

Predicting plasticity: acute context-dependent changes to vocal performance predict long-term age-dependent changes

2015· article· en· W2174065847 on OpenAlexaff
Logan S. James, Jon T. Sakata

Bibliographic record

VenueJournal of Neurophysiology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsMcGill University
Fundersnot available
KeywordsVariation (astronomy)Context (archaeology)SyllablePlasticityDegree (music)PsychologyDuration (music)BiologySpeech recognitionComputer scienceAcousticsPhysics

Abstract

fetched live from OpenAlex

Understanding the factors that predict and guide variation in behavioral change can lend insight into mechanisms of motor plasticity and individual differences in behavior. The performance of adult birdsong changes with age in a manner that is similar to rapid context-dependent changes to song. To reveal mechanisms of vocal plasticity, we analyzed the degree to which variation in the direction and magnitude of age-dependent changes to Bengalese finch song could be predicted by variation in context-dependent changes. Using a repeated-measures design, we found that variation in age-dependent changes to the timing, sequencing, and structure of vocal elements ("syllables") was significantly predicted by variation in context-dependent changes. In particular, the degree to which the duration of intersyllable gaps, syllable sequencing at branch points, and fundamental frequency of syllables within spontaneous [undirected (UD)] songs changed over time was correlated with the degree to which these features changed from UD song to female-directed (FD) song in young-adult finches (FDyoung). As such, the structure of some temporal features of UD songs converged over time onto the structure of FDyoung songs. This convergence suggested that the FDyoung song could serve as a stable target for vocal motor plasticity. Consequently, we analyzed the stability of FD song and found that the temporal structure of FD song changed significantly over time in a manner similar to UD song. Because FD song is considered a state of heightened performance, these data suggest that age-dependent changes could reflect practice-related improvements in vocal motor performance.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.048
GPT teacher head0.299
Teacher spread0.250 · 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

Citations22
Published2015
Admission routes1
Has abstractyes

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