MétaCan
Menu
Back to cohort
Record W2972239000 · doi:10.1101/761783

Dopamine in the songbird auditory cortex shapes auditory preference

2019· preprint· en· W2972239000 on OpenAlexafffund
Helena J. Barr, Erin M Wall, Sarah C. Woolley

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsMcGill UniversityCentre for Research on Brain Language and Music
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsSongbirdAuditory cortexSalience (neuroscience)DopaminePsychologyNeuroscienceSensory systemBiological neural networkCognitive psychologyBiology

Abstract

fetched live from OpenAlex

ABSTRACT In vocal communication, vocal signals can provide listeners with information and also elicit motivated responses. Auditory cortical and mesolimbic reward circuits are often considered to have distinct roles in these processes, with auditory cortical circuits responsible for detecting and discriminating sounds and mesolimbic circuits ascribing salience and modulating preference for those sounds. Here, we investigated whether dopamine within auditory cortical circuits themselves can shape the incentive salience of a vocal signal. Using female zebra finches, who show natural preferences for vocal signals produced by males (‘songs’), we found that pairing passive song playback with pharmacological manipulations of dopamine in the secondary auditory cortex drives changes to song preferences. Plasticity of song preferences by dopamine lasted for at least one week and was not influenced by norepinephrine manipulations. These data suggest that dopamine acting directly in sensory processing areas can shape the incentive salience of communication signals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.247
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2019
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicAnimal Vocal Communication and BehaviorFrench-language works237,207