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Enhanced Procedural Learning of Speech Sound Categories in a Genetic Variant of<i>FOXP2</i>

2015· article· en· W1494441336 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Neuroscience · 2015
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsCentre for Movement Disorders
FundersNational Institute on Drug AbuseNational Institutes of HealthNational Institute on Deafness and Other Communication DisordersU.S. Department of Veterans Affairs
KeywordsFOXP2Procedural memoryPsychologyMotor learningCognitive psychologyNeuroscienceGeneBiologyCognitionGeneticsTranscription factor

Abstract

fetched live from OpenAlex

A mutation of the forkhead box protein P2 (FOXP2) gene is associated with severe deficits in human speech and language acquisition. In rodents, the humanized form of FOXP2 promotes faster switching from declarative to procedural learning strategies when the two learning systems compete. Here, we examined a polymorphism of FOXP2 (rs6980093) in humans (214 adults; 111 females) for associations with non-native speech category learning success. Neurocomputational modeling results showed that individuals with the GG genotype shifted faster to procedural learning strategies, which are optimal for the task. These findings support an adaptive role for the FOXP2 gene in modulating the function of neural learning systems that have a direct bearing on human speech category learning.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.032
GPT teacher head0.308
Teacher spread0.276 · 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