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Record W2127694469 · doi:10.1093/cercor/bhu030

Sensorimotor Learning Enhances Expectations During Auditory Perception

2014· article· en· W2127694469 on OpenAlexaff
Brian Mathias, Caroline Palmėr, Fabien Perrin, Barbara Tillmann

Bibliographic record

VenueCerebral Cortex · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill University
Fundersnot available
KeywordsMelodyPsychologyN400Premotor cortexPerceptionAuditory perceptionCognitive psychologyMotor cortexAudiologyNeuroscienceCognitionEvent-related potentialBiologyMusicalMedicine

Abstract

fetched live from OpenAlex

Sounds that have been produced with one's own motor system tend to be remembered better than sounds that have only been perceived, suggesting a role of motor information in memory for auditory stimuli. To address potential contributions of the motor network to the recognition of previously produced sounds, we used event-related potential, electric current density, and behavioral measures to investigate memory for produced and perceived melodies. Musicians performed or listened to novel melodies, and then heard the melodies either in their original version or with single pitch alterations. Production learning enhanced subsequent recognition accuracy and increased amplitudes of N200, P300, and N400 responses to pitch alterations. Premotor and supplementary motor regions showed greater current density during the initial detection of alterations in previously produced melodies than in previously perceived melodies, associated with the N200. Primary motor cortex was more strongly engaged by alterations in previously produced melodies within the P300 and N400 timeframes. Motor memory traces may therefore interface with auditory pitch percepts in premotor regions as early as 200 ms following perceived pitch onsets. Outcomes suggest that auditory-motor interactions contribute to memory benefits conferred by production experience, and support a role of motor prediction mechanisms in the production effect.

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 categoriesInsufficient payload (model declined to judge)
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 score0.756
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.018
GPT teacher head0.260
Teacher spread0.242 · 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.

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

Citations53
Published2014
Admission routes1
Has abstractyes

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