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Record W2550799390 · doi:10.1121/1.4970179

On the link between the ability to reproduce rhythm and reading speed: Effects of visual grouping marks

2016· article· en· W2550799390 on OpenAlexaff
Antonin Rossier-Bisaillon, Victor J. Boucher

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRhythmReading (process)ParsingComputer scienceCommunicationLink (geometry)PsychologySpeech recognitionCognitive psychologyMathematicsLinguisticsArtificial intelligencePhysicsCombinatoricsAcoustics

Abstract

fetched live from OpenAlex

Studies have revealed a link between a deficient ability to reproduce rhythm and dyslexia. Moreover, a normal ability to reproduce rhythm has been shown to correlate with reading speed. Some associate these findings to effects of neural oscillations and a visual parsing of text input (Vidyasagar, 2013). The present study aimed to clarify the properties of the visual stimuli that support a correlation between rhythm reproduction and reading speed. The experiments were partly based on Tierney and Kraus (2014). Thirty participants were asked to reproduce heard regular (2 beats/sec) and irregular (1-3 beats/sec) rhythms by tapping on a keyboard. Then, the participants had to read out loud, at a fast rate, visually displayed sequences of words and non-words. The sequences contained either no spaces between items (baseline condition) or spaces marking regular and irregular groups. Mean reading speeds were calculated on accurately decoded sequences. Among the significant findings, strong correlations were observed between mean reading speeds of texts containing spaces and the reproduction of certain rhythm patterns. However, no significant correlations appeared for text where there were no spaces separating words and non-words. This supports the view that the ability to reproduce rhythm can link to a visual parsing.

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)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.139
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.020
GPT teacher head0.289
Teacher spread0.269 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicNeuroscience, Education and Cognitive FunctionFrench-language works237,207