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Record W4405012055 · doi:10.1016/j.isci.2024.111530

Evoked and entrained pupillary activity while moving to preferred tempo and beyond

2024· article· en· W4405012055 on OpenAlexaff
Connor Spiech, Mikael Hope, Valentin Bégel

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsConcordia UniversityInternational Laboratory for Brain, Music and Sound ResearchCentre for Research on Brain Language and Music
FundersJapan Society for the Promotion of ScienceNorges ForskningsrådUniversitetet i OsloTokyo Medical and Dental UniversitySociety for Education, Music and Psychology ResearchRIKENJapan Agency for Medical Research and DevelopmentJapan Foundation for Applied Enzymology
KeywordsPsychologyNeuroscienceCognitive science

Abstract

fetched live from OpenAlex

People synchronize their movements more easily to rhythms with tempi closer to their preferred motor rates than with faster or slower ones. More efficient coupling at one's preferred rate, compared to faster or slower rates, should be associated with lower cognitive demands and better attentional entrainment, as predicted by dynamical system theories of perception and action. We show that synchronizing one's finger taps to metronomes at tempi outside of their preferred rate evokes larger pupil sizes, a proxy for noradrenergic attention, relative to passively listening. This demonstrates that synchronizing is more cognitively demanding than listening only at tempi outside of one's preferred rate. Furthermore, pupillary phase coherence increased for all tempi while synchronizing compared to listening, indicating that synchronous movements resulted in more efficiently allocated attention. Beyond their theoretical implications, our findings suggest that rehabilitation for movement disorders should be tailored to patients' preferred rates to reduce cognitive demands.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.291
Teacher spread0.247 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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