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Record W4301606469 · doi:10.1152/jn.00021.2022

Pupil diameter tracked during motor adaptation in humans

2022· article· en· W4301606469 on OpenAlexafffund
Atsushi Yokoi, Jeffrey Weiler

Bibliographic record

VenueJournal of Neurophysiology · 2022
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsParkwood InstituteWestern University
FundersJapan Society for the Promotion of ScienceWestern UniversityMinistry of Education, Culture, Sports, Science and TechnologyNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsPupillometryPupilPupillary responsePsychologySurpriseTonic (physiology)Eye movementVisual fieldCognitive psychologyAudiologyNeuroscienceCommunicationMedicine

Abstract

fetched live from OpenAlex

Pupil diameter is known as a noninvasive window into individuals' internal states. Despite the growing use of pupillometry in cognitive learning studies, it receives little attention in motor learning studies. Here, we characterized the pupil responses in a short-term reach adaptation paradigm by measuring pupil diameter of human participants while they adapted to abrupt, gradual, or switching force field conditions. Our results demonstrate how surprise and uncertainty reflected in pupil diameter develop during motor adaptation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.077
GPT teacher head0.309
Teacher spread0.233 · 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 designObservational
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

Citations36
Published2022
Admission routes2
Has abstractyes

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