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Record W2282172764 · doi:10.1080/10888438.2015.1128435

Eye-Movement Control in RAN and Reading

2016· article· en· W2282172764 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScientific Studies of Reading · 2016
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsMcMaster University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsEye movementSaccadic maskingRapid automatized namingReading (process)ComprehensionCognitive psychologyPsychologyVisual searchControl (management)RanComputer scienceArtificial intelligenceDyslexiaLinguistics

Abstract

fetched live from OpenAlex

, which suggests that fluent oculomotor control is an important component underlying the predictive relationship between Rapid Automatized Naming (RAN) tasks and reading ability. Our approach was to isolate components of saccadic planning, articulation, and lexical retrieval in three modified RAN tasks. We analyzed two samples of undergraduate readers (age 17-27), we evaluated the incremental contributions of these components and found that saccadic planning to non-linguistic stimuli alone explained roughly one-third of the variance that conventional RAN tasks explained in eye-movements registered during text reading for comprehension. We conclude that the well-established predictive role of RAN for reading performance is in part due to the individual ability to coordinate rapid sequential eye-movements to visual non-linguistic stimuli.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.345
Teacher spread0.313 · 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