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Record W4401351949 · doi:10.31234/osf.io/48en3

Bridging reading and attention through connectivity with the frontal-eye-fields

2024· preprint· en· W4401351949 on OpenAlexaff
Shaylyn Kress, Josh Neudorf, Chelsea Ekstrand, Ron Borowsky

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsUniversity of LethbridgeSimon Fraser UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsBridging (networking)Reading (process)PsychologyCognitive psychologyComputer scienceLinguisticsPhilosophyComputer security

Abstract

fetched live from OpenAlex

Attentional processes are crucial to successful reading, and theories of dyslexia propose that dysfunctional attention networks may contribute to the observed reading deficits. The goals of the study were to localize a region of the frontal-eye-field (FEF) involved in interactive reading × attention processing and examine its connectivity with regions in the reading and attention networks, given its known role in attentional processes and theorized role in reading. In Experiment 1, we revisited the results of our previous hybrid reading and attention study (Ekstrand, Neudorf, Kress, & Borowsky., 2019). In this case, we observed a previously unreported reading × attention interaction in BOLD intensity in the ventrolateral portion of Brodmann’s Area 6 (A6vl), which corresponded to the FEF. In Experiment 2, we used Human Connectome Project diffusion data to examine the connectivity profile of the A6vl. We observed high communicability between the A6vl and basal ganglia (which plays a role in spatial neglect and rhythm processing of syllables). These connections appeared to support tract clusters which terminated in the cerebellar Crus I/II (which play roles in eye movements and semantics) and cerebral superior parietal lobule (which plays a role in attentional orienting and phonetic decoding). The results of this study suggest the A6vl-FEF supports reading and attention processes. These results will help inform research on the links between reading and attention, and may have implications for developing treatments to improve reading in individuals with dyslexia.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.661
Threshold uncertainty score0.505

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.018
GPT teacher head0.261
Teacher spread0.243 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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