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Record W2040379008 · doi:10.1109/icdim.2010.5664741

Quantitative measurements of cognitive processing involved with gaze fixations

2010· article· en· W2040379008 on OpenAlexaff
Quoc Hao Mach, Mathew D. Hunter, Ratvinder Grewal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsLaurentian University
Fundersnot available
KeywordsGazeCognitionNeurophysiologyEye movementFixation (population genetics)Computer scienceTask (project management)Cognitive loadCognitive psychologyComputer visionArtificial intelligencePsychologyNeuroscienceMedicineEngineering

Abstract

fetched live from OpenAlex

Studying eye movement has proven to be useful in the study of users and interfaces. Cognitive processing can be inferred by the length of time a user fixates on certain elements of the screen as well as the number of fixations on each element of the display. Our research looks to expand on this with the addition of neurophysiological measurements to quantify cognitive processing and ascertain whether increased fixations on differential element placements on a display alter cognitive processing. Results indicate that placements of the main and secondary menus do alter the number of fixations required for task completion. The number of fixations were also shown to be related to sustained attentional processes. Our preliminary results indicate that the addition of neurophysiological measurements corroborate and expand on theories of fixation counts.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.407
Threshold uncertainty score0.230

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 science0.0000.000
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 head0.062
GPT teacher head0.303
Teacher spread0.240 · 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 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

Citations1
Published2010
Admission routes1
Has abstractyes

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