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Record W2132042082 · doi:10.1145/985692.985738

Mouse and touchscreen selection in the upper and lower visual fields

2004· article· en· W2132042082 on OpenAlexafffund
Barry A. Po, Brian Fisher, Kellogg S. Booth

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTouchscreenSelection (genetic algorithm)Computer scienceArtificial intelligenceHuman–computer interactionComputer vision

Abstract

fetched live from OpenAlex

Neuroanatomical evidence indicates the human eye's visual field can be functionally divided into two vertical hemifields, each specialized for specific functions. The upper visual field (UVF) is specialized to support perceptual tasks in the distance, while the lower visual field (LVF) is specialized to support visually-guided motor tasks, such as pointing. We present a user study comparing mouse- and touchscreen-based pointing for items presented in the UVF and LVF on an interactive display. Consistent with the neuroscience literature, we found that mouse and touchscreen pointing were faster and more accurate for items presented in the LVF when compared to pointing at identical targets presented in the UVF. Further analysis found previously unreported performance differences between the visual fields for touchscreen pointing that were not observed for mouse pointing. This indicates that a placement of interactive items favorable to the LVF yields superior user performance, especially for systems dependent on direct touch interactions.

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: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.116

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 head0.007
GPT teacher head0.255
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 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

Citations26
Published2004
Admission routes2
Has abstractyes

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