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Record W2172201717 · doi:10.1145/2556288.2557086

Pupil responses during discrete goal-directed movements

2014· article· en· W2172201717 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsUniversity of AlbertaSimon Fraser University
Fundersnot available
KeywordsPupilPupillary responseTask (project management)Pupil sizePupil diameterComputer sciencePupillometryTask analysisCognitionCognitive psychologyHuman–computer interactionPsychologyEngineeringNeuroscience

Abstract

fetched live from OpenAlex

Pupil size is known to correlate with the changes of cognitive task workloads, but how the pupil responds to requirements of basic goal-directed motor tasks involved in human-machine interactions is not yet clear. This work conducted a user study to investigate the pupil dilations during aiming in a tele-operation setting, with the purpose of better understanding how the changes in task requirements are reflected by the changes of pupil size. The task requirements, managed by Fitts' index of difficulty (ID), i.e. the size and distance apart of the targets, were varied between tasks, and pupil responses to different task IDs were recorded. The results showed that pupil diameter can be employed as an indicator of task requirements in goal-directed movements-higher task difficulty evoked higher valley to peak pupil dilation, and the peak pupil dilation occurred after a longer delay. These findings contribute to the foundation for developing methods to objectively evaluate interactive task requirements using pupil parameters during goal-directed movements in HCI.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.157
GPT teacher head0.418
Teacher spread0.261 · 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