MétaCan
Menu
Back to cohort
Record W2793897905 · doi:10.21037/aes.2018.ab055

AB055. Eye movements in the dark: saccades to non-visual targets

2018· article· en· W2793897905 on OpenAlexaff
Vanessa Harrar, William Le, Maurice Ptito, Aarlenne Z. Khan

Bibliographic record

VenueAnnals of Eye Science · 2018
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSaccadeSaccadic maskingEye movementFixation (population genetics)Saccadic suppression of image displacementProprioceptionPsychologyComputer visionSensory systemComputer scienceCognitive psychologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Background: Saccades are rapid and abrupt eye movements that allow us to change the point of fixation very quickly. Saccades are generally made to visual points of interest, but we can also saccade to non-visual objects that attract our attention. While there is a plethora of studies investigating saccadic eye movements to visual targets, there is very little evidence of how eye movement planning occurs when individuals are performing eye movements to non-visual targets across different sensory modalities. Methods: Fifteen adults with normal, or corrected to normal, vision made saccades to either visual, auditory, tactile or proprioceptive targets. In the auditory condition a speaker was positioned at one of eight locations along a circle surrounding a central fixation point. In the proprioceptive condition the participant’s finger was placed at one of the eight locations. In the tactile condition participants were touched on their right forearm in one of four eccentric location, left and right of a central point. Eye movements were made in complete darkness. Results: We compared the precision and accuracy of the eye movements to tactile, proprioceptive, and auditory targets in the dark. Overall, both precision and accuracy of movements to non-visual targets were significantly lower compared to visual targets. Conclusions: These differences emphasize the central role of the visual system in saccade planning.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.044
GPT teacher head0.382
Teacher spread0.339 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of Eye ScienceSame topicGaze Tracking and Assistive TechnologyFrench-language works237,207