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Record W4230165093 · doi:10.1167/14.10.833

The Effects of Speed and Direction on Eye-hand Coordination for Moving Targets

2014· article· en· W4230165093 on OpenAlexaff
Melissa C. Bulloch, S.S. Prime, Jonathan J. Marotta

Bibliographic record

VenueJournal of Vision · 2014
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGazeGRASPComputer visionFixation (population genetics)Artificial intelligenceEye movementComputer scienceFixation pointIndex fingerEye–hand coordinationOrientation (vector space)Object (grammar)Movement (music)Enhanced Data Rates for GSM EvolutionCommunicationPsychologyMathematicsPhysicsMedicineGeometryAnatomyAcoustics

Abstract

fetched live from OpenAlex

Grasping moving objects involves both spatial and temporal predictions. The hand is aimed at a location where it will meet the object, rather than the position at which the object is seen when the reach is initiated. Previous eye-hand coordination research from our lab, utilizing stationary objects, has shown that participants initial gaze tends to be directed towards the eventual location of the index finger. This experiment examined how object movement affects gaze and selection of grasp points. A computer-generated target (4 x 4 cm) was presented on either the left or right edge of a 24 in. monitor, and after a 1.5 s delay, travelled horizontally across the monitor at either a "slow" (5 cm/s) or "fast" (10 cm/s) speed. Participants reached to grasp the target upon hearing a tone presented either 2.5 s or 5 s after the target appeared. Results showed that when the target first appeared, participants anticipated the targets eventual movement by fixating ahead of its leading edge. Once target movement began, participants shifted their fixation to the leading edge of the target. Upon reach initiation, participants then fixated towards the top edge of the target. Final fixations tended towards the final index finger contact point on the target. ROI analysis, and examination of the extent to which the eyes reproduced the targets motion, revealed that it was direction that most influenced fixation locations and grasp points. Interestingly, it was found that participants fixated further ahead of the targets leading edge when the direction of motion was leftward, particularly at the slower speedpossibly the result of mechanical constraints of intercepting leftward moving targets with ones right hand. Our findings suggest differences between initial fixation locations (an anticipation effect), but similar preference for final fixation locations, when reaching to grasp moving versus stationary targets. Meeting abstract presented at VSS 2014

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
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)0.0020.000

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 head0.009
GPT teacher head0.271
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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
Published2014
Admission routes1
Has abstractyes

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