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Record W4386982409 · doi:10.1101/2023.09.21.558829

Influence of Gaze, Vision, and Memory on Hand Kinematics in a Placement Task

2023· preprint· en· W4386982409 on OpenAlexafffund
Gaelle N. Luabeya, Xiaogang Yan, Erez Freud, J. Douglas Crawford

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsYork University
FundersCanada First Research Excellence FundNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsGazeOrientation (vector space)SaccadeComputer visionTask (project management)Hand positionComputer scienceArtificial intelligenceKinematicsPsychologyBlock (permutation group theory)Position (finance)CommunicationEye movementMathematicsGeometryPhysicsEngineering

Abstract

fetched live from OpenAlex

ABSTRACT People usually reach for objects to place them in some position and orientation, but the placement component of this sequence is often ignored. For example, reaches are influenced by gaze position, visual feedback, and memory delays, but their influence on object placement is unclear. Here, we tested these factors in a task where participants placed and oriented a trapezoidal block against 2D visual templates displayed on a frontally located computer screen. In Experiment 1, participants matched the block to three possible orientations: 0° (horizontal), +45° and −45°, with gaze fixated 10° to the left/right. The hand and template either remained illuminated (closed-loop), or visual feedback was removed (open-loop). In Experiment 2, a memory delay was added, and participants sometimes performed saccades (centripetal, centrifugal, or opposite side). In Experiment 1, the hand consistently overshot the template relative to gaze (similar to reaching), especially in the open-loop task. After a memory delay, location errors were influenced by both template orientation and gaze position. Based on previous reach experiments, we expected these errors to be independent of the previous eye position, but placement overshoot also depended on previous saccade metrics. Hand orientation over-rotated relative to template orientation (all conditions). Orientation was influenced by gaze position in Experiment 1, but this vanished in the presence of a memory delay. These results demonstrate interactions between gaze, location, and orientation signals in the planning and execution of hand placement and suggest different neural mechanisms for closed-loop, open-loop, and memory delay placement. NEW & NOTEWORTHY Eye-hand coordination studies usually focus on object acquisition, but placement is equally important. Here, we investigated how gaze position influences object placement toward a 2D template, with different levels of visual feedback. Like reach, placement overestimated goal location relative to gaze, but was also influenced by previous saccade metrics. Gaze also modulated hand orientation, which generally overestimated template orientation. Gaze influence was feedback-dependent, with location errors increasing but orientation errors decreasing after a memory delay.

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)0.012
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
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.022
GPT teacher head0.244
Teacher spread0.222 · 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
Published2023
Admission routes2
Has abstractyes

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