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Record W2973968886 · doi:10.1152/jn.00072.2019

Eye-head-hand coordination during visually guided reaches in head-unrestrained macaques

2019· article· en· W2973968886 on OpenAlexafffund
Harbandhan Arora, Vishal Bharmauria, Xiaogang Yan, Saihong Sun, Hongying Wang, J. Douglas Crawford

Bibliographic record

VenueJournal of Neurophysiology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
FundersCanadian Institutes of Health Research
KeywordsHead (geology)Eye–hand coordinationNeuroscienceCommunicationMacaquePsychologyComputer sciencePhysical medicine and rehabilitationMedicineBiology

Abstract

fetched live from OpenAlex

Nonhuman primates have been used extensively to study eye-head coordination and eye-hand coordination, but the combination—eye-head-hand coordination—has not been studied. Our goal was to determine whether reaching influences eye-head coordination (and vice versa) in rhesus macaques. Eye, head, and hand motion were recorded in two animals with search coil and touch screen technology, respectively. Animals were seated in a customized “chair” that allowed unencumbered head motion and reaching in depth. In the reach condition, animals were trained to touch a central LED at waist level while maintaining central gaze and were then rewarded if they touched a target appearing at 1 of 15 locations in a 40° × 20° (visual angle) array. In other variants, initial hand or gaze position was varied in the horizontal plane. In similar control tasks, animals were rewarded for gaze accuracy in the absence of reach. In the Reach task, animals made eye-head gaze shifts toward the target followed by reaches that were accompanied by prolonged head motion toward the target. This resulted in significantly higher head velocities and amplitudes (and lower eye-in-head ranges) compared with the gaze control condition. Gaze shifts had shorter latencies and higher velocities and were more precise, despite the lack of gaze reward. Initial hand position did not influence gaze, but initial gaze position influenced reach latency. These results suggest that eye-head coordination is optimized for visually guided reach, first by quickly and accurately placing gaze at the target to guide reach transport and then by centering the eyes in the head, likely to improve depth vision as the hand approaches the target. NEW & NOTEWORTHY Eye-head and eye-hand coordination have been studied in nonhuman primates but not the combination of all three effectors. Here we examined the timing and kinematics of eye-head-hand coordination in rhesus macaques during a simple reach-to-touch task. Our most novel finding was that (compared with hand-restrained gaze shifts) reaching produced prolonged, increased head rotation toward the target, tending to center the binocular field of view on the target/hand.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.365
Teacher spread0.297 · 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

Citations15
Published2019
Admission routes2
Has abstractyes

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