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Record W4378782027 · doi:10.1152/jn.00053.2023

Visual accuracy dominates over haptic speed for state estimation of a partner during collaborative sensorimotor interactions

2023· article· en· W4378782027 on OpenAlexafffund
Rakshith Lokesh, Seth R. Sullivan, Laura St. Germain, Adam M. Roth, Jan A. Calalo, John Buggeln, Truc Ngo, Vanessa R. F. Marchhart, Michael J Carter, Joshua G. A. Cashaback

Bibliographic record

VenueJournal of Neurophysiology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaNational Science Foundation
KeywordsHaptic technologyTask (project management)Computer scienceVisual feedbackStereotaxyState (computer science)Artificial intelligenceComputer visionHuman–computer interactionPsychologySimulationAlgorithm

Abstract

fetched live from OpenAlex

Physical collaboration between two or more individuals involves both visual and haptic feedback. Here, we investigated how visual and haptic feedback is used to estimate the movements of a partner during a collaboration task. Our experimental and computational modeling results parsimoniously support the notion that greater visual accuracy is more important than faster yet noisier haptic feedback when estimating the state of a partner.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0000.000

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 head0.033
GPT teacher head0.343
Teacher spread0.309 · 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 designBench or experimental
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

Citations19
Published2023
Admission routes2
Has abstractyes

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