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Record W2042807121 · doi:10.1109/haptics.2008.4479929

Validating the Performance of Haptic Motor Skill Training

2008· article· en· W2042807121 on OpenAlexafffund
Xing-Dong Yang, Walter F. Bischof, Pierre Boulanger

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHaptic technologyTraining (meteorology)Motor skillComputer scienceTerm (time)Dreyfus model of skill acquisitionTraining effectPsychologyPhysical medicine and rehabilitationSimulationMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

The effect of haptic interfaces on motor skill training has been widely studied. However, relatively little is known about whether haptic training can promote long-term motor skill acquisition. In this paper, we report two experimental studies that investigated the effectiveness of visuohaptic (visual + haptic) interfaces in helping people develop short-term and long-term motor skills. Our first study compared training outcomes of visuohaptic training, visual training, and no-assistance training. We found that the training outcomes for the tested methods were similar when helping participants develop short-term motor skills. Our second experiment assessed the potential of visual training and visuohaptic training in promoting the development of long-term motor skills. Participants were trained during a four-day-long period. The results showed that the participants gained long-term skills through both training methods, and that the training outcomes for both methods were similar. The results also showed that visuohaptic training is a promising method, but that it needs to be further developed to be useful.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.103
GPT teacher head0.289
Teacher spread0.186 · 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

Citations46
Published2008
Admission routes2
Has abstractyes

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