MétaCan
Menu
Back to cohort
Record W2931587435

Accuracy instructions modulate both visual and non-visual contributions to ongoing reaches

2018· article· en· W2931587435 on OpenAlexaffabout
John de Grosbois, Kimberley Jovanov, Luc Tremblay

Bibliographic record

VenueJournal of Exercise, Movement, and Sport (SCAPPS refereed abstracts repository) · 2018
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPredictabilityVisual feedbackComputer scienceCognitive psychologyControl (management)PsychologySensory cueVisual perceptionArtificial intelligencePerceptionNeuroscience
DOInot available

Abstract

fetched live from OpenAlex

The control of ongoing goal-directed reaches is influenced by both visual and non-visual sensorimotor processes. Further, intentions to produce accurate movements influence reaching performance as well. However, it is not known how these improvements associated with accuracy-based intentions can be attributed to changes in movement planning and/or online control. Indeed, such improvements may influence both visual and non-visual online control processes. Using frequency domain analyses, the relative online contributions of such visual and non-visual sub-processes have been previously identified (e.g., de Grosbois & Tremblay, 2015; de Grosbois & Tremblay, 2016b; de Grosbois & Tremblay, 2017). The current study tested if the relative contributions of these online control sub-processes are influenced by the intention to be accurate. Reaching movements were completed in the presence of three experimental manipulations. First, vision during voluntary reaches was either provided or occluded. Second, high- and low-accuracy instruction sets were provided. And third, the predictability of visual information was manipulated through a blocked and randomized feedback scheduling. The results indicated that the contribution of online visuomotor processes (i.e., visual sub-process) was increased by the availability of online vision and the instructed intention to be accurate. In contrast, the non-visual sub-process was promoted in the absence of online vision, but suppressed when a randomized feedback schedule was implemented with instructions to be accurate. Ultimately, the intention to be accurate increases the relative contribution of vision-based online sensorimotor processes and can decrease that of non-visual online sensorimotor processes.Acknowledgments: This research was supported by the Natural Sciences and Engineering Research Council of Canada, the Canada Foundation for Innovation, the Ontario Research Fund, and a University of Toronto Graduate Student Fellowship.

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)0.005
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.252
Teacher spread0.243 · 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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Exercise, Movement, and Sport (SCAPPS refereed abstracts repository)Same topicMuscle activation and electromyography studiesFrench-language works237,207