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Record W6925208014 · doi:10.17605/osf.io/nw3qc

Visual-motor experience and motor imagery in hand gestures

2022· other· en· W6925208014 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOpen Science Framework · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMotor imageryKinesthetic learningGestureMental imageMovement (music)Auditory imageryMotor learningMotor skill

Abstract

fetched live from OpenAlex

Our goal is to determine how the type of sensori-motor practice experience (i.e., physical and visual experience) influence the ability to perform motor imagery (the mental rehearsal of a motor task). There are differences in an individual’s ability to use visual imagery (imagining how a movement looks) and kinesthetic imagery (imagining how a movement feels), that we predict are dependent on the type of experience an individual has with the skills they are imagining. In this experiment, we measure motor imagery ability by comparing actual and imagined movement times and subjective ratings of motor imagery quality and ease of generation. We will test motor imagery ability before, during and after different types of practice experiences. Participants will be randomized into three groups and imagine performing two hand gesture sequences. These sequences will comprise 4 and 6 gestures from the American Sign Language alphabet. They will imagine these sequences before and after either 1) physical practice, 2) observational practice, or 3) no-practice (i.e., control group), according to group assignment. The physical and observational practice groups will practice in pairs, with one participant physically practicing without visual feedback (hand occluded) and the other practicing by observation only. A control group will not practice. All assessments of motor imagery ability will be completed individually. Group differences in task-specific motor imagery measures following practice will allow us to infer how different types of practice impact motor imagery ability. We will take measures of both visual (how the movement looks) and kinesthetic (how the movement feels) motor imagery. Our primary measures will be time to complete the motor imagery of each sequence in comparison to how long it takes to physically complete the sequence , so termed mental chronometry. Imagined and actual movement times are defined as the time from (imagined) movement onset to the end of the (imagined) movement, both signaled by the press of the spacebar on a keyboard by the participant. We will also ask for subjective ratings of motor imagery ability for the sequences and test performance on both hands (i.e., trained and non-trained hands).

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.004
Scholarly communication0.0030.001
Open science0.0060.006
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0250.001

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 head0.021
GPT teacher head0.362
Teacher spread0.342 · 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

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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