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Record W6962669026 · doi:10.17605/osf.io/s6y5u

Investigating the role of physical and observational experience in visual and kinesthetic imagery

2023· other· en· W6962669026 on OpenAlexaff

Bibliographic record

VenueOpen Science Framework · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsKinesthetic learningMotor imageryMental imagePerceptionModality (human–computer interaction)Representation (politics)Mental representationTask (project management)

Abstract

fetched live from OpenAlex

This process of cognitively generating the perceptual consequences of an action, whether they be visual or kinesthetic, has been termed motor imagery (MI). How imagery ability is developed and the processes underlying it’s use has generated ample debate in the literature (Moran & O’Shea, 2019). MI training has consistently been shown to benefit motor performance in sport, education, music, and medicine (Schuster et al., 2011). However, its utility is dependent on the user’s ability to generate and manipulate mental images (Martin et al., 1999). In current models of MI, it is unclear what components (motor or perceptual) of an internal representation are simulated. How does previous task experience impact the representation used in MI? Do representations created through visual and physical experience differ in how they are used in MI; potentially interacting with MI modality (visual or kinesthetic)? A prevailing theory of MI is that the motor system internally simulates the same representation used for overt action. In using the same processes as overt movement, MI is thought to be functionally equivalent to physical movement (Jeannerod, 2001), with muscle activity inhibited or occurring at a subthreshold level (Hurst & Boe, 2022). Yet, it is not well-understood if sensory and/or motor components are simulated during MI, if this simulation is moderated by the type of experience (physical vs. observational), and what this means for the generation of visual and kinesthetic MI (Hurst & Boe, 2022). MI ability can be measured using mental chronometry, by comparing the difference between actual and imagined movement times (MTs). Based on ideas of functional equivalence, imagined and actual MTs should be approximately the same if they are drawing on the same resources. Larger differences between MTs would signal deficits in MI ability or a low-fidelity action representation. There is evidence that imagined MTs are generally longer than actual movement times, due also to the increased cognitive resources required to generate the mental image (Glover & Baran, 2017). In a recent study, we tested whether visual and kinesthetic MI ability was dependent on the type and amount of physical or observational practice experiences performing novel hand gestures (Peters et al., in prep.). We expected that observational practice would primarily benefit visual imagery and physical practice (in the absence of vision) would primarily benefit kinesthetic imagery. These benefits would be evidenced in smaller differences between actual and imagined movement times and higher subjective ratings of quality and ease of generation for the mental imagery conditions. Although there was some evidence from the subjective ratings that supported our hypotheses, contrary to predictions, the physical practice group had large differences between actual and imagined movement times. These differences were due to long duration imagined movement times for both visual and kinesthetic MI for the physical practice group. The generation of kinesthetic imagery may rely on the presence of a visual representation and potentially explain why the physical practice group that practised without vision had difficulty in both visual and kinesthetic imagery. Indeed, there has been some uncertainty regarding the relationship between visual and kinesthetic MI and their distinctiveness (Klatzky, 1994). While to our knowledge, there is no empirical evidence that the generation of kinesthetic MI relies on the presence of a visual representation, in applied settings, researchers have had success in MI training designs when richer sensory representations are layered upon a basic visual representation of the task (e.g., Williams et al., 2013). In this study by Williams et al., only the group that experienced the ‘layered’ imagery intervention, in comparison to more general instructions that did not cue a rich kinesthetic representation, showed improvements in kinesthetic imagery ability. Therefore, it may be that for kinesthetic imagery to be successfully produced, it requires a basic visual representation of the task to scaffold kinesthetic-related experiences relating to how the movement feels. We are proposing here to further investigate the relationship between practice experiences and imagery modality to determine if a visual representation is necessary to act as a scaffold for the kinesthetic representation to be used in MI. In a mixed, cross-over design, comprising two different groups (Table 1), participants will be given both observational and physical practice with a hand gesture sequence. Dependent on group assignment, one group will first complete physical practice of the task with vision of their hand occluded and the other group will first complete an observation-only practice phase. The conditions will then be switched in a second phase such that both groups practice with both physical and observational practice. MI ability will be measured before and after each phase of practice. As with our previous study, we will compare actual and imagined MTs for visual and kinesthetic MI (i.e., mental chronometry difference scores) and subjective ratings of quality and ease of generation for each type of MI as well as the relative visual versus kinesthetic contribution of their MI.

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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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.005
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.059
GPT teacher head0.384
Teacher spread0.325 · 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.

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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