Testing for effector transfer after motor imagery, observational, and physical practice of an underhand dart throw
Notice bibliographique
Résumé
Motor imagery (MI), the mental representation and rehearsal of a physical action without any overt movement, has been shown to measurably induce motor learning (Toth et al. 2020) and to facilitate intermanual transfer (Yao et al., 2023). As such, its potential has been demonstrated as a method for supplementing physical practice to augment learning outcomes, or to even serve as an alternative to physical practice when necessary. However, notable knowledge gaps exist regarding the mechanisms of motor imagery processes and what is encoded and generalized after motor imagery practice. Theoretically, it has been suggested that there is a ‘functional equivalence’ between covert forms of practice and overt physical practice (Jeannerod, 2001), though the extent and details of this ‘equivalence’ have been debated. For example, it has been argued that the equivalence is specific to planning stages of a neural motor program and not present in execution components (e.g., Glover et al., 2020). Moreover, there is a lack of research into the different impacts to learning and transfer between motor imagery and observational learning, as another form of covert practice. The aim of this work is to address these gaps by investigating effector transfer following overt and covert practice of a novel motor skill. There is ongoing discourse among researchers regarding the effector independence versus dependence of MI (e.g. Dahm et al., 2023; Frank et al., 2024; Ingram et al., 2016; Kraeutner et al., 2017; Mizuguchi et al., 2014) and its similarity to actual physical action in this regard (e.g. Finke, 1979; Frank et al., 2024; Grosprêtre et al., 2016; Guillot et al., 2012; Hardwick et al., 2018; Jeannerod, 1994, 1995, 2001; Johnson, 1982; Kasess et al., 2008; Solomon et al., 2019, 2022). Based on neurophysiological and behavioural data following combinations of MI and physical practice of dart-throwing to a target, there was evidence commensurate with effector general effects associated with MI practice administered before physical practice (Kraeutner et al., 2017, 2020a,b). Practicing via MI was argued to promote a perceptual integration of the more general features of a physical action that were not specific to any particular effector. This effector independence, was in contrast to physical practice, where the features of the physical action were mapped onto the effector used in practice. While there is a limited work that looks at the impact and efficacy of MI on intermanual transfer, the research that does exist overall supports the notion that MI can function as an effective supplement, or even potentially as an alternative, to physical practice in facilitating transfer (for a scoping review, see Yao et al., 2023). Land et al. (2016) proposed that physical practice operates at both the cognitive-perceptual task level (mentally processing a potential action based on sensory input) as well as at the more externally-influenced effector level (actually utilizing an effector to physically carry out the action). MI, in contrast, stays perceptual/internal and stops short of this prone-to-interference effector level process (Land et al., 2016). Effectively, MI does not require the interaction between task level and effector level motor representations that physical execution does. The scoping meta-analytic work by Yao and colleagues (2023) affirmed that overall, MI practice can be effective in facilitating interlimb transfer, even showing similar benefits to physical practice. However, these authors pointed out gaps in the literature that impacted their overall analysis and conclusions. One of the gaps related to the type of task studied, whereby most of the studies reviewed involved sequence-type tasks, which are thought to be heavily memory dependent, as opposed to tasks that emphasized motor acuity and precision. Across the literature, studies investigating intermanual transfer typically utilize one of two primary types of experimental tasks: sequence learning tasks (e.g. Amemiya et al., 2010; Asa et al., 2014; Dahm et al., 2023; Garbarini et al., 2018; Land et al., 2016; Panzer et al., 2009) and visuomotor rotation tasks (e.g. Paparella et al., 2023; Wang et al., 2011; Wang & Sainburg, 2006) or occasionally limb abduction tasks (Alenzie, 2018; Yue & Cole, 1992). In the interest of further building upon existing research and understanding the encoding that occurs through motor imagery through an intermanual transfer paradigm, it is important to investigate transfer in task that are more specifically motor focused, involving the learning of a novel movement rather than adapting an existing one or memorizing a particular sequence of events. Dart throwing to a target involves motor acuity and the coordination of joints. In particular, underhand throwing of darts to a target on the floor introduces a novelty component, in that the specific movement required would not likely have been practiced before. Because underhanded dart throwing is a novel movement for most individuals, it is not likely to be as heavily impacted by hand-specific prior experience. By throwing to a target on the floor, some of the error involved in throwing to a target (associated with minor changes to technique and large changes to errors especially as a result of gravity) is also avoided. A standard dart throwing task has been used successfully in the past to study motor imagery and transfer (Kraeutner et al., 2020a,b), in a comprehensively measurable way, inspiring the use of such an experimental task in this current study. Moreover, such a task allows us to collect both movement time (MT) data to infer the “effectiveness” of imagery practice (with respect to the similarity of imagined and later executed movements – referred to as mental chronometry; e.g., McAvienue & Robertson, 2008), as well as a measure that is not typically collected, reaction time (RT). Given suggestions that there is only functional equivalence at the planning stages and not in actually “doing” MI (e.g., Glover & Baran, 2017), there is reason to evaluate RT data to assess where similarities or differences across practice conditions are greatest. Investigating and understanding effector in/dependence matters because any motor features that exemplify similarities and/or differences between MI and physical execution provide insight into MI processes generally, and if and how prior motor capabilities and experiences are used to perform MI. Importantly, there are outstanding questions concerning the effectiveness of MI generally for motor learning, as compared to physical practice, as well as to other types of covert practice, namely observational practice (e.g. Gatti et al., 2013; Gonzalez-Rosa et al., 2015; Romano-Smith et al., 2018; Ruffino et al., 2017; Toth et al., 2020). Watching other people practice a motor task has also been shown to lead to increased perceptions of confidence with repeated watching, despite corresponding changes in motor ability (Kardas & O’Brien, 2018; Hodges & Coppola, 2015). This result was replicated in a recent online study comparing an observation only group watching a 2-ball juggling skill in comparison to a group that also engaged in motor imagery when watching (Kraeutner et al., 2023). Motor imagery moderated perceptions of improvement with practice, leading to the conclusion that motor imagery allows the performer better or more valid access to their own motor capabilities than observational practice (at least of a skilled performer). As such, measures of confidence during covert practice give insight into people’s perception of their own ability and importantly the match of this perception to their own ability. To date, no one has studied what happens to perceptions of confidence during motor imagery practice, nor whether perceptions of confidence are matched to actual ability across hands. Observational practice has been studied in reference to intermanual transfer, but not usually in comparison to MI. In a review of experiments looking at comparisons between physical practice and observational practice in the context of intermanual transfer, Shea and colleagues highlight both differences and similarities in encoding processes and transfer outcomes between these two practice types (Shea et al., 2011). Observational learning can be as effective as actual physical practice in aiding transfer, specifically when visuo-spatial coordinates of a movement task are kept consistent through practice and transfer. However, observational learning does not show equivalent transferability as that of physical practice when the motor coordinates (such as the same muscles but in a different spatial orientation) of a learned movement are the consistent conditions maintained between practice and transfer (Shea et al., 2011). Essentially, it appears that motor coordinate information is limited in transfer across the hands after simple observation. Studying the learning and interlimb transfer of a novel motor task, through two different covert methods (observation and MI), appears warranted to help make conclusions about similarities and differences in these processes as compared to physical practice; with the idea that this would implicate how such covert methods can be best leveraged for learning and transfer. Differences have also been noted in memory consolidation processes over time between overt and covert motor learning processes (Conessa et al., 2023; Trempe et al., 2011). Performance tends to stabilize through consolidation following physical practice but appears to improve through consolidation (i.e., offline learning) following MI practice (Debarnot et al., 2015; Debarnot et al., 2022; Freitas et al., 2020). Research Objectives and Aims The aim of this work is to contribute unique knowledge related to what is acquired through motor imagery practice, by testing its transfer across limbs. In order to be able to make conclusions about
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».