Self-modelled versus skilled-peer modelled AO+MI effects on skilled sensorimotor performance: A stage 2 registered report

Eoghan McNeill, Adam J. Toth, Niall Ramsbottom, Mark J. Campbell

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Action observation (AO) and motor imagery (MI) are simulation states that have been demonstrated to independently enhance motor skill performance. Historically, AO and MI were examined in isolation from one another; however recent neurophysiological and behavioural evidence indicates that using MI during AO (AO + MI) may be more potent at enhancing performance than either simulation state alone. The AO component of AO + MI is typically delivered via a self-model or skilled-peer model paradigm, via an observation video. The purpose of the proposed study was to further examine the implementation of AO + MI states by directly comparing the effectiveness of self-modelled AO + MI and skilled-peer modelled AO + MI for augmenting performance on a golf putting task with a sample of 56 skilled golfers. Our primary hypothesis was that skilled participants who engaged with a self-model intervention would improve their performance more than those engaging with a skilled-peer model intervention. Results showed no significant differences in putting accuracy or precision improvement following the two conditions. However, the self-model group did significantly improve their club-path kinematics more than the skilled peer-model group. We suggest self-observation combined with MI can facilitate error detection and correction in skilled performers only on aspects of motor control most easily viewed during AO. These findings add to an emerging body of literature outlining the conditions in which AO + MI can effectively enhance sensorimotor skill performance. The stage 1 report can be found at https://doi.org/10.1016/j.psychsport.2020.101683.

Original languageEnglish
Article number101910
JournalPsychology of Sport and Exercise
Volume54
DOIs
Publication statusPublished - May 2021

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