Discussion
primary motor cortices, injuries, fatigue
CONTRACTION, REGRESSIONS, REGRESSION, EVENTS, CORTEX
The present study investigated how the muscular activation underlying a complex motor task changes along AOT and whether these modulations parallel the behavioral improvements to some extent. For these purposes, seventy-two healthy subjects were enrolled, randomly sub-divided into two groups (AOT and CTRL), and administered training to learn to grasp marbles with chopsticks.We observed a significant improvement for all groups in several behavioral indices, with AOT outperforming the CTRL group, especially in the number of grasping attempts, whose decrease was almost double. This finding confirms the results of previous studiesA relevant aspect of our study is represented by the chosen task, selected as a behavior not belonging to the participants' cultural and motor repertoire. The underlying reasons are threefold: 1) grasping marbles with chopsticks is highly demanding, thus limiting possible ceiling effects during learning; 2) being chopsticks uncommonly used in Western cultures, it would have been easier to recruit a large sample of participants as naïve as possible to the required motor task, further guaranteeing the initial dexterity balance; 3) finally, a previous studyThe analysis of the muscular activity did not reveal any training-related modulation of the contraction amplitude in both group. Previous studies suggested that action observation, in isolation or combined with motor imagery, enhances muscular activity during the execution of the same taskContrary to the case of contraction amplitude, the similarity between the participants and the model in muscular temporal dynamics significantly changed during the training. Specifically, we revealed a gain in EMG similarity standing only for the AOT (5%) and not for the CTRL group (0%). This result is even more relevant if one considers that both groups presented a significant behavioral improvement during the training, demonstrating that motor learning still happens in the absence of previous action observation but follows trajectories unbiasedly relative to the model. In other words, CTRL participants are free to search their ameliorative strategies, and this freedom does not increase similarity with the expert at the population level. Different instead is the case of AOT, as it exposes subjects to expert observation and rehearses their motor system accordingly, polarizing the learning trajectory towards the model and ultimately explaining why AOT participants significantly increase their similarity over time.Notably, the similarity gain achieved during the training predicted the behavioral learning rate selectively for AOT participants. Significant regressions regarded only the muscles and action phases surrounding the grasping events, namely FDI and ADM during reaching and holding. Despite further evidence being needed, the notion that during AOT, participants' improvement is driven by the absorption of some motor fingerprints of the model opens different potential applications and uses. For instance, several motor tasks require maximizing the independence between different muscular districts (agonists vs. antagonists) to increase performance and reduce the fatigue and risks of injuries. In such a scenario, AOT could thus play a decisive role in focusing the training on specific muscular districts.The role that the agent-observer motor similarity exerts on the observer's motor system is not limited to indexing the AOT outcome but also extends to supporting cognitive functions such as the capacity to predict the goal of the observed actionControl analyses ruled out an effect of the initial level of similarity on both the behavioral performance at S1 and the behavioral improvement during the training. These findings may sound in contrast with those by De Marco et al.Considering the organization of the motor system, two non-mutually exclusive models might explain the role of action observation in promoting the convergence toward the model and consequently leading to larger behavioral improvement (see Fig. Model explaining the AOT effect on behavioral improvement and the similarity convergence toward the model. Green areas represent frontal and parietal areas endowed with the mirror mechanism. Their projections to the primary motor cortex (red area) are highlighted with continuous red arrows; corticospinal projections (from premotor, parietal, and primary motor cortices) are represented with dashed arrows.This work is not without its limitations. Our sample is unbalanced in terms of participants' gender (76.39% of participants are female). Such unbalance might bias the results of our study, considering that males and females have different susceptibility to learning motor skillsAnother limitation derives from the fact that AOT impacted only some aspects of motor behavior, and the increase of similarity regards only a few combinations of muscles/phases significantly associated with behavioral improvement. As mentioned above, the complexity and specificity of our task could have mitigated the potential impact of AOT, de facto underpowering the effect of visual feedback on motor components relying mainly on proprioceptive control. However, the paucity of significant improvements can also be seen as proof of the specificity of the AOT effect, which is limited to the motor act involving a dynamic control of the movement (i.e., grasping of the marble) and to the combinations of muscles/phases mostly related to the same gesture. What remains is the need to generalize our findings employing different motor tasks, thus assessing precisely which aspects of complex motor behaviors can be targeted via AOT and which ones can be monitored via an online assessment of the EMG similarity.Finally, we must acknowledge that the effect size of the behavior-similarity link is modest, with an explained variance of around 15% for linear regression models. Here, the simplicity of our behavioral tracking and the complexity of the administered task might have tempered these associations. Future studies employing simpler tasks as well as more sensitive behavioral outcomes (e.g., kinematic tracking) might help refine the strength of this relationship, further informing about the possibility of predicting the learning rate via an online similarity assessment.