Data recording and analysis
muscular contraction
CONTRACTION
According to the video, the reach-to-place of each marble was segmented into three phases: Reaching (from the initial position to the first chopstick-marble contact), Holding (from the successful chopstick-marble contact to the lifting from the plate surface), Transport (from the marble lifting to its positioning in the hole). The duration of the three phases was calculated for each marble and then averaged in each session.For each execution session, we thus evaluated the following behavioral outcomes:The number of grasping attempts (GA), i.e., the number of contacts between the chopsticks and the marble during the attempt to grab it. In principle, the ideal execution would comprise a number of GA equal to the number of marbles. Conversely, the higher is GA, the more inaccurate the motor performance. This variable has been selected as the primary outcome because the marble grasping is the most challenging phase of the whole task due to the inexperienced participants and the shape/smoothness of the marbles and container;The number of failed liftings (FL), i.e., the number of accidental fallings of the marble during the transport phase, thus impeding its correct positioning on the board;The mean duration of the reach-to-place action (MD) (s), obtained by summing the mean duration of the three phases. In this way, we excluded the time spent failing to grasp from the mean duration, thus obtaining a temporal index completely independent from GA.As the experimental procedures encompassed the recording of EMG from three hand muscles, subjects were required to produce the maximal contraction of each muscle in separate blocks of about 10 s. These indices were later used for EMG amplitude normalization across participants.During the task execution, surface EMG signals were recorded from three muscles of the right hand using a wireless EMG system (Cometa Wave Plus, Cometa Srl, Italy). The EMG signals were amplified (× 1000), sampled at 2000 Hz, and filtered with an online first-order band-pass filter (10–500 Hz). The EMG signals were analyzed using a homemade code developed in MATLAB (R2021a) to compute the mean muscular contraction amplitude during the entire reach-to-place action (full trial). For each execution, the amount of muscular contraction was normalized according to the individual (participant and muscle) maximal contraction.While the parameter described above indexes only the amplitude of the muscular contraction, a point of interest also regards the temporal dynamics of the EMG signal. Thus, the EMG signals were enveloped (rectified and filtered using a band-pass filter 3–1000 Hz and an envelope lowpass 2 Hz filter) and segmented using the same time points extracted from the videos. Subsequently, the EMG signal of each phase was standardized in time (on a temporal axis 1–100). Through this process, we obtained a curve for each phase (reaching, holding, and transport) and muscle, matched in duration and thus comparable in terms of the temporal distribution of the muscular activity. Finally, the curves of the three phases were also concatenated to create a unique curve (full trial). The same procedure was also carried out for the EMG traces of the expert.The participant's curves were compared with the corresponding ones of the model, adopting the Linear Fit Method (LFMIn summary, we extrapolated two different EMG information from the participant's training: (1) whether the average contraction amplitude changed over time, and (2) whether the EMG similarity (R