Expertise in sports, music, dance, and craftsmanship is increasingly attracting researchers from many different backgrounds who seek to deepen their understanding of outstanding human skills - a field known as skill science. The goal of skill science is to elucidate neural, cognitive, and computational mechanisms and processes underlying superior sensorimotor functions. To this aim, cross-disciplinary approaches needed include artificial intelligence, computational intelligence, soft computing, robotics, biomechanics, cognitive science, and neuroscience.

This special issue includes a variety of paper focusing on new computational approaces and cutting-edge empirical techniques shedding light on embodied knowledge. Analytical techniques include factorial analysis, such as Principal Component Analysis (PCA) and Singular Vector Decomposition (SVD), correlation networks, machine learning such as cluster analysis, Bayesian statistics, and nonlinear dynamical modeling. Experimental paradigms and techniques include Virtual Reality (VR) environment, comparison between skilled and unskilled individuals and between individuals with and without neurological disorders, and biomechanical and physiological measurement using motion capture, ElectroMyoGraphy (EMG), functional Magnetic Resonance Imaging (fMRI), Transcranial Magnetic Stimulation (TMS), and Auditory Brainstem Response (ABR).

These approaches and techniques have successfully addressed key features of sensorimotor mechanisms behind skilled human behavior. Unique approaches in terms of abduction reasoning and observation learning of robots have quantitatively and qualitatively unraveled cognitive processes in novel skill acquisition.

Findings from these studies provide intriguing insights into developing comprehensive models of embodied knowledge and into practical applications Quantitative evaluation and precise modeling of human skills are, for example, indispensable for developing hardware and software that mimic human functions and for designing robots and Brain-Machine Interfaces (BMI) that enables dexterous human-like behavior. It is of academic and clinical importance to determine mechanisms for acquiring complex sensorimotor skills. These diverse approaches toward a unique goal are expected to build bridges among researchers with vastly different backgrounds, serving as an impetus for boosting this cross-disciplinary research area.

We believe this special issue will serve as a landmark for further developing skill science research.