ABSTRACT
This chapter presents novel digital representation techniques that take into account the ability to capture the human sensitivity to environmental variation and its ability to adapt motion behavior to compensate and regulate external processes. The chapter is inspired by early studies of Yokokohji (1996, “What you see is what you feel”) who argued how multimodal environments could be used to simulate the dynamic of physical processes and intervene on learning. At the basis for motion learning research, we assume that an internal (at least simpli–ed) model for motion exists (Flash, 1985; Kelso, 1984), but the approach also bene–ts the efforts of Henmi (1998) and Sano (1999) who conceptualized how motion models could be extracted and trained from the observation of user skills.
