摘要：The classical, disembodied approach to music cognition conceptualizes action and perception as separate, peripheral phenomena. In contrast, embodied accounts to music cognition emphasize the central role of the close coupling of action and perception. It is a commonly established fact that perception spurs action tendencies. We present a theoretical framework capturing the ways that the human motor system, and the actions it produces, can reciprocally influence the perception of music. The cornerstone of this framework is the common coding theory postulating a representational overlap in the brain between the planning, the execution, and the perception of movement. The integration of action and perception in so-called internal models is explained as a result of associative learning processes. Characteristic of internal models is that they allow intended or perceived sensory states to be transferred into corresponding motor commands (inverse modelling), and vice versa, to predict the sensory outcomes of planned actions (forward modelling). Embodied accounts typically adhere to inverse modelling to explain action effects on music perception (Leman, 2007). We extent this account by pinpointing forward modelling as an alternative mechanism by which action can modulate perception. We provide an extensive overview of recent empirical evidence in support of this idea. Additionally, we demonstrate that motor dysfunctions can cause perceptual disabilities, supporting the main idea of the paper that the human motor system plays a functional role in auditory perception. The finding that music perception is shaped by the human motor system, and the action it produces, suggests that the musical mind is highly embodied. However, we advocate for a more radical approach to embodied (music) cognition in the sense that it needs to be considered as a dynamic process, in which aspects of action, perception, introspection, and social interaction are of crucial importance.
关键词：embodied music cognition; common coding theory; sensory-motor association learning; dynamic systems; internal model
