Tutorial and simulations with ADAM: an adaptation and anticipation model of sensorimotor synchronization
Interpersonal coordination of movements often involves precise synchronization of action timing, particularly in expert domains such as ensemble music performance. According to the adaptation and anticipation model (ADAM) of sensorimotor synchronization, precise yet flexible interpersonal coordination is supported by reactive error correction mechanisms and anticipatory mechanisms that exploit systematic patterns in stimulus timing to plan future actions. Here, we provide a tutorial introduction to the computational architecture of ADAM and present a series of single- and dual-virtual agent simulations that examine the model parameters that produce ideal synchronization performance in different tempo conditions. In the single-agent simulations, a virtual agent synchronized responses to steady tempo sequence or a sequence containing gradual tempo changes. Parameters controlling basic reactive error (phase) correction were sufficient for producing ideal synchronization performance at the steady tempo, whereas parameters controlling anticipatory mechanisms were necessary for ideal performance with a tempo-changing sequence. In the dual-agent simulations, two interacting virtual agents produced temporal sequences from either congruent or incongruent internal performance templates specifying a steady tempo or tempo changes. Ideal performance was achieved with reactive error correction alone when both agents implemented the same performance template (either steady tempo or tempo change). In contrast, anticipatory mechanisms played a key role when one agent implemented a steady tempo template and the other agent implemented a tempo change template. These findings have implications for understanding the interplay between reactive and anticipatory mechanisms when agents possess compatible versus incompatible representations of task goals during human–human and human–machine interaction.
KeywordsSensorimotor synchronization Interpersonal coordination Cognitive modeling Action timing Temporal prediction
This work is supported by ARC Future Fellowship Grant FT140101162.
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