A microslip is a type of action hesitation we experience in everyday life, which highlights the gap between human action and machine action patterns. By proposing a simple computational model for microslips, we examine the microslip as an implicit parallel dynamics underneath human cognition. Here, an agent, given as a dynamical system of a simple neural architecture, takes one of two choices, whose neural net is evolved using a genetic algorithm. An evolved agent often shows a hierarchy of action primitives and intentionality, and the agent is sensitive to the subtle differences of the object's layout, which results in a complex basin structures in the action-selection landscape.
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