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Abstract: In everyday tasks, selecting actions in the proper sequence requires a continuously updated representation of temporal context. Many existing models address this problem by positing a hierarchy of processing units, mirroring the roughly hierarchical structure of naturalistic tasks themselves. Although intuitive, such an approach has led to a number of difficulties, including a reliance on overly rigid sequencing mechanisms and a limited ability to address both learning and context sensitivity in behavior. We consider here an alternative framework in which the representation of temporal context depends on learned, recurrent connections within a network that maps from environmental inputs to actions. Applying this approach to the specific, and in many ways prototypical, everyday task of coffee-making, we examine its ability to account for several central characteristics of normal and impaired human performance. The model learns to deal flexibly with a complex set of sequencing constraints, encoding contextual information at multiple time-scales within a single, distributed internal representation. Mildly degrading this context representation leads to errors resembling the everyday ``slips of action'' that normal individuals commit under distraction. More severe degradation leads to a pattern of disorganization resembling that observed in {\em action disorganization syndrome}, a variety of apraxia. Analysis of the model's function yields novel, testable predictions relevant to both normal and apraxic performance. Taken together, the results indicate that recurrent connectionist models offer a useful framework for understanding routine sequential action.
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