Understanding Routine Sequential Action

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DESCRIPTION (provided by applicant): A Mentored Research Scientist Development Award (K0l) is requested, to support the establishment of an interdisciplinary research program examining the cognitive mechanisms underlying routine sequential behavior. Routine, goal-oriented action on objects -- the kind of action involved in everyday tasks such as making a cup of coffee -- is fundamental to independent functioning in daily life. When the ability to perform such actions is impaired, as frequently seen in stroke, head injury and neurodegenerative disorders, the impact is typically devastating. Understanding the mechanisms underlying routine sequential behavior, including those involved in representing goals, sequencing actions, and selecting objects, thus represents an important public health objective. The training and research contained in the present proposal pursue this goal by drawing on three important developments in recent research: (1) the application of recurrent neural network models to routine sequential action, (2) detailed tracking of eye and hand movements during the performance of naturalistic tasks, and (3) the analysis of performance in disorders affecting routine sequential action, e.g., action disorganization syndrome (ADS). Recurrent neural networks provide a framework for understanding routine behavior that differs strongly from traditional, schema-based accounts, and which appears to overcome several of their basic problems. In the proposed work, a series of computer simulations will evaluate recurrent networks as models of sequential action on objects, with an initial focus on two theoretically important issues: how objects are selected to become targets of action, and how established procedural knowledge is extended to partially novel task circumstances. Concurrent behavioral experimentation will serve to test predictions of the modeling work, and to provide empirical constraints for the developing theory. Four specific studies are proposed, two using error analyses and chronometric techniques to test predictions about naturalistic task performance in normal subjects and patients with ADS, and two using eye- and hand-tracking techniques to test detailed predictions about object selection and behavior in partially novel settings, again involving both normal and apraxic patients. In support of these research activities, the proposal includes coursework, mentored training activities, and external laboratory rotations, designed to facilitate the acquisition of new skills relating both to computational modeling and empirical research.