Understanding Routine Sequential Action

了解常规顺序操作

• 批准号: 6455678
• 负责人: Matthew M Botvinick
• 金额: $ 13.68万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-10 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6455678
• 关键词: body movement; body physical activity; clinical research; cognition; computer simulation; eye movements; human subject; immobilization of body part; neuromuscular disorder; sequential perception

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.

描述（由申请人提供）：一名受指导的研究科学家开发 要求奖（K 0 l），以支持建立跨学科的 一项研究计划，检查日常生活中的认知机制 顺序行为常规的、目标导向的对对象的操作-- 在日常工作中，比如煮一杯咖啡， 这是日常生活中独立运作的基础。当有能力 执行这些动作是受损的，常见于中风，头部受伤， 神经退行性疾病，其影响通常是毁灭性的。理解 常规顺序行为的潜在机制，包括那些涉及 在表示目标、排序行动和选择对象方面， 这是一个重要的公共卫生目标。培训和研究 本建议所载的目标是通过利用三个 最近研究的重要进展：（1）经常性 神经网络模型，以例行顺序行动，（2）详细跟踪 在自然主义任务的执行过程中的眼睛和手的运动，和（3） 分析影响常规顺序动作的障碍的表现， 例如，在一个实施例中，动作紊乱综合征（ADS）。递归神经网络提供了 一个理解日常行为的框架， 传统的，基于模式的帐户，并似乎克服了几个 他们的基本问题。在拟议的工作中，一系列计算机模拟 将评估循环网络作为对象上的顺序动作模型， 首先关注两个重要的理论问题：物体是如何 选择成为行动的目标，以及如何建立程序知识 扩展到部分新的任务环境。并发行为 实验将用于测试建模工作的预测，并 为理论的发展提供了经验约束。四项具体研究 提出，两个使用误差分析和计时技术，以测试 正常人和病人的自然主义任务表现预测 与ADS，和两个使用眼睛和手跟踪技术，以测试详细 在部分新颖的环境中对物体选择和行为的预测， 同样涉及正常和失智患者。为了支持这些研究， 活动，该提案包括课程作业，指导培训活动， 外部实验室轮换，旨在促进获得新的 与计算建模和实证研究相关的技能。