Understanding Routine Sequential Action

了解常规顺序操作

• 批准号: 6659105
• 负责人: Matthew M Botvinick
• 金额: $ 13.87万
• 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/6659105
• 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)：指导研究科学家的发展 申请奖励(K01)，以支持建立跨学科 研究常规背后的认知机制的研究项目 顺序行为。例行公事的、面向目标的对象操作--那种 日常工作中的行动，比如煮一杯咖啡--是 对日常生活中的独立运作至关重要。当你有能力 执行此类动作受到损害，如中风、头部损伤和 神经退行性疾病，其影响通常是毁灭性的。理解 常规顺序行为背后的机制，包括涉及的机制 在表示目标、排序动作和选择对象时，因此 代表着一项重要的公共卫生目标。培训和研究 本提案中所载通过借鉴三个方面来实现这一目标 近期研究的重要进展：(1)递归函数的应用 神经网络模型的常规顺序动作，(2)详细跟踪 在执行自然任务时的眼睛和手的运动，以及(3) 影响常规顺序动作的障碍的表现分析， 例如，动作紊乱综合症(ADS)。递归神经网络提供 一个理解日常行为的框架，与 传统的、基于架构的帐户，这似乎克服了几个 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对象的顺序动作的模型， 首先关注两个理论上重要的问题：物体是如何 被选中成为行动目标，以及如何建立程序性知识 被扩展到部分新颖的任务环境。并发行为 实验将用于测试对建模工作的预测，并 为发展中的理论提供实证约束。四项具体研究 提出了两个利用误差分析和计时技术进行测试的方法 正常受试者和患者自然主义任务表现的预测 其中两个使用眼睛和手部跟踪技术来测试详细的 关于在部分新颖的环境中的对象选择和行为的预测， 再一次涉及到正常和失用症患者。以支持这些研究 活动，该提案包括课程作业、有指导的培训活动以及 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。