Representations Yielding Task-Dependent Behavior

产生任务依赖行为的表示

• 批准号: 7336787
• 负责人: YUKO MUNAKATA
• 金额: $ 24.69万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-12-15 至 2009-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7336787
• 关键词: Accounting; Adult; Behavior; Behavioral; Biological; Child; Computer Simulation; Development; Dissociation; Goals; Infant; Lead; Nature; Neural Network Simulation; Pliability; Representations, Knowledge (Computer); Research; Training; Work; behavior influence; behavior test; cognitive neuroscience; cognitive system; expectation; information organization; named group; programs; theories

DESCRIPTION (provided by applicant): The primary goal of this research is to advance understanding of the representations underlying task-dependent behavior. Infants and children can appear precocious or limited in almost any ability depending on the task administered to them. For example, infants as young as 3.5 months seem to understand the continued existence of hidden objects in violation-of-expectation studies, yet infants fail to manually search for objects hidden by occluders until around 9 months. Similarly, children can answer verbal queries about how to respond flexibly in different situations, yet they fail to show such flexibility in their actual behaviors. Such task-dependent behaviors, or dissociations, raise important questions about the organization, development, and training of our cognitive systems. This project investigates these issues via the following questions: 1.) What kinds of representations lead to task-dependent behaviors, specifically: How do graded representations contribute to task-dependent behaviors? How do distinct types of representations (active and latent) contribute to task-dependent behaviors? How do graded and distinct types of representations interact to influence behavior? 2.) How general is the graded, active-latent framework proposed for understanding infants' and children's task-dependent behaviors, specifically: What revisions are required for computational models of this framework to more fully account for behavioral and biological findings? What are the potential educational implications from this framework? What is the relevance of this framework for understanding adult behavior? The proposed studies investigate these questions through the integration of behavioral testing, neural network modeling, and findings from cognitive neuroscience. The proposed work provides a coherent program for advancing our understanding of the factors contributing to task-dependent behavior -- an essential step toward characterizing the nature of representations of knowledge, and in turn informing theory and its application to both typical and special populations.

描述（由申请人提供）：这项研究的主要目标是提高对任务依赖行为基础的表示形式的理解。婴儿和儿童几乎可以根据对其执行的任务而早熟或几乎有限。例如，3.5个月以下的婴儿似乎了解违反期望研究中隐藏物体的持续存在，但是婴儿无法手动搜索封闭器隐藏的对象，直到9个月左右。同样，孩子们可以回答有关如何在不同情况下灵活反应的口头疑问，但他们在其实际行为上没有表现出如此灵活性。这种依赖于任务的行为或解离，就我们的认知系统的组织，发展和培训提出了重要问题。该项目通过以下问题调查了这些问题： 1.）哪些形式导致任务依赖性行为，特别是：分级表示如何促进与任务有关的行为？不同类型的表示（主动和潜在）如何促进任务依赖性行为？分级和不同类型的表示形式如何相互作用以影响行为？ 2.）提出的用于理解婴儿和儿童任务依赖性行为的分级，主动置式框架是多么一般：该框架的计算模型需要哪些修订以更充分地说明行为和生物学发现？该框架的潜在教育意义是什么？该框架与理解成人行为的相关性是什么？拟议的研究通过整合行为测试，神经网络建模以及认知神经科学的发现来研究这些问题。 拟议的工作提供了一个连贯的计划，以促进我们对有助于任务依赖行为的因素的理解，这是朝着表征知识表示本质的重要一步，进而为理论及其对典型和特殊人群的应用提供信息。