Optimal neural and behavioral markers for learning to learn during infancy

婴儿期学习的最佳神经和行为标记

基本信息

批准号：
8708921
负责人：
Rachel Wu
金额：
$ 5.14万
依托单位：
UNIVERSITY OF ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-08-01 至 2015-07-15
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8708921
关键词：
Adult Animals Area Artificial Intelligence Attention Automobile Driving Behavior Behavioral Behavioral Mechanisms Buffers Child Cognitive Cognitive Science Communities Complex Computer Simulation Computers Computing Methodologies Cues Data Development Dimensions Education Environment Event Eye Eye Movements Feedback Fellowship Future Goals Head Human Infant Lead Learning Life Light Link Machine Learning Measures Mediating Methods Modeling Near-Infrared Spectroscopy Pattern Perceptual learning Performance Population Process Psychological Transfer Psychological reinforcement Psychology Regimen Research Research Personnel Research Training Scalp structure Science Social Interaction Solid Solutions Techniques Testing Time Training Training Programs Vision Work age group base career data mining distraction improved infancy innovation learned behavior neuroimaging programs relating to nervous system research study selective attention tool

项目摘要

DESCRIPTION (provided by applicant): Human infants are confronted with a complex world that is filled with ambiguity. Not only are many different features and dimensions of information present in the environment, but these cues are often unrelated to any reinforcement or feedback. There are two solutions to learning in a complex and ambiguous environment: (a) innate constraints on the cues selected for processing (bottom-up), or (b) rapid learning-to-learn mechanisms that assess cues (top-down). Learned top-down mechanisms of information selection may be tuned more to specific task demands, and thus more useful for learning. Given how much infants have to learn over the first two years of life, it is not efficient to use mainly slow but precise (top-down) search methods. My hypothesis is that the developmental progression of learning how to learn requires using bottom-up information in a systematic way, while creating top-down buffers against bottom- up distraction. The experiments in the research plan will test this hypothesis, with each experiment evaluating an additional level of learning. Sophisticated behavioral techniques (i.e., both table- and head-mounted eye- tracking) and complementary state-of-the-art neuroimaging methods (i.e., functional near-infrared spectroscopy [fNIRS], measuring spatially-localized neural activation via non-invasive light probes on the scalp), as well as data mining tools applied to infant eye movement data, will examine how infants learn to learn from both computer displays and in naturalistic settings. There are four specific aims in this research program: 1) to establish a new, robust measure of learning with both behavioral and neural measures, 2) to investigate how attentional deployment can optimally improve learning, 3) to apply the learning paradigm to the natural environment, and 4) to conduct microanalyses on and to develop computational models of infant eye movements. The training component focuses on learning to use two state-of-the-art methods in infancy research (a head-mounted eye-tracker and fNIRS), and learning to use innovative data mining tools to analyze patterns of infant eye movements to link looking behavior to cognitive abilities. This training program is essential for the applicant's career goal of identifying the optimal strategies for learning to learn that will lead to training regimens for populations with learning difficulties. The findings will benefit researchers within the larger community of developmental science, as well as artificial intelligence, perceptual learning, education, animal learning, machine learning, and evolutionary psychology. This work will contribute to a foundational understanding of the dynamics of selective attention and learning in typical development, which in turn would inform populations with learning difficulties.

描述（由申请人提供）：人类婴儿面临着充满歧义的复杂世界。环境中存在许多不同的特征和信息尺寸，而且这些提示通常与任何强化或反馈无关。在复杂且模棱两可的环境中学习有两种解决方案：（a）对选择用于处理（自下而上）的提示的先天约束，或（b）评估提示的快速学习对学习的机制（自上而下）。知识的自上而下的信息选择机制可能会更多地调整为特定的任务需求，因此对于学习更有用。鉴于婴儿在生命的头两年必须学习多少，主要使用缓慢而精确（自上而下）的搜索方法并不有效。我的假设是，学习如何学习的发展进步需要以系统的方式使用自下而上的信息，同时创建自上而下的缓冲区，以防止自发分散注意力。研究计划中的实验将检验该假设，每个实验都评估了额外的学习水平。复杂的行为技术（即桌面和头部的眼睛跟踪）和互补的最先进的神经影像学方法（即功能性近红外光谱[FNIRS]，通过非侵入性神经激活测量头皮上的光探针以及应用于婴儿眼动数据的数据挖掘工具将研究婴儿如何从计算机显示器和自然主义中学习学习。该研究计划中有四个具体的目标：1）通过行为和神经措施建立一种新的，可靠的学习量度，2）研究注意力部署如何最佳地改善学习，3）将学习范式应用于自然环境和4）在对婴儿眼运动的计算模型上进行微分析。培训组件的重点是学习在婴儿期研究中使用两种最先进的方法（头部安装的眼球和FNIRS），并学习使用创新的数据挖掘工具来分析婴儿眼动的模式来链接链接外观。认知能力的行为。该培训计划对于申请人的职业目标是确定学习学习的最佳策略至关重要的，这将导致学习困难的人群培训方案。这些发现将使更大的发展科学社区以及人工智能，感知学习，教育，动物学习，机器学习和进化心理学的研究人员受益。这项工作将有助于对典型发展中选择性关注和学习的动态的基本理解，这反过来又将带来学习困难的人群。