Optimal neural and behavioral markers for learning to learn during infancy

描述(申请人提供)：人类婴儿面对的是一个充满模棱两可的复杂世界。不仅环境中存在许多不同的信息特征和维度，而且这些线索通常与任何强化或反馈无关。在复杂和模棱两可的环境中学习有两种解决方案：(A)对选择进行加工的线索的天生限制(自下而上)，或(B)评估线索的快速学习到学习机制(自上而下)。习得的自上而下的信息选择机制可能会更适应特定的任务需求，因此对学习更有用。考虑到婴儿在出生后的头两年要学到的东西很多，主要使用缓慢但精确(自上而下)的搜索方法是没有效率的。我的假设是，学习如何学习的发展进程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，以防止自下而上的分心。研究计划中的实验将检验这一假设，每个实验都评估一个额外的学习水平。先进的行为技术(即桌上和头上的眼球跟踪)和补充的最先进的神经成像方法(即功能性近红外光谱[fNIRS]，通过头皮上的非侵入性光探测器测量空间局部的神经激活)，以及应用于婴儿眼球运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环境中学习。这项研究计划有四个具体目标：1)建立一种新的、稳健的学习测量方法，包括行为和神经测量；2)研究注意力运用如何以最佳方式改善学习；3)将学习范式应用于自然环境；4)对婴儿眼动进行微观分析和开发计算模型。培训部分的重点是学习在婴幼儿研究中使用两种最先进的方法(头盔眼球跟踪器和fNIRS)，并学习使用创新的数据挖掘工具来分析婴儿眼球运动的模式，将看起来的行为与认知能力联系起来。这一培训计划对于申请者的职业目标至关重要，即确定最佳的学习学习策略，从而为学习困难的人群提供培训方案。这些发现将使发展科学以及人工智能、知觉学习、教育、动物学习、机器学习和进化心理学等更大领域的研究人员受益。这项工作将有助于对典型发育中选择性注意和学习的动态进行基础性的理解，这反过来又将告知有学习困难的人群。 与公共健康相关：这一多学科研究计划将通过融合来自行为和神经成像方法和数据挖掘工具的证据，表明在分散注意力的情况下进行有效学习的最佳注意力部署的特征。这项工作将有助于对典型发育中选择性注意和学习的动态进行基础性的理解，这反过来又将告知有学习困难的人群。

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. PUBLIC HEALTH RELEVANCE: This multi-disciplinary research program will indicate signatures of optimal attentional deployment for efficient learning among distractions via converging evidence from behavioral and neuroimaging methods and data mining tools. 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.