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Charting the role of variability in learning across development

绘制学习过程中可变性的作用

基本信息

批准号：
10553521
负责人：
Rebecca E Martin
金额：
$ 3.49万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2022-10-08
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10553521
关键词：
Address Adolescent Adult Age Applied Research Attention deficit hyperactivity disorder Basic Science Behavioral Categories Child Childhood Clinical Complex Computer Models Decision Making Development Diagnostic Early Diagnosis Entropy Environment Exhibits Functional Magnetic Resonance Imaging Future Goals High temperature of physical object Individual Integration Host Factors Knowledge Lead Learning Learning Disabilities Literature Measures Medial Methods Modeling Neurodevelopmental Disorder Outcome Pattern Prefrontal Cortex Process Psychological reinforcement Reaction Time Research Research Personnel Research Proposals Risk Role Sampling Shapes Short-Term Memory Stimulus Temperature Testing Time Work age related autism spectrum disorder behavior measurement cognitive development cognitive skill cognitive task cold temperature computer science design executive function experience imaging modality indexing neuroimaging neuromechanism recruit relating to nervous system research and development response selective attention skills theories young adult

项目摘要

Project Summary/Abstract The way individuals learn from their environment reliably changes with age and experience. A common pattern observed across a wide range of cognitive tasks is that younger children tend to exhibit greater variability in accuracy, reaction time, and other general response patterns, and this variability decreases both within and between subjects with increasing age. While this increased variability is widely acknowledged as a feature of development, the neural and computational processes underlying such variability remain to be discovered, and its potential functions for learning remain poorly characterized. The goal of this research proposal is to chart behavioral and neural variability associated with changes and improvements in learning across development. To accomplish this goal, we will apply computational modeling and neuroimaging methods to better discriminate patterns of variability observed in children during learning, and the variability reduction process that takes place with maturation. We will explore two key measures of variability: (1) the process of exploration of choices during learning, and (2) the development of strategies used to test hypotheses about the regularities of the information to be learned. Comparing a continuous sample of children through adults, we will test three core predictions. First, in Aim 1 we predict that children will show greater variability in the form of greater exploration and choice stochasticity in an explore-exploit reinforcement learning task, and this pattern of exploration will decrease with age. Second, in Aim 2 we hypothesize that when forming strategies in order to learn how to best classify stimuli in a simple perceptual categorization task, children will demonstrate greater variability by using a random guessing strategy for a longer duration and switching between strategies more often than adults. Third, we hypothesize that these behavioral effects will be modulated by late maturing prefrontal cortical regions - the frontopolar cortex during exploration, and medial prefrontal cortex during strategy formation – and the degree of activation in these regions will increase in magnitude with age. No previous study has simultaneously examined both the behavioral and neural processes underlying exploration and strategy use during learning across development. As such, we hope to gain a more detailed and nuanced understanding of how precise measures of variability can elucidate developmental differences in the way we learn. By better clarifying the process of variability reduction in learning during development, this research may inform future translational work in identifying when and how the cognitive development of a child or adolescent may not be following a normative trajectory. This knowledge can aid in designing better early diagnostic measures for individuals at risk of developing learning disabilities or neurodevelopmental disorders associated with greater neural and behavioral variability such as ADHD and autism.

项目总结/文摘

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Neural signals predict information sharing across cultures.

DOI：
10.1073/pnas.2313175120
发表时间：
2023-10-31
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
影响因子：
11.1
作者：
Chan, Hang-Yee;Scholz, Christin;Cosme, Danielle;Martin, Rebecca E.;Benitez, Christian;Resnick, Anthony;Carreras-Tartak, Jose;Cooper, Nicole;Paul, Alexandra M.;Falk, Emily B.
通讯作者：
Falk, Emily B.