基本信息
浏览量:0
职业迁徙
个人简介
Research Interests:
Attention, curiosity, learning, computational modeling, cognitive development
whatshotResearch Description:
The Kidd Lab studies the processes involved in knowledge acquisition, especially in young children, using a combination of computational and behavioral methods.
We draw inspiration from classic learning theories in education and psychology, like those by Jean Piaget, Maria Montessori, and Lev Vygotsky. We build computational models inspired by these classic theories that allow us to make specific predictions and generate testable competing hypotheses about learning dynamics (for example, the relationship between the learning context and learning outcomes).
We also design behavioral experiments to empirically differentiate between competing learning theories. Our experiments measure how learners attend and explore throughout the process of learning. Different experiments measure how humans look, explore, and play, starting in infancy and continuing throughout childhood. We use eye-trackers to measure visual fixations to screens during passive viewing, and touchscreens to study touch-based exploration in kid-friendly apps, in addition to studying more traditional play behaviors.
Our results are quantitative theories about how data interacts with learners’ growing knowledge. These formal theories can function as the “back-end” for learning technologies, in addition to informing parenting, educational, and clinical practices.
Attention, curiosity, learning, computational modeling, cognitive development
whatshotResearch Description:
The Kidd Lab studies the processes involved in knowledge acquisition, especially in young children, using a combination of computational and behavioral methods.
We draw inspiration from classic learning theories in education and psychology, like those by Jean Piaget, Maria Montessori, and Lev Vygotsky. We build computational models inspired by these classic theories that allow us to make specific predictions and generate testable competing hypotheses about learning dynamics (for example, the relationship between the learning context and learning outcomes).
We also design behavioral experiments to empirically differentiate between competing learning theories. Our experiments measure how learners attend and explore throughout the process of learning. Different experiments measure how humans look, explore, and play, starting in infancy and continuing throughout childhood. We use eye-trackers to measure visual fixations to screens during passive viewing, and touchscreens to study touch-based exploration in kid-friendly apps, in addition to studying more traditional play behaviors.
Our results are quantitative theories about how data interacts with learners’ growing knowledge. These formal theories can function as the “back-end” for learning technologies, in addition to informing parenting, educational, and clinical practices.
研究兴趣
论文共 56 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
SCIENCEno. 6651 (2023): 1222-1223
Annual Meeting of the Cognitive Science Society (2023)
引用0浏览0EI引用
0
0
Nature human behaviourno. 10 (2023): 1609-1611
引用0浏览0WOSNATURE引用
0
0
Annual Meeting of the Cognitive Science Society (2023)
引用0浏览0EI引用
0
0
semanticscholar(2021)
Anaïs Llorens,Athina Tzovara,Ludovic Bellier,Ilina Bhaya-Grossman, Aurélie Bidet-Caulet,William K. Chang,Zachariah R. Cross, Rosa Dominguez-Faus,Adeen Flinker,Yvonne Fonken, Mark A. Gorenstein, Chris Holdgraf,
Neuronno. 13 (2021): 2047-2074
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn