CompCog: Developing a Theory of Causal Learning over Time

CompCog：随着时间的推移发展因果学习理论

• 批准号: 1430439
• 负责人: Benjamin Rottman
• 金额: $ 28.24万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1430439&HistoricalAwards=false
• 关键词: CompCog; Developing; Theory; Causal; Learning

Learning from experience is a critical tool for how humans successfully predict and adjust to their changing world, whether a teacher trying to figure out which pedagogical technique will work best for a class, a doctor trying to figure out which medicine will work best for patient, or a business person trying to figure out which marketing strategy will produce the largest sales. The goal of this project is to better understand how people learn cause-effect relations and make causal judgments in a highly complex world. This will facilitate predicting when humans are likely to make good decisions and when humans are likely to make bad decisions, which can be costly for the individual and for society. The research on how people learn causal relations has primarily focused on learning in independent and identically distributed, cross-sectional situations. However, humans also learn causal relations among events that are distributed in time, which often involves non-stationary and autocorrelated information. The first goal of this work is to identify processes people use to learn causal relationships in these longitudinal situations. The second goal is to identify people's ability to employ different learning strategies adaptively for both longitudinal and cross-sectional environments. The results will also be used to develop online simulations to help students learn how to make good causal judgments when designing and analyzing research.

从经验中学习是人类成功预测和适应不断变化的世界的关键工具，无论是教师试图找出哪种教学技术最适合一个班级，医生试图找出哪种药物最适合病人，还是商人试图找出哪种营销策略将产生最大的销售额。该项目的目标是更好地了解人们如何学习因果关系，并在高度复杂的世界中做出因果判断。这将有助于预测人类何时可能做出好的决定，何时可能做出坏的决定，这对个人和社会来说都是代价高昂的。关于人们如何学习因果关系的研究主要集中在独立和同分布的横截面情况下的学习。然而，人类也学习在时间上分布的事件之间的因果关系，这通常涉及非平稳和自相关的信息。这项工作的第一个目标是确定人们用来学习这些纵向情况下的因果关系的过程。第二个目标是确定人们的能力，采用不同的学习策略自适应的纵向和横截面的环境。研究结果还将用于开发在线模拟，以帮助学生学习如何在设计和分析研究时做出良好的因果判断。