CAREER: Connecting Human and Machine Learning through Probabilistic Models of Cognition

人们能够比计算机更快地学习新概念，通常只需要几个例子，而计算机可能需要数百个。这种非凡的能力在一定程度上是对世界的广泛经验的结果，导致了对可能形成类别的对象类型的强大先验知识。该研究项目通过开发人类类别学习的概率模型，将心理数据与计算机科学和统计学的最新理论联系起来，弥合了人类和机器学习之间的差距。这些数学和计算模型用于探索人们如何快速学习类别，捕捉先验知识对分类的影响，并建立一个人类概念目录，可用于测试心理学理论和训练机器学习系统。在每种情况下，研究结合了心理学和计算机科学中使用的思想，方法和数据来源，使用分层贝叶斯模型和马尔可夫链蒙特卡罗算法来模拟人类认知，实验室实验来测试这些模型，以及大型数据库作为指导模型预测的统计信息来源。该研究计划与教育计划相结合，该计划包括本科和研究生教学和指导，开发认知概率模型教科书，旨在增加计算机科学和心理学社区之间的联系的教程和研讨会，以及通过讲座和网站进行宣传。

People are able to learn new concepts much faster than computers, often requiring only a handful of examples where a computer might require hundreds. This remarkable ability is partly the consequence of extensive experience with the world, resulting in strong prior knowledge about the kinds of objects that are likely to form categories. This research project bridges the gap between human and machine learning by developing probabilistic models of human category learning, connecting psychological data with the latest theories from computer science and statistics. These mathematical and computational models are used to explore how people learn categories so quickly, to capture the effects of prior knowledge on categorization, and to build a catalogue of human concepts that can be used to test psychological theories and to train machine learning systems. In each case, the research combines the ideas, methods, and sources of data used in psychology and computer science, using hierarchical Bayesian models and Markov chain Monte Carlo algorithms to model human cognition, laboratory experiments to test these models, and large databases as a source of statistical information that guides model predictions. This research program is integrated with an educational plan that incorporates undergraduate and graduate teaching and mentoring, development of a textbook on probabilistic models of cognition, tutorials and workshops aimed at increasing contact between the computer science and psychology communities, and outreach through talks and a website.