Hierarchical Bayesian Models for Human Conceptual Learning

人类概念学习的分层贝叶斯模型

• 批准号: DP0773794
• 负责人: A/Prof Danielle Navarro
• 金额: $ 35.43万
• 依托单位: The University of Adelaide
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2007
• 资助国家: 澳大利亚
• 起止时间: 2007-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0773794
• 关键词: Hierarchical; Bayesian; Models; Human; Conceptual

This project seeks to understand the nature of human conceptual learning. With the shift to an information-based economy, it becomes important to understand what assumptions a real-world learning system should make. Even given the impressive growth of machine learning and artificial intelligence, the human mind remains the most successful example of such a system. In this light, the scientific study of human conceptual structure presents the opportunity to discover how an intelligent thinking system should operate. In addition, many important problems facing an information economy involve being able to understand how people behave. An understanding of the concepts people use is central to this endeavour.

这个项目试图理解人类概念学习的本质。随着转向以信息为基础的经济，理解现实世界的学习系统应该做出什么假设变得重要。即使考虑到机器学习和人工智能令人印象深刻的增长，人类大脑仍然是这种系统最成功的例子。从这个角度来看，对人类概念结构的科学研究提供了发现智能思维系统应该如何运作的机会。此外，信息经济面临的许多重要问题涉及能够理解人们的行为。对人们使用的概念的理解是这一努力的核心。