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卫健学术沙龙:基于5G+人工智能的心理/精神健康服务与管理体系建设
Gauging spacetime inversions
AI合成化学前沿与战略研讨会暨国家智能化学数据中心启动会
Macro to micro- and nano-scale fluidic engineering for analytical chemistry
报告题目:
Misrepresentation and the Discrepancy Theory of Learning
 报告人:
Malcolm Forster 教授
清华大学科技与社会研究所、美国Wisconsin-Madison大学教授
报告时间:
2011-06-03 15:00
报告地点:
新斋353
主办单位:
清华大学科技与社会研究所
  简介:
摘要:Causal theories of mental representation must solve what Fodor called the disjunction problem (1984, “Semantics, Wisconsin Style,” Synthese 59: 231□250): If representations inside the head represent what causes them, then they can never misrepresent because they must always be caused by something. There are many solutions to this problem, but my proposal is designed to explain certain empirical phenomena in animal learning that J. E. R. Staddon (1983, Adaptive Behavior and Learning) systematized in terms of what he called the discrepancy theory of learning.

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