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天文系 Colloquium: Probing Planet Formation with the Most Extreme Cases
清华大学材料科学与工程研究院《材料科学论坛》:Magnetocaloric effect: from the...
Data-driven Discovery of Optimization-based Decision-making Models
Toward the Efficient Operation of an Electrified Chemical Industry
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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