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原文連結
論文資訊
- 類型：已發表論文
- 日期：2022-08-22
摘要
Precisely how humans process relational patterns of 資訊 in knowledge, 語言, music, and society is not well understood. Prior work in the field of 統計 learning has demonstrated that humans process such 資訊 by building internal models of the underlying 網絡 structure. However, these mental maps are often inaccurate due to limitations in human 資訊 processing. The existence of such limitations raises clear questions: Given a target 網絡 that one wishes for a human to learn, what 網絡 should one present to the human? Should one simply present the target 網絡 as-is, or should one emphasize certain parts of the 網絡 to proactively mitigate expected errors in learning? To investigate these questions, we study the optimization of 網絡 learnability in a 計算 model of human learning. Evaluating an array of synthetic and
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