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原文連結
論文資訊
- 類型：已發表論文
- 日期：2020-05-08
摘要
Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of 統計 relationships should involve complex mental processes. Here we propose an alternative perspective: that higher-order associations instead arise from natural errors in learning and memory. Using the free energy principle, which bridges 資訊 theory and 貝氏 inference, we derive a maximum 熵 model of people’s internal representations of the transitions between stimuli. Importantly, our model (i) affords a concise analytic form, (ii) qualitatively explains the effects of transition 網絡 structure on human expectations, and (iii) quantitatively predicts human reaction times in probabilistic sequential motor tasks.
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