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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-09-07
摘要
Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming 認知 maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an edge. Sequences can then be generated from walks through the latent space, with different spaces giving rise to different sequence statistics. Individual or group differences in sequence learning can be modeled by changing the time scale over which estimates of transition probabilities are built, or in other words, by changing the amount of temporal discounting. Latent space models with temporal discounting bear a resemblance to models of navigation through Euclidean spaces. However, fe
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