Remembrance of inferences past: Amortization in human hypothesis generation
Remembrance of inferences past: Amortization in human hypothesis generation
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DOI：
10.1016/j.cognition.2018.04.017
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发表时间：
2018-09-01
期刊：
影响因子：
3.4
通讯作者：
Gershman, Samuel J.
中科院分区：
文献类型：
--
作者：
Dasgupta, Ishita;Schulz, Eric;Gershman, Samuel J.
贝叶斯认知模型假设人们计算假设的概率分布。然而，所需的计算通常是棘手的或昂贵的。由于人们经常遇到许多密切相关的分布，因此选择性重复使用计算（摊销推理）是对大脑有限资源的计算高效利用。我们提出了三个实验，为人类概率推理中的摊销提供了证据。当顺序回答两个有关自然场景的查询时，参与者对第二个查询的响应系统地依赖于第一个查询的结构。这种影响对查询的内容很敏感，只有在查询相关时才会出现。使用认知负荷操纵，我们发现证据表明，人们摊销汇总统计以前的推断，而不是存储整个分布。这些发现支持了大脑权衡准确性和计算成本的观点，以有效利用其有限的认知资源来近似概率推理。
Bayesian models of cognition assume that people compute probability distributions over hypotheses. However, the required computations are frequently intractable or prohibitively expensive. Since people often encounter many closely related distributions, selective reuse of computations (amortized inference) is a computationally efficient use of the brain's limited resources. We present three experiments that provide evidence for amortization in human probabilistic reasoning. When sequentially answering two related queries about natural scenes, participants' responses to the second query systematically depend on the structure of the first query. This influence is sensitive to the content of the queries, only appearing when the queries are related. Using a cognitive load manipulation, we find evidence that people amortize summary statistics of previous inferences, rather than storing the entire distribution. These findings support the view that the brain trades off accuracy and computational cost, to make efficient use of its limited cognitive resources to approximate probabilistic inference.