Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference
Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference
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DOI：
10.1126/science.1196404
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发表时间：
2011-05-27
期刊：
影响因子：
56.9
通讯作者：
Bonatti, Luca L.
中科院分区：
文献类型：
--
作者：
Teglas, Erno;Vul, Edward;Bonatti, Luca L.
许多生物体可以根据过去经验的统计数据预测未来事件，但人类也擅长通过纯粹的推理进行预测：在抽象知识的指导下整合多个信息来源，对从未直接经历过的新情况形成理性预期。在这里，我们表明，这种推理是令人惊讶的丰富，强大的，连贯的，甚至在言语前的婴儿。当12个月大的婴儿看到多个移动物体的复杂显示时，他们对未来事件形成了随时间变化的预期，这些预期是几个刺激变量的系统和理性函数。婴儿的注视时间与体现物体运动抽象原则的贝叶斯理想观察者一致。该模型解释了婴儿的统计期望和经典的定性研究结果，对象认知在年幼的婴儿，而不是最初被视为概率推理。
Many organisms can predict future events from the statistics of past experience, but humans also excel at making predictions by pure reasoning: integrating multiple sources of information, guided by abstract knowledge, to form rational expectations about novel situations, never directly experienced. Here, we show that this reasoning is surprisingly rich, powerful, and coherent even in preverbal infants. When 12-month-old infants view complex displays of multiple moving objects, they form time-varying expectations about future events that are a systematic and rational function of several stimulus variables. Infants' looking times are consistent with a Bayesian ideal observer embodying abstract principles of object motion. The model explains infants' statistical expectations and classic qualitative findings about object cognition in younger babies, not originally viewed as probabilistic inferences.