Commonsense psychology in human infants and machines

Commonsense psychology in human infants and machines
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人类婴儿和机器的常识心理学

DOI:
10.1016/j.cognition.2023.105406
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发表时间:
2023
期刊:
影响因子:
3.4
通讯作者:
Dillon, Moira R.
Dillon, Moira R.
中科院分区:
心理学2区
文献类型:
--
作者:
Stojnić, Gala;Gandhi, Kanishk;Yasuda, Shannon;Lake, Brenden M.;Dillon, Moira R.

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人类婴儿对其他人着迷。它们为人们的行为动机带来了一系列丰富而灵活的期望,使人们着迷。在这里,我们在“婴儿直觉基准(BIB)”上测试了 11 个月大的婴儿和最先进的学习驱动的神经网络模型,这是一系列挑战婴儿和机器的任务,以对智能体行为的根本原因做出高级预测。婴儿期望智能体的行动针对物体,而不是位置,并且婴儿表现出对智能体针对目标的理性有效行动的默认期望。神经网络模型未能捕捉婴儿的知识。我们的工作提供了一个全面的框架来描述婴儿的常识心理,并迈出了测试人类知识和类人人工智能是否可以在认知和发展理论假设的基础上建立的第一步。
Human infants are fascinated by other people. They bring to this fascination a constellation of rich and flexible expectations about the intentions motivating people's actions. Here we test 11-month-old infants and state-of-the-art learning-driven neural-network models on the “Baby Intuitions Benchmark (BIB),” a suite of tasks challenging both infants and machines to make high-level predictions about the underlying causes of agents' actions. Infants expected agents' actions to be directed towards objects, not locations, and infants demonstrated default expectations about agents' rationally efficient actions towards goals. The neural-network models failed to capture infants' knowledge. Our work provides a comprehensive framework in which to characterize infants' commonsense psychology and takes the first step in testing whether human knowledge and human-like artificial intelligence can be built from the foundations cognitive and developmental theories postulate.
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