Considerable research shows that causal perception emerges between 6 and 10 months of age. Yet, because this research tends to use artificial stimuli, it is unanswered how or through what mechanisms of change human infants learn about the causal properties of real-world categories such as animate entities and inanimate objects. One answer to this question is that this knowledge is innate (i.e., unlearned, evolutionarily ancient, and possibly present at birth) and underpinned by core knowledge and core cognition. An alternative perspective that is tested here through computer simulations is that infants acquire this knowledge via domain-general associative learning. This article demonstrates that associative learning alone-as instantiated in an artificial neural network-is sufficient to explain the data presented in four classic infancy studies: Spelke et al. (1995), Saxe et al. (2005), Saxe et al. (2007), and Markson and Spelke (2006). This work not only advances theoretical perspectives within developmental psychology but also has implications for the design of artificial intelligence systems inspired by human cognitive development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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关于婴儿如何学习有生命和无生命的因果行为的联想学习说明：对四项经典研究的批判性重新审视。


大量研究表明，因果知觉在 6 至 10 个月大时出现。然而，由于这项研究倾向于使用人工刺激，因此人类婴儿如何或通过什么变化机制了解现实世界类别（例如有生命的实体和无生命的物体）的因果特性尚未得到解答。这个问题的一个答案是，这种知识是与生俱来的（即，未经学习的、进化上古老的、可能在出生时就存在的）并以核心知识和核心认知为基础。这里通过计算机模拟测试的另一种观点是，婴儿通过领域通用联想学习获得这些知识。本文证明，仅在人工神经网络中实例化的联想学习就足以解释四项经典婴儿期研究中提供的数据：Spelke 等人。 (1995)，萨克斯等人。 (2005)，萨克斯等人。 (2007)、Markson 和 Spelke (2006)。这项工作不仅推进了发展心理学的理论观点，而且对受人类认知发展启发的人工智能系统的设计也具有影响。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。