From the structure of experience to concepts of structure: How the concept "cause" is attributed to objects and events.
From the structure of experience to concepts of structure: How the concept "cause" is attributed to objects and events.
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从经验结构到结构概念：“原因”概念如何归因于物体和事件。
DOI：
10.1037/xge0000594
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Thompson-Schill,SharonL
中科院分区：
文献类型：
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作者：
Leshinskaya,Anna;Thompson-Schill,SharonL
概念中关系信息的普遍存在，以及感官输入中的间接存在，提出了如何从经验中提取信息的问题。我们将经验操作为一系列事件，其中随机事件之间存在可靠的预测关系，并且学习者对于他们将学到的内容很天真（即统计学习范式）。首先，我们询问，在没有评估因果关系的说明的情况下，预测事件对是否会自发地被视为相互因果关系。我们发现，预测信息确实为后来的因果判断提供了信息，但并没有导致自发的因果关系感。因此，偶发事件与因果推理相关，但这种解释可能不会完全自下而上发生。第二个问题是如何利用这种经验来了解新奇的物体。因为事件发生在一个持续存在的对象周围或涉及一个持续存在的对象，所以我们能够将对象与事件区分开来。我们发现，对象可以通过高阶结构来归因于因果属性，其中对象的身份与其影响的可能性增加无关，而是与事件之间的预测结构（鉴于其存在）相关。这是一个重要的证明，表明对象的因果属性可以是高度抽象的：它们不需要指感官事件本身的发生，或其与对象的联系，而是指其存在的事件之间是否存在预测关系。这些学习机制对于从经验中获取抽象知识可能很重要。
The pervasive presence of relational information in concepts, and its indirect presence in sensory input, raises the question of how it is extracted from experience. We operationalized experience as a stream of events in which reliable predictive relationships exist among random ones, and in which learners are naïve as to what they will learn (ie, a statistical learning paradigm). First, we asked whether predictive event pairs would spontaneously be seen as causing each other, given no instructions to evaluate causality. We found that predictive information indeed informed later causal judgments but did not lead to a spontaneous sense of causality. Thus, event contingencies are relevant to causal inference, but such interpretations may not occur fully bottom-up. A second question was how such experience might be used to learn about novel objects. Because events occurred either around or involving a continually present object, we were able to distinguish objects from events. We found that objects can be attributed causal properties by virtue of a higher-order structure, in which the object’s identity is linked not to the increased likelihood of its effect, but rather, to the predictive structure among events, given its presence. This is an important demonstration that objects’ causal properties can be highly abstract: They need not refer to an occurrence of a sensory event per se, or its link to an object, but rather to whether or not a predictive relationship holds among events in its presence. These learning mechanisms may be important for acquiring abstract knowledge from experience.