Department of Psychological and Brain Sciences, University of Louisville, KY 40292, USA.
Dev Sci. 2012 May;15(3):436-47. doi: 10.1111/j.1467-7687.2012.01135.x. Epub 2012 Feb 28.
A core assumption of many theories of development is that children can learn indirectly from other people. However, indirect experience (or testimony) is not constrained to provide veridical information. As a result, if children are to capitalize on this source of knowledge, they must be able to infer who is trustworthy and who is not. How might a learner make such inferences while at the same time learning about the world? What biases, if any, might children bring to this problem? We address these questions with a computational model of epistemic trust in which learners reason about the helpfulness and knowledgeability of an informant. We show that the model captures the competencies shown by young children in four areas: (1) using informants' accuracy to infer how much to trust them; (2) using informants' recent accuracy to overcome effects of familiarity; (3) inferring trust based on consensus among informants; and (4) using information about mal-intent to decide not to trust. The model also explains developmental changes in performance between 3 and 4 years of age as a result of changing default assumptions about the helpfulness of other people.
许多发展理论的一个核心假设是，儿童可以从他人那里间接学习。然而，间接经验（或证词）并不局限于提供真实信息。因此，如果儿童要利用这种知识来源，他们必须能够推断出谁是可信赖的，谁是不可信赖的。学习者在了解世界的同时，如何进行这种推断？如果有的话，儿童可能会对此问题产生哪些偏见？我们通过一个认知信任的计算模型来解决这些问题，学习者通过该模型来推理信息提供者的帮助性和知识能力。我们表明，该模型可以捕捉到幼儿在四个方面表现出的能力：（1）根据信息提供者的准确性来推断应该信任他们的程度；（2）使用信息提供者最近的准确性来克服熟悉度的影响；（3）根据信息提供者之间的共识来推断信任；（4）使用有关恶意意图的信息来决定不相信。该模型还解释了 3 岁至 4 岁之间的表现变化，这是由于对他人的帮助性的默认假设发生了变化。
