The extent to which we generalize a novel property from a sample of familiar instances to novel instances depends on the sample composition. Previous property induction experiments have only used samples consisting of novel types (unique entities). Because real‐world evidence samples often contain redundant tokens (repetitions of the same entity), we studied the effects on property induction of adding types and tokens to an observed sample. In Experiments 1–3, we presented participants with a sample of birds or flowers known to have a novel property and probed whether this property generalized to novel items varying in similarity to the initial sample. Increasing the number of novel types (e.g., new birds with the target property) in a sample produced tightening, promoting property generalization to highly similar stimuli but decreasing generalization to less similar stimuli. On the other hand, increasing the number of tokens (e.g., repeated presentations of the same bird with the target property) had little effect on generalization. Experiment 4 showed that repeated tokens are encoded and can benefit recognition, but appear to be given little weight when inferring property generalization. We modified an existing Bayesian model of induction (Navarro, Dry, & Lee, 2012) to account for both the information added by new types and the discounting of information conveyed by tokens.

中文翻译：

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添加类型而不是标记会影响属性归纳。

我们将新特性从熟悉实例的样本推广到新实例的程度取决于样本组成。以前的属性归纳实验仅使用由新类型（独特实体）组成的样本。由于现实世界的证据样本通常包含冗余标记（同一实体的重复），我们研究了向观察样本添加类型和标记对属性归纳的影响。在实验 1-3 中，我们向参与者展示了一个已知具有新特性的鸟类或花卉样本，并探讨该特性是否可以推广到与初始样本相似的新项目。增加样本中新类型（例如，具有目标属性的新鸟）的数量会产生紧缩，促进属性泛化到高度相似的刺激，但降低泛化到不太相似的刺激。另一方面，增加标记的数量（例如，同一只鸟与目标属性的重复呈现）对泛化几乎没有影响。实验 4 表明，重复的标记被编码并且有利于识别，但在推断属性泛化时似乎没有什么权重。我们修改了现有的贝叶斯归纳模型 (Navarro, Dry, & Lee, 2012) 来解释新类型添加的信息和令牌传达的信息的折扣。实验 4 表明，重复的标记被编码并且有利于识别，但在推断属性泛化时似乎没有什么权重。我们修改了现有的贝叶斯归纳模型 (Navarro, Dry, & Lee, 2012) 来解释新类型添加的信息和令牌传达的信息的折扣。实验 4 表明，重复的标记被编码并且有利于识别，但在推断属性泛化时似乎没有什么权重。我们修改了现有的贝叶斯归纳模型 (Navarro, Dry, & Lee, 2012) 来解释新类型添加的信息和令牌传达的信息的折扣。