Statistical Learning Creates Novel Object Associations via Transitive Relations
作者研究了统计学习能否让人自动推断从未直接关联的物体之间的新关联。在实验中，参与者观看包含两个基础配对（如A-B、B-C）的连续序列后，会自动推断出传递性配对（如A-C），尽管这两个物体从未同时出现（实验1）。这种传递性推断在参与者没有意识到基础配对的情况下也会发生。然而，当基础配对增加到三个时（实验2），参与者无法推断出传递性配对，显示了这种推断的局限性（实验3）。进一步实验表明，这种传递性推断可以跨越类别层级（实验4-7）。这些发现揭示了统计学习的一个新结果：物体之间的新传递性关联可以被隐式推断。
A remarkable ability of the cognitive system is to make novel inferences on the basis of prior experiences. What mechanism supports such inferences? We propose that statistical learning is a process through which transitive inferences of new associations are made between objects that have never been directly associated. After viewing a continuous sequence containing two base pairs (e.g., A-B, B-C), participants automatically inferred a transitive pair (e.g., A-C) where the two objects had never co-occurred before (Experiment 1). This transitive inference occurred in the absence of explicit awareness of the base pairs. However, participants failed to infer the transitive pair from three base pairs (Experiment 2), showing the limits of the transitive inference (Experiment 3). We further demonstrated that this transitive inference can operate across the categorical hierarchy (Experiments 4-7). The findings revealed a novel consequence of statistical learning in which new transitive associations between objects are implicitly inferred.