UBC Theses and Dissertations
Statistical learning creates novel object associations via transitive relations Luo, Yu
A remarkable ability of the cognitive system is to make novel inferences based on prior experiences. What mechanism supports such inference? We propose that statistical learning is a process where 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 where new transitive associations between objects are implicitly inferred.
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