Proceedings of the Annual Meeting of the Cognitive Science Society

Most statistical learning studies focus on the learning oftransitional probabilities between adjacent elements in asequence, however, other statistical regularities may un-derpin different aspects of processing language and regu-larities in other domains. Here, we investigate how con-junctive statistical regularities (of the form A and B to-gether predict C) can be learned, and how this learningis impacted by similarity in representations analogousto that in unambiguous words, homonyms with mul-tiple unrelated meanings, and polysemes with multiplerelated meanings. We observed that provided the stimu-lus structure is relatively simple, participants are readilyable to learn conjunctive probabilities and display sen-sitivity to relatedness among representations. These re-sults open new theoretical possibilities for exploring thedomain-generality of how the learning and processingsystems merge conjunctive information in simple labo-ratory tasks and in natural language.