Mo, D. & Armstrong, B. C. (2019).  Statistical learning of conjunctive probabilities.   Proceedings of the 41st Annual Conference of the Cognitive Science Society.  Mahwah, NH: Lawrence Erlbaum Associates.


Author's self-archived version (.pdf)  (7 pages)

Official version hosted by the Cognitive Science Society


Most statistical learning studies focus on the learning of transitional probabilities between adjacent elements in a sequence, however, other statistical regularities may underpin different aspects of processing language and regularities in other domains.  Here, we investigate how conjunctive statistical regularities (of the form A and B together predict C) can be learned, and how this learning is  impacted  by  similarity  in  representations  analogous to  that  in  unambiguous  words,  homonyms  with  multiple unrelated meanings, and polysemes with multiple related meanings.  We observed that provided the stimulus structure is relatively simple, participants are readily able to learn conjunctive probabilities and display sensitivity to relatedness among representations.  These results open new theoretical possibilities for exploring the domain-generality  of  how  the  learning  and  processing systems merge conjunctive information in simple laboratory tasks and in natural language.

Keywords: statistical learning; lexical ambiguity; transitional probability; conjunctive probability 

Copyright Notice (borrowed from David Plaut): The documents distributed here have been provided as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.