title: iMinerva: a mathematical model of distributional statistical learning.
abstract: Statistical learning refers to the ability to identify structure in the input based on its statistical properties. For many linguistic structures, the relevant statistical features are distributional: They are related to the frequency and variability of exemplars in the input. These distributional regularities have been suggested to play a role in many different aspects of language learning, including phonetic categories, using phonemic distinctions in word learning, and discovering non-adjacent relations. On the surface, these different aspects share few commonalities. Despite this, we demonstrate that the same computational framework can account for learning in all of these tasks. These results support two conclusions. The first is that much, and perhaps all, of distributional statistical learning can be explained by the same underlying set of processes. The second is that some aspects of language can be learned due to domain-general characteristics of memory.
authors.affiliation: Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213, USA. thiessen@andrew.cmu.edu
authors.forename: Erik D
authors.initials: ED
authors.lastname: Thiessen
authors.forename: Philip I
authors.initials: PI
authors.lastname: Pavlik
mesh_terms: D003198:Computer Simulation; D006801:Humans; D007223:Infant; D007802:Language; D007804:Language Development; D008568:Memory; D008962:Models, Theoretical; D011337:Probability Learning; D014706:Verbal Learning