title: Statistical computations over a speech stream in a rodent.
abstract: Statistical learning is one of the key mechanisms available to human infants and adults when they face the problems of segmenting a speech stream (Saffran, Aslin, & Newport, 1996) and extracting long-distance regularities (G6mez, 2002; Peña, Bonatti, Nespor, & Mehler, 2002). In the present study, we explore statistical learning abilities in rats in the context of speech segmentation experiments. In a series of five experiments, we address whether rats can compute the necessary statistics to be able to segment synthesized speech streams and detect regularities associated with grammatical structures. Our results demonstrate that rats can segment the streams using the frequency of co-occurrence (not transitional probabilities, as human infants do) among items, showing that some basic statistical learning mechanism generalizes over nonprimate species. Nevertheless, rats did not differentiate among test items when the stream was organized over more complex regularities that involved nonadjacent elements and abstract grammar-like rules.
authors.affiliation: Universitat de Barcelona, Barcelona, Spain. jmtoro@sissa.it
authors.forename: Juan M
authors.initials: JM
authors.lastname: Toro
authors.forename: Josep B
authors.initials: JB
authors.lastname: Trobalón
mesh_terms: D000818:Animals; D007858:Learning; D008297:Male; D011601:Psychophysics / Q000706:statistics & numerical data*; D051381:Rats; D020318:Rats, Long-Evans; D013067:Speech Perception / Q000502:physiology*; D014825:Vocabulary