, Volume 67, Issue 5, pp 867-875

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 (Gómez, 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.

This research was supported by Grant JSMF-20002079 from the James S. McDonnell Foundation, HFSP Grant 301870, Catalan Government Research Grant SGR00034, and Spanish MECD Fellowship AP2000-4164. The procedure was approved by the Comité de Ética en Experimentación Animal from the Universitat de Barcelona and complied with the guidelines of the Catalan and Spanish governments for the treatment of laboratory animals.