title: New evidence for chunk-based models in word segmentation.
abstract: There is large evidence that infants are able to exploit statistical cues to discover the words of their language. However, how they proceed to do so is the object of enduring debates. The prevalent position is that words are extracted from the prior computation of statistics, in particular the transitional probabilities between syllables. As an alternative, chunk-based models posit that the sensitivity to statistics results from other processes, whereby many potential chunks are considered as candidate words, then selected as a function of their relevance. These two classes of models have proven to be difficult to dissociate. We propose here a procedure, which leads to contrasted predictions regarding the influence of a first language, L1, on the segmentation of a second language, L2. Simulations run with PARSER (Perruchet & Vinter, 1998), a chunk-based model, predict that when the words of L1 become word-external transitions of L2, learning of L2 should be depleted until reaching below chance level, at least before extensive exposure to L2 reverses the effect. In the same condition, a transitional-probability based model predicts above-chance performance whatever the duration of exposure to L2. PARSER's predictions were confirmed by experimental data: Performance on a two-alternative forced choice test between words and part-words from L2 was significantly below chance even though part-words were less cohesive in terms of transitional probabilities than words.
authors.affiliation: Université de Bourgogne, LEAD/CNRS, UMR5022, Pôle AAFE, 11 Esplanade Erasme, 21000 Dijon, France. Electronic address: pierre.perruchet@u-bourgogne.fr.
authors.forename: Pierre
authors.initials: P
authors.lastname: Perruchet
authors.affiliation: Université de Bourgogne, LEAD/CNRS, UMR5022, Pôle AAFE, 11 Esplanade Erasme, 21000 Dijon, France.
authors.forename: Bénédicte
authors.initials: B
authors.lastname: Poulin-Charronnat
authors.affiliation: CNRS, UMR5292, INSERM U1028, Lyon Neuroscience Research Center, Auditory Cognition and Psychoacoustics Team, Université of Lyon I, Lyon, France.
authors.forename: Barbara
authors.initials: B
authors.lastname: Tillmann
authors.affiliation: Laboratoire de Psychologie et Neurocognition, CNRS UMR5105, Université Grenoble Alpes, Bâtiment Sciences de l'Homme et Mathématiques, BP47, 38040 Grenoble Cedex 9, France.
authors.forename: Ronald
authors.initials: R
authors.lastname: Peereman
mesh_terms: D003463:Cues; D006801:Humans; D007802:Language; D007804:Language Development; D007858:Learning; D008962:Models, Theoretical
keywords: Artificial language; Chunking; Modeling; Word segmentation