TRACX: A Recognition-Based Connectionist Framework for Sequence Segmentation and Chunk Extraction.
Catégorie
Journal Article
Auteurs
French, R. M., Addyman, C., Mareschal, D.
Année
2011
Titre
TRACX: A Recognition-Based Connectionist Framework for Sequence Segmentation and Chunk Extraction.
Journal / Livre / Conférence
Psychological Review
Résumé
Individuals of all ages extract structure from the sequences of patterns they encounter in their environment, an ability that is at the very heart of cognition. Exactly what underlies this ability has been the subject of much debate over the years. A novel mechanism, implicit chunk recognition (ICR), is proposed for sequence segmentation and chunk extraction. The mechanism relies on the recognition of previously encountered subsequences (chunks) in the input rather than on the prediction of upcoming items in the input sequence. A connectionist autoassociator model of ICR, truncated recursive autoassociative chunk extractor (TRACX), is presented in which chunks are extracted by means of truncated recursion. The performance and robustness of the model is demonstrated in a series of 9 simulations of empirical data, covering a wide range of phenomena from the infant statistical learning and adult implicit learning literatures, as well as 2 simulations demonstrating the models ability to generalize to new input and to develop internal representations whose structure reflects that of the items in the input sequence. TRACX outperforms PARSER (Perruchet & Vintner, 1998) and the simple recurrent network (SRN, Cleeremans & McClelland, 1991) in matching human sequence segmentation on existing data. A new study is presented exploring 8-month-olds use of backward transitional probabilities to segment auditory sequences.
Issue
4
Volume
118
Pages
614-636
Mots-clés
chunk extraction, statistical learning, implicit learning, recursive autoassociative memory, autoassociators