TRACX: A Recognition-Based Connectionist Framework for Sequence Segmentation and Chunk Extraction

Autor/inn/en	French, Robert M.; Addyman, Caspar; Mareschal, Denis
Titel	TRACX: A Recognition-Based Connectionist Framework for Sequence Segmentation and Chunk Extraction
Quelle	In: Psychological Review, 118 (2011) 4, S.614-636 (23 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-295X
DOI	10.1037/a0025255
Schlagwörter	Infants; Probability; Learning Processes; Pattern Recognition; Sequential Approach; Sequential Learning; Cognitive Processes; Simulation; Data Analysis; Child Development; Memory; Auditory Stimuli; Adult Education; Models + Suchen Sie Ihr Suchwort? Infant; Toddler; Toddlers; Kleinkind; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Learning process; Lernprozess; Mustererkennung; Schrittfolge; Didaktische Sequenzierung; Lernsequenz; Cognitive process; Kognitiver Prozess; Simulation program; Simulationsprogramm; Auswertung; Kindesentwicklung; Gedächtnis; Auditive Stimulation; Adult; Adults; Education; Adult basic education; Adult training; Erwachsenenbildung; Analogiemodell
Abstract	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 model's 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. (Contains 10 footnotes, 3 tables, and 7 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10