How Many Mechanisms Are Needed to Analyze Speech? A Connectionist Simulation of Structural Rule Learning in Artificial Language Acquisition
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Cognitive Science 35 (7):1243-1281 (2011)
Some empirical evidence in the artificial language acquisition literature has been taken to suggest that statistical learning mechanisms are insufficient for extracting structural information from an artificial language. According to the more than one mechanism (MOM) hypothesis, at least two mechanisms are required in order to acquire language from speech: (a) a statistical mechanism for speech segmentation; and (b) an additional rule-following mechanism in order to induce grammatical regularities. In this article, we present a set of neural network studies demonstrating that a single statistical mechanism can mimic the apparent discovery of structural regularities, beyond the segmentation of speech. We argue that our results undermine one argument for the MOM hypothesis.
|Keywords||Language acquisition Speech processing Artificial grammar learning More than one mechanism hypothesis Connectionism Statistical learning|
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Citations of this work BETA
Whitney Tabor, Pyeong W. Cho & Harry Dankowicz (2013). Birth of an Abstraction: A Dynamical Systems Account of the Discovery of an Elsewhere Principle in a Category Learning Task. Cognitive Science 37 (7):1193-1227.
Sofoklis Kakouros & Okko Räsänen (2015). Perception of Sentence Stress in Speech Correlates With the Temporal Unpredictability of Prosodic Features. Cognitive Science 40 (1):n/a-n/a.
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