Modeling human performance in statistical word segmentation |
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| Authors: | Michael C Frank Sharon Goldwater Thomas L Griffiths Joshua B Tenenbaum |
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| Institution: | 1. Department of Psychology, Stanford University, United States;2. School of Informatics, University of Edinburgh, United Kingdom;3. Department of Psychology, University of California, Berkeley, United States;4. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, United States;1. University of Tokyo, Japan;2. Tamagawa University, Japan;1. Cluster Languages of Emotion, Freie Universität Berlin, Habelschwerdter Allee 45, 14195 Berlin, Germany;2. Centre for Music and Science, Faculty of Music, University of Cambridge, United Kingdom;1. Brain and Cognitive Sciences Department, University of Rochester, United States;2. Psychology Department, Carnegie Mellon University, United States;3. Sackler Institute for Developmental Psychobiology, Weill-Cornell Medical School, United States;4. Haskins Laboratories, United States |
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| Abstract: | The ability to discover groupings in continuous stimuli on the basis of distributional information is present across species and across perceptual modalities. We investigate the nature of the computations underlying this ability using statistical word segmentation experiments in which we vary the length of sentences, the amount of exposure, and the number of words in the languages being learned. Although the results are intuitive from the perspective of a language learner (longer sentences, less training, and a larger language all make learning more difficult), standard computational proposals fail to capture several of these results. We describe how probabilistic models of segmentation can be modified to take into account some notion of memory or resource limitations in order to provide a closer match to human performance. |
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