Sentence processing in an artificial language: Learning and using combinatorial constraints |
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| Authors: | Michael S Amato Maryellen C MacDonald |
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| Institution: | 1. Division of Speech and Hearing Sciences, University of Hong Kong, Hong Kong Special Administrative Region;2. Department of Special Education and Counseling, The Hong Kong Institute of Education, Hong Kong Special Administrative Region;1. English Department, Faculty of Philology, University of Barcelona, Barcelona, Spain;2. Department of Psychology, Faculty of Education and Psychology, University of Girona, Girona, Spain;1. Western University, Department of Psychology, Brain & Mind Institute, London, ON, Canada;2. Northwestern University, Department of Psychology, Evanston, IL, USA;1. Department of Cognitive Science, University of California, San Diego, United States;2. Department of Linguistics, University of California, San Diego, United States;1. Department of Psychology, Tufts University, USA;2. Department of Linguistics, University of California, Davis, USA;3. Department of Linguistics, University of Maryland, USA;4. Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA |
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| Abstract: | A study combining artificial grammar and sentence comprehension methods investigated the learning and online use of probabilistic, nonadjacent combinatorial constraints. Participants learned a small artificial language describing cartoon monsters acting on objects. Self-paced reading of sentences in the artificial language revealed comprehenders’ sensitivity to nonadjacent combinatorial constraints, without explicit awareness of the probabilities embedded in the language. These results show that even newly-learned constraints have an identifiable effect on online sentence processing. The rapidity of learning in this paradigm relative to others has implications for theories of implicit learning and its role in language acquisition. |
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