Readings from previous years can be found below:
2011-2012
2010-2011
Discussed Spring 2013
Kwiatkowski, T., Goldwater, S., Zettlemoyer, L., & Steedman, M. 2012. A Probabilistic Model of Syntactic and Semantic Acquisition from Child-Directed Utterances and their Meanings. Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics.
Lignos, C. 2012. Infant Word Segmentation: An Incremental, Integrated Model. Proceedings of the 30th West Coast Conference on Formal Linguistics, ed. Nathan Arnett and Ryan Bennett, 237-247. Somerville, MA: Cascadilla Proceedings Project.
Martin, A. 2011. Grammars leak: Modeling how phonotactic generalizations interact within the grammar. Language, 87(4), 751-770.
Perfors, A. 2012. When do memory limitations lead to regularization? An experimental and computational investigation. Journal of Memory and Language, 67, 486-506.
Reference Material to Readings
Chan, E. & Lignos, C. 2011. Investigating the Relationship Between Linguistic Representation and Computation through an Unsupervised Model of Human Morphology Learning. Research on Language and Computation, 8(2-3), 209-238.
Lignos, C. 2011. Modeling Infant Word Segmentation. Proceedings of the Fifteenth Conference on Computational Natural Language Learning, Portland, Oregon. 29-38.
Perfors, A. 2011. Memory limitations alone do not lead to over-regularization: An experimental and computational investigation. In L. Carlson, C. Hoelscher & T. F. Shipley (eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society: 3274-3279.
Perfors, A. 2012. Probability matching vs over-regularization in language: Participant behavior depends on their interpretation of the task. In N. Miyake, D. Peebles, & R. P. Cooper (eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society: 845-850.
Lignos, C. 2012. Infant Word Segmentation: An Incremental, Integrated Model. Proceedings of the 30th West Coast Conference on Formal Linguistics, ed. Nathan Arnett and Ryan Bennett, 237-247. Somerville, MA: Cascadilla Proceedings Project.
Martin, A. 2011. Grammars leak: Modeling how phonotactic generalizations interact within the grammar. Language, 87(4), 751-770.
Perfors, A. 2012. When do memory limitations lead to regularization? An experimental and computational investigation. Journal of Memory and Language, 67, 486-506.
Reference Material to Readings
Chan, E. & Lignos, C. 2011. Investigating the Relationship Between Linguistic Representation and Computation through an Unsupervised Model of Human Morphology Learning. Research on Language and Computation, 8(2-3), 209-238.
Lignos, C. 2011. Modeling Infant Word Segmentation. Proceedings of the Fifteenth Conference on Computational Natural Language Learning, Portland, Oregon. 29-38.
Perfors, A. 2011. Memory limitations alone do not lead to over-regularization: An experimental and computational investigation. In L. Carlson, C. Hoelscher & T. F. Shipley (eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society: 3274-3279.
Perfors, A. 2012. Probability matching vs over-regularization in language: Participant behavior depends on their interpretation of the task. In N. Miyake, D. Peebles, & R. P. Cooper (eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society: 845-850.
Discussed Winter 2013
Frank, S., Bod, R., & Christiansen, M. 2012. How hierarchical is language use? Proc. R. Soc. B, published online 12 September 2012. doi: 10.1098/rspb.2012.1741.
Stabler, E. 2009a. Mathematics of language learning. Revised version appears in Histoire, Epistemologie, Langage, 31, 1, 127-145.
Stabler, E. 2009b. Computational models of language universals. Revised version appears in M. H. Christiansen, C. Collins, and S. Edelman, eds., Language Universals, Oxford: Oxford University Press, 200-223.
Thiessen, E., & Pavlik, P. 2012 forthcoming. iMinerva: A Mathematical Model of Distributional Statistical Learning. Cognitive Science.
Reference Material to Readings
Chater, N. & Christiansen, M. 2010. Language Acquisition Meets Language Evolution. Cognitive Science, 34, 1131-1157.
Heinz, J. 2006. Learning quantity insensitive stress systems via local inference. In Proceedings of the Eighth Meeting of the ACL Special Interest Group in Computational Phonology at HLT-NAACL, 21-30. NY.
Thiessen, E. & Saffran, J. 2007. Learning to Learn: Infants' Acquisition of Stress-Based Strategies for Word Segmentation. Language Learning and Development, 3(1), 73-100.
Stabler, E. 2009a. Mathematics of language learning. Revised version appears in Histoire, Epistemologie, Langage, 31, 1, 127-145.
Stabler, E. 2009b. Computational models of language universals. Revised version appears in M. H. Christiansen, C. Collins, and S. Edelman, eds., Language Universals, Oxford: Oxford University Press, 200-223.
Thiessen, E., & Pavlik, P. 2012 forthcoming. iMinerva: A Mathematical Model of Distributional Statistical Learning. Cognitive Science.
Reference Material to Readings
Chater, N. & Christiansen, M. 2010. Language Acquisition Meets Language Evolution. Cognitive Science, 34, 1131-1157.
Heinz, J. 2006. Learning quantity insensitive stress systems via local inference. In Proceedings of the Eighth Meeting of the ACL Special Interest Group in Computational Phonology at HLT-NAACL, 21-30. NY.
Thiessen, E. & Saffran, J. 2007. Learning to Learn: Infants' Acquisition of Stress-Based Strategies for Word Segmentation. Language Learning and Development, 3(1), 73-100.
Discussed Fall 2012
Feldman, N., Griffiths, T., Goldwater, S., & Morgan, J. 2012. A role for the developing lexicon in phonetic category acquisition. Manuscript, University of Maryland, University of California at Berkeley, University of Edinburgh, and Brown University. Note: Please do not cite without permission from Naomi Feldman.
Frank, M. 2012. Computational models of early language acquisition. Manuscript, Stanford University. Note: Please do not cite without permission from Michael Frank.
Gagliardi, A., Bennett, E., Lidz, J., & Feldman, N. 2012. Children's Inferences in Generalizing Novel Nouns and Adjectives. In N. Miyake, D. Peebles, & R. Cooper (Eds.), Proceedings of the 34th Annual Meeting of the Cognitive Science Society, 354-359.
Hsu, A., Chater, N., & Vitanyi, P. 2011. The probabilistic analysis of language acquisition: Theoretical, computational, and experimental analysis. Cognition, 120, 380-390.
Reference Material to Readings
Dillon, B., Dunbar, E., & Idsardi, W. forthcoming. A single stage approach to learning phonological categories: insights from inuktitut. Cognitive Science.
Feldman, N., Myers, E., White, K., Griffiths, T., & Morgan, J. 2011. Learners use word-level statistics in phonetic category acquisition. In N. Danis, K. Mesh, & H. Sung (Eds.), Proceedings of the 35th Boston University Conference on Language Development, 197-209. Somerville, MA: Cascadilla Press.
Goldwater, S., Griffiths, T. L., & Johnson, M. 2009. A Bayesian framework for word segmentation: Exploring the effects of context. Cognition, 112(1), 21-54.
Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. 2008. A rational analysis of rule-based concept learning. Cognitive Science, 32, 108-154.
Griffiths, T., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. 2010. Probabilistic models of cognition: Exploring representations and inductive biasesG. Trends in Cognitive Sciences, 14, 357-364.
Hsu, A., & Chater, N. 2010. The Logical Problem of Language Acquisition: A Probabilistic Perspective. Cognitive Science, 34, 972-1016.
Vallabha, G. K., McClelland, J. L., Pons, F., Werker, J. F., & Amano, S. 2007. Unsupervised learning of vowel categories from infant-directed speech. Proceedings of the National Academy of Sciences, 104, 13273-13278.
Xu, F. & Tenenbaum, J. B. 2007. Word learning as Bayesian inference. Psychological Review, 114(2), 245-272.
Frank, M. 2012. Computational models of early language acquisition. Manuscript, Stanford University. Note: Please do not cite without permission from Michael Frank.
Gagliardi, A., Bennett, E., Lidz, J., & Feldman, N. 2012. Children's Inferences in Generalizing Novel Nouns and Adjectives. In N. Miyake, D. Peebles, & R. Cooper (Eds.), Proceedings of the 34th Annual Meeting of the Cognitive Science Society, 354-359.
Hsu, A., Chater, N., & Vitanyi, P. 2011. The probabilistic analysis of language acquisition: Theoretical, computational, and experimental analysis. Cognition, 120, 380-390.
Reference Material to Readings
Dillon, B., Dunbar, E., & Idsardi, W. forthcoming. A single stage approach to learning phonological categories: insights from inuktitut. Cognitive Science.
Feldman, N., Myers, E., White, K., Griffiths, T., & Morgan, J. 2011. Learners use word-level statistics in phonetic category acquisition. In N. Danis, K. Mesh, & H. Sung (Eds.), Proceedings of the 35th Boston University Conference on Language Development, 197-209. Somerville, MA: Cascadilla Press.
Goldwater, S., Griffiths, T. L., & Johnson, M. 2009. A Bayesian framework for word segmentation: Exploring the effects of context. Cognition, 112(1), 21-54.
Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. 2008. A rational analysis of rule-based concept learning. Cognitive Science, 32, 108-154.
Griffiths, T., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. 2010. Probabilistic models of cognition: Exploring representations and inductive biasesG. Trends in Cognitive Sciences, 14, 357-364.
Hsu, A., & Chater, N. 2010. The Logical Problem of Language Acquisition: A Probabilistic Perspective. Cognitive Science, 34, 972-1016.
Vallabha, G. K., McClelland, J. L., Pons, F., Werker, J. F., & Amano, S. 2007. Unsupervised learning of vowel categories from infant-directed speech. Proceedings of the National Academy of Sciences, 104, 13273-13278.
Xu, F. & Tenenbaum, J. B. 2007. Word learning as Bayesian inference. Psychological Review, 114(2), 245-272.