When does abstraction occur in semantic memory: insights from distributional models

References

• Ashby, F. G., & Maddox, W. T. (1993). Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology, 37(3), 372–400.
   Web of Science ®Google Scholar
• Ashby, F. G., & Rosedahl, L. (2017). A neural implementation of exemplar theory. Psychological Review, 124(4), 472–482.
   PubMed Web of Science ®Google Scholar
• Ashby, F. G, & Valentin, V. V. (2016). Multiple systems of percep- tual category learning: Theory and cognitive tests. In H. Cohen & C. Lefebvre (Eds.), Handbook of categorization in cognitive science, second edition (p. in press). New York: Elsevier.
   Google Scholar
• Barsalou, L. W. (1999). Perceptions of perceptual symbols. Behavioral and Brain Sciences, 22(4), 637–660.
   Web of Science ®Google Scholar
• Becker, S. (2005). A computational principle for hippocampal learning and neurogenesis. Hippocampus, 15(6), 722–738.
   PubMed Web of Science ®Google Scholar
• Bordes, A., Usunier, N., Chopra, S., & Weston, J. (2015). Large-scale simple question answering with memory networks (arXiv preprint arXiv:1506.02075).
   Google Scholar
• Daelemans, W., Van Den Bosch, A., & Zavrel, J. (1999). Forgetting exceptions is harmful in language learning. Machine Learning, 34(1–3), 11–41.
   Web of Science ®Google Scholar
• Damasio, A. R. (1989). Time-locked multiregional retroactivation: A systems-level proposal for the neural substrates of recall and recognition. Cognition, 33(1), 25–62.
   PubMed Web of Science ®Google Scholar
• Dennis, S. (2005). A memory-based theory of verbal cognition. Cognitive Science, 29, 145–193.
   PubMed Web of Science ®Google Scholar
• Erk, K. (2009). Representing words as regions in vector space. Proceedings of the thirteenth conference on computational natural language learning (pp. 57–65). Association for Computational Linguistics.
   Google Scholar
• Erk, K., & Padó, S. (2010). Exemplar-based models for word meaning in context. Proceedings of the acl 2010 conference short papers (pp. 92–97). Association for Computational Linguistics.
   Google Scholar
• Firth, J. R. (1957). A synopsis of linguistic theory (pp. 1930–1955). Oxford: Blackwell.
   Google Scholar
• Goldberg, A. E. (2006). Constructions at work: The nature of generalization in language. New York: Oxford University Press.
   Google Scholar
• Goldinger, S. D. (1998). Echoes of echoes? An episodic theory of lexical access. Psychological Review, 105(2), 251–279.
   PubMed Web of Science ®Google Scholar
• Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114(2), 211.
   PubMed Web of Science ®Google Scholar
• Hélie, S., Paul, E. J., & Ashby, F. G. (2012). A neurocomputational account of cognitive deficits in Parkinson's disease. Neuropsychologia, 50(9), 2290–2302.
   PubMed Web of Science ®Google Scholar
• Hintzman, D. L. (1984). MINERVA 2: A simulation model of human memory. Behavior Research Methods, Instruments, & Computers, 16(2), 96–101.
   Google Scholar
• Hintzman, D. L. (1986). “Schema abstraction” in a multiple-trace memory model. Psychological Review, 93, 411–428.
   Web of Science ®Google Scholar
• Hintzman, D. L. (1990). Human learning and memory: Connections and dissociations. Annual Review of Psychology, 41(1), 109–139.
   PubMedGoogle Scholar
• Johns, B. T., Jamieson, R. K., Crump, M. J. C., Jones, M. N., & Mewhort, D. J. K. (2016). The combinatorial power of experience. Proceedings of the 37th meeting of the cognitive science society.
   Google Scholar
• Johns, B. T., & Jones, M. N. (2010). Evaluating the random representation assumption of lexical semantics in cognitive models. Psychonomic Bulletin and Review, 17, 662–672.
   PubMed Web of Science ®Google Scholar
• Johns, B. T., & Jones, M. N. (2014). Generating structure from experience: The role of memory in language. Proceedings of the 35th annual conference of the cognitive science society. Austin, TX: Cognitive Science Society.
   Google Scholar
• Johns, B. T., & Jones, M. N. (2015). Generating structure from experience: A retrieval-based model of language processing. Canadian Journal of Experimental Psychology, 69, 233–251.
   PubMed Web of Science ®Google Scholar
• Jones, M. N., Dye, M., & Johns, B. T. (2016). Context as an organizing principle of the lexicon. In B. Ross (Ed.), The psychology of learning and motivationion (Vol. 67, pp. 239–283). https://doi.org/10.1016/bs.plm.2017.03.008
   Google Scholar
• Jones, M. N., & Mewhort, D. J. K. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114(1), 1–37.
   PubMed Web of Science ®Google Scholar
• Jones, M. N., Willits, J. A., & Dennis, S. (2015). Models of semantic memory. In J. R. Busemeyer & J. T. Townsend (Eds.), Oxford handbook of mathematical and computational psychology (pp. 232–254). New York: Oxford University Press.
   Google Scholar
• Kintsch, W. (2001). Predication. Cognitive Science, 25(2), 173–202.
   Web of Science ®Google Scholar
• Kintsch, W., & Mangalath, P. (2011). The construction of meaning. Topics in Cognitive Science, 3(2), 346–370.
   PubMed Web of Science ®Google Scholar
• Kruschke, J. K. (2008). Models of categorization. In R. Sun (Ed.), The Cambridge Handbook of Computational Psychology (pp. 267–301). New York: Cambridge University Press.
   Google Scholar
• Kwantes, P. J. (2005). Using context to build semantics. Psychonomic Bulletin & Review, 12, 703–710.
   PubMed Web of Science ®Google Scholar
• Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2), 211–240.
   Web of Science ®Google Scholar
• Levy, O., & Goldberg, Y. (2014). Neural word embedding as implicit matrix factorization. In Z. Ghahramani, M. Welling, & C. Cortes (Eds.), Advances in neural information processing systems (pp. 2177–2185). Cambridge, MA: MIT Press Cambridge.
   Google Scholar
• Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85(3), 207–238.
   Web of Science ®Google Scholar
• Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111–3119).
   Google Scholar
• Minda, J. P., & Smith, J. D. (2001). Prototypes in category learning: The effects of category size, category structure, andstimulus complexity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(3), 775.
   PubMed Web of Science ®Google Scholar
• Murphy, G. L. (2016). Is there an exemplar theory of concepts? Psychonomic Bulletin & Review, 23(4), 1035–1042.
   PubMed Web of Science ®Google Scholar
• Norman, G., Young, M., & Brooks, L. (2007). Non-analytical models of clinical reasoning: The role of experience. Medical Education, 41, 1140–1145.
   PubMed Web of Science ®Google Scholar
• Nosofsky, R. M. (1986). Attention, similarity, and the identification–categorization relationship. Journal of Experimental Psychology: General, 115(1), 39–57.
   PubMed Web of Science ®Google Scholar
• Nosofsky, R. M. (1988). Exemplar-based accounts of relations between classification, recognition, and typicality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(4), 700–708.
   Web of Science ®Google Scholar
• Nosofsky, R. M., Palmeri, T. J., & McKinley, S. C. (1994). Rule-plus-exception model of classification learning. Psychological Review, 101(1), 53–79.
   PubMed Web of Science ®Google Scholar
• Nosofsky, R. M., Sanders, C. A., & McDaniel, M. A. (2017). Tests of an Exemplar-Memory Model of Classification Learning in a High-Dimensional Natural-Science Category Domain. Journal of Experimental Psychology: General. http://psycnet.apa.org/doi/10.1037/xge0000369
   PubMed Web of Science ®Google Scholar
• Pickering, A. D. (1997). New approaches to the study of amnesic patients: What can a neurofunctional philosophy and neural network methods offer? Memory, 5(1–2), 255–300.
   PubMed Web of Science ®Google Scholar
• Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77(3p1), 353–363.
   PubMedGoogle Scholar
• Reed, S. K. (1972). Pattern recognition and categorization. Cognitive Psychology, 3(3), 382–407.
   Web of Science ®Google Scholar
• Reisinger, J., & Mooney, R. J. (2010). Multi-prototype vector-space models of word meaning. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, June 2010.
   Google Scholar
• Rogers, T. T., & McClelland, J. L. (2004). Semantic cognition: A parallel distributed processing approach. Cambridge, MA: MIT Press
   Google Scholar
• Rogers, T. T., & McClelland, J. L. (2011). 5 semantics without categorization. In E. M. Pothos & A. J. Wills (Eds.), Formal Approaches in Categorization (pp. 88–119). New York: Cambridge University Press.
   Google Scholar
• Rosch, E. H. (1973). Natural categories. Cognitive Psychology, 4(3), 328–350.
   Web of Science ®Google Scholar
• Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7(4), 573–605.
   Web of Science ®Google Scholar
• Stanton, R. D., Nosofsky, R. M., & Zaki, S. R. (2002). Comparisons between exemplar similarity and mixed prototype models using a linearly separable category structure. Memory & Cognition, 30(6), 934–944.
   PubMed Web of Science ®Google Scholar
• Thiessen, E. D. (2017). What’s statistical about learning? Insights from modelling statistical learning as a set of memory processes. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1711), 20160056.
   PubMed Web of Science ®Google Scholar
• Thiessen, E. D., & Pavlik, P. I. (2013). Iminerva: A mathematical model of distributional statistical learning. Cognitive Science, 37(2), 310–343.
   PubMed Web of Science ®Google Scholar
• Tomasello, M. (2003). Constructing a language: A usage-based theory of language acquisition. Cambridge, MA: Harvard University Press.
   Google Scholar
• Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson (Eds.), Organization of memory (pp. 381–403). New York, NY: Academic Press.
   Google Scholar
• Valentin, V. V., Maddox, W. T., & Ashby, F. G. (2014). A computational model of the temporal dynamics of plasticity in procedural learning: sensitivity to feedback timing. Frontiers in Psychology, 5, 1–9.
   PubMed Web of Science ®Google Scholar
• Vilnis, L., & McCallum, A. (2015). Word representations via gaussian embedding. arXiv preprint arXiv:1412.6623.
   Google Scholar
• Weston, J., Chopra, S., & Bordes, A. (2015). Memory networks. arXiv preprint arXiv:1410.3916.
   Google Scholar