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Paul Smolensky [24]P. Smolensky [2]
  1. Paul Smolensky, Matthew Goldrick & Donald Mathis (2014). Optimization and Quantization in Gradient Symbol Systems: A Framework for Integrating the Continuous and the Discrete in Cognition. Cognitive Science 38 (6):1102-1138.
    Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, (...)
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  2. Jennifer Culbertson, Paul Smolensky & Colin Wilson (2013). Cognitive Biases, Linguistic Universals, and Constraint‐Based Grammar Learning. Topics in Cognitive Science 5 (3):392-424.
    According to classical arguments, language learning is both facilitated and constrained by cognitive biases. These biases are reflected in linguistic typology—the distribution of linguistic patterns across the world's languages—and can be probed with artificial grammar experiments on child and adult learners. Beginning with a widely successful approach to typology (Optimality Theory), and adapting techniques from computational approaches to statistical learning, we develop a Bayesian model of cognitive biases and show that it accounts for the detailed pattern of results of artificial (...)
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  3. Jennifer Culbertson & Paul Smolensky (2012). A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word‐Order Universal. Cognitive Science 36 (8):1468-1498.
  4. Jennifer Culbertson, Paul Smolensky & Géraldine Legendre (2012). Learning Biases Predict a Word Order Universal. Cognition 122 (3):306-329.
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  5. Géraldine Legendre & Paul Smolensky (2012). On the Asymmetrical Difficulty of Acquiring Person Reference in French: Production Versus Comprehension. [REVIEW] Journal of Logic, Language and Information 21 (1):7-30.
    Young French children freely produce subject pronouns by the age of 2. However, by age 2 and a half they fail to interpret 3rd person pronouns in an experimental setting designed to select a referent among three participants (speaker, hearer, and other). No such problems are found with 1st and 2nd person pronouns. We formalize our analysis of these empirical results in terms of direction-sensitive optimizations, showing that uni-directionality of optimization, when combined with non-adult-like constraint rankings, explains the general acquisition (...)
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  6. Paul Smolensky (2012). Subsymbolic Computation Theory for the Human Intuitive Processor. In. In S. Barry Cooper (ed.), How the World Computes. 675--685.
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  7. William Bechtel, Marlene Behrmann, Nick Chater, Robert J. Glushko, Robert L. Goldstone & Paul Smolensky (2010). The Rumelhart Prize at 10. Cognitive Science 34 (5):713-715.
  8. Lotte Hogeweg, Géraldine Legendre & Paul Smolensky (2010). Kinship Terminology: Polysemy or Categorization? Behavioral and Brain Sciences 33 (5):386-387.
    The target article offers an analysis of the categorization of kin types and empirical evidence that cross-cultural universals may be amenable to OT explanation. Since the analysis concerns the structuring of conceptual categories rather than the use of words, it differs from previous OT analyses in lexical semantics in what is considered to be the input and output of optimization.
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  9. Paul Smolensky, Emmanuel Dupoux, Nicholas Evans & Stephen C. Levinson (2009). Universals in Cognitive Theories of Language. Behavioral and Brain Sciences 32 (5):468.
    Generative linguistics' search for linguistic universals (1) is not comparable to the vague explanatory suggestions of the article; (2) clearly merits a more central place than linguistic typology in cognitive science; (3) is fundamentally untouched by the article's empirical arguments; (4) best explains the important facts of linguistic diversity; and (5) illuminates the dominant component of language's nature: biology.
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  10. Paul Smolensky (2008). Introduction to the 2006 Rumelhart Prize Special Issue Honoring Roger Shepard. Cognitive Science 32 (1):1-2.
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  11. Paul Smolensky (2006). Harmony in Linguistic Cognition. Cognitive Science 30 (5):779-801.
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  12. Paul Smolensky (2005). Optimization in Neural Networks and in Universal Grammar. Journal of Semantics 17 (34):185314.
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  13. D. E. Rumelhart, P. Smolensky, J. L. McClelland & G. E. Hinton (2004). Is Achieved. Prior to Stabilization, Neural Networks Do Not Jump Around Between Points in Activation Space. Stabiliza-Tion is the Process Whereby a Network First Generates a de-Terminate Activation Pattern, and Thereby Arrives at a Point in Activation Space. [REVIEW] Behavioral and Brain Sciences 27:2.
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  14. P. K. Monteiro, M. R. Pascoa & P. Smolensky (1999). Grammar-Based Connectionist Approaches to Language-A Connectionist Representation of Rule, Variables, and Dynamic Bindings Using Temporal Synchrony. Cognitive Science 23 (4):589-613.
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  15. Paul Smolensky (1999). Grammar‐Based Connectionist Approaches to Language. Cognitive Science 23 (4):589-613.
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  16. Paul Smolensky (1995). Constituent Structure and Explanation in an Integrated Connectionist/Symbolic Cognitive Architecture. In C. Macdonald (ed.), Connectionism: Debates on Psychological Explanation. Blackwell.
     
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  17. Paul Smolensky (1995). On the Projectable Predicates of Connectionist Psychology: A Case for Belief. In C. Macdonald & Graham F. Macdonald (eds.), Connectionism: Debates on Psychological Explanation. Blackwell.
     
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  18. Paul Smolensky (1991). Connectionism, Constituency and the Language of Thought. In Barry M. Loewer & Georges Rey (eds.), Meaning in Mind: Fodor and His Critics. Blackwell.
  19. Paul Smolensky (1990). In Defense of PTC. Behavioral and Brain Sciences 13 (2):407-412.
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  20. Paul Smolensky (1990). Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems. Artificial Intelligence 46:159-216.
  21. Paul Smolensky (1988). On the Proper Treatment of Connectionism. Behavioral and Brain Sciences 11 (1):1-23.
    A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...)
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  22. Paul Smolensky (1988). Putting Together Connectionism – Again. Behavioral and Brain Sciences 11 (1):59.
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  23. Paul Smolensky (1988). The Constituent Structure of Connectionist Mental States: A Reply to Fodor and Pylyshyn. Southern Journal of Philosophy 26 (S1):137-161.
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  24. Paul Smolensky (1987). Connectionism and Implementation. Behavioral and Brain Sciences 10 (3):492.
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  25. Paul Smolensky (1987). Connectionist, Symbolic, and the Brain. AI Review 1:95-109.
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  26. Paul Smolensky (1987). The Constituent Structure of Connectionist Mental States. Southern Journal of Philosophy Supplement 26:137-60.
     
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