28 found
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  1.  86
    Vincent G. Berthiaume, Thomas R. Shultz & Kristine H. Onishi (2013). A Constructivist Connectionist Model of Transitions on False-Belief Tasks. Cognition 126 (3):441-458.
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  2.  17
    Gert Westermann, Sylvain Sirois, Thomas R. Shultz & Denis Mareschal (2006). Modeling Developmental Cognitive Neuroscience. Trends in Cognitive Sciences 10 (5):227-232.
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  3.  13
    Fredéric Dandurand & Thomas R. Shultz (2009). Modeling Acquisition of a Torque Rule on the Balance-Scale Task. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 1541--6.
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  4.  10
    Thomas R. Shultz & Yoshio Takane (2007). Rule Following and Rule Use in the Balance-Scale Task. Cognition 103 (3):460-472.
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  5.  13
    Thomas R. Shultz, Max Hartshorn & Artem Kaznatcheev (2009). Why is Ethnocentrism More Common Than Humanitarianism. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 2100--2105.
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  6.  16
    Dirk Schlimm & Thomas R. Shultz (2009). Learning the Structure of Abstract Groups. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 2100--5.
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  7.  7
    David Buckingham & Thomas R. Shultz (1996). Computational Power and Realistic Cognitive Development. In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum 507--511.
  8.  10
    Frédéric Dandurand & Thomas R. Shultz (2002). Modeling Consciousness. Behavioral and Brain Sciences 25 (3):334-334.
    Perruchet & Vinter do not fully resolve issues about the role of consciousness and the unconscious in cognition and learning, and it is doubtful that consciousness has been computationally implemented. The cascade-correlation (CC) connectionist model develops high-order feature detectors as it learns a problem. We describe an extension, knowledge-based cascade-correlation (KBCC), that uses knowledge to learn in a hierarchical fashion.
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  9.  4
    Thomas R. Shultz (1991). The Rationality of Causal Inference. Behavioral and Brain Sciences 14 (3):503-504.
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  10.  5
    David Buckingham & Thomas R. Shultz (1994). A Connectionist Model of the Development of Velocity, Time, and Distance Concepts. In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum 72--77.
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  11.  5
    Thomas R. Shultz (1996). A Generative Neural Network Analysis of Conservation. In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum 18--65.
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  12.  9
    Thomas R. Shultz, Jean-Philippe Thivierge & Kristin Laurin (2008). Acquisition of Concepts with Characteristic and Defining Features. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society 531--536.
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  13.  5
    Thomas R. Shultz, Max Hartshorn & Ross A. Hammond (2008). Stages in the Evolution of Ethnocentrism. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society 1244--1249.
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  14.  20
    Thomas R. Shultz & Alan C. Bale (2006). Neural Networks Discover a Near-Identity Relation to Distinguish Simple Syntactic Forms. Minds and Machines 16 (2):107-139.
    Computer simulations show that an unstructured neural-network model [Shultz, T. R., & Bale, A. C. (2001). Infancy, 2, 501–536] covers the essential features␣of infant learning of simple grammars in an artificial language [Marcus, G. F., Vijayan, S., Bandi Rao, S., & Vishton, P. M. (1999). Science, 283, 77–80], and generalizes to examples both outside and inside of the range of training sentences. Knowledge-representation analyses confirm that these networks discover that duplicate words in the sentences are nearly identical and that they (...)
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  15.  3
    Artem Kaznatcheev & Thomas R. Shultz (2013). Limitations of the Dirac Formalism as a Descriptive Framework for Cognition. Behavioral and Brain Sciences 36 (3):292 - 293.
    We highlight methodological and theoretical limitations of the authors' Dirac formalism and suggest the von Neumann open systems approach as a resolution. The open systems framework is a generalization of classical probability and we hope it will allow cognitive scientists to extend quantum probability from perception, categorization, memory, decision making, and similarity judgments to phenomena in learning and development.
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  16.  3
    Thomas R. Shultz (2008). Toward Automatic Constructive Learning. Behavioral and Brain Sciences 31 (3):344-345.
    Neuroconstructivist modeling can be usefully extended with algorithms that build their own topology and recruit existing knowledge, effectively constructing a hierarchy of network modules. Possible benefits include allowing abilities to emerge naturally, in a way that affords objective study, deeper insights, and more rapid progress, and provides more serious consideration of the implications of constructivism.
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  17. Thomas R. Shultz & Mark R. Lepper (1996). Cognitive Dissonance Reduction as Constraint Satisfaction. Psychological Review 103 (2):219-240.
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  18.  5
    Thomas R. Shultz (1992). Choosing a Unifying Theory for Cognitive Development. Behavioral and Brain Sciences 15 (3):456-457.
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  19.  5
    Thomas R. Shultz (1994). The Challenge of Representational Redescription. Behavioral and Brain Sciences 17 (4):728.
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  20.  10
    Denis Mareschal & Thomas R. Shultz (1997). From Neural Constructivism to Children's Cognitive Development: Bridging the Gap. Behavioral and Brain Sciences 20 (4):571-572.
    Missing from Quartz & Sejnowski's (Q&S's) unique and valuable effort to relate cognitive development to neural constructivism is an examination of the global emergent properties of adding new neural circuits. Such emergent properties can be studied with computational models. Modeling with generative connectionist networks shows that synaptogenic mechanisms can account for progressive increases in children's representational power.
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  21.  3
    Gert Westermann, Sylvain Sirois, Thomas R. Shultz & Denis Mareschal (2006). Brain and Cognitive Development. Trends in Cognitive Sciences 10 (5):227-232.
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  22.  1
    Kim Plunkett & Thomas R. Shultz (1996). Computational Models of Development: A Symposium. In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum 18--61.
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  23.  1
    Thomas R. Shultz, Arlene Dover & Eric Amsel (1979). The Logical and Empirical Bases of Conservation Judgements. Cognition 7 (2):99-123.
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  24.  4
    Thomas R. Shultz (2000). Prototypes and Portability in Artificial Neural Network Models. Behavioral and Brain Sciences 23 (4):493-494.
    The Page target article is interesting because of apparent coverage of many psychological phenomena with simple, unified neural techniques. However, prototype phenomena cannot be covered because the strongest response would be to the first-learned stimulus in each category rather than to a prototype stimulus or most frequently presented stimuli. Alternative methods using distributed coding can also achieve portability of network knowledge.
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  25.  1
    Thomas R. Shultz & Peter J. LaFrenière (1988). Deception and Adaptation: Multidisciplinary Perspectives on Presenting a Neutral Image. Behavioral and Brain Sciences 11 (2):263.
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  26. Dalbir Bindra, Kenneth A. Clarke & Thomas R. Shultz (1980). Understanding Predictive Relations of Necessity and Sufficiency in Formally Equivalent "Causal" and "Logical" Problems. Journal of Experimental Psychology: General 109 (4):422-443.
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  27. Ryotaro Kamimura, Taeko Kamimura & Thomas R. Shultz (2001). Information Theoretic Competitive Learning and Linguistic Rule Acquisition. Transactions of the Japanese Society for Artificial Intelligence 16:287-298.
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  28. Nicolas Szilas & Thomas R. Shultz (1997). Prospects for Automatic Recoding of Inputs in Connectionist Learning. Behavioral and Brain Sciences 20 (1):81-82.
    Clark & Thornton present the well-established principle that recoding inputs can make learning easier. A useful goal would be to make such recoding automatic. We discuss some ways in which incrementality and transfer in connectionist networks could attain this goal.
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