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  1.  23
    Barbara J. Knowlton, Robert G. Morrison, John E. Hummel & Keith J. Holyoak (2012). A Neurocomputational System for Relational Reasoning. Trends in Cognitive Sciences 16 (7):373-381.
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  2. Keith J. Holyoak & John E. Hummel (2000). The Proper Treatment of Symbols in a Connectionist Architecture. In Eric Dietrich Art Markman (ed.), Cognitive Dynamics: Conceptual Change in Humans and Machines. Lawrence Erlbaum 229--263.
     
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  3.  60
    David H. Landy, Erin L. Jones & John E. Hummel (2008). Why Spatial-Numeric Associations Aren't Evidence for a Mental Number Line. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society 357--362.
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  4.  1
    Wookyoung Jung & John E. Hummel (2015). Making Probabilistic Relational Categories Learnable. Cognitive Science 39 (6):1259-1291.
    Theories of relational concept acquisition based on structured intersection discovery predict that relational concepts with a probabilistic structure ought to be extremely difficult to learn. We report four experiments testing this prediction by investigating conditions hypothesized to facilitate the learning of such categories. Experiment 1 showed that changing the task from a category-learning task to choosing the “winning” object in each stimulus greatly facilitated participants' ability to learn probabilistic relational categories. Experiments 2 and 3 further investigated the mechanisms underlying this (...)
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  5.  30
    John E. Hummel (2010). Symbolic Versus Associative Learning. Cognitive Science 34 (6):958-965.
    Ramscar and colleagues (2010, this volume) describe the “feature-label-order” (FLO) effect on category learning and characterize it as a constraint on symbolic learning. I argue that FLO is neither a constraint on symbolic learning in the sense of “learning elements of a symbol system” (instead, it is an effect on nonsymbolic, association learning) nor is it, more than any other constraint on category learning, a constraint on symbolic learning in the sense of “solving the symbol grounding problem.”.
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  6. John E. Hummel & Irving Biederman (1992). Dynamic Binding in a Neural Network for Shape Recognition. Psychological Review 99 (3):480-517.
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  7.  27
    Daniel C. Krawczyk, Keith J. Holyoak & John E. Hummel (2004). Structural Constraints and Object Similarity in Analogical Mapping and Inference. Thinking and Reasoning 10 (1):85 – 104.
    Theories of analogical reasoning have viewed relational structure as the dominant determinant of analogical mapping and inference, while assigning lesser importance to similarity between individual objects. An experiment is reported in which these two sources of constraints on analogy are placed in competition under conditions of high relational complexity. Results demonstrate equal importance for relational structure and object similarity, both in analogical mapping and in inference generation. The human data were successfully simulated using a computational analogy model (LISA) that treats (...)
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  8. John E. Hummel & Keith J. Holyoak (1997). Distributed Representations of Structure: A Theory of Analogical Access and Mapping. Psychological Review 104 (3):427-466.
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  9.  62
    Derek C. Penn, Patricia W. Cheng, Keith J. Holyoak, John E. Hummel & Daniel J. Povinelli (2009). There is More to Thinking Than Propositions. Behavioral and Brain Sciences 32 (2):221-223.
    We are big fans of propositions. But we are not big fans of the proposed by Mitchell et al. The authors ignore the critical role played by implicit, non-inferential processes in biological cognition, overestimate the work that propositions alone can do, and gloss over substantial differences in how different kinds of animals and different kinds of cognitive processes approximate propositional representations.
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  10.  3
    John E. Hummel & Keith J. Holyoak (1996). LISA: A Computational Model of Analogical Inference and Schema Induction. In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum 352--357.
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  11.  3
    John E. Hummel & Keith J. Holyoak (1993). Distributing Structure Over Time. Behavioral and Brain Sciences 16 (3):464.
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  12.  14
    Leonidas A. A. Doumas & John E. Hummel (2010). A Computational Account of the Development of the Generalization of Shape Information. Cognitive Science 34 (4):698-712.
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  13.  6
    Daniel C. Krawczyk, Keith J. Holyoak & John E. Hummel (2005). The One‐to‐One Constraint in Analogical Mapping and Inference. Cognitive Science 29 (5):797-806.
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  14. John E. Hummel & Keith J. Holyoak (2003). A Symbolic-Connectionist Theory of Relational Inference and Generalization. Psychological Review 110 (2):220-264.
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  15.  13
    John E. Hummel, Keith J. Holyoak, Collin Green, Leonidas Aa Doumas, Derek Devnich, Aniket Kittur & Donald J. Kalar (2004). A Solution to the Binding Problem for Compositional Connectionism. In Simon D. Levy & Ross Gayler (eds.), Compositional Connectionism in Cognitive Science. Aaai Press
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  16.  10
    Leonidas A. A. Doumas, Keith J. Holyoak & John E. Hummel (2006). The Problem with Using Associations to Carry Binding Information. Behavioral and Brain Sciences 29 (1):74-75.
    van der Velde & de Kamps argue for the importance of considering the binding problem in accounts of human mental representation. However, their proposed solution fails as a complete account because it represents the bindings between roles and their fillers through associations (or connections). In addition, many criticisms leveled by the authors towards synchrony-based bindings models do not hold.
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  17. Leonidas A. A. Doumas, John E. Hummel & Catherine M. Sandhofer (2008). A Theory of the Discovery and Predication of Relational Concepts. Psychological Review 115 (1):1-43.
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  18.  4
    James K. Kroger, Keith J. Holyoak & John E. Hummel (2004). Varieties of Sameness: The Impact of Relational Complexity on Perceptual Comparisons. Cognitive Science 28 (3):335-358.
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  19.  19
    John E. Hummel & Philip J. Kellman (1998). Finding the Pope in the Pizza: Abstract Invariants and Cognitive Constraints on Perceptual Learning. Behavioral and Brain Sciences 21 (1):30-30.
    Schyns, Goldstone & Thibaut argue that categorization experience results in the learning of new perceptual features that are not derivable from the learner's existing feature set. We explore the meaning and implications of this “nonderivability” claim and relate it to the question of whether perceptual invariants are learnable, and if so, what might be entailed in learning them.
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  20.  11
    John E. Hummel (2000). Localism as a First Step Toward Symbolic Representation. Behavioral and Brain Sciences 23 (4):480-481.
    Page argues convincingly for several important properties of localist representations in connectionist models of cognition. I argue that another important property of localist representations is that they serve as the starting point for connectionist representations of symbolic (relational) structures because they express meaningful properties independent of one another and their relations.
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  21.  2
    John E. Hummel (2003). “Effective Systematicity” in, “Effective Systematicity” Out: A Reply to Edelman and Intrator (2003). Cognitive Science 27 (2):327-329.
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  22.  4
    Keith J. Holyoak & John E. Hummel (2008). No Way to Start a Space Program: Associationism as a Launch Pad for Analogical Reasoning. Behavioral and Brain Sciences 31 (4):388-389.
    Humans, including preschool children, exhibit role-based relational reasoning, of which analogical reasoning is a canonical example. The connectionist model proposed in the target article is only capable of conditional paired-associate learning.
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  23. Jessica M. Choplin & John E. Hummel (2002). Magnitude Comparisons Distort Mental Representations of Magnitude. Journal of Experimental Psychology: General 131 (2):270-286.
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  24. Leonidas A. A. Doumas, John E. Hummel & Catherine M. Sandhofer (2013). "A Theory of the Discovery and Predication of Relational Concepts": Correction to Doumas, Hummel, and Sandhofer. Psychological Review 120 (3):543-543.
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  25. John E. Hummel, John Licato & Selmer Bringsjord (2014). Analogy, Explanation, and Proof. Frontiers in Human Neuroscience 8.
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