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  1. James L. McClelland, Daniel Mirman, Donald J. Bolger & Pranav Khaitan (2014). Interactive Activation and Mutual Constraint Satisfaction in Perception and Cognition. Cognitive Science 38 (6):1139-1189.
    In a seminal 1977 article, Rumelhart argued that perception required the simultaneous use of multiple sources of information, allowing perceivers to optimally interpret sensory information at many levels of representation in real time as information arrives. Building on Rumelhart's arguments, we present the Interactive Activation hypothesis—the idea that the mechanism used in perception and comprehension to achieve these feats exploits an interactive activation process implemented through the bidirectional propagation of activation among simple processing units. We then examine the interactive activation (...)
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  2. Timothy T. Rogers & James L. McClelland (2014). Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition. Cognitive Science 38 (6):1024-1077.
    This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...)
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  3. Pavlos Kollias & James L. McClelland (2013). Context, Cortex, and Associations: A Connectionist Developmental Approach to Verbal Analogies. Frontiers in Psychology 4.
    We present a PDP model of binary choice verbal analogy problems (A:B as C:[D1|D2], where D1 and D2 represent choice alternatives). We train a recurrent neural network in item-relation- item triples and use this network to test performance on analogy questions. Without training on analogy problems per se, the model explains the developmental shift from associative to relational responding as an emergent consequence of learning upon the environment’s statistics. Such learning allows gradual, item-specific acquisition of relational knowledge to overcome the (...)
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  4. James L. McClelland (2013). Integrating Probabilistic Models of Perception and Interactive Neural Networks: A Historical and Tutorial Review. Frontiers in Psychology 4.
    This article seeks to establish a rapprochement between explicitly Bayesian models of contextual effects in perception and neural network models of such effects, particularly the connectionist interactive activation model of perception. The article is in part an historical review and in part a tutorial, reviewing the probabilistic Bayesian approach to understanding perception and how it may be shaped by context, and also reviewing ideas about how such probabilistic computations may be carried out in neural networks, focusing on the role of (...)
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  5. Tiago V. Maia & James L. McClelland (2012). A Neurocomputational Approach to Obsessive-Compulsive Disorder. Trends in Cognitive Sciences 16 (1):14-15.
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  6. James L. McClelland (2010). Emergence in Cognitive Science. Topics in Cognitive Science 2 (4):751-770.
    The study of human intelligence was once dominated by symbolic approaches, but over the last 30 years an alternative approach has arisen. Symbols and processes that operate on them are often seen today as approximate characterizations of the emergent consequences of sub- or nonsymbolic processes, and a wide range of constructs in cognitive science can be understood as emergents. These include representational constructs (units, structures, rules), architectural constructs (central executive, declarative memory), and developmental processes and outcomes (stages, sensitive periods, neurocognitive (...)
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  7. James L. McClelland, Matthew M. Botvinick, David C. Noelle, David C. Plaut, Timothy T. Rogers, Mark S. Seidenberg & Linda B. Smith (2010). Letting Structure Emerge: Connectionist and Dynamical Systems Approaches to Cognition. Trends in Cognitive Sciences 14 (8):348-356.
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  8. James L. McClelland (2009). The Place of Modeling in Cognitive Science. Topics in Cognitive Science 1 (1):11-38.
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  9. James L. McClelland & Axel Cleeremans (2009). Connectionist Models. In Bayne Tim, Cleeremans Axel & Wilken Patrick (eds.), The Oxford Companion to Consciousness. Oxford University Press.
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  10. Anna C. Schapiro & James L. McClelland (2009). A Connectionist Model of a Continuous Developmental Transition in the Balance Scale Task. Cognition 110 (3):395-411.
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  11. D. Sternberg & James L. McClelland (2009). When Should We Expect Indirect Effects in Human Contingency Learning. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 206--211.
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  12. Daniel A. Sternberg & James L. McClelland (2009). How Do We Get From Propositions to Behavior? Behavioral and Brain Sciences 32 (2):226-227.
    Mitchell et al. describe many fascinating studies, and in the process, propose what they consider to be a unified framework for human learning in which effortful, controlled learning results in propositional knowledge. However, it is unclear how any of their findings privilege a propositional account, and we remain concerned that embedding all knowledge in propositional representations obscures the tight interdependence between learning from experiences and the use of the results of learning as a basis for action.
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  13. P. H. Thibodeau, James L. McClelland & Lera Boroditsky (2009). When a Bad Metaphor May Not Be a Victimless Crime: The Role of Metaphor in Social Policy. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 809--814.
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  14. Daniel Mirman, James L. McClelland, Lori L. Holt & James S. Magnuson (2008). Effects of Attention on the Strength of Lexical Influences on Speech Perception: Behavioral Experiments and Computational Mechanisms. Cognitive Science 32 (2):398-417.
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  15. Timothy T. Rogers & James L. McClelland (2008). A Simple Model From a Powerful Framework That Spans Levels of Analysis. Behavioral and Brain Sciences 31 (6):729-749.
    The commentaries reflect three core themes that pertain not just to our theory, but to the enterprise of connectionist modeling more generally. The first concerns the relationship between a cognitive theory and an implemented computer model. Specifically, how does one determine, when a model departs from the theory it exemplifies, whether the departure is a useful simplification or a critical flaw? We argue that the answer to this question depends partially upon the model's intended function, and we suggest that connectionist (...)
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  16. Timothy T. Rogers & James L. McClelland (2008). Précis of Semantic Cognition: A Parallel Distributed Processing Approach. Behavioral and Brain Sciences 31 (6):689-714.
    In this prcis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (...)
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  17. Michael Sc Thomas & James L. McClelland (2008). Connectionist Models of Cognition. In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press.
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  18. Marius Usher, Anat Elhalal & James L. McClelland (2008). The Neurodynamics of Choice, Value-Based Decisions, and Preference Reversal. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oup Oxford. 277--300.
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  19. James L. McClelland, Daniel Mirman & Lori L. Holt (2006). Are There Interactive Processes in Speech Perception? Trends in Cognitive Sciences 10 (8):363-369.
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  20. Daniel Mirman, James L. McClelland & Lori L. Holt (2006). Response to McQueen Et Al.: Theoretical and Empirical Arguments Support Interactive Processing. Trends in Cognitive Sciences 10 (12):534.
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  21. Tiago V. Maia & James L. McClelland (2005). The Somatic Marker Hypothesis: Still Many Questions but No Answers. Trends in Cognitive Sciences 9 (4):162-164.
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  22. Tiago V. Maia & James L. McClelland (2005). The Somatic Marker Hypothesis: Still Many Questions but No Answers: Response to Bechara Et Al. Trends in Cognitive Sciences 9 (4):162-164.
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  23. James L. McClelland & Karalyn Patterson (2003). Differentiation and Integration in Human Language. Trends in Cognitive Sciences 7 (2):63-64.
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  24. James L. McClelland, David C. Plaut, Stephen J. Gotts & Tiago V. Maia (2003). Developing a Domain-General Framework for Cognition: What is the Best Approach? Behavioral and Brain Sciences 26 (5):611-614.
    We share with Anderson & Lebiere (A&L) (and with Newell before them) the goal of developing a domain-general framework for modeling cognition, and we take seriously the issue of evaluation criteria. We advocate a more focused approach than the one reflected in Newell's criteria, based on analysis of failures as well as successes of models brought into close contact with experimental data. A&L attribute the shortcomings of our parallel-distributed processing framework to a failure to acknowledge a symbolic level of thought. (...)
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  25. James L. McClelland & Gary Lupyan (2002). Double Dissociations Never License Simple Inferences About Underlying Brain Organization, Especially in Developmental Cases. Behavioral and Brain Sciences 25 (6):763-764.
    Different developmental anomalies produce contrasting deficits in a single, integrated system. In a network that inflects regular and exception verbs correctly, a disproportionate deficit with exceptions occurs if connections are deleted, whereas a disproportionate deficit with regulars occurs when an auditory deficit impairs perception of the regular inflection. In general, contrasting deficits do not license the inference of underlying modularity.
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  26. James L. McClelland & Karalyn Patterson (2002). Rules or Connections in Past-Tense Inflections: What Does the Evidence Rule Out? Trends in Cognitive Sciences 6 (11):465-472.
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  27. James L. McClelland & Karalyn Patterson (2002). 'Words or Rules' Cannot Exploit the Regularity in Exceptions. Trends in Cognitive Sciences 6 (11):464-465.
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  28. David C. Plaut & James L. McClelland (2000). Stipulating Versus Discovering Representations. Behavioral and Brain Sciences 23 (4):489-491.
    Page's proposal to stipulate representations in which individual units correspond to meaningful entities is too unconstrained to support effective theorizing. An approach combining general computational principles with domain-specific assumptions, in which learning is used to discover representations that are effective in solving tasks, provides more insight into why cognitive and neural systems are organized the way they are.
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  29. James L. McClelland (1996). The Basis of Organization in Interactive Processing Systems. In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum. 18--41.
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  30. David E. Rumelhart, James L. McClelland & Adele Diamond (1996). Plenary Addresses. In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum. 18--1.
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  31. James L. McClelland (1993). The GRAIN Model: A Framework for Modeling the Dynamics of Information Processing. In David E. Meyer & Sylvan Kornblum (eds.), Attention and Performance Xiv. The Mit Press. 655--688.
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  32. Javier R. Movellan & James L. McClelland (1993). Learning Continuous Probability Distributions with Symmetric Diffusion Networks. Cognitive Science 17 (4):463-496.
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  33. James L. McClelland (1985). Putting Knowledge in its Place: A Scheme for Programming Parallel Processing Structures on the Fly. Cognitive Science 9 (1):113-146.
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  34. James L. McClelland & David E. Rumelhart (1985). Distributed Memory and the Representation of General and Specific Information. Journal of Experimental Psychology 114 (2).
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