56 found
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  1.  30
    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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  2.  38
    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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  3.  16
    James L. McClelland & David E. Rumelhart (1985). Distributed Memory and the Representation of General and Specific Information. Journal of Experimental Psychology 114 (2):159-188.
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  4.  9
    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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  5.  48
    James L. McClelland (2009). The Place of Modeling in Cognitive Science. Topics in Cognitive Science 1 (1):11-38.
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  6. David C. Plaut, James L. McClelland, Mark S. Seidenberg & Karalyn Patterson (1996). Understanding Normal and Impaired Word Reading: Computational Principles in Quasi-Regular Domains. Psychological Review 103 (1):56-115.
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  7. 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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  8.  24
    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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  9. Marius Usher & James L. McClelland (2001). The Time Course of Perceptual Choice: The Leaky, Competing Accumulator Model. Psychological Review 108 (3):550-592.
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  10.  2
    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. Mark S. Seidenberg & James L. McClelland (1989). A Distributed, Developmental Model of Word Recognition and Naming. Psychological Review 96 (4):523-568.
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  12.  1
    Jonathan D. Cohen, Kevin Dunbar & James L. McClelland (1990). On the Control of Automatic Processes: A Parallel Distributed Processing Account of the Stroop Effect. Psychological Review 97 (3):332-361.
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  13. James L. McClelland, Bruce L. McNaughton & Randall C. O'Reilly (1995). Why There Are Complementary Learning Systems in the Hippocampus and Neocortex: Insights From the Successes and Failures of Connectionist Models of Learning and Memory. Psychological Review 102 (3):419-457.
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  14.  1
    James L. McClelland & David E. Rumelhart (1981). An Interactive Activation Model of Context Effects in Letter Perception: I. An Account of Basic Findings. Psychological Review 88 (5):375-407.
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  15.  58
    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.
  16.  15
    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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  17.  22
    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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  18. 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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  19.  2
    James L. McClelland (2013). Incorporating Rapid Neocortical Learning of New Schema-Consistent Information Into Complementary Learning Systems Theory. Journal of Experimental Psychology: General 142 (4):1190-1210.
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  20.  14
    Dharshan Kumaran, Demis Hassabis & James L. McClelland (2016). What Learning Systems Do Intelligent Agents Need? Complementary Learning Systems Theory Updated. Trends in Cognitive Sciences 20 (7):512-534.
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  21.  62
    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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  22.  10
    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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  23.  18
    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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  24.  7
    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.
  25. James L. McClelland & Mark Chappell (1998). Familiarity Breeds Differentiation: A Subjective-Likelihood Approach to the Effects of Experience in Recognition Memory. Psychological Review 105 (4):724-760.
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  26. James L. McClelland (1979). On the Time Relations of Mental Processes: An Examination of Systems of Processes in Cascade. Psychological Review 86 (4):287-330.
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  27. Axel Cleeremans & James L. McClelland (1991). Learning the Structure of Event Sequences. Journal of Experimental Psychology: General 120 (3):235-253.
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  28.  10
    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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  29. Dharshan Kumaran & James L. McClelland (2012). Generalization Through the Recurrent Interaction of Episodic Memories: A Model of the Hippocampal System. Psychological Review 119 (3):573-616.
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  30.  2
    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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  31.  13
    James L. McClelland & Karalyn Patterson (2003). Differentiation and Integration in Human Language. Trends in Cognitive Sciences 7 (2):63-64.
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  32.  1
    David E. Rumelhart & James L. McClelland (1982). An Interactive Activation Model of Context Effects in Letter Perception: II. The Contextual Enhancement Effect and Some Tests and Extensions of the Model. Psychological Review 89 (1):60-94.
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  33. Javier R. Movellan & James L. McClelland (2001). The Morton-Massaro Law of Information Integration: Implications for Models of Perception. Psychological Review 108 (1):113-148.
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  34.  29
    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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  35.  2
    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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  36. Timothy T. Rogers, Matthew A. Lambon Ralph, Peter Garrard, Sasha Bozeat, James L. McClelland, John R. Hodges & Karalyn Patterson (2004). Structure and Deterioration of Semantic Memory: A Neuropsychological and Computational Investigation. Psychological Review 111 (1):205-235.
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  37. Marius Usher & James L. McClelland (2004). Loss Aversion and Inhibition in Dynamical Models of Multialternative Choice. Psychological Review 111 (3):757-769.
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  38. Yuko Munakata, James L. McClelland, Mark H. Johnson & Robert S. Siegler (1997). Rethinking Infant Knowledge: Toward an Adaptive Process Account of Successes and Failures in Object Permanence Tasks. Psychological Review 104 (4):686-713.
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  39.  2
    James L. McClelland & Axel Cleeremans (2009). Connectionist Models. In Bayne Tim, Cleeremans Axel & Wilken Patrick (eds.), The Oxford Companion to Consciousness. Oxford University Press
  40.  2
    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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  41. Martha J. Farah & James L. McClelland (1991). A Computational Model of Semantic Memory Impairment: Modality Specificity and Emergent Category Specificity. Journal of Experimental Psychology: General 120 (4):339-357.
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  42.  14
    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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  43.  10
    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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  44.  1
    Jonathan D. Cohen, Marius Usher & James L. McClelland (1998). A PDP Approach to Set Size Effects Within the Stroop Task: Reply to Kanne, Balota, Spieler, and Faust. Psychological Review 105 (1):188-194.
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  45. Mark S. Seidenberg & James L. McClelland (1990). More Words but Still No Lexicon: Reply to Besner Et Al. Psychological Review 97 (3):447-452.
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  46. 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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  47. 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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  48.  29
    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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  49.  1
    Mark D. Jackson & James L. McClelland (1979). Processing Determinants of Reading Speed. Journal of Experimental Psychology: General 108 (2):151-181.
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  50.  1
    David E. Rumelhart & James L. McClelland (1985). Levels Indeed! A Response to Broadbent. Journal of Experimental Psychology: General 114 (2):193-197.
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