40 found
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  1.  25
    Understanding normal and impaired word reading: Computational principles in quasi-regular domains.David C. Plaut, James L. McClelland, Mark S. Seidenberg & Karalyn Patterson - 1996 - Psychological Review 103 (1):56-115.
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  2.  25
    Doing Without Schema Hierarchies: A Recurrent Connectionist Approach to Normal and Impaired Routine Sequential Action.Matthew Botvinick & David C. Plaut - 2004 - Psychological Review 111 (2):395-429.
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  3.  21
    Computational insights into human perceptual expertise for familiar and unfamiliar face recognition.Nicholas M. Blauch, Marlene Behrmann & David C. Plaut - 2021 - Cognition 208 (C):104341.
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  4.  62
    Language acquisition in the absence of explicit negative evidence: how important is starting small?Douglas L. T. Rohde & David C. Plaut - 1999 - Cognition 72 (1):67-109.
  5.  22
    Individual and developmental differences in semantic priming: Empirical and computational support for a single-mechanism account of lexical processing.David C. Plaut & James R. Booth - 2000 - Psychological Review 107 (4):786-823.
  6. Distributed circuits, not circumscribed centers, mediate visual recognition.Marlene Behrmann & David C. Plaut - 2013 - Trends in Cognitive Sciences 17 (5):210-219.
  7.  32
    Short-term memory for serial order: A recurrent neural network model.Matthew M. Botvinick & David C. Plaut - 2006 - Psychological Review 113 (2):201-233.
  8.  12
    Deep learning of shared perceptual representations for familiar and unfamiliar faces: Reply to commentaries.Nicholas M. Blauch, Marlene Behrmann & David C. Plaut - 2021 - Cognition 208 (C):104484.
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  9.  77
    The joint development of hemispheric lateralization for words and faces.Eva M. Dundas, David C. Plaut & Marlene Behrmann - 2013 - Journal of Experimental Psychology: General 142 (2):348.
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  10.  29
    SD-squared: On the association between semantic dementia and surface dyslexia.Anna M. Woollams, Matthew A. Lambon Ralph, David C. Plaut & Karalyn Patterson - 2007 - Psychological Review 114 (2):316-339.
  11.  13
    More modeling but still no stages: Reply to Borowsky and Besner.David C. Plaut & James R. Booth - 2006 - Psychological Review 113 (1):196-200.
  12.  24
    Locating object knowledge in the brain: Comment on Bowers’s (2009) attempt to revive the grandmother cell hypothesis.David C. Plaut & James L. McClelland - 2010 - Psychological Review 117 (1):284-288.
  13.  77
    “Shallow Draughts Intoxicate the Brain”: Lessons from Cognitive Science for Cognitive Neuropsychology.Karalyn Patterson & David C. Plaut - 2009 - Topics in Cognitive Science 1 (1):39-58.
    This article presents a sobering view of the discipline of cognitive neuropsychology as practiced over the last three or four decades. Our judgment is that, although the study of abnormal cognition resulting from brain injury or disease in previously normal adults has produced a catalogue of fascinating and highly selective deficits, it has yielded relatively little advance in understanding how the brain accomplishes its cognitive business. We question the wisdom of the following three “choices” in mainstream cognitive neuropsychology: (a) single‐case (...)
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  14.  25
    A Connectionist Approach to Word Reading and Acquired Dyslexia: Extension to Sequential Processing.David C. Plaut - 1999 - Cognitive Science 23 (4):543-568.
    A connectionist approach to word reading, based on the principles of distributed representation, graded learning of statistical structure, and interactivity in processing, has led to the development of explicit computational models which account for a wide range of data on normal skilled reading and on patterns of reading impairment due to brain damage. There have, however, been recent empirical challenges to these models, and the approach in general, relating to the influence of orthographic length on the naming latencies of both (...)
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  15.  60
    Quasiregularity and Its Discontents: The Legacy of the Past Tense Debate.Mark S. Seidenberg & David C. Plaut - 2014 - Cognitive Science 38 (6):1190-1228.
    Rumelhart and McClelland's chapter about learning the past tense created a degree of controversy extraordinary even in the adversarial culture of modern science. It also stimulated a vast amount of research that advanced the understanding of the past tense, inflectional morphology in English and other languages, the nature of linguistic representations, relations between language and other phenomena such as reading and object recognition, the properties of artificial neural networks, and other topics. We examine the impact of the Rumelhart and McClelland (...)
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  16.  24
    Large‐Scale Modeling of Wordform Learning and Representation.Daragh E. Sibley, Christopher T. Kello, David C. Plaut & Jeffrey L. Elman - 2008 - Cognitive Science 32 (4):741-754.
    The forms of words as they appear in text and speech are central to theories and models of lexical processing. Nonetheless, current methods for simulating their learning and representation fail to approach the scale and heterogeneity of real wordform lexicons. A connectionist architecture termed thesequence encoderis used to learn nearly 75,000 wordform representations through exposure to strings of stress‐marked phonemes or letters. First, the mechanisms and efficacy of the sequence encoder are demonstrated and shown to overcome problems with traditional slot‐based (...)
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  17.  20
    Such stuff as habits are made on: A reply to Cooper and Shallice (2006).Matthew M. Botvinick & David C. Plaut - 2006 - Psychological Review 113 (4):917-927.
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  18.  26
    Modeling the N400 ERP component as transient semantic over-activation within a neural network model of word comprehension.Samuel J. Cheyette & David C. Plaut - 2017 - Cognition 162 (C):153-166.
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  19.  41
    Representational development and theory-of-mind computations.David C. Plaut & Annette Karmiloff-Smith - 1993 - Behavioral and Brain Sciences 16 (1):70-71.
  20.  39
    Attractor dynamics in word recognition: converging evidence from errors by normal subjects, dyslexic patients and a connectionist model.Peter McLeod, Tim Shallice & David C. Plaut - 2000 - Cognition 74 (1):91-114.
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  21.  14
    Dissociations in Performance on Novel Versus Irregular Items: Single‐Route Demonstrations With Input Gain in Localist and Distributed Models.Christopher T. Kello, Daragh E. Sibley & David C. Plaut - 2005 - Cognitive Science 29 (4):627-654.
    Four pairs of connectionist simulations are presented in which quasi‐regular mappings are computed using localist and distributed representations. In each simulation, a control parameter termed input gain was modulated over the only level of representation that mapped inputs to outputs. Input gain caused both localist and distributed models to shift between regularity‐based and item‐based modes of processing. Performance on irregular items was selectively impaired in the regularity‐based modes, whereas performance on novel items was selectively impaired in the item‐based modes. Thus, (...)
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  22.  40
    Stipulating versus discovering representations.David C. Plaut & James L. McClelland - 2000 - 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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  23. Settling dynamics in distributed networks explain task differences in semantic ambiguity effects: Computational and behavioral evidence.Blair C. Armstrong & David C. Plaut - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 273--278.
     
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  24.  29
    SD-squared revisited: Reply to Coltheart, Tree, and Saunders (2010).Anna M. Woollams, Matthew A. Lambon Ralph, David C. Plaut & Karalyn Patterson - 2010 - Psychological Review 117 (1):273-281.
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  25.  30
    Empirical and computational support for context-dependent representations of serial order: Reply to Bowers, Damian, and Davis (2009).Matthew M. Botvinick & David C. Plaut - 2009 - Psychological Review 116 (4):998-1001.
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  26.  96
    Developing a domain-general framework for cognition: What is the best approach?James L. McClelland, David C. Plaut, Stephen J. Gotts & Tiago V. Maia - 2003 - 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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  27.  92
    Response to Susilo and Duchaine: beyond neuropsychological dissociations in understanding face and word representations.David C. Plaut & Marlene Behrmann - 2013 - Trends in Cognitive Sciences 17 (11):546.
  28. Beyond Functional Architecture in Cognitive Neuropsychology: A Reply to Coltheart (2010).David C. Plaut & Karalyn Patterson - 2010 - Topics in Cognitive Science 2 (1):12-14.
    We (Patterson & Plaut, 2009) argued that cognitive neuropsychology has had a limited impact on cognitive science due to a nearly exclusive reliance on (a) single‐case studies, (b) dissociations in cognitive performance, and (c) shallow, box‐and‐arrow theorizing, and we advocated adopting a case‐series methodology, considering associations as well as dissociations, and employing explicit computational modeling in studying “how the brain does its cognitive business.” In reply, Coltheart (2010) claims that our concern is misplaced because cognitive neuropsychology is concerned only with (...)
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  29. Yoked criteria shifts in decision system adaptation: Computational and behavioral investigations.Blair C. Armstrong, Steve Joordens & David C. Plaut - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
     
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  30.  22
    Postscript: The way forward: Comment.Matthew M. Botvinick & David C. Plaut - 2006 - Psychological Review 113 (4):928-928.
  31.  23
    Postscript: Winnowing out some take-home points.Matthew M. Botvinick & David C. Plaut - 2009 - Psychological Review 116 (4):1001-1002.
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  32.  24
    Giving theories of reading a sporting chance.David C. Plaut - 2012 - Behavioral and Brain Sciences 35 (5):301-302.
    The search for a universal theory of reading is misguided. Instead, theories should articulate general principles of neural computation that interact with language-specific learning environments to explain the full diversity of observed reading-related phenomena across the world's languages.
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  33. Lesioned attractor networks as models of neuropsychological deficits.David C. Plaut - 1995 - In Michael A. Arbib (ed.), Handbook of Brain Theory and Neural Networks. MIT Press. pp. 540--543.
  34.  20
    Neural networks and computational theory: Solving the right problem.David C. Plaut - 1989 - Behavioral and Brain Sciences 12 (3):411-413.
  35.  25
    Parallel distributed processing challenges the strong modularity hypothesis, not the locality assumption.David C. Plaut - 1994 - Behavioral and Brain Sciences 17 (1):77-78.
  36.  31
    Postscript: Parallel distributed processing in localist models without thresholds.David C. Plaut & James L. McClelland - 2010 - Psychological Review 117 (1):289-290.
  37.  14
    A neural network model of the effect of prior experience with regularities on subsequent category learning.Casey L. Roark, David C. Plaut & Lori L. Holt - 2022 - Cognition 222 (C):104997.
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  38.  33
    Simple recurrent networks can distinguish non-occurring from ungrammatical sentences given appropriate task structure: reply to Marcus.Douglas L. T. Rohde & David C. Plaut - 1999 - Cognition 73 (3):297-300.
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  39.  38
    Sequence Encoders Enable Large‐Scale Lexical Modeling: Reply to Bowers and Davis (2009).Daragh E. Sibley, Christopher T. Kello, David C. Plaut & Jeffrey L. Elman - 2009 - Cognitive Science 33 (7):1187-1191.
    Sibley, Kello, Plaut, and Elman (2008) proposed the sequence encoder as a model that learns fixed‐width distributed representations of variable‐length sequences. In doing so, the sequence encoder overcomes problems that have restricted models of word reading and recognition to processing only monosyllabic words. Bowers and Davis (2009) recently claimed that the sequence encoder does not actually overcome the relevant problems, and hence it is not a useful component of large‐scale word‐reading models. In this reply, it is noted that the sequence (...)
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  40.  17
    Postscript: SD-squared revisited again.Anna M. Woollams, Matthew A. Lambon Ralph, David C. Plaut & Karalyn Patterson - 2010 - Psychological Review 117 (1):282-283.
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