11 found
  1.  88
    Letting structure emerge: connectionist and dynamical systems approaches to cognition.James L. McClelland, Matthew M. Botvinick, David C. Noelle, David C. Plaut, Timothy T. Rogers, Mark S. Seidenberg & Linda B. Smith - 2010 - Trends in Cognitive Sciences 14 (8):348-356.
  2.  26
    Structure and Deterioration of Semantic Memory: A Neuropsychological and Computational Investigation.Timothy T. Rogers, Matthew A. Lambon Ralph, Peter Garrard, Sasha Bozeat, James L. McClelland, John R. Hodges & Karalyn Patterson - 2004 - Psychological Review 111 (1):205-235.
  3.  46
    Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition.Timothy T. Rogers & James L. McClelland - 2014 - 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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  4. Précis of semantic cognition: A parallel distributed processing approach.Timothy T. Rogers & James L. McClelland - 2008 - 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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  5.  38
    A simple model from a powerful framework that spans levels of analysis.Timothy T. Rogers & James L. McClelland - 2008 - 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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  6.  60
    Can semi-supervised learning explain incorrect beliefs about categories?Charles W. Kalish, Timothy T. Rogers, Jonathan Lang & Xiaojin Zhu - 2011 - Cognition 120 (1):106-118.
    Three experiments with 88 college-aged participants explored how unlabeled experiences—learning episodes in which people encounter objects without information about their category membership—influence beliefs about category structure. Participants performed a simple one-dimensional categorization task in a brief supervised learning phase, then made a large number of unsupervised categorization decisions about new items. In all three experiments, the unsupervised experience altered participants’ implicit and explicit mental category boundaries, their explicit beliefs about the most representative members of each category, and even their memory (...)
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  7.  78
    Human Semi-Supervised Learning.Bryan R. Gibson, Timothy T. Rogers & Xiaojin Zhu - 2013 - Topics in Cognitive Science 5 (1):132-172.
    Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and (...)
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  8. Semi-supervised learning is observed in a speeded but not an unspeeded 2D categorization task.Timothy T. Rogers, Charles Kalish, Bryan R. Gibson, Joseph Harrison & Xiaojin Zhu - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
  9.  43
    Impact of dialect use on a basic component of learning to read.Megan C. Brown, Daragh E. Sibley, Julie A. Washington, Timothy T. Rogers, Jan R. Edwards, Maryellen C. MacDonald & Mark S. Seidenberg - 2015 - Frontiers in Psychology 6.
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    Distinguishing literal from metaphorical applications of Bayesian approaches.Timothy T. Rogers & Mark S. Seidenberg - 2011 - Behavioral and Brain Sciences 34 (4):211-212.
    We distinguish between literal and metaphorical applications of Bayesian models. When intended literally, an isomorphism exists between the elements of representation assumed by the rational analysis and the mechanism that implements the computation. Thus, observation of the implementation can externally validate assumptions underlying the rational analysis. In other applications, no such isomorphism exists, so it is not clear how the assumptions that allow a Bayesian model to fit data can be independently validated.
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  11. Learning a hierarchical organization of categories.Steven Verheyen, Eef Ameel, Timothy T. Rogers & Gert Storms - 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.
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