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  1. 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.
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  • The Place of Modeling in Cognitive Science.James L. McClelland - 2009 - Topics in Cognitive Science 1 (1):11-38.
  • Learning Representations of Wordforms With Recurrent Networks: Comment on Sibley, Kello, Plaut, & Elman (2008).Jeffrey S. Bowers & Colin J. Davis - 2009 - Cognitive Science 33 (7):1183-1186.
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  • Protein Analysis Meets Visual Word Recognition: A Case for String Kernels in the Brain.Thomas Hannagan & Jonathan Grainger - 2012 - Cognitive Science 36 (4):575-606.
    It has been recently argued that some machine learning techniques known as Kernel methods could be relevant for capturing cognitive and neural mechanisms (Jäkel, Schölkopf, & Wichmann, 2009). We point out that ‘‘String kernels,’’ initially designed for protein function prediction and spam detection, are virtually identical to one contending proposal for how the brain encodes orthographic information during reading. We suggest some reasons for this connection and we derive new ideas for visual word recognition that are successfully put to the (...)
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  • 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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  • Learning Orthographic Structure With Sequential Generative Neural Networks.Alberto Testolin, Ivilin Stoianov, Alessandro Sperduti & Marco Zorzi - 2016 - Cognitive Science 40 (3):579-606.
    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine, a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual (...)
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  • Parallel Distributed Processing Theory in the Age of Deep Networks.Jeffrey S. Bowers - 2017 - Trends in Cognitive Sciences 21 (12):950-961.