21 found
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  1.  56
    Towards a Universal Model of Reading.Ram Frost, Christina Behme, Madeleine El Beveridge, Thomas H. Bak, Jeffrey S. Bowers, Max Coltheart, Stephen Crain, Colin J. Davis, S. Hélène Deacon & Laurie Beth Feldman - 2012 - Behavioral and Brain Sciences 35 (5):263.
    In the last decade, reading research has seen a paradigmatic shift. A new wave of computational models of orthographic processing that offer various forms of noisy position or context-sensitive coding have revolutionized the field of visual word recognition. The influx of such models stems mainly from consistent findings, coming mostly from European languages, regarding an apparent insensitivity of skilled readers to letter order. Underlying the current revolution is the theoretical assumption that the insensitivity of readers to letter order reflects the (...)
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  2.  13
    On the Biological Plausibility of Grandmother Cells: Implications for Neural Network Theories in Psychology and Neuroscience.Jeffrey S. Bowers - 2009 - Psychological Review 116 (1):220-251.
  3.  13
    The Practical and Principled Problems With Educational Neuroscience.Jeffrey S. Bowers - forthcoming - Psychological Review.
  4.  11
    Why Do Some Neurons in Cortex Respond to Information in a Selective Manner? Insights From Artificial Neural Networks.Jeffrey S. Bowers, Ivan I. Vankov, Markus F. Damian & Colin J. Davis - 2016 - Cognition 148:47-63.
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  5.  17
    Interfering Neighbours: The Impact of Novel Word Learning on the Identification of Visually Similar Words.Jeffrey S. Bowers, Colin J. Davis & Derek A. Hanley - 2005 - Cognition 97 (3):B45-B54.
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  6.  25
    Neural Networks Learn Highly Selective Representations in Order to Overcome the Superposition Catastrophe.Jeffrey S. Bowers, Ivan I. Vankov, Markus F. Damian & Colin J. Davis - 2014 - Psychological Review 121 (2):248-261.
  7.  22
    Parallel Distributed Processing Theory in the Age of Deep Networks.Jeffrey S. Bowers - 2017 - Trends in Cognitive Sciences 21 (12):950-961.
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  8.  15
    A Fundamental Limitation of the Conjunctive Codes Learned in PDP Models of Cognition: Comment on Botvinick and Plaut.Jeffrey S. Bowers, Markus F. Damian & Colin J. Davis - 2009 - Psychological Review 116 (4):986-995.
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  9.  10
    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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  10.  4
    Priming is Not All Bias: Commentary on Ratcliff and McKoon.Jeffrey S. Bowers - 1999 - Psychological Review 106 (3):582-596.
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  11. Psychology, Not Educational Neuroscience, is the Way Forward for Improving Educational Outcomes for All Children: Reply to Gabrieli (2016) and Howard-Jones Et Al. (2016). [REVIEW]Jeffrey S. Bowers - 2016 - Psychological Review 123 (5):628-635.
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  12.  22
    Age-of-Acquisition Effects in Visual Word Recognition: Evidence From Expert Vocabularies.Hans Stadthagen-Gonzalez, Jeffrey S. Bowers & Markus F. Damian - 2004 - Cognition 93 (1):B11-B26.
  13.  8
    Postscript: More Problems with Botvinick and Plaut’s PDP Model of Short-Term Memory.Jeffrey S. Bowers, Markus F. Damian & Colin J. Davis - 2009 - Psychological Review 116 (4):995-997.
  14.  54
    Is Speech Perception Modular or Interactive?Jeffrey S. Bowers & Colin J. Davis - 2004 - Trends in Cognitive Sciences 8 (1):3-5.
  15.  11
    Grossberg and Colleagues Solved the Hyperonym Problem Over a Decade Ago.Jeffrey S. Bowers - 1999 - Behavioral and Brain Sciences 22 (1):38-39.
    Levelt et al. describe a model of speech production in which lemma access is achieved via input from nondecompositional conceptual representations. They claim that existing decompositional theories are unable to account for lexical retrieval because of the so-called hyperonym problem. However, existing decompositional models have solved a formally equivalent problem.
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  16.  18
    Position-Invariant Letter Identification is a Key Component of Any Universal Model of Reading.Jeffrey S. Bowers - 2012 - Behavioral and Brain Sciences 35 (5):281-282.
    A universal property of visual word identification is position-invariant letter identification, such that the letter is coded in the same way in CAT and ACT. This should provide a fundamental constraint on theories of word identification, and, indeed, it inspired some of the theories that Frost has criticized. I show how the spatial coding scheme of Colin Davis can, in principle, account for contrasting transposed letter priming effects, and at the same time, position-invariant letter identification.
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  17.  29
    Further Arguments in Support of Localist Coding in Connectionist Networks.Jeffrey S. Bowers - 2000 - Behavioral and Brain Sciences 23 (4):471-471.
    Two additional sources of evidence are provided in support of localist coding within connectionist networks. First, only models with localist codes can currently represent multiple pieces of information simultaneously or represent order among a set of items on-line. Second, recent priming data appear problematic for theories that rely on distributed representations. However, a faulty argument advanced by Page is also pointed out.
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  18.  15
    Converging Methods for Understanding Reading and Dyslexia.Jeffrey S. Bowers - 2000 - Trends in Cognitive Sciences 4 (12):476.
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  19.  27
    The Visual Categories for Letters and Words Reside Outside Any Informationally Encapsulated Perceptual System.Jeffrey S. Bowers - 1999 - Behavioral and Brain Sciences 22 (3):368-369.
    According to Pylyshyn, the early visual system is able to categorize perceptual inputs into shape classes based on visual similarity criteria; it is also suggested that written words may be categorized within early vision. This speculation is contradicted by the fact that visually unrelated exemplars of a given letter (e.g., a/A) or word (e.g., read/READ) map onto common visual categories.
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  20.  2
    Researchers Keep Rejecting Grandmother Cells After Running the Wrong Experiments: The Issue Is How Familiar Stimuli Are Identified.Jeffrey S. Bowers, Nicolas D. Martin & Ella M. Gale - 2019 - Bioessays 41 (8):1800248.
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  21.  10
    More Varieties of Bayesian Theories, but No Enlightenment.Jeffrey S. Bowers & Colin J. Davis - 2011 - Behavioral and Brain Sciences 34 (4):193-194.
    We argue that Bayesian models are best categorized as methodological or theoretical. That is, models are used as tools to constrain theories, with no commitment to the processes that mediate cognition, or models are intended to approximate the underlying algorithmic solutions. We argue that both approaches are flawed, and that the Enlightened Bayesian approach is unlikely to help.
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