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Jeffrey L. Elman [19]Jeff Elman [6]J. L. Elman [3]J. Elman [3]
Julie Passanante Elman [1]Jeff L. Elman [1]
  1.  93
    Finding Structure in Time.Jeffrey L. Elman - 1990 - Cognitive Science 14 (2):179-211.
    Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: (...)
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  2. {Finding structure in time}.J. Elman - 1993 - {Cognitive Science} 48:71-99.
     
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  3. Learning and development in neural networks: the importance of starting small.Jeffrey L. Elman - 1993 - Cognition 48 (1):71-99.
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  4.  84
    On the Meaning of Words and Dinosaur Bones: Lexical Knowledge Without a Lexicon.Jeffrey L. Elman - 2009 - Cognitive Science 33 (4):547-582.
    Although for many years a sharp distinction has been made in language research between rules and words—with primary interest on rules—this distinction is now blurred in many theories. If anything, the focus of attention has shifted in recent years in favor of words. Results from many different areas of language research suggest that the lexicon is representationally rich, that it is the source of much productive behavior, and that lexically specific information plays a critical and early role in the interpretation (...)
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  5.  31
    Prediction‐Based Learning and Processing of Event Knowledge.Ken McRae, Kevin S. Brown & Jeffrey L. Elman - 2021 - Topics in Cognitive Science 13 (1):206-223.
    McRae, Brown and Elman argue against the view that events are structured as frequently‐occurring sequences of world stimuli. They underline the importance of temporal structure defining event types and advance a more complex temporal structure, which allows for some variance in the component elements.
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  6.  59
    Coherence and coreference revisited.Andrew Kehler, Laura Kertz, Hannah Rohde & Jeffrey L. Elman - 2008 - Journal of Semantics 25 (1):1-44.
    For more than three decades, research into the psycholinguistics of pronoun interpretation has argued that hearers use various interpretation ‘preferences’ or ‘strategies’ that are associated with specific linguistic properties of antecedent expressions. This focus is a departure from the type of approach outlined in Hobbs , who argues that the mechanisms supporting pronoun interpretation are driven predominantly by semantics, world knowledge and inference, with particular attention to how these are used to establish the coherence of a discourse. On the basis (...)
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  7.  19
    Learning and morphological change.Mary Hare & Jeffrey L. Elman - 1995 - Cognition 56 (1):61-98.
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  8.  27
    A model of event knowledge.Jeffrey L. Elman & Ken McRae - 2019 - Psychological Review 126 (2):252-291.
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  9.  52
    Language as a dynamical system.Jeffrey L. Elman - 1995 - In Tim van Gelder & Robert Port (eds.), Mind as Motion: Explorations in the Dynamics of Cognition. MIT Press. pp. 195--223.
  10.  21
    Learning to use words: Event-related potentials index single-shot contextual word learning.Arielle Borovsky, Marta Kutas & Jeff Elman - 2010 - Cognition 116 (2):289-296.
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  11.  11
    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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  12.  38
    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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  13.  36
    The Wind Chilled the Spectators, but the Wine Just Chilled: Sense, Structure, and Sentence Comprehension.Mary Hare, Jeffrey L. Elman, Tracy Tabaczynski & Ken McRae - 2009 - Cognitive Science 33 (4):610-628.
    Anticipation plays a role in language comprehension. In this article, we explore the extent to which verb sense influences expectations about upcoming structure. We focus on change of state verbs like shatter, which have different senses that are expressed in either transitive or intransitive structures, depending on the sense that is used. In two experiments we influence the interpretation of verb sense by manipulating the thematic fit of the grammatical subject as cause or affected entity for the verb, and test (...)
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  14.  17
    Why is that? Structural prediction and ambiguity resolution in a very large corpus of English sentences.Douglas Roland, Jeffrey L. Elman & Victor S. Ferreira - 2006 - Cognition 98 (3):245-272.
  15.  33
    Online expectations for verbal arguments conditional on event knowledge.Klinton Bicknell, Jeffrey L. Elman, Mary Hare, Ken McRae & Marta Kutas - 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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  16.  14
    Innateness and Emergentism.Elizabeth Bates, Jeffrey L. Elman, Mark H. Johnson, Annette Karmiloff-Smith, Domenico Parisi & Kim Plunkett - 2017 - In William Bechtel & George Graham (eds.), A Companion to Cognitive Science. Oxford, UK: Blackwell. pp. 590–601.
    The nature–nurture controversy has been with us since it was first outlined by Plato and Aristotle. Nobody likes it anymore. All reasonable scholars today agree that genes and environment interact to determine complex cognitive outcomes. So why does the controversy persist? First, it persists because it has practical implications that cannot be postponed (i.e., what can we do to avoid bad outcomes and insure better ones?), a state of emergency that sometimes tempts scholars to stake out claims they cannot defend. (...)
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  17. Cairns, HS, 193.G. Cossu, J. Davidoff, J. L. Elman, R. A. Griggs, D. G. Hall, F. G. E. Happt & Hsu Jr - 1993 - Cognition 48:307.
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  18.  32
    Cripping Safe Sex: Life Goes On’s Queer/disabled Alliances.Julie Passanante Elman - 2012 - Journal of Bioethical Inquiry 9 (3):317-326.
    Life Goes On (1989–1993) was the first television series in U.S. history not only to introduce a recurring teenaged HIV-positive character but also to feature an actor with Down syndrome in a leading role. Drawing new connections among disability studies, queer theory, and bioethics, I argue that Life responded to American disability rights activism and the AIDS epidemic of the early 1990s by depicting sex education as disability activism. By portraying fulfilling sexual relationships for its disabled protagonists, Life challenged heteronormative (...)
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  19.  17
    Marr Prize Committee.Chris Barker, Marlene Behrmann, Charles Elkan, Jeff Elman & Keith Holyoak - 1996 - In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum.
  20. Cultural learning.E. Bates, J. Elman, H. Beilin, A. Bourguigon, M. Bunge, R. Case, D. Ciccetti, L. Cosmides & J. Tobby - 1993 - Behavioral and Brain Sciences 16 (3):495-552.
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  21.  29
    Semantic integration of novel word meanings after a single exposure in context.Arielle Borovsky, Jeff Elman & Marta Kutas - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
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  22. Liu, Y., B21 Massey, C., B75 Mattingley, JB, 53 Melinger, A., B11 Meseguer, E., B1.J. L. Bradshaw, A. M. Burton, J. I. D. Campbell, K. Christianson, S. Dehaene, J. L. Elman, F. Ferreira, V. S. Ferreira, G. Gigerenzer & R. Jenkins - 2006 - Cognition 98:309.
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  23.  7
    Toddlers’ Ability to Leverage Statistical Information to Support Word Learning.Erica M. Ellis, Arielle Borovsky, Jeffrey L. Elman & Julia L. Evans - 2021 - Frontiers in Psychology 12.
    PurposeThis study investigated whether the ability to utilize statistical regularities from fluent speech and map potential words to meaning at 18-months predicts vocabulary at 18- and again at 24-months.MethodEighteen-month-olds were exposed to an artificial language with statistical regularities within the speech stream, then participated in an object-label learning task. Learning was measured using a modified looking-while-listening eye-tracking design. Parents completed vocabulary questionnaires when their child was 18-and 24-months old.ResultsAbility to learn the object-label pairing for words after exposure to the artificial (...)
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  24.  2
    Connectionism, Artificial Life, and Dynamical Systems.Jeffrey L. Elman - 2017 - In William Bechtel & George Graham (eds.), A Companion to Cognitive Science. Oxford, UK: Blackwell. pp. 488–505.
    Periodically in science there arrive on the scene what appear to be dramatically new theoretical frameworks (what the philosopher of science Thomas Kuhn has called paradigm shifts). Characteristic of such changes in perspective is the recasting of old problems in new terms. By altering the conceptual vocabulary we use to think about problems, we may discover solutions which were obscured by prior ways of thinking about things. Connectionism, artificial life, and dynamical systems are all approaches to cognition which are relatively (...)
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  25. Computational approaches to language acquisition.Jeff Elman - 2006 - In Encyclopedia of Language and Linguistics. pp. 2--726.
  26. Encyclopedia of Language and Linguistics.Jeff Elman - 2006
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  27. JONATHAN St. BT EVANS (University of Plymouth) The mental model theory of conditional reasoning: critical appraisal and revision, l-20.Jeffrey L. Elman, Francesca Ge Happe, Richard D. Platt & Richard A. Griggs - 1993 - Cognition 48:30-5.
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  28.  11
    Questions for future research.Jeffrey L. Elman - 2005 - Trends in Cognitive Sciences 9 (3):111-117.
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  29. Situated Agents [Special issue].J. G. Green, M. T. H. Chi, W. J. Clancey & J. Elman - 1993 - Cognitive Science 17.
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  30.  35
    Constraints on the construction of cognition.Mark H. Johnson, Liz Bates, Jeff Elman, Annette Karmiloff-Smith & Kim Plunkett - 1997 - Behavioral and Brain Sciences 20 (4):569-570.
    We add to the constructivist approach of Quartz & Sejnowski (Q&S) by outlining a specific classification of sources of constraint on the emergence of representations from Elman et al. (1996). We suggest that it is important to consider behavioral constructivism in addition to neural constructivism.
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  31.  14
    What Does It Mean to Claim that Something Is 'Innate'? Response to Clark, Harris, Lightfoot and Samuels.Annette Karmiloff-Smith, Kim Plunkett, Mark H. Johnson, Jeff L. Elman & Elizabeth A. Bates - 1998 - Mind and Language 13 (4):588-597.
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  32. Generalized event knowledge activated during online language comprehension.R. Metusalem, M. Kutas, M. Hare, K. McRae & J. L. Elman - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
     
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  33.  21
    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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