Search results for 'Learnability' (try it on Scholar)

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  1. David Lightfoot (1989). The Child's Trigger Experience: Degree-0 Learnability. Behavioral and Brain Sciences 12 (2):321.score: 21.0
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  2. Geoffrey K. Pullum (1996). Learnability, Hyperlearning, and the Poverty of the Stimulus. In J. Johnson, M. L. Juge & J. L. Moxley (eds.), Proceedings of the 22nd Annual Meeting: General Session and Parasession on the Role of Learnability in Grammatical Theory. Berkeley, California: Berkeley Linguistics Society. 498-513.score: 18.0
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  3. Douglas Patterson (2005). Learnability and Compositionality. Mind and Language 20 (3):326–352.score: 18.0
    In recent articles Fodor and Lepore have argued that not only do considerations of learnability dictate that meaning must be compositional in the wellknown sense that the meanings of all sentences are determined by the meanings of a finite number of primitive expressions and a finite number of operations on them, but also that meaning must be 'reverse compositional' as well, in the sense that the meanings of the primitive expressions of which a complex expression is composed must be (...)
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  4. T. Hunter & J. Lidz (2013). Conservativity and Learnability of Determiners. Journal of Semantics 30 (3):315-334.score: 18.0
    A striking cross-linguistic generalisation about the semantics of determiners is that they never express non-conservative relations. To account for this one might hypothesise that the mechanisms underlying human language acquisition are unsuited to non-conservative determiner meanings. We present experimental evidence that 4- and 5-year-olds fail to learn a novel non-conservative determiner but succeed in learning a comparable conservative determiner, consistent with the learnability hypothesis.
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  5. Denis Béchet, Annie Foret & Isabelle Tellier (2007). Learnability of Pregroup Grammars. Studia Logica 87 (2-3):225 - 252.score: 18.0
    This paper investigates the learnability by positive examples in the sense of Gold of Pregroup Grammars. In a first part, Pregroup Grammars are presented and a new parsing strategy is proposed. Then, theoretical learnability and non-learnability results for subclasses of Pregroup Grammars are proved. In the last two parts, we focus on learning Pregroup Grammars from a special kind of input called feature-tagged examples. A learning algorithm based on the parsing strategy presented in the first part is (...)
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  6. Barbara Dziemidowicz-Gryz (2007). On Learnability of Restricted Classes of Categorial Grammars. Studia Logica 85 (2):153 - 169.score: 18.0
    In this paper we present learning algorithms for classes of categorial grammars restricted by negative constraints. We modify learning functions of Kanazawa [10] and apply them to these classes of grammars. We also prove the learnability of intersection of the class of minimal grammars with the class of k-valued grammars.
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  7. A. Gualmini & B. Schwarz (2009). Solving Learnability Problems in the Acquisition of Semantics. Journal of Semantics 26 (2):185-215.score: 18.0
    This paper proposes solutions to two semantic learnability problems that have featured prominently in the literature on language acquisition. Both problems have often been deemed unsolvable for language learners as a matter of logic, and they have accordingly been taken to motivate principles making sure they will not actually arise in the course of language acquisition. One problem concerns the acquisition of ambiguous sentences whose readings are related by entailment. Crain et al.'s (1994) Semantic Subset Principle is intended to (...)
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  8. Matthew Schlesinger & Dima Amso (2013). Image Free-Viewing as Intrinsically-Motivated Exploration: Estimating the Learnability of Center-of-Gaze Image Samples in Infants and Adults. Frontiers in Psychology 4.score: 18.0
    We propose that free viewing of natural images in human infants can be understood and analyzed as the product of intrinsically-motivated visual exploration. We examined this idea by first generating five sets of center-of-gaze (COG) image samples, which were derived by presenting a series of natural images to groups of both real observers (i.e., 9-month-olds and adults) and artificial observers (i.e., an image-saliency model, an image-entropy model, and a random-gaze model). In order to assess the sequential learnability of the (...)
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  9. Heidi Waterfall & Shimon Edelman (2009). The Neglected Universals: Learnability Constraints and Discourse Cues. Behavioral and Brain Sciences 32 (5):471-472.score: 16.0
    Converging findings from English, Mandarin, and other languages suggest that observed may be algorithmic. First, computational principles behind recently developed algorithms that acquire productive constructions from raw texts or transcribed child-directed speech impose family resemblance on learnable languages. Second, child-directed speech is particularly rich in statistical (and social) cues that facilitate learning of certain types of structures.
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  10. David Kirsh (1992). PDP Learnability and Innate Knowledge of Language. Connectionism 3:297-322.score: 15.0
    It is sometimes argued that if PDP networks can be trained to make correct judgements of grammaticality we have an existence proof that there is enough information in the stimulus to permit learning grammar by inductive means alone. This seems inconsistent superficially with Gold's theorem and at a deeper level with the fact that networks are designed on the basis of assumptions about the domain of the function to be learned. To clarify the issue I consider what we should learn (...)
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  11. John Tooby, Leda Cosmides & H. Clark Barrett (2005). Resolving the Debate on Innate Ideas: Learnability Constraints and the Evolved Interpenetration of Motivational and Conceptual Functions. In Peter Carruthers, Stephen Laurence & Stephen Stich (eds.), The Innate Mind: Structure and Contents. New York: Oxford University Press New York. 305--337.score: 15.0
    In P. Carruthers, S. Laurence, & S. Stich (Eds.). The innate mind: Structure and content. (pp. 305-337). New York: Oxford University Press.
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  12. Dorrit Billman (1987). Language Learnability and Language Development. Mind and Language 2 (3):252-263.score: 15.0
  13. Reinhard Blutner (2002). Bruce Tesar and Paul Smolensky, Learnability in Optimality Theory. Linguistics and Philosophy 25 (1):65-80.score: 15.0
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  14. Robin Clark (2010). On the Learnability of Quantifiers. In Johan Van Benthem & Alice Ter Meulen (eds.), Handbook of Logic and Language, 2nd Edition.score: 15.0
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  15. M. Rita Manzini (1991). Learnability and Cognition. Mind and Language 6 (4):382-385.score: 15.0
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  16. W. J. M. Levelt (1990). On Learnability, Empirical Foundations, and Naturalness. Behavioral and Brain Sciences 13 (3):501.score: 15.0
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  17. Harry S. Broudy (1963). Professor Kircher, Utopianism, and Learnability. Studies in Philosophy and Education 2 (4):350-355.score: 15.0
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  18. Zhixiang Chen & Steven Homer (1996). The Bounded Injury Priority Method and the Learnability of Unions of Rectangles. Annals of Pure and Applied Logic 77 (2):143-168.score: 15.0
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  19. Ulrich Kohlenbach & Pavol Safarik (2014). Fluctuations, Effective Learnability and Metastability in Analysis. Annals of Pure and Applied Logic 165 (1):266-304.score: 15.0
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  20. Jun Lai & Fenna H. Poletiek (2011). The Impact of Adjacent-Dependencies and Staged-Input on the Learnability of Center-Embedded Hierarchical Structures. Cognition 118 (2):265-273.score: 15.0
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  21. Ansgar D. Endress, Ghislaine Dehaene-Lambertz & Jacques Mehler (2007). Perceptual Constraints and the Learnability of Simple Grammars. Cognition 105 (3):577-614.score: 15.0
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  22. K. Higuchi & T. Kihara (2014). Inside the Muchnik Degrees I: Discontinuity, Learnability and Constructivism. Annals of Pure and Applied Logic 165 (5):1058-1114.score: 15.0
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  23. William O'Grady (1989). Two Perspectives on Learnability. Behavioral and Brain Sciences 12 (2):354.score: 15.0
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  24. Albert Silverstein & Richard A. Dienstbier (1968). Can the Superior Learnability of Meaningful and Pleasant Words Be Transferred to Nonsense Syllables? Journal of Experimental Psychology 78 (2p1):292.score: 15.0
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  25. Marc Ettlinger Anna N. Rafferty, Thomas L. Griffiths (2013). Greater Learnability is Not Sufficient to Produce Cultural Universals. Cognition 129 (1):70.score: 15.0
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  26. C. L. Baker (1989). Some Observations on Degree of Learnability. Behavioral and Brain Sciences 12 (2):334.score: 15.0
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  27. W. Demopoulos (1989). On Applying Learnability Theory to the Rationalism-Empiricism Controversy in An Intimate Relation. Studies in the History and Philosophy of Science. Boston Studies in the Philosophy of Science 116:429-440.score: 15.0
     
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  28. Meinou H. de Vries, Padraic Monaghan, Stefan Knecht & Pienie Zwitserlood (2008). Syntactic Structure and Artificial Grammar Learning: The Learnability of Embedded Hierarchical Structures. Cognition 107 (2):763-774.score: 15.0
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  29. Daniel L. Everett (2012). Breathability, Learnability, and the Illusion of Design: A Response to Haselager. Pragmatics and Cognition 20 (2):386-387.score: 15.0
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  30. Jane Grimshaw (1987). The Components of Learnability Theory. In Jay L. Garfield (ed.), Modularity in Knowledge Representation and Natural-Language Understanding. Mit Press. 207--220.score: 15.0
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  31. J. Johnson, M. L. Juge & J. L. Moxley (eds.) (1996). Proceedings of the 22nd Annual Meeting: General Session and Parasession on the Role of Learnability in Grammatical Theory. Berkeley, California: Berkeley Linguistics Society.score: 15.0
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  32. Aravind K. Joshi (1989). A Possible Mathematical Specification of “Degree-0” or “Degree-0 Plus a Little” Learnability. Behavioral and Brain Sciences 12 (2):345.score: 15.0
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  33. Keith R. Kluender (1998). Locus Equations Reveal Learnability. Behavioral and Brain Sciences 21 (2):273-274.score: 15.0
    Although neural encoding by bats and owls presents seductive analogies, the major contribution of locus equations and orderly output constraints discussed by Sussman et al. is the demonstration that important acoustic information for speech perception can be captured by elegant and neurally-plausible learning processes.
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  34. Jun Lai & Fenna H. Poletiek (2010). The Impact of Starting Small on the Learnability of Recursion. In. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.score: 15.0
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  35. James L. Morgan (1989). Learnability Considerations and the Nature of Trigger Experiences in Language Acquisition. Behavioral and Brain Sciences 12 (2):352.score: 15.0
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  36. Jutta L. Mueller, Jörg Bahlmann & Angela D. Friederici (2010). Learnability of Embedded Syntactic Structures Depends on Prosodic Cues. Cognitive Science 34 (2):338-349.score: 15.0
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  37. Amy Perfors, Joshua B. Tenenbaum & Terry Regier (2011). The Learnability of Abstract Syntactic Principles. Cognition 118 (3):306-338.score: 15.0
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  38. Anna N. Rafferty, Thomas L. Griffiths & Marc Ettlinger (2013). Greater Learnability is Not Sufficient to Produce Cultural Universals. Cognition 129 (1):70-87.score: 15.0
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  39. Kenneth Wexler (1982). On Extensional Learnability. Cognition 11 (1):89-95.score: 15.0
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  40. Edwin Williams (1989). Linguistic Variation and Learnability. Behavioral and Brain Sciences 12 (2):363.score: 15.0
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  41. Steven R. Quartz & Terrence J. Sejnowski (1997). The Neural Basis of Cognitive Development: A Constructivist Manifesto. Behavioral and Brain Sciences 20 (4):537-556.score: 9.0
    How do minds emerge from developing brains? According to the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: minimizes the need for prespecification in accordance with recent neurobiological evidence (...)
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  42. Makoto Kanazawa (1996). Identification in the Limit of Categorial Grammars. Journal of Logic, Language and Information 5 (2):115-155.score: 7.0
    It is proved that for any k, the class of classical categorial grammars that assign at most k types to each symbol in the alphabet is learnable, in the Gold (1967) sense of identification in the limit from positive data. The proof crucially relies on the fact that the concept known as finite elasticity in the inductive inference literature is preserved under the inverse image of a finite-valued relation. The learning algorithm presented here incorporates Buszkowski and Penn's (1990) algorithm for (...)
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  43. Neal E. Miller & Edward J. Murray (1952). Displacement and Conflict; Learnable Drive as a Basis for the Steeper Gradient of Avoidance Than of Approach. Journal of Experimental Psychology 43 (3):227.score: 7.0
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  44. Kent Johnson (2004). Gold's Theorem and Cognitive Science. Philosophy of Science 70 (4):571-592.score: 6.0
    A variety of inaccurate claims about Gold's Theorem have appeared in the cognitive science literature. I begin by characterizing the logic of this theorem and its proof. I then examine several claims about Gold's Theorem, and I show why they are false. Finally, I assess the significance of Gold's Theorem for cognitive science.
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  45. Terry Horgan & Matjaž Potrč (2006). Particularist Semantic Normativity. Acta Analytica 21 (1):45-61.score: 6.0
    We sketch the view we call contextual semantics. It asserts that truth is semantically correct affirmability under contextually variable semantic standards, that truth is frequently an indirect form of correspondence between thought/language and the world, and that many Quinean commitments are not genuine ontological commitments. We argue that contextualist semantics fits very naturally with the view that the pertinent semantic standards are particularist rather than being systematizable as exceptionless general principles.
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  46. Giorgio Magri (2013). A Note on the GLA's Choice of the Current Loser From the Perspective of Factorizability. Journal of Logic, Language and Information 22 (2):231-247.score: 6.0
    Boersma’s (1997, 1998) Gradual Learning Algorithm (GLA) performs a sequence of slight re-rankings of the constraint set triggered by mistakes on the incoming stream of data. Data consist of underlying forms paired with the corresponding winner forms. At each iteration, the algorithm needs to complete the current data pair with a corresponding loser form. Tesar and Smolensky (Linguist Inq 29:229–268, 1998) suggest that this current loser should be set equal to the winner predicted by the current ranking. This paper develops (...)
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  47. C. P. Schnorr & P. Fuchs (1977). General Random Sequences and Learnable Sequences. Journal of Symbolic Logic 42 (3):329-340.score: 6.0
    We formalise the notion of those infinite binary sequences z that admit a single program P which expresses the entire algorithmical structure of z. Such a program P minimizes the information which must be used in a relative computation for z. We propose two concepts with different strength for this notion, the learnable and the super-learnable sequences. We establish three different equivalent characterizations of learnable (super-learnable, resp.) sequences. In particular, we prove that a sequences z is learnable (super-learnable, resp.) if (...)
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  48. Derek Bickerton (1984). The Language Bioprogram Hypothesis. Behavioral and Brain Sciences 7 (2):173.score: 6.0
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  49. Bruce Tesar (2006). Faithful Contrastive Features in Learning. Cognitive Science 30 (5):863-903.score: 6.0
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