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Amy Perfors [9]Amy F. Perfors [1]Amy Francesca Perfors [1]
  1.  16
    Thomas L. Griffiths, Nick Chater, Charles Kemp, Amy Perfors & Joshua B. Tenenbaum (2010). Probabilistic Models of Cognition: Exploring Representations and Inductive Biases. Trends in Cognitive Sciences 14 (8):357-364.
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  2. Amy Perfors, Joshua B. Tenenbaum, Thomas L. Griffiths & Fei Xu (2011). A Tutorial Introduction to Bayesian Models of Cognitive Development. Cognition 120 (3):302-321.
  3.  8
    Amy Perfors, Joshua B. Tenenbaum & Terry Regier (2011). The Learnability of Abstract Syntactic Principles. Cognition 118 (3):306-338.
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  4.  9
    Amy Perfors & Daniel J. Navarro (2014). Language Evolution Can Be Shaped by the Structure of the World. Cognitive Science 38 (4):775-793.
    Human languages vary in many ways but also show striking cross-linguistic universals. Why do these universals exist? Recent theoretical results demonstrate that Bayesian learners transmitting language to each other through iterated learning will converge on a distribution of languages that depends only on their prior biases about language and the quantity of data transmitted at each point; the structure of the world being communicated about plays no role (Griffiths & Kalish, , ). We revisit these findings and show that (...)
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  5.  19
    Stephani Foraker, Terry Regier, Naveen Khetarpal, Amy Perfors & Joshua Tenenbaum (2009). Indirect Evidence and the Poverty of the Stimulus: The Case of Anaphoric One. Cognitive Science 33 (2):287-300.
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  6.  12
    Fei Xu, Kathryn Dewar & Amy Perfors (2009). Induction, Overhypotheses, and the Shape Bias: Some Arguments and Evidence for Rational Constructivism. In Bruce M. Hood & Laurie Santos (eds.), The Origins of Object Knowledge. Oxford University Press 263--284.
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  7.  33
    Amy Perfors (2012). Bayesian Models of Cognition: What's Built in After All? Philosophy Compass 7 (2):127-138.
    This article explores some of the philosophical implications of the Bayesian modeling paradigm. In particular, it focuses on the ramifications of the fact that Bayesian models pre‐specify an inbuilt hypothesis space. To what extent does this pre‐specification correspond to simply ‘‘building the solution in''? I argue that any learner must have a built‐in hypothesis space in precisely the same sense that Bayesian models have one. This has implications for the nature of learning, Fodor's puzzle of concept acquisition, and (...)
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  8.  4
    Keith J. Ransom, Amy Perfors & Daniel J. Navarro (2015). Leaping to Conclusions: Why Premise Relevance Affects Argument Strength. Cognitive Science 40 (2).
    Everyday reasoning requires more evidence than raw data alone can provide. We explore the idea that people can go beyond this data by reasoning about how the data was sampled. This idea is investigated through an examination of premise non-monotonicity, in which adding premises to a category-based argument weakens rather than strengthens it. Relevance theories explain this phenomenon in terms of people's sensitivity to the relationships among premise items. We show that a Bayesian model of category-based induction taking premise sampling (...)
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  9.  6
    Luke Maurits, Amy F. Perfors & Daniel J. Navarro (2009). Joint Acquisition of Word Order and Word Reference. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 36.
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  10.  1
    Daniel Joseph Navarro & Amy Francesca Perfors (2011). Enlightenment Grows From Fundamentals. Behavioral and Brain Sciences 34 (4):207-208.
    Jones & Love (J&L) contend that the Bayesian approach should integrate process constraints with abstract computational analysis. We agree, but argue that the fundamentalist/enlightened dichotomy is a false one: Enlightened research is deeply intertwined with the basic, fundamental work upon which it is based.
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  11. Nick Chater, Alexander Clark, John A. Goldsmith & Amy Perfors (2015). Empiricism and Language Learnability. Oxford University Press Uk.
    This interdisciplinary new work explores one of the central theoretical problems in linguistics: learnability. The authors, from different backgrounds---linguistics, philosophy, computer science, psychology and cognitive science-explore the idea that language acquisition proceeds through general purpose learning mechanisms, an approach that is broadly empiricist both methodologically and psychologically. Written by four researchers in the full range of relevant fields: linguistics, psychology, computer science, and cognitive science, the book sheds light on the central problems of learnability and language, and traces their implications (...)
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