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  1.  98
    Language Evolution by Iterated Learning With Bayesian Agents.Thomas L. Griffiths & Michael L. Kalish - 2007 - Cognitive Science 31 (3):441-480.
    Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages from this posterior (...)
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  2.  32
    Population of Linear Experts: Knowledge Partitioning and Function Learning.Michael L. Kalish, Stephan Lewandowsky & John K. Kruschke - 2004 - Psychological Review 111 (4):1072-1099.
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  3.  45
    Using Category Structures to Test Iterated Learning as a Method for Identifying Inductive Biases.Thomas L. Griffiths, Brian R. Christian & Michael L. Kalish - 2008 - Cognitive Science 32 (1):68-107.
    Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases—assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed data. This article explores a novel experimental method for identifying the biases that guide human inductive inferences. The idea behind this method is simple: This article uses the responses (...)
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  4.  56
    The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning.Stephan Lewandowsky, Thomas L. Griffiths & Michael L. Kalish - 2009 - Cognitive Science 33 (6):969-998.
    Determining the knowledge that guides human judgments is fundamental to understanding how people reason, make decisions, and form predictions. We use an experimental procedure called ‘‘iterated learning,’’ in which the responses that people give on one trial are used to generate the data they see on the next, to pinpoint the knowledge that informs people's predictions about everyday events (e.g., predicting the total box office gross of a movie from its current take). In particular, we use this method to discriminate (...)
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  5.  23
    A test of two processes: The effect of training on deductive and inductive reasoning.Rachel G. Stephens, John C. Dunn, Brett K. Hayes & Michael L. Kalish - 2020 - Cognition 199 (C):104223.
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  6. The Effects of Cultural Transmission Are Modulated by the Amount of Information Transmitted.Thomas L. Griffiths, Stephan Lewandowsky & Michael L. Kalish - 2013 - Cognitive Science 37 (5):953-967.
    Information changes as it is passed from person to person, with this process of cultural transmission allowing the minds of individuals to shape the information that they transmit. We present mathematical models of cultural transmission which predict that the amount of information passed from person to person should affect the rate at which that information changes. We tested this prediction using a function-learning task, in which people learn a functional relationship between two variables by observing the values of those variables. (...)
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  7.  54
    Cognitive penetration: Would we know it if we saw it?Gillian Rhodes & Michael L. Kalish - 1999 - Behavioral and Brain Sciences 22 (3):390-391.
    How can the impenetrability hypothesis be empirically tested? We comment on the role of signal detection measures, suggesting that context effects on discriminations for which post-perceptual cues are irrelevant, or on neural activity associated with early vision, would challenge impenetrability. We also note the great computational power of the proposed pre-perceptual attention processes and consider the implications for testability of the theory.
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