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Michael L. Kalish [5]Michael Kalish [4]
  1. Thomas L. Griffiths, Stephan Lewandowsky & Michael L. Kalish (2013). The Effects of Cultural Transmission Are Modulated by the Amount of Information Transmitted. 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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  2. Jonathan Trigg & Michael Kalish (2011). Explaining How the Mind Works: On the Relation Between Cognitive Science and Philosophy. Topics in Cognitive Science 3 (2):399-424.
    In this paper, we argue that under certain prevalent interpretations of the nature and aims of cognitive science, theories of cognition generate a forced choice between a conception of cognition which depends on the possibility of a private language, and a conception of cognition which depends on mereological confusions. We argue, further, that this should not pose a fundamental problem for cognitive scientists since a plausible interpretation of the nature and aims of cognitive science is available that does not generate (...)
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  3. Stephan Lewandowsky, Thomas L. Griffiths & Michael L. Kalish (2009). The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning. Cognitive Science 33 (6):969-998.
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  4. Thomas L. Griffiths, Brian R. Christian & Michael L. Kalish (2008). Using Category Structures to Test Iterated Learning as a Method for Identifying Inductive Biases. Cognitive Science 32 (1):68-107.
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  5. Thomas L. Griffiths & Michael L. Kalish (2007). Language Evolution by Iterated Learning With Bayesian Agents. 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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  6. Gillian Rhodes & Michael L. Kalish (1999). Cognitive Penetration: Would We Know It If We Saw It? 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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  7. Michael Kalish (1994). Adaptive Learning of Gaussian Categories Leads to Decision Bounds and Response Surfaces Incompatible with Optimal Decision Making. In. In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. 16--479.
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  8. Michael Kalish (1992). Limitations on the What Reaching Can Tell Us About Sensorimotor Transformations. Behavioral and Brain Sciences 15 (2):344.
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