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  1. Fragmentation and information access.Adam Elga & Agustin Rayo - 2021 - In Cristina Borgoni, Dirk Kindermann & Andrea Onofri (eds.), The Fragmented Mind. Oxford: Oxford University Press.
    In order to predict and explain behavior, one cannot specify the mental state of an agent merely by saying what information she possesses. Instead one must specify what information is available to an agent relative to various purposes. Specifying mental states in this way allows us to accommodate cases of imperfect recall, cognitive accomplishments involved in logical deduction, the mental states of confused or fragmented subjects, and the difference between propositional knowledge and know-how .
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  • Tensions Between Science and Intuition Across the Lifespan.Andrew Shtulman & Kelsey Harrington - 2016 - Topics in Cognitive Science 8 (1):118-137.
    The scientific knowledge needed to engage with policy issues like climate change, vaccination, and stem cell research often conflicts with our intuitive theories of the world. How resilient are our intuitive theories in the face of contradictory scientific knowledge? Here, we present evidence that intuitive theories in 10 domains of knowledge—astronomy, evolution, fractions, genetics, germs, matter, mechanics, physiology, thermodynamics, and waves—persist more than four decades beyond the acquisition of a mutually exclusive scientific theory. Participants were asked to verify two types (...)
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  • Bayesian computation and mechanism: Theoretical pluralism drives scientific emergence.David K. Sewell, Daniel R. Little & Stephan Lewandowsky - 2011 - Behavioral and Brain Sciences 34 (4):212-213.
    The breadth-first search adopted by Bayesian researchers to map out the conceptual space and identify what the framework can do is beneficial for science and reflective of its collaborative and incremental nature. Theoretical pluralism among researchers facilitates refinement of models within various levels of analysis, which ultimately enables effective cross-talk between different levels of analysis.
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  • Testing adaptive toolbox models: A Bayesian hierarchical approach.Benjamin Scheibehenne, Jörg Rieskamp & Eric-Jan Wagenmakers - 2013 - Psychological Review 120 (1):39-64.
  • 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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