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  1. The Now-or-Never bottleneck: A fundamental constraint on language.Morten H. Christiansen & Nick Chater - 2016 - Behavioral and Brain Sciences 39:e62.
    Memory is fleeting. New material rapidly obliterates previous material. How, then, can the brain deal successfully with the continual deluge of linguistic input? We argue that, to deal with this “Now-or-Never” bottleneck, the brain must compress and recode linguistic input as rapidly as possible. This observation has strong implications for the nature of language processing: (1) the language system must “eagerly” recode and compress linguistic input; (2) as the bottleneck recurs at each new representational level, the language system must build (...)
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  • SWIFT: A Dynamical Model of Saccade Generation During Reading.Ralf Engbert, Antje Nuthmann, Eike M. Richter & Reinhold Kliegl - 2005 - Psychological Review 112 (4):777-813.
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  • Toward a model of eye movement control in reading.Erik D. Reichle, Alexander Pollatsek, Donald L. Fisher & Keith Rayner - 1998 - Psychological Review 105 (1):125-157.
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  • The Bayesian reader: Explaining word recognition as an optimal Bayesian decision process.Dennis Norris - 2006 - Psychological Review 113 (2):327-357.
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  • The spatial coding model of visual word identification.Colin J. Davis - 2010 - Psychological Review 117 (3):713-758.
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  • OB1-reader: A model of word recognition and eye movements in text reading.Joshua Snell, Sam van Leipsig, Jonathan Grainger & Martijn Meeter - 2018 - Psychological Review 125 (6):969-984.
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  • Comprehenders model the nature of noise in the environment.Rachel Ryskin, Richard Futrell, Swathi Kiran & Edward Gibson - 2018 - Cognition 181 (C):141-150.
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  • Task effects reveal cognitive flexibility responding to frequency and predictability: Evidence from eye movements in reading and proofreading.Elizabeth R. Schotter, Klinton Bicknell, Ian Howard, Roger Levy & Keith Rayner - 2014 - Cognition 131 (1):1-27.
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  • The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations Using an Interactive Shiny App.Sarah Depaoli, Sonja D. Winter & Marieke Visser - 2020 - Frontiers in Psychology 11.
    The current paper highlights a new, interactive Shiny App that can be used to aid in understanding and teaching the important task of conducting a prior sensitivity analysis when implementing Bayesian estimation methods. In this paper, we discuss the importance of examining prior distributions through a sensitivity analysis. We argue that conducting a prior sensitivity analysis is equally important when so-called diffuse priors are implemented as it is with subjective priors. As a proof of concept, we conducted a small simulation (...)
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