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  1. For a Few Neurons More: Tractability and Neurally Informed Economic Modelling.Matteo Colombo - 2015 - British Journal for the Philosophy of Science 66 (4):713-736.
    There continues to be significant confusion about the goals, scope, and nature of modelling practice in neuroeconomics. This article aims to dispel some such confusion by using one of the most recent critiques of neuroeconomic modelling as a foil. The article argues for two claims. First, currently, for at least some economic model of choice behaviour, the benefits derivable from neurally informing an economic model do not involve special tractability costs. Second, modelling in neuroeconomics is best understood within Marr’s three-level (...)
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  • Rejection positivity predicts trial-to-trial reaction times in an auditory selective attention task: a computational analysis of inhibitory control.Sufen Chen & Robert D. Melara - 2014 - Frontiers in Human Neuroscience 8.
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  • A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making.Prezenski Sabine, Brechmann André, Wolff Susann & Russwinkel Nele - 2017 - Frontiers in Psychology 8.
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  • Representational geometry: integrating cognition, computation, and the brain.Nikolaus Kriegeskorte & Rogier A. Kievit - 2013 - Trends in Cognitive Sciences 17 (8):401-412.
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  • Model-based theorising in cognitive neuroscience.Elizabeth Irvine - unknown
    Weisberg (2006) and Godfrey-Smith (2006, 2009) distinguish between two forms of theorising: data-driven ‘abstract direct representation’ and modeling. The key difference is that when using a data-driven approach, theories are intended to represent specific phenomena, so directly represent them, while models may not be intended to represent anything, so represent targets indirectly, if at all. The aim here is to compare and analyse these practices, in order to outline an account of model-based theorising that involves direct representational relationships. This is (...)
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