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  1. Being Realist About Bayes, and the Predictive Processing Theory of Mind.Matteo Colombo, Lee Elkin & Stephan Hartmann - forthcoming - British Journal for the Philosophy of Science.
    Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science. In this paper, we argue that this realist attitude is unwarranted. The Bayesian research program in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty. In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives. It is also contentious that the Bayesian approach (...)
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  • Transcending the Evidentiary Boundary: Prediction Error Minimization, Embodied Interaction, and Explanatory Pluralism.Regina E. Fabry - 2017 - Philosophical Psychology 30 (4):395-414.
    In a recent paper, Jakob Hohwy argues that the emerging predictive processing perspective on cognition requires us to explain cognitive functioning in purely internalistic and neurocentric terms. The purpose of the present paper is to challenge the view that PP entails a wholesale rejection of positions that are interested in the embodied, embedded, extended, or enactive dimensions of cognitive processes. I will argue that Hohwy’s argument from analogy, which forces an evidentiary boundary into the picture, lacks the argumentative resources to (...)
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  • Explanatory Pluralism: An Unrewarding Prediction Error for Free Energy Theorists.Matteo Colombo & Cory Wright - 2017 - Brain and Cognition 112:3–12.
    Courtesy of its free energy formulation, the hierarchical predictive processing theory of the brain (PTB) is often claimed to be a grand unifying theory. To test this claim, we examine a central case: activity of mesocorticolimbic dopaminergic (DA) systems. After reviewing the three most prominent hypotheses of DA activity—the anhedonia, incentive salience, and reward prediction error hypotheses—we conclude that the evidence currently vindicates explanatory pluralism. This vindication implies that the grand unifying claims of advocates of PTB are unwarranted. More generally, (...)
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  • How Could a Rational Analysis Model Explain?Samuli Reijula - 2017 - COGSCI 2017: 39th Annual Conference of the Cognitive Science Society,.
    Rational analysis is an influential but contested account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this paper, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances.
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  • Bayesian Cognitive Science, Predictive Brains, and the Nativism Debate.Matteo Colombo - 2018 - Synthese 195 (11):4817-4838.
    The rise of Bayesianism in cognitive science promises to shape the debate between nativists and empiricists into more productive forms—or so have claimed several philosophers and cognitive scientists. The present paper explicates this claim, distinguishing different ways of understanding it. After clarifying what is at stake in the controversy between nativists and empiricists, and what is involved in current Bayesian cognitive science, the paper argues that Bayesianism offers not a vindication of either nativism or empiricism, but one way to talk (...)
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  • Solely Generic Phenomenology.Ned Block - 2015 - Open MIND 2015.
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  • Bayesian Cognitive Science, Monopoly, and Neglected Frameworks.Matteo Colombo, Lee Elkin & Stephan Hartmann - unknown
    A widely shared view in the cognitive sciences is that discovering and assessing explanations of cognitive phenomena whose production involves uncertainty should be done in a Bayesian framework. One assumption supporting this modelling choice is that Bayes provides the best approach for representing uncertainty. However, it is unclear that Bayes possesses special epistemic virtues over alternative modelling frameworks, since a systematic comparison has yet to be attempted. Currently, it is then premature to assert that cognitive phenomena involving uncertainty are best (...)
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  • Bayesian Cognitive Science, Predictive Brains, and the Nativism Debate.Matteo Colombo - 2017 - Synthese:1-22.
    The rise of Bayesianism in cognitive science promises to shape the debate between nativists and empiricists into more productive forms—or so have claimed several philosophers and cognitive scientists. The present paper explicates this claim, distinguishing different ways of understanding it. After clarifying what is at stake in the controversy between nativists and empiricists, and what is involved in current Bayesian cognitive science, the paper argues that Bayesianism offers not a vindication of either nativism or empiricism, but one way to talk (...)
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  • What, When and How Do Rational Analysis Models Explain?Samuli Poyhonen - unknown
    Probabilistic modeling is a highly influential method of theorizing in cognitive science. Rational analysis is an account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this article, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances.
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  • Computational Cognitive Neuroscience.Carlos Zednik - forthcoming - In Mark Sprevak & Matteo Colombo (eds.), The Routledge Handbook of the Computational Mind. Routledge.
    This chapter provides an overview of the basic research strategies and analytic techniques deployed in computational cognitive neuroscience. On the one hand, “top-down” strategies are used to infer, from formal characterizations of behavior and cognition, the computational properties of underlying neural mechanisms. On the other hand, “bottom-up” research strategies are used to identify neural mechanisms and to reconstruct their computational capacities. Both of these strategies rely on experimental techniques familiar from other branches of neuroscience, including functional magnetic resonance imaging, single-cell (...)
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  • Bayesian Reverse-Engineering Considered as a Research Strategy for Cognitive Science.Carlos Zednik & Frank Jäkel - 2016 - Synthese 193 (12):3951-3985.
    Bayesian reverse-engineering is a research strategy for developing three-level explanations of behavior and cognition. Starting from a computational-level analysis of behavior and cognition as optimal probabilistic inference, Bayesian reverse-engineers apply numerous tweaks and heuristics to formulate testable hypotheses at the algorithmic and implementational levels. In so doing, they exploit recent technological advances in Bayesian artificial intelligence, machine learning, and statistics, but also consider established principles from cognitive psychology and neuroscience. Although these tweaks and heuristics are highly pragmatic in character and (...)
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  • Descending Marr's Levels: Standard Observers Are No Panacea.Carlos Zednik & Frank Jäkel - 2018 - Behavioral and Brain Sciences 41.
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