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  1. Subjective Probability as Sampling Propensity.Thomas Icard - 2016 - Review of Philosophy and Psychology 7 (4):863-903.
    Subjective probability plays an increasingly important role in many fields concerned with human cognition and behavior. Yet there have been significant criticisms of the idea that probabilities could actually be represented in the mind. This paper presents and elaborates a view of subjective probability as a kind of sampling propensity associated with internally represented generative models. The resulting view answers to some of the most well known criticisms of subjective probability, and is also supported by empirical work in neuroscience and (...)
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  • Rational Analysis, Intractability, and the Prospects of ‘as If’-Explanations.Iris van Rooij, Cory D. Wright, Johan Kwisthout & Todd Wareham - 2018 - Synthese 195 (2):491-510.
    Despite their success in describing and predicting cognitive behavior, the plausibility of so-called ‘rational explanations’ is often contested on the grounds of computational intractability. Several cognitive scientists have argued that such intractability is an orthogonal pseudoproblem, however, since rational explanations account for the ‘why’ of cognition but are agnostic about the ‘how’. Their central premise is that humans do not actually perform the rational calculations posited by their models, but only act as if they do. Whether or not the problem (...)
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  • How Intractability Spans the Cognitive and Evolutionary Levels of Explanation.Patricia Rich, Mark Blokpoel, Ronald Haan & Iris Rooij - 2020 - Topics in Cognitive Science 12 (4):1382-1402.
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  • How Do Beliefs Simplify Reasoning?Julia Staffel - 2019 - Noûs 53 (4):937-962.
    According to an increasingly popular epistemological view, people need outright beliefs in addition to credences to simplify their reasoning. Outright beliefs simplify reasoning by allowing thinkers to ignore small error probabilities. What is outright believed can change between contexts. It has been claimed that thinkers manage shifts in their outright beliefs and credences across contexts by an updating procedure resembling conditionalization, which I call pseudo-conditionalization (PC). But conditionalization is notoriously complicated. The claim that thinkers manage their beliefs via PC is (...)
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  • A Parallel Architecture Perspective on Pre-Activation and Prediction in Language Processing.Falk Huettig, Jenny Audring & Ray Jackendoff - 2022 - Cognition 224:105050.
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  • Self-Organization Takes Time Too.Iris van Rooij - 2012 - Topics in Cognitive Science 4 (1):63-71.
    Four articles in this issue of topiCS (volume 4, issue 1) argue against a computational approach in cognitive science in favor of a dynamical approach. I concur that the computational approach faces some considerable explanatory challenges. Yet the dynamicists’ proposal that cognition is self-organized seems to only go so far in addressing these challenges. Take, for instance, the hypothesis that cognitive behavior emerges when brain and body (re-)configure to satisfy task and environmental constraints. It is known that for certain systems (...)
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  • Bayes, Bounds, and Rational Analysis.Thomas F. Icard - 2018 - Philosophy of Science 85 (1):79-101.
    While Bayesian models have been applied to an impressive range of cognitive phenomena, methodological challenges have been leveled concerning their role in the program of rational analysis. The focus of the current article is on computational impediments to probabilistic inference and related puzzles about empirical confirmation of these models. The proposal is to rethink the role of Bayesian methods in rational analysis, to adopt an independently motivated notion of rationality appropriate for computationally bounded agents, and to explore broad conditions under (...)
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  • Being Realist About Bayes, and the Predictive Processing Theory of Mind.Matteo Colombo, Lee Elkin & Stephan Hartmann - 2021 - British Journal for the Philosophy of Science 72 (1):185-220.
    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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  • One and Done? Optimal Decisions From Very Few Samples.Edward Vul, Noah Goodman, Thomas L. Griffiths & Joshua B. Tenenbaum - 2014 - Cognitive Science 38 (4):599-637.
    In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian (...)
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  • Demons of Ecological Rationality.Maria Otworowska, Mark Blokpoel, Marieke Sweers, Todd Wareham & Iris van Rooij - 2018 - Cognitive Science 42 (3):1057-1066.
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  • Adversariality and Ideal Argumentation: A Second-Best Perspective.Marc-Kevin Daoust - 2021 - Topoi 40 (5):887-898.
    What is the relevance of ideals for determining virtuous argumentative practices? According to Bailin and Battersby (2016), the telos of argumentation is to improve our cognitive systems, and adversariality plays no role in ideally virtuous argumentation. Stevens and Cohen (2019) grant that ideal argumentation is collaborative, but stress that imperfect agents like us should not aim at approximating the ideal of argumentation. Accordingly, it can be virtuous, for imperfect arguers like us, to act as adversaries. Many questions are left unanswered (...)
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  • Naturalism, Tractability and the Adaptive Toolbox.Iris van Rooij, Todd Wareham, Marieke Sweers, Maria Otworowska, Ronald de Haan, Mark Blokpoel & Patricia Rich - 2019 - Synthese 198 (6):5749-5784.
    Many compelling examples have recently been provided in which people can achieve impressive epistemic success, e.g. draw highly accurate inferences, by using simple heuristics and very little information. This is possible by taking advantage of the features of the environment. The examples suggest an easy and appealing naturalization of rationality: on the one hand, people clearly can apply simple heuristics, and on the other hand, they intuitively ought do so when this brings them high accuracy at little cost.. The ‘ought-can’ (...)
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  • When Can Predictive Brains Be Truly Bayesian?Mark Blokpoel, Johan Kwisthout & Iris van Rooij - 2012 - Frontiers in Psychology 3.
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  • Taking Problem-Solving Seriously.Emmanuel Genot & Justine Jacot - unknown
    Instructions in Wason’s Selection Task underdetermine empirical subjects’ representation of the underlying problem, and its admissible solutions. We model the Selection Task as an interrogative learning problem, and reasoning to solutions as: selection of a representation of the problem; and: strategic planning from that representation. We argue that recovering Wason’s ‘normative’ selection is possible only if both stages are constrained further than they are by Wason’s formulation. We conclude comparing our model with other explanatory models, w.r.t. to empirical adequacy, and (...)
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  • Bayesian Cognitive Science, Monopoly, and Neglected Frameworks.Matteo Colombo & Stephan Hartmann - 2015 - British Journal for the Philosophy of Science 68 (2):451–484.
    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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  • 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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  • A Computational Model of the Cultural Co-Evolution of Language and Mindreading.Marieke Woensdregt, Chris Cummins & Kenny Smith - 2020 - Synthese 199 (1-2):1347-1385.
    Several evolutionary accounts of human social cognition posit that language has co-evolved with the sophisticated mindreading abilities of modern humans. It has also been argued that these mindreading abilities are the product of cultural, rather than biological, evolution. Taken together, these claims suggest that the evolution of language has played an important role in the cultural evolution of human social cognition. Here we present a new computational model which formalises the assumptions that underlie this hypothesis, in order to explore how (...)
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  • Holistic Reinforcement Learning: The Role of Structure and Attention.Angela Radulescu, Yael Niv & Ian Ballard - 2019 - Trends in Cognitive Sciences 23 (4):278-292.
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