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  1. Bayes in the Brain—On Bayesian Modelling in Neuroscience.Matteo Colombo & Peggy Seriès - 2012 - British Journal for the Philosophy of Science 63 (3):697-723.
    According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) From the use of Bayesian (...)
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  • Mental models, computational explanation and Bayesian cognitive science: Commentary on Knauff and Gazzo Castañeda (2023).Mike Oaksford - 2023 - Thinking and Reasoning 29 (3):371-382.
    Knauff and Gazzo Castañeda (2022) object to using the term “new paradigm” to describe recent developments in the psychology of reasoning. This paper concedes that the Kuhnian term “paradigm” may be queried. What cannot is that the work subsumed under this heading is part of a new, progressive movement that spans the brain and cognitive sciences: Bayesian cognitive science. Sampling algorithms and Bayes nets used to explain biases in JDM can implement the Bayesian new paradigm approach belying any advantages of (...)
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  • Confirmation bias without rhyme or reason.Matthias Michel & Megan A. K. Peters - 2020 - Synthese 199 (1-2):2757-2772.
    Having a confirmation bias sometimes leads us to hold inaccurate beliefs. So, the puzzle goes: why do we have it? According to the influential argumentative theory of reasoning, confirmation bias emerges because the primary function of reason is not to form accurate beliefs, but to convince others that we’re right. A crucial prediction of the theory, then, is that confirmation bias should be found only in the reasoning domain. In this article, we argue that there is evidence that confirmation bias (...)
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  • Can resources save rationality? ‘Anti-Bayesian’ updating in cognition and perception.Eric Mandelbaum, Isabel Won, Steven Gross & Chaz Firestone - 2020 - Behavioral and Brain Sciences 143:e16.
    Resource rationality may explain suboptimal patterns of reasoning; but what of “anti-Bayesian” effects where the mind updates in a direction opposite the one it should? We present two phenomena — belief polarization and the size-weight illusion — that are not obviously explained by performance- or resource-based constraints, nor by the authors’ brief discussion of reference repulsion. Can resource rationality accommodate them?
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  • Self-evaluation of decision-making: A general Bayesian framework for metacognitive computation.Stephen M. Fleming & Nathaniel D. Daw - 2017 - Psychological Review 124 (1):91-114.
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  • (1 other version)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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  • Perceptual Confidence.John Morrison - 2016 - Analytic Philosophy 57 (1):15-48.
    Perceptual Confidence is the view that perceptual experiences assign degrees of confidence. After introducing, clarifying, and motivating Perceptual Confidence, I catalogue some of its more interesting consequences, such as the way it blurs the distinction between veridical and illusory experiences, a distinction that is sometimes said to carry a lot of metaphysical weight. I also explain how Perceptual Confidence fills a hole in our best scientific theories of perception and why it implies that experiences don't have objective accuracy conditions.
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  • Rationalizable Irrationalities of Choice.Peter Dayan - 2014 - Topics in Cognitive Science 6 (2):204-228.
    Although seemingly irrational choice abounds, the rules governing these mis‐steps that might provide hints about the factors limiting normative behavior are unclear. We consider three experimental tasks, which probe different aspects of non‐normative choice under uncertainty. We argue for systematic statistical, algorithmic, and implementational sources of irrationality, including incomplete evaluation of long‐run future utilities, Pavlovian actions, and habits, together with computational and statistical noise and uncertainty. We suggest structural and functional adaptations that minimize their maladaptive effects.
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  • Two Kinds of Information Processing in Cognition.Mark Sprevak - 2020 - Review of Philosophy and Psychology 11 (3):591-611.
    What is the relationship between information and representation? Dating back at least to Dretske (1981), an influential answer has been that information is a rung on a ladder that gets one to representation. Representation is information, or representation is information plus some other ingredient. In this paper, I argue that this approach oversimplifies the relationship between information and representation. If one takes current probabilistic models of cognition seriously, information is connected to representation in a new way. It enters as a (...)
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  • Learning What to See in a Changing World.Katharina Schmack, Veith Weilnhammer, Jakob Heinzle, Klaas E. Stephan & Philipp Sterzer - 2016 - Frontiers in Human Neuroscience 10.
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  • Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model.Sebastian Bitzer, Hame Park, Felix Blankenburg & Stefan J. Kiebel - 2014 - Frontiers in Human Neuroscience 8.
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  • A Bayesian generative model for learning semantic hierarchies.Roni Mittelman, Min Sun, Benjamin Kuipers & Silvio Savarese - 2014 - Frontiers in Psychology 5.
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  • 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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  • (1 other version)Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2017 - British Journal for the Philosophy of Science 68 (2).
    It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be (...)
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  • Neural evidence for Bayesian trial-by-trial adaptation on the N400 during semantic priming.Nathaniel Delaney-Busch, Emily Morgan, Ellen Lau & Gina R. Kuperberg - 2019 - Cognition 187 (C):10-20.
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  • Representing Probability in Perception and Experience.Geoffrey Lee & Nico Orlandi - 2022 - Review of Philosophy and Psychology 13 (4):907-945.
    It is increasingly common in cognitive science and philosophy of perception to regard perceptual processing as a probabilistic engine, taking into account uncertainty in computing representations of the distal environment. Models of this kind often postulate probabilistic representations, or what we will call probabilistic states,. These are states that in some sense mark or represent information about the probabilities of distal conditions. It has also been argued that perceptual experience itself in some sense represents uncertainty (Morrison _Analytic Philosophy_ 57 (1): (...)
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  • Can the Brain Build Probability Distributions?Marcus Lindskog, Pär Nyström & Gustaf Gredebäck - 2021 - Frontiers in Psychology 12.
    How humans efficiently operate in a world with massive amounts of data that need to be processed, stored, and recalled has long been an unsettled question. Our physical and social environment needs to be represented in a structured way, which could be achieved by reducing input to latent variables in the form of probability distributions, as proposed by influential, probabilistic accounts of cognition and perception. However, few studies have investigated the neural processes underlying the brain’s potential ability to represent a (...)
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  • Overrepresentation of extreme events in decision making reflects rational use of cognitive resources.Falk Lieder, Thomas L. Griffiths & Ming Hsu - 2018 - Psychological Review 125 (1):1-32.
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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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  • Top-down influences on ambiguous perception: the role of stable and transient states of the observer.Lisa Scocchia, Matteo Valsecchi & Jochen Triesch - 2014 - Frontiers in Human Neuroscience 8.
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  • Looking for Image Statistics: Active Vision With Avatars in a Naturalistic Virtual Environment.Dominik Straub & Constantin A. Rothkopf - 2021 - Frontiers in Psychology 12.
    The efficient coding hypothesis posits that sensory systems are tuned to the regularities of their natural input. The statistics of natural image databases have been the topic of many studies, which have revealed biases in the distribution of orientations that are related to neural representations as well as behavior in psychophysical tasks. However, commonly used natural image databases contain images taken with a camera with a planar image sensor and limited field of view. Thus, these images do not incorporate the (...)
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  • A detailed comparison of optimality and simplicity in perceptual decision making.Shan Shen & Wei Ji Ma - 2016 - Psychological Review 123 (4):452-480.
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  • Learning what to expect.Peggy Seriès & Aaron R. Seitz - 2013 - Frontiers in Human Neuroscience 7.
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  • When the world becomes 'too real': a Bayesian explanation of autistic perception.Elizabeth Pellicano & David Burr - 2012 - Trends in Cognitive Sciences 16 (10):504-510.
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  • A decision network account of reasoning about other people’s choices.Alan Jern & Charles Kemp - 2015 - Cognition 142 (C):12-38.
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  • From Amateur to Professional: A Neuro-cognitive Model of Categories and Expert Development. [REVIEW]Michael S. Harré - 2013 - Minds and Machines 23 (4):443-472.
    The ability to group perceptual objects into functionally relevant categories is vital to our comprehension of the world. Such categorisation aids in how we search for objects in familiar scenes and how we identify an object and its likely uses despite never having seen that specific object before. The systems that mediate this process are only now coming to be understood through considerable research efforts combining neurological, psychological and behavioural studies. What is much less well understood are the differences between (...)
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  • Bayesian reasoning with ifs and ands and ors.Nicole Cruz, Jean Baratgin, Mike Oaksford & David E. Over - 2015 - Frontiers in Psychology 6.
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  • The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers.Matthew M. Botvinick - 2014 - Cognitive Science 38 (6):1249-1285.
    Cognitive control has long been one of the most active areas of computational modeling work in cognitive science. The focus on computational models as a medium for specifying and developing theory predates the PDP books, and cognitive control was not one of the areas on which they focused. However, the framework they provided has injected work on cognitive control with new energy and new ideas. On the occasion of the books' anniversary, we review computational modeling in the study of cognitive (...)
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