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  1. Does Perceptual Consciousness Overflow Cognitive Access? The Challenge From Probabilistic, Hierarchical Processes.Steven Gross & Jonathan Flombaum - 2017 - Mind and Language 32 (3):358-391.
    Does perceptual consciousness require cognitive access? Ned Block argues that it does not. Central to his case are visual memory experiments that employ post-stimulus cueing—in particular, Sperling's classic partial report studies, change-detection work by Lamme and colleagues, and a recent paper by Bronfman and colleagues that exploits our perception of ‘gist’ properties. We argue contra Block that these experiments do not support his claim. Our reinterpretations differ from previous critics' in challenging as well a longstanding and common view of visual (...)
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  • Phenomenal Variability and Introspective Reliability.Jakob Hohwy - 2011 - Mind and Language 26 (3):261-286.
    There is surprising evidence that introspection of our phenomenal states varies greatly between individuals and within the same individual over time. This puts pressure on the notion that introspection gives reliable access to our own phenomenology: introspective unreliability would explain the variability, while assuming that the underlying phenomenology is stable. I appeal to a body of neurocomputational, Bayesian theory and neuroimaging findings to provide an alternative explanation of the evidence: though some limited testing conditions can cause introspection to be unreliable, (...)
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  • Perception and Cognition Are Largely Independent, but Still Affect Each Other in Systematic Ways: Arguments From Evolution and the Consciousness-Attention Dissociation.Carlos Montemayor & Harry Haroutioun Haladjian - 2017 - Frontiers in Psychology 8:1-15.
    The main thesis of this paper is that two prevailing theories about cognitive penetration are too extreme, namely, the view that cognitive penetration is pervasive and the view that there is a sharp and fundamental distinction between cognition and perception, which precludes any type of cognitive penetration. These opposite views have clear merits and empirical support. To eliminate this puzzling situation, we present an alternative theoretical approach that incorporates the merits of these views into a broader and more nuanced explanatory (...)
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  • Dream to Predict? REM Dreaming as Prospective Coding.Sue Llewellyn - 2015 - Frontiers in Psychology 6.
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  • The New Tweety Puzzle: Arguments Against Monistic Bayesian Approaches in Epistemology and Cognitive Science.Matthias Unterhuber & Gerhard Schurz - 2013 - Synthese 190 (8):1407-1435.
    In this paper we discuss the new Tweety puzzle. The original Tweety puzzle was addressed by approaches in non-monotonic logic, which aim to adequately represent the Tweety case, namely that Tweety is a penguin and, thus, an exceptional bird, which cannot fly, although in general birds can fly. The new Tweety puzzle is intended as a challenge for probabilistic theories of epistemic states. In the first part of the paper we argue against monistic Bayesians, who assume that epistemic states can (...)
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  • A Framework for the First‑Person Internal Sensation of Visual Perception in Mammals and a Comparable Circuitry for Olfactory Perception in Drosophila.Kunjumon Vadakkan - 2015 - Springerplus 4 (833):1-23.
    Perception is a first-person internal sensation induced within the nervous system at the time of arrival of sensory stimuli from objects in the environment. Lack of access to the first-person properties has limited viewing perception as an emergent property and it is currently being studied using third-person observed findings from various levels. One feasible approach to understand its mechanism is to build a hypothesis for the specific conditions and required circuit features of the nodal points where the mechanistic operation of (...)
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  • Probabilistic Models of Language Processing and Acquisition.Nick Chater & Christopher D. Manning - 2006 - Trends in Cognitive Sciences 10 (7):335-344.
  • A Hierarchical Generative Framework of Language Processing: Linking Language Perception, Interpretation, and Production Abnormalities in Schizophrenia.Meredith Brown & Gina R. Kuperberg - 2015 - Frontiers in Human Neuroscience 9.
  • Reconciling Intuitive Physics and Newtonian Mechanics for Colliding Objects.Adam N. Sanborn, Vikash K. Mansinghka & Thomas L. Griffiths - 2013 - Psychological Review 120 (2):411-437.
  • Accuracy of Motor Error Predictions for Different Sensory Signals.Michael Joch, Mathias Hegele, Heiko Maurer, Hermann Müller & Lisa K. Maurer - 2018 - Frontiers in Psychology 9.
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  • The Coherent Organization of Mental Life Depends on Mechanisms for Context-Sensitive Gain-Control That Are Impaired in Schizophrenia.William A. Phillips & Steven M. Silverstein - 2013 - Frontiers in Psychology 4.
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  • Radical Predictive Processing.Andy Clark - 2015 - Southern Journal of Philosophy 53 (S1):3-27.
  • Active Inference, Enactivism and the Hermeneutics of Social Cognition.Shaun Gallagher & Micah Allen - 2018 - Synthese 195 (6):2627-2648.
    We distinguish between three philosophical views on the neuroscience of predictive models: predictive coding, predictive processing and predictive engagement. We examine the concept of active inference under each model and then ask how this concept informs discussions of social cognition. In this context we consider Frith and Friston’s proposal for a neural hermeneutics, and we explore the alternative model of enactivist hermeneutics.
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  • A Bayesian Framework for Knowledge Attribution: Evidence From Semantic Integration.Derek Powell, Zachary Horne, Ángel Pinillos & Keith Holyoak - 2015 - Cognition 139:92-104.
    We propose a Bayesian framework for the attribution of knowledge, and apply this framework to generate novel predictions about knowledge attribution for different types of “Gettier cases”, in which an agent is led to a justified true belief yet has made erroneous assumptions. We tested these predictions using a paradigm based on semantic integration. We coded the frequencies with which participants falsely recalled the word “thought” as “knew” (or a near synonym), yielding an implicit measure of conceptual activation. Our experiments (...)
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  • Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults.Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik - 2011 - Cognitive Science 35 (8):1407-1455.
    People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which (...)
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  • Are We Predictive Engines? Perils, Prospects, and the Puzzle of the Porous Perceiver.Andy Clark - 2013 - Behavioral and Brain Sciences 36 (3):233-253.
    The target article sketched and explored a mechanism (action-oriented predictive processing) most plausibly associated with core forms of cortical processing. In assessing the attractions and pitfalls of the proposal we should keep that element distinct from larger, though interlocking, issues concerning the nature of adaptive organization in general.
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  • Bayesian Decision Theory in Sensorimotor Control.Konrad P. Körding & Daniel M. Wolpert - 2006 - Trends in Cognitive Sciences 10 (7):319-326.
  • Probabilistic Models of Cognition: Conceptual Foundations.Nick Chater & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):287-291.
    Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, ‘sophisticated’ probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, explore (...)
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  • Perspectives on Modeling in Cognitive Science.Richard M. Shiffrin - 2010 - Topics in Cognitive Science 2 (4):736-750.
    This commentary gives a personal perspective on modeling and modeling developments in cognitive science, starting in the 1950s, but focusing on the author’s personal views of modeling since training in the late 1960s, and particularly focusing on advances since the official founding of the Cognitive Science Society. The range and variety of modeling approaches in use today are remarkable, and for many, bewildering. Yet to come to anything approaching adequate insights into the infinitely complex fields of mind, brain, and intelligent (...)
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  • Popper's Severity of Test as an Intuitive Probabilistic Model of Hypothesis Testing.Fenna H. Poletiek - 2009 - Behavioral and Brain Sciences 32 (1):99-100.
    Severity of Test (SoT) is an alternative to Popper's logical falsification that solves a number of problems of the logical view. It was presented by Popper himself in 1963. SoT is a less sophisticated probabilistic model of hypothesis testing than Oaksford & Chater's (O&C's) information gain model, but it has a number of striking similarities. Moreover, it captures the intuition of everyday hypothesis testing.
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  • The Uncertain Reasoner: Bayes, Logic, and Rationality.Mike Oaksford & Nick Chater - 2009 - Behavioral and Brain Sciences 32 (1):105-120.
    Human cognition requires coping with a complex and uncertain world. This suggests that dealing with uncertainty may be the central challenge for human reasoning. In Bayesian Rationality we argue that probability theory, the calculus of uncertainty, is the right framework in which to understand everyday reasoning. We also argue that probability theory explains behavior, even on experimental tasks that have been designed to probe people's logical reasoning abilities. Most commentators agree on the centrality of uncertainty; some suggest that there is (...)
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  • Whatever Next? Predictive Brains, Situated Agents, and the Future of Cognitive Science.Andy Clark - 2013 - Behavioral and Brain Sciences 36 (3):181-204.
    Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to (...)
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  • Probabilistic Models of Cognition: Where Next?Nick Chater, Joshua B. Tenenbaum & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):292-293.
  • The Close Proximity of Threat: Altered Distance Perception in the Anticipation of Pain.Abby Tabor, Mark J. Catley, Simon C. Gandevia, Michael A. Thacker, Charles Spence & G. L. Moseley - 2015 - Frontiers in Psychology 6.
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  • Perception of Features and Perception of Objects.Daniel Burnston & Jonathan Cohen - 2012 - Croatian Journal of Philosophy 12 (3):283-314.
    There is a long and distinguished tradition in philosophy and psychology according to which the mind’s fundamental, foundational connection to the world is made by connecting perceptually to features of objects. On this picture, which we’ll call feature prioritarianism, minds like ours first make contact with the colors, shapes, and sizes of distal items, and then, only on the basis of the representations so obtained, build up representations of the objects that bear these features. The feature priority view maintains, then, (...)
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  • Statistically Optimal Perception and Learning: From Behavior to Neural Representations.József Fiser, Pietro Berkes, Gergő Orbán & Máté Lengyel - 2010 - Trends in Cognitive Sciences 14 (3):119-130.
  • A Bayesian Framework for Word Segmentation: Exploring the Effects of Context.Sharon Goldwater, Thomas L. Griffiths & Mark Johnson - 2009 - Cognition 112 (1):21-54.
  • A Rational Account of Perceptual Compensation for Coarticulation.Morgan Sonderegger & Alan Yu - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 375--380.
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  • The Predictive Mind and the Experience of Visual Art Work.Ladislav Kesner - 2014 - Frontiers in Psychology 5.
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  • The Imaginary Fundamentalists: The Unshocking Truth About Bayesian Cognitive Science.Nick Chater, Noah Goodman, Thomas L. Griffiths, Charles Kemp, Mike Oaksford & Joshua B. Tenenbaum - 2011 - Behavioral and Brain Sciences 34 (4):194-196.
    If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.
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  • The Bayesian Boom: Good Thing or Bad?Ulrike Hahn - 2014 - Frontiers in Psychology 5.
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  • Active Inference and Free Energy.Karl Friston - 2013 - Behavioral and Brain Sciences 36 (3):212-213.
    Why do brains have so many connections? The principles exposed by Andy Clark provide answers to questions like this by appealing to the notion that brains distil causal regularities in the sensorium and embody them in models of their world. For example, connections embody the fact that causes have particular consequences. This commentary considers the imperatives for this form of embodiment.
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  • Sculpting the Space of Actions. Explaining Human Action by Integrating Intentions and Mechanisms.Machiel Keestra - 2014 - Dissertation, University of Amsterdam
    How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...)
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  • The Ouroboros Model.Dr Knud Thomsen - unknown
    At the core of the Ouroboros Model lies a self-referential recursive process with alternating phases of data acquisition and evaluation. Memory entries are organized in schemata. Activation at a time of part of a schema biases the whole structure and, in particular, missing features, thus triggering expectations. An iterative recursive monitor process termed ‘consumption analysis’ is then checking how well such expectations fit with successive activations. A measure for the goodness of fit, “emotion”, provides feedback as (self-) monitoring signal. Contradictions (...)
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  • Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. [REVIEW]Frederick Eberhardt & David Danks - 2011 - Minds and Machines 21 (3):389-410.
    Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the (...)
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  • Regimes of Expectations: An Active Inference Model of Social Conformity and Human Decision Making.Axel Constant, Maxwell J. D. Ramstead, Samuel P. L. Veissière & Karl Friston - 2019 - Frontiers in Psychology 10.
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  • Gravity as a Strong Prior: Implications for Perception and Action.López-Moliner Joan & Jörges Björn - 2017 - Frontiers in Human Neuroscience 11.
  • Is Statistical Learning Constrained by Lower Level Perceptual Organization?Lauren L. Emberson, Ran Liu & Jason D. Zevin - 2013 - Cognition 128 (1):82-102.
  • The Non-­‐Redundant Contributions of Marr’s Three Levels of Analysis for Explaining Information Processing Mechanisms.William Bechtel & Oron Shagrir - 2015 - Topics in Cognitive Science 7 (2):312-322.
    Are all three of Marr's levels needed? Should they be kept distinct? We argue for the distinct contributions and methodologies of each level of analysis. It is important to maintain them because they provide three different perspectives required to understand mechanisms, especially information-processing mechanisms. The computational perspective provides an understanding of how a mechanism functions in broader environments that determines the computations it needs to perform. The representation and algorithmic perspective offers an understanding of how information about the environment is (...)
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  • Evaluating the Inverse Reasoning Account of Object Discovery.Christopher D. Carroll & Charles Kemp - 2015 - Cognition 139:130-153.
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  • Consciousness for the Ouroboros Model.Knud Thomsen - 2011 - International Journal of Machine Consciousness 3 (01):163-175.
  • Cognitive Penetration and the Tribunal of Experience.Jona Vance - 2015 - Review of Philosophy and Psychology 6 (4):641-663.
    Perception purports to help you gain knowledge of the world even if the world is not the way you expected it to be. Perception also purports to be an independent tribunal against which you can test your beliefs. It is natural to think that in order to serve these and other central functions, perceptual representations must not causally depend on your prior beliefs and expectations. In this paper, I clarify and then argue against the natural thought above. All perceptual systems (...)
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  • Scrutinizing Visual Images: The Role of Gaze in Mental Imagery and Memory.Bruno Laeng, Ilona M. Bloem, Stefania D’Ascenzo & Luca Tommasi - 2014 - Cognition 131 (2):263-283.
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  • A Multi‐Factor Account of Degrees of Awareness.Peter Fazekas & Morten Overgaard - 2018 - Cognitive Science 42 (6):1833-1859.
    In this paper we argue that awareness comes in degrees, and we propose a novel multi-factor account that spans both subjective experiences and perceptual representations. At the subjective level, we argue that conscious experiences can be degraded by being fragmented, less salient, too generic, or flash-like. At the representational level, we identify corresponding features of perceptual representations—their availability for working memory, intensity, precision, and stability—and argue that the mechanisms that affect these features are what ultimately modulate the degree of awareness. (...)
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  • Bayesian Intractability Is Not an Ailment That Approximation Can Cure.Johan Kwisthout, Todd Wareham & Iris van Rooij - 2011 - Cognitive Science 35 (5):779-784.
    Bayesian models are often criticized for postulating computations that are computationally intractable (e.g., NP-hard) and therefore implausibly performed by our resource-bounded minds/brains. Our letter is motivated by the observation that Bayesian modelers have been claiming that they can counter this charge of “intractability” by proposing that Bayesian computations can be tractably approximated. We would like to make the cognitive science community aware of the problematic nature of such claims. We cite mathematical proofs from the computer science literature that show intractable (...)
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  • Prior Divergence: Do Researchers and Participants Share the Same Prior Probability Distributions?Christina Fang, Sari Carp & Zur Shapira - 2011 - Cognitive Science 35 (4):744-762.
    Do participants bring their own priors to an experiment? If so, do they share the same priors as the researchers who design the experiment? In this article, we examine the extent to which self-generated priors conform to experimenters’ expectations by explicitly asking participants to indicate their own priors in estimating the probability of a variety of events. We find in Study 1 that despite being instructed to follow a uniform distribution, participants appear to have used their own priors, which deviated (...)
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  • Feature Selection for Inductive Generalization.Na-Yung Yu, Takashi Yamauchi, Huei-Fang Yang, Yen-Lin Chen & Ricardo Gutierrez-Osuna - 2010 - Cognitive Science 34 (8):1574-1593.
    Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our (...)
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  • The Perception of Size and Shape.Christopher S. Hill & David J. Bennett - 2008 - Philosophical Issues 18 (1):294-315.
  • Seeing Other Minds: Attributed Mental States Influence Perception.Christoph Teufel, Paul C. Fletcher & Greg Davis - 2010 - Trends in Cognitive Sciences 14 (8):376-382.
  • Was Reid a Direct Realist?Jake Quilty-Dunn - 2013 - British Journal for the History of Philosophy 21 (2):302 - 323.
    There are issues in Reid scholarship as well as the primary texts that seem to suggest that Reid is not a direct realist about visual perception. In this paper, I examine two key issues ? colour perception and visible figure ? and attempt to defend the direct realism of Reid's theory through an interpretation of ?directness? as well as what Reid calls ?acquired perception?, which is ?mediate? in that it requires prior perception of signs, but nonetheless constitutes direct perception.
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