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  1. Normative Theories of Argumentation: Are Some Norms Better Than Others?Adam Corner & Ulrike Hahn - 2013 - Synthese 190 (16):3579-3610.
    Norms—that is, specifications of what we ought to do—play a critical role in the study of informal argumentation, as they do in studies of judgment, decision-making and reasoning more generally. Specifically, they guide a recurring theme: are people rational? Though rules and standards have been central to the study of reasoning, and behavior more generally, there has been little discussion within psychology about why (or indeed if) they should be considered normative despite the considerable philosophical literature that bears on this (...)
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  • Prospects for Probabilistic Theories of Natural Information.Ulrich E. Stegmann - 2015 - Erkenntnis 80 (4):869-893.
    Much recent work on natural information has focused on probabilistic theories, which construe natural information as a matter of probabilistic relations between events or states. This paper assesses three variants of probabilistic theories. I distinguish between probabilistic theories as attempts to reveal why probabilistic relations are important for human and non-human animals and as explications of the information concept employed in the sciences. I argue that the strength of probabilistic theories lies in the first project. Probability-raising can enable organisms to (...)
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  • GIRL Special Issue Introduction.Justine Jacot & Philip Pärnamets - 2018 - Synthese 195 (2):483-490.
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  • Probabilities, Beliefs, and Dual Processing: The Paradigm Shift in the Psychology of Reasoning.Shira Elqayam & David Over - 2012 - Mind and Society 11 (1):27-40.
    In recent years, the psychology of reasoning has been undergoing a paradigm shift, with general Bayesian, probabilistic approaches replacing the older, much more restricted binary logic paradigm. At the same time, dual processing theories have been gaining influence. We argue that these developments should be integrated and moreover that such integration is already underway. The new reasoning paradigm should be grounded in dual processing for its algorithmic level of analysis just as it uses Bayesian theory for its computational level of (...)
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  • Norm Conflicts and Conditionals.Niels Skovgaard-Olsen, David Kellen, Ulrike Hahn & Karl Christoph Klauer - 2019 - Psychological Review (5):611-633.
    Suppose that two competing norms, N1 and N2, can be identified such that a given person’s response can be interpreted as correct according to N1 but incorrect according to N2. Which of these two norms, if any, should one use to interpret such a response? In this paper we seek to address this fundamental problem by studying individual variation in the interpretation of conditionals by establishing individual profiles of the participants based on their case judgments and reflective attitudes. To investigate (...)
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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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  • Assessing the Role of the ‘Unity Assumption’ on Multisensory Integration: A Review.Yi-Chuan Chen & Charles Spence - 2017 - Frontiers in Psychology 8.
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  • 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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  • Explaining Cognitive Phenomena with Internal Representations: A Mechanistic Perspective.Paweł Gładziejewski - 2015 - Studies in Logic, Grammar and Rhetoric 40 (1):63-90.
    Despite the fact that the notion of internal representation has - at least according to some - a fundamental role to play in the sciences of the mind, not only has its explanatory utility been under attack for a while now, but it also remains unclear what criteria should an explanation of a given cognitive phenomenon meet to count as a representational explanation in the first place. The aim of this article is to propose a solution to this latter problem. (...)
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  • Tuning Your Priors to the World.Jacob Feldman - 2013 - Topics in Cognitive Science 5 (1):13-34.
    The idea that perceptual and cognitive systems must incorporate knowledge about the structure of the environment has become a central dogma of cognitive theory. In a Bayesian context, this idea is often realized in terms of “tuning the prior”—widely assumed to mean adjusting prior probabilities so that they match the frequencies of events in the world. This kind of “ecological” tuning has often been held up as an ideal of inference, in fact defining an “ideal observer.” But widespread as this (...)
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  • Conditionals, Context, and the Suppression Effect.Fabrizio Cariani & Lance J. Rips - 2017 - Cognitive Science 41 (3):540-589.
    Modus ponens is the argument from premises of the form If A, then B and A to the conclusion B. Nearly all participants agree that the modus ponens conclusion logically follows when the argument appears in this Basic form. However, adding a further premise can lower participants’ rate of agreement—an effect called suppression. We propose a theory of suppression that draws on contemporary ideas about conditional sentences in linguistics and philosophy. Semantically, the theory assumes that people interpret an indicative conditional (...)
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  • Model-Based Theorizing in Cognitive Neuroscience.Elizabeth Irvine - 2016 - British Journal for the Philosophy of Science 67 (1):143-168.
    Weisberg and Godfrey-Smith 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 based on the (...)
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  • Character and Theory of Mind: An Integrative Approach.Evan Westra - 2018 - Philosophical Studies 175 (5):1217-1241.
    Traditionally, theories of mindreading have focused on the representation of beliefs and desires. However, decades of social psychology and social neuroscience have shown that, in addition to reasoning about beliefs and desires, human beings also use representations of character traits to predict and interpret behavior. While a few recent accounts have attempted to accommodate these findings, they have not succeeded in explaining the relation between trait attribution and belief-desire reasoning. On my account, character-trait attribution is part of a hierarchical system (...)
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  • Chains of Inferences and the New Paradigm in the Psychology of Reasoning.Ulf Hlobil - 2016 - Review of Philosophy and Psychology 7 (1):1-16.
    The new paradigm in the psychology of reasoning draws on Bayesian formal frameworks, and some advocates of the new paradigm think of these formal frameworks as providing a computational-level theory of rational human inference. I argue that Bayesian theories should not be seen as providing a computational-level theory of rational human inference, where by “Bayesian theories” I mean theories that claim that all rational credal states are probabilistically coherent and that rational adjustments of degrees of belief in the light of (...)
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  • Commentary/Elqayam & Evans: Subtracting “Ought” From “Is”.Natalie Gold, Andrew M. Colman & Briony D. Pulfordb - 2011 - Behavioral and Brain Sciences 34 (5).
    Normative theories can be useful in developing descriptive theories, as when normative subjective expected utility theory is used to develop descriptive rational choice theory and behavioral game theory. “Ought” questions are also the essence of theories of moral reasoning, a domain of higher mental processing that could not survive without normative considerations.
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  • A Unified Cognitive Model of Visual Filling-In Based on an Emergic Network Architecture.David Pierre Leibovitz - 2013 - Dissertation, Carleton University
    The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well). -/- ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can interact to raise (...)
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  • Perceptual Consciousness and Cognitive Access From the Perspective of Capacity-Unlimited Working Memory.Steven Gross - forthcoming - Philosophical Transactions of the Royal Society B.
    Theories of consciousness divide over whether perceptual consciousness is rich or sparse in specific representational content and whether it requires cognitive access. These two issues are often treated in tandem because of a shared assumption that the representational capacity of cognitive access is fairly limited. Recent research on working memory challenges this shared assumption. This paper argues that abandoning the assumption undermines post-cue-based “overflow” arguments, according to which perceptual conscious is rich and does not require cognitive access. Abandoning it also (...)
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  • Troubles with Bayesianism: An Introduction to the Psychological Immune System.Eric Mandelbaum - 2019 - Mind and Language 34 (2):141-157.
    A Bayesian mind is, at its core, a rational mind. Bayesianism is thus well-suited to predict and explain mental processes that best exemplify our ability to be rational. However, evidence from belief acquisition and change appears to show that we do not acquire and update information in a Bayesian way. Instead, the principles of belief acquisition and updating seem grounded in maintaining a psychological immune system rather than in approximating a Bayesian processor.
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  • The Practical and Principled Problems With Educational Neuroscience.Jeffrey S. Bowers - forthcoming - Psychological Review.
  • 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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  • Surprisingly Rational: Probability Theory Plus Noise Explains Biases in Judgment.Fintan Costello & Paul Watts - 2014 - Psychological Review 121 (3):463-480.
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  • Goal-Directed Decision Making as Probabilistic Inference: A Computational Framework and Potential Neural Correlates.Alec Solway & Matthew M. Botvinick - 2012 - Psychological Review 119 (1):120-154.
  • Language Processing as Cue Integration: Grounding the Psychology of Language in Perception and Neurophysiology.Andrea E. Martin - 2016 - Frontiers in Psychology 7.
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  • ¿Es el principio de la energía libre una teoría normativa o descriptiva de la cognición?Eduardo A. Aponte - 2015 - Pensamiento y Cultura 18 (1):6-45.
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  • Testing Bayesian and Heuristic Predictions of Mass Judgments of Colliding Objects.Adam N. Sanborn - 2014 - Frontiers in Psychology 5.
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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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  • The Influence of Initial Beliefs on Judgments of Probability.Erica C. Yu & David A. Lagnado - 2012 - Frontiers in Psychology 3.
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  • From Cognitivism to Autopoiesis: Towards a Computational Framework for the Embodied Mind.Micah Allen & Karl J. Friston - 2018 - Synthese 195 (6):2459-2482.
    Predictive processing approaches to the mind are increasingly popular in the cognitive sciences. This surge of interest is accompanied by a proliferation of philosophical arguments, which seek to either extend or oppose various aspects of the emerging framework. In particular, the question of how to position predictive processing with respect to enactive and embodied cognition has become a topic of intense debate. While these arguments are certainly of valuable scientific and philosophical merit, they risk underestimating the variety of approaches gathered (...)
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  • Beyond Single‐Level Accounts: The Role of Cognitive Architectures in Cognitive Scientific Explanation.Richard P. Cooper & David Peebles - 2015 - Topics in Cognitive Science 7 (2):243-258.
    We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level or Marr's implementational level and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system into a set (...)
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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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  • Can Quantum Probability Provide a New Direction for Cognitive Modeling?Emmanuel M. Pothos & Jerome R. Busemeyer - 2013 - Behavioral and Brain Sciences 36 (3):255-274.
    Classical (Bayesian) probability (CP) theory has led to an influential research tradition for modeling cognitive processes. Cognitive scientists have been trained to work with CP principles for so long that it is hard even to imagine alternative ways to formalize probabilities. However, in physics, quantum probability (QP) theory has been the dominant probabilistic approach for nearly 100 years. Could QP theory provide us with any advantages in cognitive modeling as well? Note first that both CP and QP theory share the (...)
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  • Editorial: From Is to Ought: The Place of Normative Models in the Study of Human Thought.Shira Elqayam & David E. Over - 2016 - Frontiers in Psychology 7.
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  • Sensorimotor Grounding of Musical Embodiment and the Role of Prediction: A Review.Pieter-Jan Maes - 2016 - Frontiers in Psychology 7.
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  • Will Understanding Vision Require a Wholly Empirical Paradigm?Dale Purves, Yaniv Morgenstern & William T. Wojtach - 2015 - Frontiers in Psychology 6.
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  • Throwing Out the Bayesian Baby with the Optimal Bathwater: Response to Endress.Michael C. Frank - 2013 - Cognition 128 (3):417-423.
  • 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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  • The Predictive Mind and the Experience of Visual Art Work.Ladislav Kesner - 2014 - Frontiers in Psychology 5.
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  • The Bayesian Boom: Good Thing or Bad?Ulrike Hahn - 2014 - Frontiers in Psychology 5.
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  • Modeling Language and Cognition with Deep Unsupervised Learning: A Tutorial Overview.Marco Zorzi, Alberto Testolin & Ivilin P. Stoianov - 2013 - Frontiers in Psychology 4.
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  • Measuring the Crowd Within Again: A Pre-Registered Replication Study.Sara Steegen, Laura Dewitte, Francis Tuerlinckx & Wolf Vanpaemel - 2014 - Frontiers in Psychology 5.
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  • Towards a Descriptivist Psychology of Reasoning and Decision Making.Jonathan St Bt Evans & Shira Elqayam - 2011 - Behavioral and Brain Sciences 34 (5):275-290.
    Our target article identified normativism as the view that rationality should be evaluated against unconditional normative standards. We believe this to be entrenched in the psychological study of reasoning and decision making and argued that it is damaging to this empirical area of study, calling instead for a descriptivist psychology of reasoning and decision making. The views of 29 commentators (from philosophy and cognitive science as well as psychology) were mixed, including some staunch defences of normativism, but also a number (...)
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  • Norms and High-Level Cognition: Consequences, Trends, and Antidotes.Simon McNair & Aidan Feeney - 2011 - Behavioral and Brain Sciences 34 (5):260-261.
    We are neither as pessimistic nor as optimistic as Elqayam & Evans (E&E). The consequences of normativism have not been uniformly disastrous, even among the examples they consider. However, normativism won't be going away any time soon and in the literature on causal Bayes nets new debates about normativism are emerging. Finally, we suggest that to concentrate on expert reasoners as an antidote to normativism may limit the contribution of research on thinking to basic psychological science.
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  • Grounding Quantum Probability in Psychological Mechanism.Bradley C. Love - 2013 - Behavioral and Brain Sciences 36 (3):296-296.
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  • 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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  • Why Are There Descriptive Norms? Because We Looked for Them.Ryan Muldoon, Chiara Lisciandra & Stephan Hartmann - 2014 - Synthese 191 (18):4409-4429.
    In this work, we present a mathematical model for the emergence of descriptive norms, where the individual decision problem is formalized with the standard Bayesian belief revision machinery. Previous work on the emergence of descriptive norms has relied on heuristic modeling. In this paper we show that with a Bayesian model we can provide a more general picture of the emergence of norms, which helps to motivate the assumptions made in heuristic models. In our model, the priors formalize the belief (...)
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  • Solely Generic Phenomenology.Ned Block - 2015 - Open MIND 2015.
  • 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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  • 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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  • Judging the Probability of Hypotheses Versus the Impact of Evidence: Which Form of Inductive Inference Is More Accurate and Time‐Consistent?Katya Tentori, Nick Chater & Vincenzo Crupi - 2016 - Cognitive Science 40 (3):758-778.
    Inductive reasoning requires exploiting links between evidence and hypotheses. This can be done focusing either on the posterior probability of the hypothesis when updated on the new evidence or on the impact of the new evidence on the credibility of the hypothesis. But are these two cognitive representations equally reliable? This study investigates this question by comparing probability and impact judgments on the same experimental materials. The results indicate that impact judgments are more consistent in time and more accurate than (...)
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  • A Bayesian Perspective on Magnitude Estimation.Frederike H. Petzschner, Stefan Glasauer & Klaas E. Stephan - 2015 - Trends in Cognitive Sciences 19 (5):285-293.