Results for 'Bayesian inference of ACT‐R parameters'

970 found
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  1.  76
    Building an ACT‐R Reader for Eye‐Tracking Corpus Data.Jakub Dotlačil - 2018 - Topics in Cognitive Science 10 (1):144-160.
    Cognitive architectures have often been applied to data from individual experiments. In this paper, I develop an ACT-R reader that can model a much larger set of data, eye-tracking corpus data. It is shown that the resulting model has a good fit to the data for the considered low-level processes. Unlike previous related works, the model achieves the fit by estimating free parameters of ACT-R using Bayesian estimation and Markov-Chain Monte Carlo techniques, rather than by relying on the (...)
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  2.  38
    Capturing Dynamic Performance in a Cognitive Model: Estimating ACT‐R Memory Parameters With the Linear Ballistic Accumulator.Maarten van der Velde, Florian Sense, Jelmer P. Borst, Leendert van Maanen & Hedderik van Rijn - 2022 - Topics in Cognitive Science 14 (4):889-903.
    The parameters governing our behavior are in constant flux. Accurately capturing these dynamics in cognitive models poses a challenge to modelers. Here, we demonstrate a mapping of ACT-R's declarative memory onto the linear ballistic accumulator (LBA), a mathematical model describing a competition between evidence accumulation processes. We show that this mapping provides a method for inferring individual ACT-R parameters without requiring the modeler to build and fit an entire ACT-R model. Existing parameter estimation methods for the LBA can (...)
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  3.  26
    Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT–R.Rhiannon Weaver - 2008 - Cognitive Science 32 (8):1349-1375.
    Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a “model fixed, data variable” paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood‐based, “data fixed, model variable” paradigm in which models (...)
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  4.  63
    Too Many Cooks: Bayesian Inference for Coordinating Multi‐Agent Collaboration.Sarah A. Wu, Rose E. Wang, James A. Evans, Joshua B. Tenenbaum, David C. Parkes & Max Kleiman-Weiner - 2021 - Topics in Cognitive Science 13 (2):414-432.
    Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by (...)
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  5.  35
    Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation.Antti Kangasrääsiö, Jussi P. P. Jokinen, Antti Oulasvirta, Andrew Howes & Samuel Kaski - 2019 - Cognitive Science 43 (6):e12738.
    This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress of (...)
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  6.  47
    Nested sets theory, full stop: Explaining performance on bayesian inference tasks without dual-systems assumptions.David R. Mandel - 2007 - Behavioral and Brain Sciences 30 (3):275-276.
    Consistent with Barbey & Sloman (B&S), it is proposed that performance on Bayesian inference tasks is well explained by nested sets theory (NST). However, contrary to those authors' view, it is proposed that NST does better by dispelling with dual-systems assumptions. This article examines why, and sketches out a series of NST's core principles, which were not previously defined.
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  7.  40
    Capturing Dynamic Performance in a Cognitive Model: Estimating ACT‐R Memory Parameters With the Linear Ballistic Accumulator.Maarten Velde, Florian Sense, Jelmer P. Borst, Leendert Maanen & Hedderik Rijn - 2022 - Topics in Cognitive Science 14 (4):889-903.
    The parameters governing our behavior are in constant flux, and capturing these dynamics in cognitive models remains a challenge. We demonstrate how a mapping between ACT‐R's model of declarative memory and the linear ballistic accumulator enables efficient estimation of memory parameters from data. The resulting estimates provide a cognitively meaningful explanation for observed differences in behavior over time and between individuals.
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  8. Multivariate Higher-Order IRT Model and MCMC Algorithm for Linking Individual Participant Data From Multiple Studies.Eun-Young Mun, Yan Huo, Helene R. White, Sumihiro Suzuki & Jimmy de la Torre - 2019 - Frontiers in Psychology 10.
    Many clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise when underlying domain-specific, (...)
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  9.  88
    Bayesian statistics in medical research: an intuitive alternative to conventional data analysis.Lyle C. Gurrin, Jennifer J. Kurinczuk & Paul R. Burton - 2000 - Journal of Evaluation in Clinical Practice 6 (2):193-204.
  10.  36
    Instruction in information structuring improves Bayesian judgment in intelligence analysts.David R. Mandel - 2015 - Frontiers in Psychology 6:137593.
    An experiment was conducted to test the effectiveness of brief instruction in information structuring (i.e., representing and integrating information) for improving the coherence of probability judgments and binary choices among intelligence analysts. Forty-three analysts were presented with comparable sets of Bayesian judgment problems before and immediately after instruction. After instruction, analysts’ probability judgments were more coherent (i.e., more additive and compliant with Bayes theorem). Instruction also improved the coherence of binary choices regarding category membership: after instruction, subjects were more (...)
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  11.  78
    Non-bayesian foundations for statistical estimation, prediction, and the ravens example.Malcolm R. Forster - 1994 - Erkenntnis 40 (3):357 - 376.
    The paper provides a formal proof that efficient estimates of parameters, which vary as as little as possible when measurements are repeated, may be expected to provide more accurate predictions. The definition of predictive accuracy is motivated by the work of Akaike (1973). Surprisingly, the same explanation provides a novel solution for a well known problem for standard theories of scientific confirmation — the Ravens Paradox. This is significant in light of the fact that standard Bayesian analyses of (...)
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  12.  55
    Bayesian model learning based on predictive entropy.Jukka Corander & Pekka Marttinen - 2006 - Journal of Logic, Language and Information 15 (1):5-20.
    Bayesian paradigm has been widely acknowledged as a coherent approach to learning putative probability model structures from a finite class of candidate models. Bayesian learning is based on measuring the predictive ability of a model in terms of the corresponding marginal data distribution, which equals the expectation of the likelihood with respect to a prior distribution for model parameters. The main controversy related to this learning method stems from the necessity of specifying proper prior distributions for all (...)
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  13.  66
    A Hierarchical Bayesian Modeling Approach to Searching and Stopping in Multi-Attribute Judgment.Don van Ravenzwaaij, Chris P. Moore, Michael D. Lee & Ben R. Newell - 2014 - Cognitive Science 38 (7):1384-1405.
    In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the (...)
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  14.  21
    Syncopation as Probabilistic Expectation: Conceptual, Computational, and Experimental Evidence.Noah R. Fram & Jonathan Berger - 2023 - Cognitive Science 47 (12):e13390.
    Definitions of syncopation share two characteristics: the presence of a meter or analogous hierarchical rhythmic structure and a displacement or contradiction of that structure. These attributes are translated in terms of a Bayesian theory of syncopation, where the syncopation of a rhythm is inferred based on a hierarchical structure that is, in turn, learned from the ongoing musical stimulus. Several experiments tested its simplest possible implementation, with equally weighted priors associated with different meters and independence of auditory events, which (...)
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  15.  17
    Meta-learning as a bridge between neural networks and symbolic Bayesian models.R. Thomas McCoy & Thomas L. Griffiths - 2024 - Behavioral and Brain Sciences 47:e155.
    Meta-learning is even more broadly relevant to the study of inductive biases than Binz et al. suggest: Its implications go beyond the extensions to rational analysis that they discuss. One noteworthy example is that meta-learning can act as a bridge between the vector representations of neural networks and the symbolic hypothesis spaces used in many Bayesian models.
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  16. Bayesian inference, predictive coding and delusions.Rick A. Adams, Harriet R. Brown & Karl J. Friston - 2014 - Avant: Trends in Interdisciplinary Studies 5 (3):51-88.
  17.  59
    How Forgetting Aids Heuristic Inference.Lael J. Schooler & Ralph Hertwig - 2005 - Psychological Review 112 (3):610-628.
    Some theorists, ranging from W. James to contemporary psychologists, have argued that forgetting is the key to proper functioning of memory. The authors elaborate on the notion of beneficial forgetting by proposing that loss of information aids inference heuristics that exploit mnemonic information. To this end, the authors bring together 2 research programs that take an ecological approach to studying cognition. Specifically, they implement fast and frugal heuristics within the ACT-R cognitive architecture. Simulations of the recognition heuristic, which relies (...)
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  18.  18
    Inferences for Generalized Pareto Distribution Based on Progressive First-Failure Censoring Scheme.Rashad M. El-Sagheer, Taghreed M. Jawa & Neveen Sayed-Ahmed - 2021 - Complexity 2021:1-11.
    In this article, we consider estimation of the parameters of a generalized Pareto distribution and some lifetime indices such as those relating to reliability and hazard rate functions when the failure data are progressive first-failure censored. Both classical and Bayesian techniques are obtained. In the Bayesian framework, the point estimations of unknown parameters under both symmetric and asymmetric loss functions are discussed, after having been estimated using the conjugate gamma and discrete priors for the shape and (...)
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  19.  76
    Bayesian inferences about the self : A review.Michael Moutoussis, Pasco Fearon, Wael El-Deredy, Raymond J. Dolan & Karl J. Friston - 2014 - Consciousness and Cognition 25:67-76.
    Viewing the brain as an organ of approximate Bayesian inference can help us understand how it represents the self. We suggest that inferred representations of the self have a normative function: to predict and optimise the likely outcomes of social interactions. Technically, we cast this predict-and-optimise as maximising the chance of favourable outcomes through active inference. Here the utility of outcomes can be conceptualised as prior beliefs about final states. Actions based on interpersonal representations can therefore be (...)
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  20. Universal bayesian inference?David Dowe & Graham Oppy - 2001 - Behavioral and Brain Sciences 24 (4):662-663.
    We criticise Shepard's notions of “invariance” and “universality,” and the incorporation of Shepard's work on inference into the general framework of his paper. We then criticise Tenenbaum and Griffiths' account of Shepard (1987b), including the attributed likelihood function, and the assumption of “weak sampling.” Finally, we endorse Barlow's suggestion that minimum message length (MML) theory has useful things to say about the Bayesian inference problems discussed by Shepard and Tenenbaum and Griffiths. [Barlow; Shepard; Tenenbaum & Griffiths].
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  21. Generalization, similarity, and bayesian inference.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - Behavioral and Brain Sciences 24 (4):629-640.
    Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...)
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  22.  75
    Non-Bayesian Inference: Causal Structure Trumps Correlation.Bénédicte Bes, Steven Sloman, Christopher G. Lucas & Éric Raufaste - 2012 - Cognitive Science 36 (7):1178-1203.
    The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of (...)
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  23.  38
    Bayesian Word Learning in Multiple Language Environments.Benjamin D. Zinszer, Sebi V. Rolotti, Fan Li & Ping Li - 2018 - Cognitive Science 42 (S2):439-462.
    Infant language learners are faced with the difficult inductive problem of determining how new words map to novel or known objects in their environment. Bayesian inference models have been successful at using the sparse information available in natural child-directed speech to build candidate lexicons and infer speakers’ referential intentions. We begin by asking how a Bayesian model optimized for monolingual input generalizes to new monolingual or bilingual corpora and find that, especially in the case of the bilingual (...)
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  24. Performing Bayesian inference with exemplar models.Lei Shi, Naomi H. Feldman & Thomas L. Griffiths - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky, Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 745--750.
     
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  25.  48
    Using Category Structures to Test Iterated Learning as a Method for Identifying Inductive Biases.Thomas L. Griffiths, Brian R. Christian & Michael L. Kalish - 2008 - Cognitive Science 32 (1):68-107.
    Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases—assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed data. This article explores a novel experimental method for identifying the biases that guide human inductive inferences. The idea behind this method is simple: This article uses the responses (...)
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  26. Picturing classical and quantum Bayesian inference.Bob Coecke & Robert W. Spekkens - 2012 - Synthese 186 (3):651 - 696.
    We introduce a graphical framework for Bayesian inference that is sufficiently general to accommodate not just the standard case but also recent proposals for a theory of quantum Bayesian inference wherein one considers density operators rather than probability distributions as representative of degrees of belief. The diagrammatic framework is stated in the graphical language of symmetric monoidal categories and of compact structures and Frobenius structures therein, in which Bayesian inversion boils down to transposition with respect (...)
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  27. Irrationality: An Essay on Akrasia, Self-Deception, and Self-Control.Alfred R. Mele - 1987 - Oxford: Oxford University Press USA.
    Although much human action serves as proof that irrational behavior is remarkably common, certain forms of irrationality--most notably, incontinent action and self-deception--pose such difficult theoretical problems that philosophers have rejected them as logically or psychologically impossible. Here, Mele shows that, and how, incontinent action and self-deception are indeed possible. Drawing upon recent experimental work in the psychology of action and inference, he advances naturalized explanations of akratic action and self-deception while resolving the paradoxes around which the philosophical literature revolves. (...)
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  28. Epistemic Irrationality in the Bayesian Brain.Daniel Williams - 2021 - British Journal for the Philosophy of Science 72 (4):913-938.
    A large body of research in cognitive psychology and neuroscience draws on Bayesian statistics to model information processing within the brain. Many theorists have noted that this research seems to be in tension with a large body of experimental results purportedly documenting systematic deviations from Bayesian updating in human belief formation. In response, proponents of the Bayesian brain hypothesis contend that Bayesian models can accommodate such results by making suitable assumptions about model parameters. To make (...)
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  29. Is human cognition adaptive?John R. Anderson - 1991 - Behavioral and Brain Sciences 14 (3):471-485.
    Can the output of human cognition be predicted from the assumption that it is an optimal response to the information-processing demands of the environment? A methodology called rational analysis is described for deriving predictions about cognitive phenomena using optimization assumptions. The predictions flow from the statistical structure of the environment and not the assumed structure of the mind. Bayesian inference is used, assuming that people start with a weak prior model of the world which they integrate with experience (...)
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  30.  35
    A Bayesian Baseline for Belief in Uncommon Events.Vesa Palonen - 2017 - European Journal for Philosophy of Religion 9 (3):159-175.
    The plausibility of uncommon events and miracles based on testimony of such an event has been much discussed. When analyzing the probabilities involved, it has mostly been assumed that the common events can be taken as data in the calculations. However, we usually have only testimonies for the common events. While this difference does not have a significant effect on the inductive part of the inference, it has a large influence on how one should view the reliability of testimonies. (...)
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  31.  23
    (1 other version)Bayesian Inference with Indeterminate Probabilities.Stephen Spielman - 1976 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1976:185 - 196.
    The theory of personal probability needs to be developed as a logic of credibility in order to provide an adequate basis for the theories of scientific inference and rational decision making. But standard systems of personal probability impose formal structures on probability relationships which are too restrictive to qualify them as logics of credibility. Moreover, some rules for conditional probability have no justification as principles of credibility. A formal system of qualitative probability which is free of these defects and (...)
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  32.  97
    Dynamic Epistemic Logic for Implicit and Explicit Beliefs.Fernando R. Velázquez-Quesada - 2014 - Journal of Logic, Language and Information 23 (2):107-140.
    Epistemic logic with its possible worlds semantic model is a powerful framework that allows us to represent an agent’s information not only about propositional facts, but also about her own information. Nevertheless, agents represented in this framework are logically omniscient: their information is closed under logical consequence. This property, useful in some applications, is an unrealistic idealisation in some others. Many proposals to solve this problem focus on weakening the properties of the agent’s information, but some authors have argued that (...)
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  33.  59
    Relevance theory, pragmatic inference and cognitive architecture.Wen Yuan, Francis Y. Lin & Richard P. Cooper - 2019 - Philosophical Psychology 32 (1):98-122.
    Relevance Theory (RT) argues that human language comprehension processes tend to maximize “relevance,” and postulates that there is a relevance-based procedure that a hearer follows when trying to understand an utterance. Despite being highly influential, RT has been criticized for its failure to explain how speaker-related information, either the speaker’s abilities or her/his preferences, is incorporated into the hearer’s inferential, pragmatic process. An alternative proposal is that speaker-related information gains prominence due to representation of the speaker within higher level goal-directed (...)
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  34.  32
    Vice and Naturalistic Ontology.Christopher R. - 2008 - Philosophy, Psychiatry, and Psychology 15 (1):39-41.
    In lieu of an abstract, here is a brief excerpt of the content:Vice and Naturalistic OntologyChristopher R. Williams (bio)Keywordscausality, criminality, determinism, medical model, positivismThese questions have been posed: Is vice (encompassing criminal and other wrongful conduct) best regarded as “sick” behavior, “immoral” behavior, or some other type altogether? Are we to understand vice in natural-medical terms, or are we better served by utilizing a moral framework? Is criminality reducible to and best categorized as a metaphysical type the essential features of (...)
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  35.  50
    Fast, frugal, and surprisingly accurate heuristics.R. Duncan Luce - 2000 - Behavioral and Brain Sciences 23 (5):757-758.
    A research program is announced, and initial, exciting progress described. Many inference problems, poorly modeled by some traditional approaches, are surprisingly well handled by kinds of simple-minded Bayesian approximations. Fuller Bayesian approaches are typically more accurate but rarely are they either fast or frugal. Open issues include codifying when to use which heuristic and to give detailed evolutionary explanations.
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  36.  13
    Guess who? Identity attribution as Bayesian inference.Francesco Rigoli - 2025 - Philosophical Psychology 38 (2):875-896.
    An influential argument is that mental processes can be explained at three different levels of analysis: the functional, algorithmic, and implementation level. Identity attribution (the process whereby an identity is attributed to another individual or to the self) has been rarely explored at the functional level. To address this, here I propose a theory of identity attribution grounded on Bayesian inference, being the latter a well-established functional perspective in cognitive science. The theory posits that an identity is inferred (...)
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  37.  78
    New Semantics for Bayesian Inference: The Interpretive Problem and Its Solutions.Olav Benjamin Vassend - 2019 - Philosophy of Science 86 (4):696-718.
    Scientists often study hypotheses that they know to be false. This creates an interpretive problem for Bayesians because the probability assigned to a hypothesis is typically interpreted as the probability that the hypothesis is true. I argue that solving the interpretive problem requires coming up with a new semantics for Bayesian inference. I present and contrast two new semantic frameworks, and I argue that both of them support the claim that there is pervasive pragmatic encroachment on whether a (...)
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  38.  5
    Perception as Bayesian Inference.David C. Knill & Whitman Richards (eds.) - 1996 - Cambridge University Press.
    In recent years, Bayesian probability theory has emerged not only as a powerful tool for building computational theories of vision, but also as a general paradigm for studying human visual perception. This book provides an introduction to and critical analysis of the Bayesian paradigm. Leading researchers in computer vision and experimental vision science describe general theoretical frameworks for modeling vision, detailed applications to specific problems and implications for experimental studies of human perception. The book provides a dialogue between (...)
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  39.  53
    Competing Fairly in the New Economy: Lessons from the Browser Wars.R. A. Spinello - 2005 - Journal of Business Ethics 57 (4):343-361.
    The browser wars case is a useful springboard for considering the principle of positive competition and the proper regulation of platform technologies. There are lessons to be culled about policy, the application of antitrust law, and the parameters of fair competition. We argue that despite Microsofts opportunistic exploitation of its proprietary code, policy makers should resist the temptation to mandate an open source code model. Vigilant anti-trust enforcement is a preferable alternative. But courts must refrain from using antitrust law (...)
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  40. Bayesian Evidence Test for Precise Hypotheses.Julio Michael Stern - 2003 - Journal of Statistical Planning and Inference 117 (2):185-198.
    The full Bayesian signi/cance test (FBST) for precise hypotheses is presented, with some illustrative applications. In the FBST we compute the evidence against the precise hypothesis. We discuss some of the theoretical properties of the FBST, and provide an invariant formulation for coordinate transformations, provided a reference density has been established. This evidence is the probability of the highest relative surprise set, “tangential” to the sub-manifold (of the parameter space) that defines the null hypothesis.
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  41.  35
    Delusion: Cognitive Approaches—Bayesian Inference and Compartmentalisation.Martin Davies & Andy Egan - 2013 - In K. W. M. Fulford, Martin Davies, Richard Gipps, George Graham, John Sadler, Giovanni Stanghellini & Tim Thornton, The Oxford handbook of philosophy and psychiatry. Oxford: Oxford University Press. pp. 689-727.
    Cognitive approaches contribute to our understanding of delusions by providing an explanatory framework that extends beyond the personal level to the sub personal level of information-processing systems. According to one influential cognitive approach, two factors are required to account for the content of a delusion, its initial adoption as a belief, and its persistence. This chapter reviews Bayesian developments of the two-factor framework.
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  42. Delusion: Cognitive approaches—Bayesian inference and compartmentalisation.Martin Davies - 2013 - In K. W. M. Fulford, Martin Davies, Richard Gipps, George Graham, John Sadler, Giovanni Stanghellini & Tim Thornton, The Oxford handbook of philosophy and psychiatry. Oxford: Oxford University Press. pp. 689–727.
    The question posed by Dunn and Kirsner (D&K) is an instance of a more general one: What can we infer from data? One answer, if we are talking about logically valid deductive inference, is that we cannot infer theories from data. A theory is supposed to explain the data and so cannot be a mere summary of the data to be explained. The truth of an explanatory theory goes beyond the data and so is never logically guaranteed by the (...)
     
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  43. A dual approach to Bayesian inference and adaptive control.Leigh Tesfatsion - 1982 - Theory and Decision 14 (2):177-194.
    Probability updating via Bayes' rule often entails extensive informational and computational requirements. In consequence, relatively few practical applications of Bayesian adaptive control techniques have been attempted. This paper discusses an alternative approach to adaptive control, Bayesian in spirit, which shifts attention from the updating of probability distributions via transitional probability assessments to the direct updating of the criterion function, itself, via transitional utility assessments. Results are illustrated in terms of an adaptive reinvestment two-armed bandit problem.
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  44. Genericity and Inductive Inference.Henry Ian Schiller - 2023 - Philosophy of Science:1-18.
    We are often justified in acting on the basis of evidential confirmation. I argue that such evidence supports belief in non-quantificational generic generalizations, rather than universally quantified generalizations. I show how this account supports, rather than undermines, a Bayesian account of confirmation. Induction from confirming instances of a generalization to belief in the corresponding generic is part of a reasoning instinct that is typically (but not always) correct, and allows us to approximate the predictions that formal epistemology would make.
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  45.  42
    Renascent Rationalism. [REVIEW]E. D. R. - 1976 - Review of Metaphysics 30 (1):137-138.
    This volume is a revival and updating of the rationalism initiated by the Cartesian cogito. Even the four main divisions of the work give evidence of this: Perception, the Real World, Real Mind, and the Suprarational. The order of treatment is not identical in every respect with that of Descartes, but the four main themes are indubitably Cartesian. While the protagonist is Descartes, the antagonist to whom this volume is consciously addressed is the empiricist and the positivist. Professor Robinson seems (...)
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  46.  48
    Assessing interactive causal influence.Laura R. Novick & Patricia W. Cheng - 2004 - Psychological Review 111 (2):455-485.
    The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a) the conditions under which covariation implies conjunctive causation (...)
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  47. Learning from games: Inductive bias and Bayesian inference.Michael H. Coen & Yue Gao - 2009 - In N. A. Taatgen & H. van Rijn, Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 2729--2734.
     
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  48.  10
    Delusion: Cognitive Approaches—Bayesian Inference and Compartmentalisation.Andy Egan & Martin Davies - 2013 - In K. W. M. Fulford, Martin Davies, Richard Gipps, George Graham, John Sadler, Giovanni Stanghellini & Tim Thornton, The Oxford handbook of philosophy and psychiatry. Oxford: Oxford University Press. pp. 689–727.
    Cognitive approaches contribute to our understanding of delusions by providing an explanatory framework that extends beyond the personal level to the sub personal level of information-processing systems. According to one influential cognitive approach, two factors are required to account for the content of a delusion, its initial adoption as a belief, and its persistence. This chapter reviews Bayesian developments of the two-factor framework.
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    Theology From The Scripture.John R. Shook - 2010 - In The God debates: a 21st century guide for atheists and believers (and everyone in between). Malden, Mass.: Wiley-Blackwell. pp. 47–83.
    This chapter contains sections titled: Scientific History Scientific History and Scripture The Argument from Divine Signs The Argument from Apostolic Faith The Argument from Divine Character The Argument from Pseudo‐history.
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    Empty Words: Buddhist Philosophy and Cross-Cultural Interpretation (review).Edward R. Falls - 2005 - Buddhist-Christian Studies 25 (1):196-200.
    In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Empty Words: Buddhist Philosophy and Cross-Cultural InterpretationEdward R. FallsEmpty Words: Buddhist Philosophy and Cross-Cultural Interpretation. By Jay L. Garfield. Oxford and New York: Oxford University Press, 2002. 306 + xi pp.Jay L. Garfield's Empty Words is a collection of (mostly) previously published essays bearing on the interpretation of Buddhist thought. Emphasizing the Indo-Tibetan tradition while indebted to Euro-American philosophy, Empty Words belongs in a class with books such (...)
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