73 found
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  1.  40
    Bayesian Rationality: The Probabilistic Approach to Human Reasoning.Mike Oaksford & Nick Chater - 2007 - Oxford University Press.
    Are people rational? This question was central to Greek thought and has been at the heart of psychology and philosophy for millennia. This book provides a radical and controversial reappraisal of conventional wisdom in the psychology of reasoning, proposing that the Western conception of the mind as a logical system is flawed at the very outset. It argues that cognition should be understood in terms of probability theory, the calculus of uncertain reasoning, rather than in terms of logic, the calculus (...)
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  2.  13
    A Rational Analysis of the Selection Task as Optimal Data Selection.Mike Oaksford & Nick Chater - 1994 - Psychological Review 101 (4):608-631.
  3.  21
    The Rationality of Informal Argumentation: A Bayesian Approach to Reasoning Fallacies.Ulrike Hahn & Mike Oaksford - 2007 - Psychological Review 114 (3):704-732.
  4. Précis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning.Mike Oaksford & Nick Chater - 2009 - Behavioral and Brain Sciences 32 (1):69-84.
    According to Aristotle, humans are the rational animal. The borderline between rationality and irrationality is fundamental to many aspects of human life including the law, mental health, and language interpretation. But what is it to be rational? One answer, deeply embedded in the Western intellectual tradition since ancient Greece, is that rationality concerns reasoning according to the rules of logic – the formal theory that specifies the inferential connections that hold with certainty between propositions. Piaget viewed logical reasoning as defining (...)
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  5.  58
    Against Logicist Cognitive Science.Mike Oaksford & Nick Chater - 1991 - Mind and Language 6 (1):1-38.
  6.  55
    The Probabilistic Mind: Prospects for Bayesian Cognitive Science.Nick Chater & Mike Oaksford (eds.) - 2008 - Oxford University Press.
    'The Probabilistic Mind' is a follow-up to the influential and highly cited 'Rational Models of Cognition'. It brings together developments in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods.
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  7.  11
    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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  8.  21
    Dynamic Inference and Everyday Conditional Reasoning in the New Paradigm.Mike Oaksford & Nick Chater - 2013 - Thinking and Reasoning 19 (3-4):346-379.
  9.  23
    Cognition and Conditionals: Probability and Logic in Human Thought.Mike Oaksford & Nick Chater (eds.) - 2010 - Oxford University Press.
    This book shows how these developments have led researchers to view people's conditional reasoning behaviour more as succesful probabilistic reasoning rather ...
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  10.  65
    Logicism, Mental Models and Everyday Reasoning: Reply to Garnham.Nick Chater & Mike Oaksford - 1993 - Mind and Language 8 (1):72-89.
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  11. A Bayesian Approach to Informal Argument Fallacies.Ulrike Hahn & Mike Oaksford - 2006 - Synthese 152 (2):207-236.
    We examine in detail three classic reasoning fallacies, that is, supposedly ``incorrect'' forms of argument. These are the so-called argumentam ad ignorantiam, the circular argument or petitio principii, and the slippery slope argument. In each case, the argument type is shown to match structurally arguments which are widely accepted. This suggests that it is not the form of the arguments as such that is problematic but rather something about the content of those examples with which they are typically justified. This (...)
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  12.  43
    Autonomy, Implementation and Cognitive Architecture: A Reply to Fodor and Pylyshyn.Nick Chater & Mike Oaksford - 1990 - Cognition 34 (1):93-107.
  13. The Probabilistic Approach to Human Reasoning.Mike Oaksford & Nick Chater - 2001 - Trends in Cognitive Sciences 5 (8):349-357.
    A recent development in the cognitive science of reasoning has been the emergence of a probabilistic approach to the behaviour observed on ostensibly logical tasks. According to this approach the errors and biases documented on these tasks occur because people import their everyday uncertain reasoning strategies into the laboratory. Consequently participants' apparently irrational behaviour is the result of comparing it with an inappropriate logical standard. In this article, we contrast the probabilistic approach with other approaches to explaining rationality, and then (...)
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  14.  36
    The Mental Representation of Causal Conditional Reasoning: Mental Models or Causal Models.Nilufa Ali, Nick Chater & Mike Oaksford - 2011 - Cognition 119 (3):403-418.
  15.  93
    Ten Years of the Rational Analysis of Cognition.Nick Chater & Mike Oaksford - 1999 - Trends in Cognitive Sciences 3 (2):57-65.
  16.  2
    Rational Explanation of the Selection Task.Mike Oaksford & Nick Chater - 1996 - Psychological Review 103 (2):381-391.
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  17.  82
    The Burden of Proof and Its Role in Argumentation.Ulrike Hahn & Mike Oaksford - 2007 - Argumentation 21 (1):39-61.
    The notion of “the burden of proof” plays an important role in real-world argumentation contexts, in particular in law. It has also been given a central role in normative accounts of argumentation, and has been used to explain a range of classic argumentation fallacies. We argue that in law the goal is to make practical decisions whereas in critical discussion the goal is frequently simply to increase or decrease degree of belief in a proposition. In the latter case, it is (...)
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  18.  15
    Probability Logic and the Modus Ponens-Modus Tollens Asymmetry in Conditional Inference.Mike Oaksford & Nick Chater - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press. pp. 97--120.
  19.  29
    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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  20.  8
    Fast, Frugal, and Rational: How Rational Norms Explain Behavior.Nick Chater, Mike Oaksford, Ramin Nakisa & Martin Redington - 2003 - Organizational Behavior and Human Decision Processes 90 (1):63-86.
    Much research on judgment and decision making has focussed on the adequacy of classical rationality as a description of human reasoning. But more recently it has been argued that classical rationality should also be rejected even as normative standards for human reasoning. For example, Gigerenzer and Goldstein and Gigerenzer and Todd argue that reasoning involves “fast and frugal” algorithms which are not justified by rational norms, but which succeed in the environment. They provide three lines of argument for this view, (...)
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  21.  15
    Mental Models and the Tractability of Everyday Reasoning.Mike Oaksford - 1993 - Behavioral and Brain Sciences 16 (2):360-361.
  22.  60
    A Normative Theory of Argument Strength.Ulrike Hahn & Mike Oaksford - 2006 - Informal Logic 26 (1):1-24.
    In this article, we argue for the general importance of normative theories of argument strength. We also provide some evidence based on our recent work on the fallacies as to why Bayesian probability might, in fact, be able to supply such an account. In the remainder of the article we discuss the general characteristics that make a specifically Bayesian approach desirable, and critically evaluate putative flaws of Bayesian probability that have been raised in the argumentation literature.
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  23.  62
    Probabilistic Effects in Data Selection.Mike Oaksford, Nick Chater & Becki Grainger - 1999 - Thinking and Reasoning 5 (3):193 – 243.
    Four experiments investigated the effects of probability manipulations on the indicative four card selection task (Wason, 1966, 1968). All looked at the effects of high and low probability antecedents (p) and consequents (q) on participants' data selections when determining the truth or falsity of a conditional rule, if p then q . Experiments 1 and 2 also manipulated believability. In Experiment 1, 128 participants performed the task using rules with varied contents pretested for probability of occurrence. Probabilistic effects were observed (...)
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  24.  33
    The New Paradigm and Mental Models.Jean Baratgin, Igor Douven, Jonathan StB. T. Evans, Mike Oaksford, David Over & Guy Politzer - 2015 - Trends in Cognitive Sciences 19 (10):547-548.
  25.  71
    Conditional Probability and the Cognitive Science of Conditional Reasoning.Mike Oaksford & Nick Chater - 2003 - Mind and Language 18 (4):359–379.
  26.  28
    Dual Processes, Probabilities, and Cognitive Architecture.Mike Oaksford & Nick Chater - 2012 - Mind and Society 11 (1):15-26.
    It has been argued that dual process theories are not consistent with Oaksford and Chater’s probabilistic approach to human reasoning (Oaksford and Chater in Psychol Rev 101:608–631, 1994 , 2007 ; Oaksford et al. 2000 ), which has been characterised as a “single-level probabilistic treatment[s]” (Evans 2007 ). In this paper, it is argued that this characterisation conflates levels of computational explanation. The probabilistic approach is a computational level theory which is consistent with theories of general cognitive architecture that invoke (...)
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  27.  31
    Probabilistic Single Function Dual Process Theory and Logic Programming as Approaches to Non-Monotonicity in Human Vs. Artificial Reasoning.Mike Oaksford & Nick Chater - 2014 - Thinking and Reasoning 20 (2):269-295.
  28.  28
    Adaptive Non‐Interventional Heuristics for Covariation Detection in Causal Induction: Model Comparison and Rational Analysis.Masasi Hattori & Mike Oaksford - 2007 - Cognitive Science 31 (5):765-814.
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  29. The Rational Analysis of Mind and Behavior.Nick Chater & Mike Oaksford - 2000 - Synthese 122 (1-2):93-131.
    Rational analysis (Anderson 1990, 1991a) is an empiricalprogram of attempting to explain why the cognitive system isadaptive, with respect to its goals and the structure of itsenvironment. We argue that rational analysis has two importantimplications for philosophical debate concerning rationality. First,rational analysis provides a model for the relationship betweenformal principles of rationality (such as probability or decisiontheory) and everyday rationality, in the sense of successfulthought and action in daily life. Second, applying the program ofrational analysis to research on human reasoning (...)
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  30.  54
    Local and Global Inferential Relations: Response to Over (2009).Nick Chater & Mike Oaksford - 2009 - Thinking and Reasoning 15 (4):439-446.
  31.  20
    Deontic Reasoning With Emotional Content: Evolutionary Psychology or Decision Theory?Nick Perham & Mike Oaksford - 2005 - Cognitive Science 29 (5):681-718.
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  32.  55
    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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  33.  23
    Imaging Deductive Reasoning and the New Paradigm.Mike Oaksford - 2015 - Frontiers in Human Neuroscience 9.
  34.  37
    Rational Argument, Rational Inference.Ulrike Hahn, Adam J. L. Harris & Mike Oaksford - 2013 - Argument and Computation 4 (1):21 - 35.
    (2013). Rational argument, rational inference. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 21-35. doi: 10.1080/19462166.2012.689327.
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  35.  36
    Contrast Classes and Matching Bias as Explanations of the Effects of Negation on Conditional Reasoning.Mike Oaksford - 2002 - Thinking and Reasoning 8 (2):135 – 151.
    In this paper the arguments for optimal data selection and the contrast class account of negations in the selection task and the conditional inference task are summarised, and contrasted with the matching bias approach. It is argued that the probabilistic contrast class account provides a unified, rational explanation for effects across these tasks. Moreover, there are results that are only explained by the contrast class account that are also discussed. The only major anomaly is the explicit negations effect in the (...)
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  36.  40
    Programs as Causal Models: Speculations on Mental Programs and Mental Representation.Nick Chater & Mike Oaksford - 2013 - Cognitive Science 37 (6):1171-1191.
    Judea Pearl has argued that counterfactuals and causality are central to intelligence, whether natural or artificial, and has helped create a rich mathematical and computational framework for formally analyzing causality. Here, we draw out connections between these notions and various current issues in cognitive science, including the nature of mental “programs” and mental representation. We argue that programs (consisting of algorithms and data structures) have a causal (counterfactual-supporting) structure; these counterfactuals can reveal the nature of mental representations. Programs can also (...)
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  37.  28
    Theories of Reasoning and the Computational Explanation of Everyday Inference.Mike Oaksford & Nick Chater - 1995 - Thinking and Reasoning 1 (2):121 – 152.
  38.  20
    Quantum Probability, Intuition, and Human Rationality.Mike Oaksford - 2013 - Behavioral and Brain Sciences 36 (3):303-303.
    This comment suggests that Pothos & Busmeyer (P&B) do not provide an intuitive rational foundation for quantum probability (QP) theory to parallel standard logic and classical probability (CP) theory. In particular, the intuitive foundation for standard logic, which underpins CP, is the elimination of contradictions – that is, believing p and not-p is bad. Quantum logic, which underpins QP, explicitly denies non-contradiction, which seems deeply counterintuitive for the macroscopic world about which people must reason. I propose a possible resolution in (...)
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  39.  16
    Corrigendum: 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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  40.  7
    Human Rationality and the Psychology of Reasoning: Where Do We Go From Here?Nick Chater & Mike Oaksford - 2001 - British Journal of Psychology 92 (1):193-216.
    British psychologists have been at the forefront of research into human reasoning for 40 years. This article describes some past research milestones within this tradition before outlining the major theoretical positions developed in the UK. Most British reasoning researchers have contributed to one or more of these positions. We identify a common theme that is emerging in all these approaches, that is, the problem of explaining how prior general knowledge affects reasoning. In our concluding comments we outline the challenges for (...)
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  41.  17
    The “is-Ought Fallacy” Fallacy.Mike Oaksford & Nick Chater - 2011 - Behavioral and Brain Sciences 34 (5):262-263.
    Mere facts about how the world is cannot determine how we ought to think or behave. Elqayam & Evans (E&E) argue that this undercuts the use of rational analysis in explaining how people reason, by ourselves and with others. But this presumed application of the fallacy is itself fallacious. Rational analysis seeks to explain how people do reason, for example in laboratory experiments, not how they ought to reason. Thus, no ought is derived from an is; and rational analysis is (...)
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  42.  23
    Causal Discounting and Conditional Reasoning in Children.Nilufa Ali, Anne Schlottmann, Abigail Shaw, Nick Chater & Mike Oaksford - 2010 - In M. Oaksford & N. Chater (eds.), Cognition and Conditionals: Probability and Logic in Human Thought. Oxford University Press.
  43.  23
    Bayesian Argumentation and the Pragmatic Approach: Comment on Darmstadter.Mike Oaksford - 2013 - Thinking and Reasoning 19 (3-4):495-499.
  44.  20
    Probabilities, Causation, and Logic Programming in Conditional Reasoning: Reply to Stenning and Van Lambalgen.Mike Oaksford & Nick Chater - 2016 - Thinking and Reasoning 22 (3):336-354.
    ABSTRACTOaksford and Chater critiqued the logic programming approach to nonmonotonicity and proposed that a Bayesian probabilistic approach to conditional reasoning provided a more empirically adequate theory. The current paper is a reply to Stenning and van Lambalgen's rejoinder to this earlier paper entitled ‘Logic programming, probability, and two-system accounts of reasoning: a rejoinder to Oaksford and Chater’ in Thinking and Reasoning. It is argued that causation is basic in human cognition and that explaining how abnormality lists are created in LP (...)
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  45.  8
    On the Source of Human Irrationality.Mike Oaksford & Simon Hall - 2016 - Trends in Cognitive Sciences 20 (5):336-344.
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  46.  74
    Connectionism, Classical Cognitive Science and Experimental Psychology.Mike Oaksford, Nick Chater & Keith Stenning - 1990 - AI and Society 4 (1):73-90.
    Classical symbolic computational models of cognition are at variance with the empirical findings in the cognitive psychology of memory and inference. Standard symbolic computers are well suited to remembering arbitrary lists of symbols and performing logical inferences. In contrast, human performance on such tasks is extremely limited. Standard models donot easily capture content addressable memory or context sensitive defeasible inference, which are natural and effortless for people. We argue that Connectionism provides a more natural framework in which to model this (...)
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  47.  16
    Deontic Reasoning, Modules and Innateness: A Second Look.Nick Chater & Mike Oaksford - 1996 - Mind and Language 11 (2):191-202.
  48.  50
    Two and Three Stage Models of Deontic Reasoning.Mike Oaksford & Nick Chater - 1995 - Thinking and Reasoning 1 (4):350 – 357.
  49.  24
    Computational and Biological Constraints in the Psychology of Reasoning.Mike Oaksford & Mike Malloch - 1993 - Behavioral and Brain Sciences 16 (3):468-469.
  50.  29
    Discussion Task Demands and Revising Probabilities in the Selection Task: A Comment on Green, Over, and Pyne.Mike Oaksford - 1998 - Thinking and Reasoning 4 (2):179 – 186.
    Green, Over, and Pyne's (1997) paper (hereafter referred to as ''GOP") seems to provide a novel approach to examining probabilistic effects in Wason's selection task. However, in this comment, it is argued that their chosen experimental paradigm confounds most of their results. The task demands of the externalisation procedure (Green, 1995) enforce a correlation between card selections and the probability of finding a counterexample, which was the main finding of GOP's experiments. Consequently GOP cannot argue that their data support Kirby's (...)
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