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Reasoning With Causal Cycles

Cognitive Science 41 (S5):944-1002 (2017)

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  1. Causality: Models, Reasoning and Inference.Christopher Hitchcock & Judea Pearl - 2001 - Philosophical Review 110 (4):639.
    Judea Pearl has been at the forefront of research in the burgeoning field of causal modeling, and Causality is the culmination of his work over the last dozen or so years. For philosophers of science with a serious interest in causal modeling, Causality is simply mandatory reading. Chapter 2, in particular, addresses many of the issues familiar from works such as Causation, Prediction and Search by Peter Spirtes, Clark Glymour, and Richard Scheines. But philosophers with a more general interest in (...)
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  • Learning Causes: Psychological Explanations of Causal Explanation. [REVIEW]Clark Glymour - 1998 - Minds and Machines 8 (1):39-60.
    I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between ‘mechanist’ and ‘associationist’ psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, (...)
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  • Causal Categorization in Children and Adults.Brett K. Hayes & Bob Rehder - 2012 - Cognitive Science 36 (6):1102-1128.
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  • The Development of Causal Categorization.Brett K. Hayes & Bob Rehder - 2012 - Cognitive Science 36 (6):1102-1128.
    Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status, and relational centrality. Adult classification was driven primarily by coherence when causal links were deterministic (...)
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  • Learning to Learn Causal Models.Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum - 2010 - Cognitive Science 34 (7):1185-1243.
    Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the (...)
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  • Two Causal Theories of Counterfactual Conditionals.Lance J. Rips - 2010 - Cognitive Science 34 (2):175-221.
    Bayes nets are formal representations of causal systems that many psychologists have claimed as plausible mental representations. One purported advantage of Bayes nets is that they may provide a theory of counterfactual conditionals, such as If Calvin had been at the party, Miriam would have left early. This article compares two proposed Bayes net theories as models of people's understanding of counterfactuals. Experiments 1-3 show that neither theory makes correct predictions about backtracking counterfactuals (in which the event of the if-clause (...)
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  • Causal‐Based Property Generalization.Bob Rehder - 2009 - Cognitive Science 33 (3):301-344.
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  • Children?S Causal Inferences From Indirect Evidence: Backwards Blocking and Bayesian Reasoning in Preschoolers.D. Sobel - 2004 - Cognitive Science 28 (3):303-333.
  • Review: The Grand Leap; Reviewed Work: Causation, Prediction, and Search. [REVIEW]Peter Spirtes, Clark Glymour & Richard Scheines - 1996 - British Journal for the Philosophy of Science 47 (1):113-123.
  • Do We “Do‘?Steven A. Sloman & David A. Lagnado - 2005 - Cognitive Science 29 (1):5-39.
    A normative framework for modeling causal and counterfactual reasoning has been proposed by Spirtes, Glymour, and Scheines. The framework takes as fundamental that reasoning from observation and intervention differ. Intervention includes actual manipulation as well as counterfactual manipulation of a model via thought. To represent intervention, Pearl employed the do operator that simplifies the structure of a causal model by disconnecting an intervened-on variable from its normal causes. Construing the do operator as a psychological function affords predictions about how people (...)
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  • Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Tijdschrift Voor Filosofie 64 (1):201-202.
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  • Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.J. Pearl, F. Bacchus, P. Spirtes, C. Glymour & R. Scheines - 1988 - Synthese 104 (1):161-176.
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  • Structure Induction in Diagnostic Causal Reasoning.Björn Meder, Ralf Mayrhofer & Michael R. Waldmann - 2014 - Psychological Review 121 (3):277-301.
  • Agents and Causes: Dispositional Intuitions As a Guide to Causal Structure.Ralf Mayrhofer & Michael R. Waldmann - 2015 - Cognitive Science 39 (1):65-95.
    Currently, two frameworks of causal reasoning compete: Whereas dependency theories focus on dependencies between causes and effects, dispositional theories model causation as an interaction between agents and patients endowed with intrinsic dispositions. One important finding providing a bridge between these two frameworks is that failures of causes to generate their effects tend to be differentially attributed to agents and patients regardless of their location on either the cause or the effect side. To model different types of error attribution, we augmented (...)
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  • From Covariation to Causation: A Causal Power Theory.Patricia W. Cheng - 1997 - Psychological Review 104 (2):367-405.
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  • The Impact of Goal Specificity on Strategy Use and the Acquisition of Problem Structure.Regina Vollmeyer, Bruce D. Burns & Keith J. Holyoak - 1996 - Cognitive Science 20 (1):75-100.
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  • Inferring Causal Networks From Observations and Interventions.Mark Steyvers, Joshua B. Tenenbaum, Eric-Jan Wagenmakers & Ben Blum - 2003 - Cognitive Science 27 (3):453-489.
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  • Feature Centrality and Conceptual Coherence.Steven A. Sloman, Bradley C. Love & Woo-Kyoung Ahn - 1998 - Cognitive Science 22 (2):189-228.
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  • Inference and Explanation in Counterfactual Reasoning.Lance J. Rips & Brian J. Edwards - 2013 - Cognitive Science 37 (6):1107-1135.
    This article reports results from two studies of how people answer counterfactual questions about simple machines. Participants learned about devices that have a specific configuration of components, and they answered questions of the form “If component X had not operated [failed], would component Y have operated?” The data from these studies indicate that participants were sensitive to the way in which the antecedent state is described—whether component X “had not operated” or “had failed.” Answers also depended on whether the device (...)
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  • Analogical Mapping by Constraint Satisfaction.Keith J. Holyoak & Paul Thagard - 1989 - Cognitive Science 13 (3):295-355.
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  • Homeostasis, Species, and Higher Taxa.Richard Boyd - 1999 - In R. A. Wilson (ed.), Species: New Interdisciplinary Essays. MIT Press. pp. 141-85.
  • Cognitive Dissonance Reduction as Constraint Satisfaction.Thomas R. Shultz & Mark R. Lepper - 1996 - Psychological Review 103 (2):219-240.
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  • Bayesian Generic Priors for Causal Learning.Hongjing Lu, Alan L. Yuille, Mimi Liljeholm, Patricia W. Cheng & Keith J. Holyoak - 2008 - Psychological Review 115 (4):955-984.
  • Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.Henry E. Kyburg - 1991 - Journal of Philosophy 88 (8):434-437.
  • Structured Statistical Models of Inductive Reasoning.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (1):20-58.
  • A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.Alison Gopnik, Clark Glymour, Laura Schulz, Tamar Kushnir & David Danks - 2004 - Psychological Review 111 (1):3-32.
    We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...)
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  • Analogical and Category-Based Inference: A Theoretical Integration with Bayesian Causal Models.Keith J. Holyoak, Hee Seung Lee & Hongjing Lu - 2010 - Journal of Experimental Psychology: General 139 (4):702-727.
  • Causal Models and the Acquisition of Category Structure.Michael R. Waldmann, Keith J. Holyoak & Angela Fratianne - 1995 - Journal of Experimental Psychology: General 124 (2):181.
  • Inductive Reasoning About Causally Transmitted Properties.Patrick Shafto, Charles Kemp, Elizabeth Baraff Bonawitz, John D. Coley & Joshua B. Tenenbaum - 2008 - Cognition 109 (2):175-192.
  • Modelling Mechanisms with Causal Cycles.Brendan Clarke, Bert Leuridan & Jon Williamson - 2014 - Synthese 191 (8):1-31.
    Mechanistic philosophy of science views a large part of scientific activity as engaged in modelling mechanisms. While science textbooks tend to offer qualitative models of mechanisms, there is increasing demand for models from which one can draw quantitative predictions and explanations. Casini et al. (Theoria 26(1):5–33, 2011) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. The RBN formalism is an extension of the standard Bayesian net formalism, an extension that allows for modelling the hierarchical (...)
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  • An Interactive Activation Model of Context Effects in Letter Perception: I. An Account of Basic Findings.James L. McClelland & David E. Rumelhart - 1981 - Psychological Review 88 (5):375-407.
  • Clinical Psychologists' Theory-Based Representations of Mental Disorders Predict Their Diagnostic Reasoning and Memory.Nancy S. Kim & Woo-Kyoung Ahn - 2002 - Journal of Experimental Psychology: General 131 (4):451-476.
  • Causal Relations and Feature Similarity in Children's Inductive Reasoning.Brett K. Hayes & Susan P. Thompson - 2007 - Journal of Experimental Psychology: General 136 (3):470-484.
  • “Structured Statistical Models of Inductive Reasoning”: Correction.Charles Kemp & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (2):461-461.
  • Theory-Based Causal Induction.Thomas L. Griffiths & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (4):661-716.
  • Causal Knowledge and Categories: The Effects of Causal Beliefs on Categorization, Induction, and Similarity.Bob Rehder & Reid Hastie - 2001 - Journal of Experimental Psychology 130 (3):323-360.
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  • Decision Makers Conceive of Their Choices as Interventions.York Hagmayer & Steven A. Sloman - 2009 - Journal of Experimental Psychology: General 138 (1):22-38.
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