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  1. A Hypothetical Learning Progression for Quantifying Phenomena in Science.Hui Jin, Cesar Delgado, Malcolm I. Bauer, E. Caroline Wylie, Dante Cisterna & Kenneth F. Llort - forthcoming - Science & Education.
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  • Explaining Disease: Correlations, Causes, and Mechanisms. [REVIEW]Paul Thagard - 1998 - Minds and Machines 8 (1):61-78.
    Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference from correlation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how causal (...)
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  • Do Explanation Formats in Elementary Chemistry Depend on Agent Causality?Rom Harré - 2011 - Foundations of Chemistry 13 (3):187-200.
    By setting out the grammar of event causality, as developed by Hume and Mackie, in contrast to the grammar of agent causality in the natural sciences, a kind of hybrid hierarchical format for chemical explanations is sketched. From this starting point the history of agentive concepts in chemistry is displayed as a progression from Newton’s ‘forces’, through the nineteenth century concepts of ‘affinity’ and ‘valency’ to recent theories of molecular binding in terms of the migration of electrons and protons as (...)
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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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  • The Relevance Effect and Conditionals.Niels Skovgaard-Olsen, Henrik Singmann & Karl Christoph Klauer - 2016 - Cognition 150:26-36.
    More than a decade of research has found strong evidence for P(if A, then C) = P(C|A) (“the Equation”). We argue, however, that this hypothesis provides an overly simplified picture due to its inability to account for relevance. We manipulated relevance in the evaluation of the probability and acceptability of indicative conditionals and found that relevance moderates the effect of P(C|A). This corroborates the Default and Penalty Hypothesis put forward in this paper. Finally, the probability and acceptability of concessive conditionals (...)
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  • Dynamic Inference and Everyday Conditional Reasoning in the New Paradigm.Mike Oaksford & Nick Chater - 2013 - Thinking and Reasoning 19 (3-4):346-379.
  • Words, Images and Concepts.Daniel A. Weiskopf - 2015 - Analysis 75 (1):99-109.
    Christopher Gauker proposes that all cognition can be divided into nonconceptual image-based thought and conceptual language-based thought. The division between the two hinges on the representational powers of their respective mediums. I argue that a richer variety of representational states and processes is necessary in order to explain both human and nonhuman cognition. There are aspects of nonhuman cognition that cannot be explained simply by images, and there are aspects of human conceptual thought, particularly those dealing with causal reasoning, that (...)
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  • Stable Causal Relationships Are Better Causal Relationships.Nadya Vasilyeva, Thomas Blanchard & Tania Lombrozo - 2018 - Cognitive Science 42 (4):1265-1296.
    We report three experiments investigating whether people’s judgments about causal relationships are sensitive to the robustness or stability of such relationships across a range of background circumstances. In Experiment 1, we demonstrate that people are more willing to endorse causal and explanatory claims based on stable (as opposed to unstable) relationships, even when the overall causal strength of the relationship is held constant. In Experiment 2, we show that this effect is not driven by a causal generalization’s actual scope of (...)
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  • : Developing Reason.Deanna Kuhn, Jared B. Katz & David Dean Jr - 2004 - Thinking and Reasoning 10 (2):197 – 219.
    We argue in favour of the general proposition that the nature of reasoning is best understood within a context of its origins and development. A major dimension of what develops in the years from childhood to adulthood, we propose, is increasing meta-level monitoring and management of cognition. Two domains are examined in presenting support for these claims—multivariable causal reasoning and argumentive reasoning.
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  • Actual Causation by Probabilistic Active Paths.Charles R. Twardy & Kevin B. Korb - 2011 - Philosophy of Science 78 (5):900-913.
    We present a probabilistic extension to active path analyses of token causation (Halpern & Pearl 2001, forthcoming; Hitchcock 2001). The extension uses the generalized notion of intervention presented in (Korb et al. 2004): we allow an intervention to set any probability distribution over the intervention variables, not just a single value. The resulting account can handle a wide range of examples. We do not claim the account is complete --- only that it fills an obvious gap in previous active-path approaches. (...)
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  • The Propositional Nature of Human Associative Learning.Chris J. Mitchell, Jan De Houwer & Peter F. Lovibond - 2009 - Behavioral and Brain Sciences 32 (2):183-198.
    The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved (...)
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  • Depressive Realism and Outcome Density Bias in Contingency Judgments: The Effect of the Context and Intertrial Interval.Rachel M. Msetfi, Robin A. Murphy, Jane Simpson & Diana E. Kornbrot - 2005 - Journal of Experimental Psychology: General 134 (1):10-22.
  • The Folk Psychology of Free Will: Fits and Starts.Shaun Nichols - 2004 - Mind and Language 19 (5):473-502.
    According to agent-causal accounts of free will, agents have the capacity to cause actions, and for a given action, an agent could have done otherwise. This paper uses existing results and presents experimental evidence to argue that young children deploy a notion of agent-causation. If young children do have such a notion, however, it remains quite unclear how they acquire it. Several possible acquisition stories are canvassed, including the possibility that the notion of agent-causation develops from a prior notion of (...)
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  • Learning, Prediction and Causal Bayes Nets.Clark Glymour - 2003 - Trends in Cognitive Sciences 7 (1):43-48.
  • Explanation in Contexts of Causal Complexity: Lessons From Psychiatric Genetics.Lauren N. Ross - unknown
    Over the past decade there have been increasingly common claims that psychiatry is in a “crisis”. These claims often target the lack of known or identifiable causal etiologies for psychiatric diseases, suggesting that they are “among the most intractable enigmas in medicine”. While the intractable nature of these disorders is often associated with their “causal complexity”, it is not always clear exactly what is meant by this. How should we understand causal complexity in this domain How does it challenge scientific (...)
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  • The Preference for Joint Attributions Over Contrast-Factor Attributions in Causal Contrast Situations.Moyun Wang & Mingyi Zhu - 2019 - Frontiers in Psychology 10.
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  • Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment.Samuel G. B. Johnson & Woo-Kyoung Ahn - 2015 - Cognitive Science 39 (7):1468-1503.
    Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, (...)
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  • How Contrast Situations Affect the Assignment of Causality in Symmetric Physical Settings.Sieghard Beller & Andrea Bender - 2014 - Frontiers in Psychology 5.
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  • Naive Probability: Model‐Based Estimates of Unique Events.Sangeet S. Khemlani, Max Lotstein & Philip N. Johnson-Laird - 2015 - Cognitive Science 39 (6):1216-1258.
    We describe a dual-process theory of how individuals estimate the probabilities of unique events, such as Hillary Clinton becoming U.S. President. It postulates that uncertainty is a guide to improbability. In its computer implementation, an intuitive system 1 simulates evidence in mental models and forms analog non-numerical representations of the magnitude of degrees of belief. This system has minimal computational power and combines evidence using a small repertoire of primitive operations. It resolves the uncertainty of divergent evidence for single events, (...)
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  • A Causal Power Semantics for Generic Sentences.Robert van Rooij & Katrin Schulz - forthcoming - Topoi:1-16.
    Many generic sentences express stable inductive generalizations. Stable inductive generalizations are typically true for a causal reason. In this paper we investigate to what extent this is also the case for the generalizations expressed by generic sentences. More in particular, we discuss the possibility that many generic sentences of the form ‘ks have feature e’ are true because kind k have the causal power to ‘produce’ feature e. We will argue that such an analysis is quite close to a probabilistic (...)
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  • Generics and Typicality: A Bounded Rationality Approach.Robert van Rooij & Katrin Schulz - forthcoming - Linguistics and Philosophy:1-35.
    Cimpian et al. observed that we accept generic statements of the form ‘Gs are f’ on relatively weak evidence, but that if we are unfamiliar with group G and we learn a generic statement about it, we still treat it inferentially in a much stronger way: all Gs are f. This paper makes use of notions like ‘representativeness’, ‘contingency’ and ‘relative difference’ from psychology to provide a uniform semantics of generics that explains why people accept generics based on weak evidence. (...)
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  • The Problem of Variable Choice.James Woodward - 2016 - Synthese 193 (4):1047-1072.
    This paper explores some issues about the choice of variables for causal representation and explanation. Depending on which variables a researcher employs, many causal inference procedures and many treatments of causation will reach different conclusions about which causal relationships are present in some system of interest. The assumption of this paper is that some choices of variables are superior to other choices for the purpose of causal analysis. A number of possible criteria for variable choice are described and defended within (...)
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  • Singular Clues to Causality and Their Use in Human Causal Judgment.Peter A. White - 2014 - Cognitive Science 38 (1):38-75.
    It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as heuristics for generating causal judgments under uncertainty and are a pervasive source of bias in causal judgment. More sophisticated clues such as mechanism clues and repeated interventions (...)
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  • Combining Minds: A Defence of the Possibility of Experiential Combination.Luke Roelofs - 2015 - Dissertation, University of Toronto
    This thesis explores the possibility of composite consciousness: phenomenally conscious states belonging to a composite being in virtue of the consciousness of, and relations among, its parts. We have no trouble accepting that a composite being has physical properties entirely in virtue of the physical properties of, and relations among, its parts. But a long­standing intuition holds that consciousness is different: my consciousness cannot be understood as a complex of interacting component consciousnesses belonging to parts of me. I ask why: (...)
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  • Cue Competition Effects and Young Children's Causal and Counterfactual Inferences.Teresa McCormack, Stephen Andrew Butterfill, Christoph Hoerl & Patrick Burns - 2009 - Developmental Psychology 45 (6):1563-1575.
    The authors examined cue competition effects in young children using the blicket detector paradigm, in which objects are placed either singly or in pairs on a novel machine and children must judge which objects have the causal power to make the machine work. Cue competition effects were found in a 5- to 6-year-old group but not in a 4-year-old group. Equivalent levels of forward and backward blocking were found in the former group. Children's counterfactual judgments were subsequently examined by asking (...)
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  • Putting Inferentialism and the Suppositional Theory of Conditionals to the Test (Second Dissertation, Psychology).Niels Skovgaard-Olsen - 2017 - Dissertation, University of Freiburg
    This dissertation is devoted to empirically contrasting the Suppositional Theory of conditionals, which holds that indicative conditionals serve the purpose of engaging in hypothetical thought, and Inferentialism, which holds that indicative conditionals express reason relations. Throughout a series of experiments, probabilistic and truth-conditional variants of Inferentialism are investigated using new stimulus materials, which manipulate previously overlooked relevance conditions. These studies are some of the first published studies to directly investigate the central claims of Inferentialism empirically. In contrast, the Suppositional Theory (...)
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  • Preemption in Singular Causation Judgments: A Computational Model.Simon Stephan & Michael R. Waldmann - 2018 - Topics in Cognitive Science 10 (1):242-257.
    Causal queries about singular cases are ubiquitous, yet the question of how we assess whether a particular outcome was actually caused by a specific potential cause turns out to be difficult to answer. Relying on the causal power framework, Cheng and Novick () proposed a model of causal attribution intended to help answer this question. We challenge this model, both conceptually and empirically. We argue that the central problem of this model is that it treats causal powers that are probabilistically (...)
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  • Reasoning With Causal Cycles.Bob Rehder - 2017 - Cognitive Science 41 (S5):944-1002.
    This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new formalisms that allow cycles are introduced and evaluated. Dynamic Bayesian networks represent cycles by unfolding them over time. Chain graphs augment CGMs by allowing the presence of undirected links (...)
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  • A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.Hongjing Lu, Randall R. Rojas, Tom Beckers & Alan L. Yuille - 2016 - Cognitive Science 40 (2):404-439.
    Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that (...)
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  • Are Causal Structure and Intervention Judgments Inextricably Linked? A Developmental Study.Caren A. Frosch, Teresa McCormack, David A. Lagnado & Patrick Burns - 2012 - Cognitive Science 36 (2):261-285.
    The application of the formal framework of causal Bayesian Networks to children’s causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. Three experiments examined whether children are able to make correct inferences about interventions on different causal structures. The first two experiments examined whether children’s causal structure and intervention judgments were consistent with one another. In Experiment 1, children (...)
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  • Four-Dimensional Consciousness.Richard Allen Sieb - 2017 - Activitas Nervosa Superior 59 (2):(43-60).
    Conscious experience is the direct observation of conscious events. Human conscious experience is four-dimensional. Conscious events are linked (associated) by spacetime intervals to produce a coherent conscious experience. This explains why conscious experience appears to us the way it does. Conscious experience is an orientation in space and time, an understanding of the position of the observer in space and time. Causality, past-future relations, learning, memory, cognitive processing, and goal-directed actions all evolve from four-dimensional conscious experience. A neural correlate for (...)
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  • 4 What is a Medical Theory?Paul Thagard - unknown
    Modern medicine has produced many successful theories concerning the causes of diseases. For example, we know that tuberculosis is caused by the bacterium Mycobacterium tuberculosis, and that scurvy is caused by a deficiency of vitamin C. This chapter discusses the nature of medical theories from the perspective of the philosophy, history, and psychology of science. I will review prominent philosophical accounts of what constitutes a scientific theory, and develop a new account of medical theories as representations of mechanisms that explain (...)
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  • Neural Correlates of Causal Power Judgments.Denise Dellarosa Cummins - 2014 - Frontiers in Human Neuroscience 8.
  • Superstition and Belief as Inevitable by-Products of an Adaptive Learning Strategy.Jan Beck & Wolfgang Forstmeier - 2007 - Human Nature 18 (1):35-46.
    The existence of superstition and religious beliefs in most, if not all, human societies is puzzling for behavioral ecology. These phenomena bring about various fitness costs ranging from burial objects to celibacy, and these costs are not outweighed by any obvious benefits. In an attempt to resolve this problem, we present a verbal model describing how humans and other organisms learn from the observation of coincidence (associative learning). As in statistical analysis, learning organisms need rules to distinguish between real patterns (...)
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  • The Folk Probably Don't Think What You Think They Think: Experiments on Causation by Absence.Jonathan Livengood & Edouard Machery - 2007 - Midwest Studies in Philosophy 31 (1):107–127.
    Folk theories—untutored people’s (often implicit) theories about various features of the world—have been fashionable objects of inquiry in psychology for almost two decades now (e.g., Hirschfeld and Gelman 1994), and more recently they have been of interest in experimental philosophy (Nichols 2004). Folk theories of psy- chology, physics, biology, and ethics have all come under investigation. Folk meta- physics, however, has not been as extensively studied. That so little is known about folk metaphysics is unfortunate for (at least) two reasons. (...)
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  • Causal Powers.Eric Hiddleston - 2005 - British Journal for the Philosophy of Science 56 (1):27-59.
    Nancy Cartwright offers an account of causal powers, and argues that it explains some important general features of scientific method. Patricia Cheng argues that this theory is superior as a psychological theory of learning to standard models of conditioning. I extend and develop the theory, and argue that it provides the best explanation of a number of problem cases for philosophical theories of causation, including preemption, overdetermination and puzzles about transitivity. Hitchcock and Halpern & Pearl on ‘actual causes’ Problems and (...)
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  • Contrasting Associative and Statistical Theories of Contingency Judgments.Rick R. Mehta - unknown
    "Blocking" refers to judgments of a moderate contingency being lowered when contrasted with a strong contingency. The Rescorla-Wagner model and causal model theory account for blocking through different mechanisms. To examine the predictions from these two models, seven experiments tested the extent to which "causal scenario" and "causal order" would influence whether blocking was observed in human contingency learning tasks. "Causal scenario" was manipulated by contrasting responses to two causes of one effect or to one cause of two effects; "causal (...)
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  • Causal‐Based Property Generalization.Bob Rehder - 2009 - Cognitive Science 33 (3):301-344.
  • Sensitive and Insensitive Causation.James Woodward - 2006 - Philosophical Review 115 (1):1-50.
  • Edge Replacement and Nonindependence in Causation.D. Buchanan, J. Tenenbaum & D. Sobel - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
  • Explaining Compound Generalization in Associative and Causal Learning Through Rational Principles of Dimensional Generalization.Fabian A. Soto, Samuel J. Gershman & Yael Niv - 2014 - Psychological Review 121 (3):526-558.
  • Reconciling Intuitive Physics and Newtonian Mechanics for Colliding Objects.Adam N. Sanborn, Vikash K. Mansinghka & Thomas L. Griffiths - 2013 - Psychological Review 120 (2):411-437.
  • Learning a Theory of Causality.Noah D. Goodman, Tomer D. Ullman & Joshua B. Tenenbaum - 2011 - Psychological Review 118 (1):110-119.
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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.
  • BUCKLE: A Model of Unobserved Cause Learning.Christian C. Luhmann & Woo-Kyoung Ahn - 2007 - Psychological Review 114 (3):657-677.
  • The Meaning and Computation of Causal Power: Comment on Cheng and Novick and Cheng.Christian C. Luhmann & Woo-Kyoung Ahn - 2005 - Psychological Review 112 (3):685-692.
  • 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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  • A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.Alison Gopnik, Clark Glymour, Laura Schulz, Tamar Kushnir & David Danks - 2002 - 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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  • Causal Inference and the Hierarchical Structure of Experience.Samuel G. B. Johnson & Frank C. Keil - 2014 - Journal of Experimental Psychology: General 143 (6):2223-2241.
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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.