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  1. 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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  • The role of mechanism knowledge in singular causation judgments.Simon Stephan & Michael R. Waldmann - 2022 - Cognition 218 (C):104924.
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  • Time and Singular Causation—A Computational Model.Simon Stephan, Ralf Mayrhofer & Michael R. Waldmann - 2020 - Cognitive Science 44 (7).
    Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co‐occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a (...)
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  • 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.
  • Effects of question formats on causal judgments and model evaluation.Yiyun Shou & Michael Smithson - 2015 - Frontiers in Psychology 6.
  • How Does Explanatory Virtue Determine Probability Estimation?—Empirical Discussion on Effect of Instruction.Asaya Shimojo, Kazuhisa Miwa & Hitoshi Terai - 2020 - Frontiers in Psychology 11.
    It is important to reveal how humans evaluate an explanation of the recent development of explainable artificial intelligence. So, what makes people feel that one explanation is more likely than another? In the present study, we examine how explanatory virtues affect the process of estimating subjective posterior probability. Through systematically manipulating two virtues, Simplicity—the number of causes used to explain effects—and Scope—the number of effects predicted by causes—in three different conditions, we clarified two points in Experiment 1: that Scope's effect (...)
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  • Successful structure learning from observational data.Anselm Rothe, Ben Deverett, Ralf Mayrhofer & Charles Kemp - 2018 - Cognition 179 (C):266-297.
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  • Children Use Temporal Cues to Learn Causal Directionality.Benjamin M. Rottman, Jonathan F. Kominsky & Frank C. Keil - 2014 - Cognitive Science 38 (3):489-513.
    The ability to learn the direction of causal relations is critical for understanding and acting in the world. We investigated how children learn causal directionality in situations in which the states of variables are temporally dependent (i.e., autocorrelated). In Experiment 1, children learned about causal direction by comparing the states of one variable before versus after an intervention on another variable. In Experiment 2, children reliably inferred causal directionality merely from observing how two variables change over time; they interpreted Y (...)
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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 framework for knowledge attribution: Evidence from semantic integration.Derek Powell, Zachary Horne, Ángel Pinillos & Keith Holyoak - 2015 - Cognition 139 (C):92-104.
    We propose a Bayesian framework for the attribution of knowledge, and apply this framework to generate novel predictions about knowledge attribution for different types of “Gettier cases”, in which an agent is led to a justified true belief yet has made erroneous assumptions. We tested these predictions using a paradigm based on semantic integration. We coded the frequencies with which participants falsely recalled the word “thought” as “knew” (or a near synonym), yielding an implicit measure of conceptual activation. Our experiments (...)
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  • A tutorial introduction to Bayesian models of cognitive development.Amy Perfors, Joshua B. Tenenbaum, Thomas L. Griffiths & Fei Xu - 2011 - Cognition 120 (3):302-321.
  • Confidence and gradation in causal judgment.Kevin O'Neill, Paul Henne, Paul Bello, John Pearson & Felipe De Brigard - 2022 - Cognition 223 (C):105036.
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  • Sufficiency and Necessity Assumptions in Causal Structure Induction.Ralf Mayrhofer & Michael R. Waldmann - 2016 - Cognitive Science 40 (8):2137-2150.
    Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found (...)
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  • 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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  • When children are better (or at least more open-minded) learners than adults: Developmental differences in learning the forms of causal relationships.Christopher G. Lucas, Sophie Bridgers, Thomas L. Griffiths & Alison Gopnik - 2014 - Cognition 131 (2):284-299.
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  • Learning the Form of Causal Relationships Using Hierarchical Bayesian Models.Christopher G. Lucas & Thomas L. Griffiths - 2010 - Cognitive Science 34 (1):113-147.
  • Bayesian analogy with relational transformations.Hongjing Lu, Dawn Chen & Keith J. Holyoak - 2012 - Psychological Review 119 (3):617-648.
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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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  • How multiple causes combine: independence constraints on causal inference.Mimi Liljeholm - 2015 - Frontiers in Psychology 6.
  • 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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  • What the Bayesian framework has contributed to understanding cognition: Causal learning as a case study.Keith J. Holyoak & Hongjing Lu - 2011 - Behavioral and Brain Sciences 34 (4):203-204.
    The field of causal learning and reasoning (largely overlooked in the target article) provides an illuminating case study of how the modern Bayesian framework has deepened theoretical understanding, resolved long-standing controversies, and guided development of new and more principled algorithmic models. This progress was guided in large part by the systematic formulation and empirical comparison of multiple alternative Bayesian models.
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  • Dual frames for causal induction: the normative and the heuristic.Ikuko Hattori, Masasi Hattori, David E. Over, Tatsuji Takahashi & Jean Baratgin - 2017 - Thinking and Reasoning 23 (3):292-317.
    Causal induction in the real world often has to be quick and efficient as well as accurate. We propose that people use two different frames to achieve these goals. The A-frame consists of heuristic processes that presuppose rarity and can detect causally relevant factors quickly. The B-frame consists of analytic processes that can be highly accurate in detecting actual causes. Our dual frame theory implies that several factors affect whether people use the A-frame or the B-frame in causal induction: among (...)
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  • Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults.Thomas L. Griffiths, David M. Sobel, Joshua B. Tenenbaum & Alison Gopnik - 2011 - Cognitive Science 35 (8):1407-1455.
    People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which (...)
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  • 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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  • Cognitive shortcuts in causal inference.Philip M. Fernbach & Bob Rehder - 2013 - Argument and Computation 4 (1):64 - 88.
    (2013). Cognitive shortcuts in causal inference. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 64-88. doi: 10.1080/19462166.2012.682655.
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  • Bayesian learning and the psychology of rule induction.Ansgar D. Endress - 2013 - Cognition 127 (2):159-176.
  • Causal Pluralism in Philosophy: Empirical Challenges and Alternative Proposals.Phuong Dinh & David Danks - 2021 - Philosophy of Science 88 (5):761-772.
    An increasing number of arguments for causal pluralism invoke empirical psychological data. Different aspects of causal cognition—specifically, causal perception and causal inference—are thought to involve distinct cognitive processes and representations, and they thereby distinctively support transference and dependency theories of causation, respectively. We argue that this dualistic picture of causal concepts arises from methodological differences, rather than from an actual plurality of concepts. Hence, philosophical causal pluralism is not particularly supported by the empirical data. Serious engagement with cognitive science reveals (...)
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  • Causal Structure Learning in Continuous Systems.Zachary J. Davis, Neil R. Bramley & Bob Rehder - 2020 - Frontiers in Psychology 11.
    Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e. those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via interactions with (...)
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  • Privileged Causal Cognition: A Mathematical Analysis.David Danks - 2018 - Frontiers in Psychology 9.
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  • Generative Inferences Based on Learned Relations.Dawn Chen, Hongjing Lu & Keith J. Holyoak - 2017 - Cognitive Science 41 (S5):1062-1092.
    A key property of relational representations is their generativity: From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from non-relational inputs. In the present paper, we show that a bottom-up model of relation learning, initially developed to discriminate between positive and negative examples of comparative relations, can be extended to make generative inferences. (...)
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  • Analytic Causal Knowledge for Constructing Useable Empirical Causal Knowledge: Two Experiments on Pre‐schoolers.Patricia W. Cheng, Catherine M. Sandhofer & Mimi Liljeholm - 2022 - Cognitive Science 46 (5):e13137.
    Cognitive Science, Volume 46, Issue 5, May 2022.
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  • Evaluating the inverse reasoning account of object discovery.Christopher D. Carroll & Charles Kemp - 2015 - Cognition 139:130-153.
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  • How do humans want causes to combine their effects? The role of analytically-defined causal invariance for generalizable causal knowledge.Jeffrey K. Bye, Pei-Jung Chuang & Patricia W. Cheng - 2023 - Cognition 230 (C):105303.
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  • Bayesian Occam's Razor Is a Razor of the People.Thomas Blanchard, Tania Lombrozo & Shaun Nichols - 2018 - Cognitive Science 42 (4):1345-1359.
    Occam's razor—the idea that all else being equal, we should pick the simpler hypothesis—plays a prominent role in ordinary and scientific inference. But why are simpler hypotheses better? One attractive hypothesis known as Bayesian Occam's razor is that more complex hypotheses tend to be more flexible—they can accommodate a wider range of possible data—and that flexibility is automatically penalized by Bayesian inference. In two experiments, we provide evidence that people's intuitive probabilistic and explanatory judgments follow the prescriptions of BOR. In (...)
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  • 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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  • A Bayesian Framework for False Belief Reasoning in Children: A Rational Integration of Theory-Theory and Simulation Theory.Nobuhiko Asakura & Toshio Inui - 2016 - Frontiers in Psychology 7.
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  • The induction of hidden causes: Causal mediation and violations of independent causal influence.Christopher D. Carroll & Patricia W. Cheng - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 913--918.
     
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  • Causal Control: A Rationale for Causal Selection.Lauren N. Ross - 2015
    Causal selection has to do with the distinction we make between background conditions and “the” true cause or causes of some outcome of interest. A longstanding consensus in philosophy views causal selection as lacking any objective rationale and as guided, instead, by arbitrary, pragmatic, and non-scientific considerations. I argue against this position in the context of causal selection for disease traits. In this domain, causes are selected on the basis of the type of causal control they exhibit over a disease (...)
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