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  1. 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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  • 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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  • Transfer Effects Between Moral Dilemmas: A Causal Model Theory.Alex Wiegmann & Michael R. Waldmann - 2014 - Cognition 131 (1):28-43.
  • The Interplay of Conflict and Analogy in Multidisciplinary Teams.Susannah Bf Paletz, Christian D. Schunn & Kevin H. Kim - 2013 - Cognition 126 (1):1-19.
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  • The Role of Causal Models in Multiple Judgments Under Uncertainty.Brett K. Hayes, Guy E. Hawkins, Ben R. Newell, Martina Pasqualino & Bob Rehder - 2014 - Cognition 133 (3):611-620.
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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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  • 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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  • 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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