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Christopher Lucas
University of Edinburgh
  1.  35
    Learning the Form of Causal Relationships Using Hierarchical Bayesian Models.Christopher G. Lucas & Thomas L. Griffiths - 2010 - Cognitive Science 34 (1):113-147.
  2.  23
    When Children Are Better 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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  3.  50
    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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  4.  9
    People Learn Other People’s Preferences Through Inverse Decision-Making.Alan Jern, Christopher G. Lucas & Charles Kemp - 2017 - Cognition 168:46-64.
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    Corrigendum to “People Learn Other People’s Preferences Through Inverse Decision-Making” [Cognition 168 46–64].Alan Jern, Christopher G. Lucas & Charles Kemp - 2018 - Cognition 175:201.
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  6. An Improved Probabilistic Account of Counterfactual Reasoning.Christopher G. Lucas & Charles Kemp - 2015 - Psychological Review 122 (4):700-734.
    When people want to identify the causes of an event, assign credit or blame, or learn from their mistakes, they often reflect on how things could have gone differently. In this kind of reasoning, one considers a counterfactual world in which some events are different from their real-world counterparts and considers what else would have changed. Researchers have recently proposed several probabilistic models that aim to capture how people do (or should) reason about counterfactuals. We present a new model and (...)
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