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David A. Lagnado [16]David Lagnado [7]
  1.  5
    James W. Moore, David Lagnado, Darvany C. Deal & Patrick Haggard (2009). Feelings of Control: Contingency Determines Experience of Action. Cognition 110 (2):279-283.
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  2.  7
    David A. Lagnado & Shelley Channon (2008). Judgments of Cause and Blame: The Effects of Intentionality and Foreseeability. Cognition 108 (3):754-770.
  3.  8
    Steven A. Sloman & David A. Lagnado (2005). Do We “Do”? Cognitive Science 29 (1):5-39.
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  4.  11
    York Hagmayer, Steven A. Sloman, David A. Lagnado & Michael R. Waldmann (2007). Causal Reasoning Through Intervention. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press
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  5.  21
    David A. Lagnado, Tobias Gerstenberg & Ro'I. Zultan (2013). Causal Responsibility and Counterfactuals. Cognitive Science 37 (6):1036-1073.
    How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in multiple agent contexts. We draw on the structural model account of actual causation (e.g., Halpern & Pearl, 2005) and its extension to responsibility judgments (Chockler & Halpern, 2004). We review the main (...)
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  6.  4
    Christos Bechlivanidis & David A. Lagnado (2016). Time Reordered: Causal Perception Guides the Interpretation of Temporal Order. Cognition 146:58-66.
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  7.  9
    Tobias Gerstenberg & David A. Lagnado (2010). Spreading the Blame: The Allocation of Responsibility Amongst Multiple Agents. Cognition 115 (1):166-171.
  8.  11
    Jonathan F. Kominsky, Jonathan Phillips, Tobias Gerstenberg, David Lagnado & Joshua Knobe (2015). Causal Superseding. Cognition 137:196-209.
    When agents violate norms, they are typically judged to be more of a cause of resulting outcomes. In this paper, we suggest that norm violations also affect the causality attributed to other agents, a phenomenon we refer to as "causal superseding." We propose and test a counterfactual reasoning model of this phenomenon in four experiments. Experiments 1 and 2 provide an initial demonstration of the causal superseding effect and distinguish it from previously studied effects. Experiment 3 shows that this causal (...)
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  9.  29
    Norman Fenton, Martin Neil & David A. Lagnado (2013). A General Structure for Legal Arguments About Evidence Using Bayesian Networks. Cognitive Science 37 (1):61-102.
    A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where (...)
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  10.  48
    Jonathan Kominsky, Jonathan Phillips, Tobias Gerstenberg, David Lagnado & Joshua Knobe (2016). Causal Superseding. Cognition 137:196-209.
    When agents violate norms, they are typically judged to be more of a cause of resulting outcomes. In this paper, we suggest that norm violations also affect the causality attributed to other agents, a phenomenon we refer to as ‘‘causal superseding.’’ We propose and test a counterfactual reasoning model of this phenomenon in four experiments. Experiments 1 and 2 provide an initial demonstration of the causal superseding effect and distinguish it from previously studied effects. Experiment 3 shows that this causal (...)
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  11.  7
    David A. Lagnado, Norman Fenton & Martin Neil (2013). Legal Idioms: A Framework for Evidential Reasoning. Argument and Computation 4 (1):46 - 63.
    (2013). Legal idioms: a framework for evidential reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 46-63. doi: 10.1080/19462166.2012.682656.
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  12.  9
    David A. Lagnado, Michael R. Waldmann, York Hagmayer & Steven A. Sloman (2007). Beyond Covariation. In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press
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  13.  14
    Robert L. Goldstone, Steven A. Sloman, David A. Lagnado, Mark Steyvers, Joshua B. Tenenbaum, Saskia Jaarsveld, Cees van Leeuwen, Murray Shanahan, Terry Dartnall & Simon Dennis (2005). Subject Index to Volume 29. Cognitive Science 29:1093-1096.
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  14.  32
    Caren A. Frosch, Teresa McCormack, David A. Lagnado & Patrick Burns (2012). Are Causal Structure and Intervention Judgments Inextricably Linked? A Developmental Study. 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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  15.  9
    David Lagnado (2011). Thinking About Evidence. In Philip Dawid, William Twining & Mimi Vasilaki (eds.), Evidence, Inference and Enquiry. OUP/British Academy 183-223.
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  16. David A. Lagnado & David R. Shanks (2003). The Influence of Hierarchy on Probability Judgment. Cognition 89 (2):157-178.
    Consider the task of predicting which soccer team will win the next World Cup. The bookmakers may judge Brazil to be the team most likely to win, but also judge it most likely that a European rather than a Latin American team will win. This is an example of a non-aligned hierarchy structure: the most probable event at the subordinate level (Brazil wins) appears to be inconsistent with the most probable event at the superordinate level (a European team wins). In (...)
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  17.  22
    Marion Vorms & David Lagnado, The Role of Models in Mind and Science.
    During the last few decades, models have become the centre of attention in both cognitive science and philosophy of science. In cognitive science, the claim that humans reason with mental models, rather than mentally manipulate linguistic symbols, is the majority view. Similarly, philosophers of science almost unanimously acknowledge that models have to be taken as a central unit of analysis. Moreover, some philosophers of science and cognitive scientists have suggested that the cognitive hypothesis of mental models is a promising way (...)
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  18.  12
    David R. Shanks & David Lagnado (2000). Sub-Optimal Reasons for Rejecting Optimality. Behavioral and Brain Sciences 23 (5):761-762.
    Although we welcome Gigerenzer, Todd, and the ABC Research Group's shift of emphasis from “coherence” to “correspondence” criteria, their rejection of optimality in human decision making is premature: In many situations, experts can achieve near-optimal performance. Moreover, this competence does not require implausible computing power. The models Gigerenzer et al. evaluate fail to account for many of the most robust properties of human decision making, including examples of optimality.
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  19.  22
    David A. Lagnado (2009). A Causal Framework for Integrating Learning and Reasoning. Behavioral and Brain Sciences 32 (2):211-212.
    Can the phenomena of associative learning be replaced wholesale by a propositional reasoning system? Mitchell et al. make a strong case against an automatic, unconscious, and encapsulated associative system. However, their propositional account fails to distinguish inferences based on actions from those based on observation. Causal Bayes networks remedy this shortcoming, and also provide an overarching framework for both learning and reasoning. On this account, causal representations are primary, but associative learning processes are not excluded a priori.
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  20.  12
    David A. Lagnado (2007). Perspectives on Daniel Kahneman. Thinking and Reasoning 13 (1):1 – 4.
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  21.  10
    David A. Lagnado & David R. Shanks (2007). Dual Concerns with the Dualist Approach. Behavioral and Brain Sciences 30 (3):271-272.
    Barbey & Sloman (B&S) attribute all instances of normative base-rate usage to a rule-based system, and all instances of neglect to an associative system. As it stands, this argument is too simplistic, and indeed fails to explain either good or bad performance on the classic Medical Diagnosis problem.
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  22. David Carmel, Shlomo Bentin, Chang Hong Liu, Avi Chaudhuri, David A. Lagnado & David R. Shanks (2002). Alexandre Pouget, Jean-Christophe Ducom, Jeffrey Torri and Daphne Bavelier (University of Rochester) Multisensory Spatial Representations in Eye-Centered Coordinates for Reaching, B1–B11. Cognition 83:323-325.
     
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  23. David Lagnado (2011). Causal Thinking. In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. OUP Oxford
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