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David A. Lagnado [30]David Lagnado [17]
  1. Causal Superseding.Jonathan F. Kominsky, Jonathan Phillips, Tobias Gerstenberg, David Lagnado & Joshua Knobe - 2015 - 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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  2. Temporal Binding, Causation and Agency: Developing a New Theoretical Framework.Christoph Hoerl, Sara Lorimer, Teresa McCormack, David A. Lagnado, Emma Blakey, Emma C. Tecwyn & Marc J. Buehner - 2020 - Cognitive Science 44 (5):e12843.
    In temporal binding, the temporal interval between one event and another, occurring some time later, is subjectively compressed. We discuss two ways in which temporal binding has been conceptualized. In studies showing temporal binding between a voluntary action and its causal consequences, such binding is typically interpreted as providing a measure of an implicit or pre-reflective “sense of agency”. However, temporal binding has also been observed in contexts not involving voluntary action, but only the passive observation of a cause-effect sequence. (...)
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  3.  39
    Judgments of Cause and Blame: The Effects of Intentionality and Foreseeability.David A. Lagnado & Shelley Channon - 2008 - Cognition 108 (3):754-770.
  4. A General Structure for Legal Arguments About Evidence Using Bayesian Networks.Norman Fenton, Martin Neil & David A. Lagnado - 2013 - 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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  5.  81
    Causal Responsibility and Counterfactuals.David A. Lagnado, Tobias Gerstenberg & Ro'I. Zultan - 2013 - 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.  32
    Feelings of Control: Contingency Determines Experience of Action.James W. Moore, David Lagnado, Darvany C. Deal & Patrick Haggard - 2009 - Cognition 110 (2):279-283.
    The experience of causation is a pervasive product of the human mind. Moreover, the experience of causing an event alters subjective time: actions are perceived as temporally shifted towards their effects [Haggard, P., Clark, S., & Kalogeras, J.. Voluntary action and conscious awareness. Nature Neuroscience, 5, 382-385]. This temporal shift depends partly on advance prediction of the effects of action, and partly on inferential "postdictive" explanations of sensory effects of action. We investigated whether a single factor of statistical contingency could (...)
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  7.  27
    Do We “Do‘?Steven A. Sloman & David A. Lagnado - 2005 - Cognitive Science 29 (1):5-39.
    A normative framework for modeling causal and counterfactual reasoning has been proposed by Spirtes, Glymour, and Scheines. The framework takes as fundamental that reasoning from observation and intervention differ. Intervention includes actual manipulation as well as counterfactual manipulation of a model via thought. To represent intervention, Pearl employed the do operator that simplifies the structure of a causal model by disconnecting an intervened-on variable from its normal causes. Construing the do operator as a psychological function affords predictions about how people (...)
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  8.  12
    When Causality Shapes the Experience of Time: Evidence for Temporal Binding in Young Children.Emma Blakey, Emma Tecwyn, Teresa McCormack, David A. Lagnado, Christoph Hoerl, Sara Lorimer & Marc J. Buehner - 2019 - Developmental Science 22 (3):e12769.
    It is well established that the temporal proximity of two events is a fundamental cue to causality. Recent research with adults has shown that this relation is bidirectional: events that are believed to be causally related are perceived as occurring closer together in time—the so‐called temporal binding effect. Here, we examined the developmental origins of temporal binding. Participants predicted when an event that was either caused by a button press, or preceded by a non‐causal signal, would occur. We demonstrate for (...)
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  9.  41
    Causal Responsibility and Robust Causation.Guy Grinfeld, David Lagnado, Tobias Gerstenberg, James F. Woodward & Marius Usher - 2020 - Frontiers in Psychology 11:1069.
    How do people judge the degree of causal responsibility that an agent has for the outcomes of her actions? We show that a relatively unexplored factor -- the robustness of the causal chain linking the agent’s action and the outcome -- influences judgments of causal responsibility of the agent. In three experiments, we vary robustness by manipulating the number of background circumstances under which the action causes the effect, and find that causal responsibility judgments increase with robustness. In the first (...)
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  10.  29
    Subject Index to Volume 29.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 - Cognitive Science 29 (1):1093-1096.
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  11.  33
    Causal Reasoning Through Intervention.York Hagmayer, Steven A. Sloman, David A. Lagnado & Michael R. Waldmann - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press.
  12.  3
    Formalizing Neurath’s Ship: Approximate Algorithms for Online Causal Learning.Neil R. Bramley, Peter Dayan, Thomas L. Griffiths & David A. Lagnado - 2017 - Psychological Review 124 (3):301-338.
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  13.  18
    Time Reordered: Causal Perception Guides the Interpretation of Temporal Order.Christos Bechlivanidis & David A. Lagnado - 2016 - Cognition 146:58-66.
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  14.  10
    Lucky or Clever? From Expectations to Responsibility Judgments.Tobias Gerstenberg, Tomer D. Ullman, Jonas Nagel, Max Kleiman-Weiner, David A. Lagnado & Joshua B. Tenenbaum - 2018 - Cognition 177:122-141.
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  15.  5
    Dependencies in Evidential Reports: The Case for Informational Advantages.Toby D. Pilditch, Ulrike Hahn, Norman Fenton & David Lagnado - 2020 - Cognition 204:104343.
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  16.  24
    Spreading the Blame: The Allocation of Responsibility Amongst Multiple Agents.Tobias Gerstenberg & David A. Lagnado - 2010 - Cognition 115 (1):166-171.
  17.  36
    Modelling Competing Legal Arguments Using Bayesian Model Comparison and Averaging.Martin Neil, Norman Fenton, David Lagnado & Richard David Gill - 2019 - Artificial Intelligence and Law 27 (4):403-430.
    Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent (...)
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  18.  23
    Legal Idioms: A Framework for Evidential Reasoning.David A. Lagnado, Norman Fenton & Martin Neil - 2013 - 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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  19.  23
    Beyond Covariation.David A. Lagnado, Michael R. Waldmann, York Hagmayer & Steven A. Sloman - 2007 - In Alison Gopnik & Laura Schulz (eds.), Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press.
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  20.  6
    Causal Judgments About Atypical Actions Are Influenced by Agents' Epistemic States.Lara Kirfel & David Lagnado - 2021 - Cognition 212:104721.
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  21.  6
    Widening Access to Bayesian Problem Solving.Nicole Cruz, Saoirse Connor Desai, Stephen Dewitt, Ulrike Hahn, David Lagnado, Alice Liefgreen, Kirsty Phillips, Toby Pilditch & Marko Tešić - 2020 - Frontiers in Psychology 11.
  22.  12
    Insight and Strategy in Multiple-Cue Learning.David A. Lagnado, Ben R. Newell, Steven Kahan & David R. Shanks - 2006 - Journal of Experimental Psychology: General 135 (2):162-183.
  23.  15
    Argumentative Explanations for Interactive Recommendations.Antonio Rago, Oana Cocarascu, Christos Bechlivanidis, David Lagnado & Francesca Toni - 2021 - Artificial Intelligence 296:103506.
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  24.  5
    Analyzing the Simonshaven Case Using Bayesian Networks.Norman Fenton, Martin Neil, Barbaros Yet & David Lagnado - 2020 - Topics in Cognitive Science 12 (4):1092-1114.
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  25.  19
    Finding Fault: Causality and Counterfactuals in Group Attributions.Ro’I. Zultan, Tobias Gerstenberg & David A. Lagnado - 2012 - Cognition 125 (3):429-440.
  26.  91
    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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  27. The Developmental Profile of Temporal Binding: From Childhood to Adulthood.Sara Lorimer, Teresa McCormack, Emma Blakey, David A. Lagnado, Christoph Hoerl, Emma Tecwyn & Marc J. Buehner - 2020 - Quarterly Journal of Experimental Psychology 73 (10):1575-1586.
    Temporal binding refers to a phenomenon whereby the time interval between a cause and its effect is perceived as shorter than the same interval separating two unrelated events. We examined the developmental profile of this phenomenon by comparing the performance of groups of children (aged 6-7-, 7-8-, and 9-10- years) and adults on a novel interval estimation task. In Experiment 1, participants made judgments about the time interval between i) their button press and a rocket launch, and ii) a non-causal (...)
     
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  28.  37
    Thinking About Evidence.David Lagnado - 2011 - In Philip Dawid, William Twining & Mimi Vasilaki (eds.), Evidence, Inference and Enquiry. Oup/British Academy. pp. 183-223.
    This chapter argues that people reason about legal evidence using small-scale qualitative networks. These cognitive networks are typically qualitative and incomplete, and based on people's causal beliefs about the specifics of the case as well as the workings of the physical and social world in general. A key feature of these networks is their ability to represent qualitative relations between hypotheses and evidence, allowing reasoners to capture the concepts of dependency and relevance critical in legal contexts. In support of this (...)
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  29.  14
    Causation Without Realism.Christos Bechlivanidis, Anne Schlottmann & David A. Lagnado - 2019 - Journal of Experimental Psychology: General 148 (5):785-804.
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  30. Causal Thinking.David Lagnado - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
     
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  31.  7
    What’s Fair? How Children Assign Reward to Members of Teams with Differing Causal Structures.Karla Koskuba, Tobias Gerstenberg, Hannah Gordon, David Lagnado & Anne Schlottmann - 2018 - Cognition 177:234-248.
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  32.  4
    The Influence of Hierarchy on Probability Judgment.David A. Lagnado & David R. Shanks - 2003 - 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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  33.  36
    Endowment Effect Despite the Odds.Lukasz Walasek, Erica C. Yu & David A. Lagnado - 2018 - Thinking and Reasoning 24 (1):79-96.
    Can ownership status influence probability judgements under condition of uncertainty? In three experiments, we presented our participants with a recording of a real horse race. We endowed half of our sample with a wager on a single horse to win the race, and the other half with money to spend to acquire the same wager. Across three large studies, we found the endowment effect – owners demanded significantly more for the wager than buyers were willing to pay to acquire it. (...)
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  34.  18
    Causality Influences Children's and Adults' Experience of Temporal Order.Emma C. Tecwyn, Christos Bechlivanidis, David A. Lagnado, Christoph Hoerl, Sara Lorimer, Emma Blakey, Teresa McCormack & Marc J. Buehner - 2020 - Developmental Psychology 56 (4):739-755.
    Although it has long been known that time is a cue to causation, recent work with adults has demonstrated that causality can also influence the experience of time. In causal reordering (Bechlivanidis & Lagnado, 2013, 2016) adults tend to report the causally consistent order of events, rather than the correct temporal order. However, the effect has yet to be demonstrated in children. Across four pre-registered experiments, 4- to 10-year-old children (N=813) and adults (N=178) watched a 3-object Michotte-style ‘pseudocollision’. While in (...)
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  35. The Role of Models in Mind and Science.Marion Vorms & David Lagnado - unknown
    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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  36. Alexandre Pouget, Jean-Christophe Ducom, Jeffrey Torri and Daphne Bavelier (University of Rochester) Multisensory Spatial Representations in Eye-Centered Coordinates for Reaching, B1–B11.David Carmel, Shlomo Bentin, Chang Hong Liu, Avi Chaudhuri, David A. Lagnado & David R. Shanks - 2002 - Cognition 83:323-325.
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  37. Coherence and Credibility in the Story-Model of Jurors’ Decision-Making: Does Mental Simulation Really Drive the Evaluation of the Evidence?David Lagnado & Marion Vorms - 2019 - In Matthieu Fontaine, Cristina Barés-Gómez, Francisco Salguero-Lamillar, Lorenzo Magnani & Ángel Nepomuceno-Fernández (eds.), Model-Based Reasoning in Science and Technology. Springer Verlag.
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  38.  1
    Motive on the Mind: Explanatory Preferences at Multiple Stages of the Legal-Investigative Process.Alice Liefgreen, Sami R. Yousif, Frank C. Keil & David A. Lagnado - 2021 - Cognition 217:104892.
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  39.  38
    A Causal Framework for Integrating Learning and Reasoning.David A. Lagnado - 2009 - 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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  40.  34
    Perspectives on Daniel Kahneman.David A. Lagnado - 2007 - Thinking and Reasoning 13 (1):1 – 4.
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  41.  4
    Explaining Away, Augmentation, and the Assumption of Independence.Nicole Cruz, Ulrike Hahn, Norman Fenton & David Lagnado - 2020 - Frontiers in Psychology 11.
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  42.  3
    The Influence of Initial Beliefs on Judgments of Probability.Erica C. Yu & David A. Lagnado - 2012 - Frontiers in Psychology 3.
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  43.  4
    Propensities and Second Order Uncertainty: A Modified Taxi Cab Problem.Stephen H. Dewitt, Norman E. Fenton, Alice Liefgreen & David A. Lagnado - 2020 - Frontiers in Psychology 11.
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  44.  11
    Dynamics of Decision-Making: From Evidence Accumulation to Preference and Belief.Marius Usher, Konstantinos Tsetsos, Erica C. Yu & David A. Lagnado - 2013 - Frontiers in Psychology 4.
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  45.  21
    Dual Concerns with the Dualist Approach.David A. Lagnado & David R. Shanks - 2007 - Behavioral and Brain Sciences 30 (3):271-272.
    Barbey & Sloman 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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  46.  3
    Are Jurors Intuitive Statisticians? Bayesian Causal Reasoning in Legal Contexts.Tamara Shengelia & David Lagnado - 2021 - Frontiers in Psychology 11.
    In criminal trials, evidence often involves a degree of uncertainty and decision-making includes moving from the initial presumption of innocence to inference about guilt based on that evidence. The jurors’ ability to combine evidence and make accurate intuitive probabilistic judgments underpins this process. Previous research has shown that errors in probabilistic reasoning can be explained by a misalignment of the evidence presented with the intuitive causal models that people construct. This has been explored in abstract and context-free situations. However, less (...)
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  47.  29
    Sub-Optimal Reasons for Rejecting Optimality.David R. Shanks & David Lagnado - 2000 - 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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