In some practical uncertain situations decision makers are presented with described events that are out of sequence when having to make a causal attribution. A theoretical perspective concerning the causal coherence of the explanation is developed to predict the effect of this on causal attribution. Three experiments investigated the effect on causal judgement when the described order of events did not correspond to their causal order. Participants had to judge the relative probability of two possible causes of an outcome in (...) scenarios in which presentation order varied. All three experiments found that there was a preference to judge that the cause associated with events described in causal order was more responsible for the outcome when events associated with one cause were interleaved in their presentation order with those from a second cause. This occurred when there was a strong causal relationship between events. The results were consistent with the causal coherence explanation. (shrink)

Causal pre-emption scenarios are problematic for the counterfactual framework of causation because people judge an action to be the actual cause of an outcome although the outcome would have...

ABSTRACTAccording to difference-based models of causal judgement, the epistemic state of the agent should not affect judgements of cause. Four experiments examined opportunity chains in which a physical event enabled a subsequent proximal cause to produce an outcome. All four experiments showed that when the proximal cause was a human action, it was judged as more causal if the agent was aware of his opportunity than if he was not or if the proximal cause was a physical event. The first (...) two experiments showed that these preferences could not be explained in terms of differences in perceived conditional probability, social controllability or perceptions of the causal sequence as forming a single unit. The third experiment showed that awareness affected the perceived deliberateness with which the action brought the outcome about but not its perceived voluntariness. The fourth experiment showed that... (shrink)

This paper outlines a theory and computer implementation of causal meanings and reasoning. The meanings depend on possibilities, and there are four weak causal relations: A causes B, A prevents B, A allows B, and A allows not‐B, and two stronger relations of cause and prevention. Thus, A causes B corresponds to three possibilities: A and B, not‐A and B, and not‐A and not‐B, with the temporal constraint that B does not precede A; and the stronger relation conveys only the (...) first and last of these possibilities. Individuals represent these relations in mental models of what is true in the various possibilities. The theory predicts a number of phenomena, and, contrary to many accounts, it implies that the meaning of causation is not probabilistic, differs from the meaning of enabling conditions, and does not depend on causal powers or mechanisms. The theory also implies that causal deductions do not depend on schemas or rules. (shrink)

Previous research on counterfactual thoughts about prevention suggests that people tend to focus on enabling rather than causing events and controllable rather than uncontrollable events. Two experiments explore whether counterfactual thinking about enablers is distinct from counterfactual thinking about controllable events. We presented participants with scenarios in which a cause and an enabler contributed to a negative outcome. We systematically manipulated the controllability of the cause and the enabler and asked participants to generate counterfactuals. The results indicate that when only (...) the cause or the enabler is controllable participants undid the controllable event more often. However, when the cause and enabler are matched in controllability participants undid the enabler slightly more often. The findings are discussed in the context of the mental model, functional and judgement dissociation theories as well as previous research on counterfactual thinking. The importance of controllability and.. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...) the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly. (shrink)

Many theories in philosophy, law, and psychology, make no distinction in meaning between causing and enabling conditions. Yet, psychologically people readily make such distinctions each day. In this paper we report three experiments, showing that individuals distinguish between causes and enabling conditions in brief descriptions of wrongful outcomes. Respondents rate actions that bring about outcomes as causes, and actions that make possible the causal relation as enablers. Likewise, causers (as opposed to enablers) are rated as more responsible for the outcome, (...) as liable to longer prison sentences, and as liable to pay higher fines. Moreover, the more actors involved, the more blame volume there is psychologically to be apportioned between them. The implication is that theories and the law in practice, both criminal and civil, may dangerously mismatch the intuitions of those to whom they are supposed to apply. The findings are discussed in light of contemporary psychological theories of how people reason about cause. -/- The paper presents the extended findings supplementary to the conference paper: Frosch, C. A., Johnson-Laird, P. N., Cowley, M. (2007). Don’t blame me your Honor, I’m only the enabler. Proceedings of the Twenty–Ninth Annual Conference of the Cognitive Science Society, p. 1755, Mahweh, N. J: Erlbaum. Nashville, USA. *Main findings now cited in the Oxford Handbook of Causal Reasoning. (shrink)