Previous research suggests that children infer the presence of unobserved causes when objects appear to move spontaneously. Are such inferences limited to motion events or do children assume that unexplained physical events have causes more generally? Here we introduce an apparently spontaneous event and ask whether, even in the absence of spatiotemporal and co-variation cues linking the events, toddlers treat a plausible variable as a cause of the event. Toddlers (24 months) saw a toy that appeared to light up either (...) spontaneously or after an experimenter’s action. Toddlers were also introduced to a button but were not shown any predictive relation between the button and the light. Across three different dependent measures of exploration, predictive looking (Study 1), prompted intervention (Study 2), and spontaneous exploration (Study 3), toddlers were more likely to represent the button as a cause of the light when the event appeared to occur spontaneously. In Study 4, we found that even in the absence of a plausible candidate cause, toddlers engaged in selective exploration when the light appeared to activate spontaneously. These results suggest that toddlers’ exploration is guided by the causal explanatory power of events. (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)

Experimental philosophy is often presented as a new movement that avoids many of the difficulties that face traditional philosophy. This article distinguishes two views of experimental philosophy: a narrow view in which philosophers conduct empirical investigations of intuitions, and a broad view which says that experimental philosophy is just the colocation in the same body of (i) philosophical naturalism and (ii) the actual practice of cognitive science. These two positions are rarely clearly distinguished in the literature about experimental philosophy, both (...) pro and con. The article argues, first, that the broader view is the only plausible one; discussions of experimental philosophy should recognize that the narrow view is a caricature of experimental philosophy as it is currently done. It then shows both how objections to experimental philosophy are transformed and how positive recommendations can be provided by adopting a broad conception of experimental philosophy. (shrink)

In this paper, two central questions will be addressed: ought we to implement medical AI technology in the medical domain? If yes, how ought we to implement this technology? I will critically engage with three options that exist with respect to these central questions: the Neo-Luddite option, the Assistive option, and the Substitutive option. I will first address key objections on behalf of the Neo-Luddite option: the Objection from Bias, the Objection from Artificial Autonomy, the Objection from Status Quo, and (...) the Objection from Inscrutability. I will thereafter present the Demographic Trends Argument and the Human Enhancement Argument in support of alternatives to the Neo-Luddite option. In the second half of the paper, I will argue against the Substitutive option and in favour of the Assistive option, given the existence of two chief formal deficits in medical AI technology: the causality deficit and the care deficit. (shrink)

Young children's social learning is a topic of great interest. Here, we examined preschoolers’ help-seeking as a social information gathering activity that may optimize and support children's opportunities for learning. In a toy assembly task, we assessed each child's competency at assembling toys and the difficulty of each step of the task. We hypothesized that children's help-seeking would be a function of both initial competency and task difficulty. The results confirmed this prediction; all children were more likely to seek assistance (...) on difficult steps and less competent children sought assistance more often. Moreover, the magnitude of the help-seeking requests similarly related to both competency and difficulty. The results provide support for viewing children's help-seeking as an information gathering activity, indicating that preschoolers flexibly adjust the level and amount of assistance to optimize their opportunities for learning. (shrink)

This article explores some ways in which philosophical theories of causation and empirical investigations into causal learning and judgment can mutually inform one another.

My research program proposes that children have representations and learning mechanisms that can be characterized as causal models of the world Bayesian Fundamentalism.”.

Debates in animal cognition are frequently polarized between the romantic view that some species have human-like causal understanding and the killjoy view that human causal reasoning is unique. These apparently endless debates are often characterized by conceptual confusions and accusations of straw-men positions. What is needed is an account of causal understanding that enables researchers to investigate both similarities and differences in cognitive abilities in an incremental evolutionary framework. Here we outline the ways in which a three-dimensional model of causal (...) understanding fulfills these criteria. We describe how this approach clarifies what is at stake, illuminates recent experiments on both physical and social cognition, and plots a path for productive future research that avoids the romantic/killjoy dichotomy. (shrink)

This paper defends an interventionist treatment of mechanisms and contrasts this with Waskan (forthcoming). Interventionism embodies a difference-making conception of causation. I contrast such conceptions with geometrical/mechanical or “actualist” conceptions, associating Waskan’s proposals with the latter. It is argued that geometrical/mechanical conceptions of causation cannot replace difference-making conceptions in characterizing the behavior of mechanisms, but that some of the intuitions behind the geometrical/mechanical approach can be captured by thinking in terms of spatio-temporally organized difference-making information.

This paper explores some interactions between normative/ philosophical/theoretical theorizing about causation and empirical research into causal reasoning and judgment of the sort conducted by psychologists and others. I attempt to extract some general morals regarding the kinds of interactions between the empirical and the more traditionally philosophical that in my experience have been most fruitful. I also compare the experimental work on which I focus with some of the research strategies employed in experimental philosophy.

Woodward’s interventionist theory of causation is beset by a problem of circularity: the analysis of causes is in terms of interventions, and the analysis of interventions is in terms of causes. This is not in itself an argument against the correctness of the analysis. But by requiring us to have causal knowledge prior to making any judgements about causation, Woodward’s theory does make it mysterious how we can ever start acquiring causal knowledge. We present a solution to this problem by (...) showing how the interventionist notion of causation can be rationally generated from a more primitive agency notion of causation. The agency notion is easily and non-circularly applicable, but fails when we attempt to capture causal relations between non-actions. We show that the interventionist notion of causation serves as an appropriate generalisation of the agency notion. Furthermore, the causal judgements based on the latter generally remain true when rephrased in terms of the former, which allows one to use the causal knowledge gained by applying the agency notion as a basis for applying Woodward’s interventionist theory. We then present an overview of relevant empirical evidence from developmental psychology which shows that our proposed rational reconstruction lines up neatly with the actual development of causal reasoning in children. This gives additional plausibility to our proposal. The article thus provides a solution to one of the main problems of interventionism while keeping Woodward’s analysis intact. (shrink)

It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as heuristics for generating causal judgments under uncertainty and are a pervasive source of bias in causal judgment. More sophisticated clues such as mechanism clues and repeated interventions (...) are derived from the 14. Research on the use of empirical information and conditional probabilities to identify causes has used scenarios in which several of the clues are present, and the use of empirical association information for causal judgment depends on the presence of singular clues. It is the singular clues and their origin that are basic to causal understanding, not multiple instance clues such as empirical association, contingency, and conditional probabilities. (shrink)

Children posit unobserved causes when events appear to occur spontaneously. What about when events appear to occur probabilistically? Here toddlers saw arbitrary causal relationships in a fixed, alternating order. The relationships were then changed in one of two ways. In the Deterministic condition, the event order changed ; in the Probabilistic condition, the causal relationships changed. As intended, toddlers looked equally long at both changes. We then introduced a previously unseen candidate cause. Toddlers looked longer at the appearance of a (...) hand and novel agent in the Deterministic than the Probabilistic conditions, but looked equally long at novel non-agents, suggesting that by 2 years of age, toddlers connect probabilistic events with unobserved agents. (shrink)