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. (...) In those contexts, it has been interpreted as a top-down effect on perception reflecting a belief in causality. These two views need not be in conflict with one another, if one thinks of them as concerning two separate mechanisms through which temporal binding can occur. In this paper, we explore an alternative possibility: that there is a unitary way of explaining temporal binding both within and outside the context of voluntary action as a top-down effect on perception reflecting a belief in causality. Any such explanation needs to account for ways in which agency, and factors connected with agency, have been shown to affect the strength of temporal binding. We show that principles of causal inference and causal selection already familiar from the literature on causal learning have the potential to explain why the strength of people’s causal beliefs can be affected by the extent to which they are themselves actively involved in bringing about events, thus in turn affecting binding. (shrink)

This article provides a conceptual account of causal understanding by connecting current psychological research on time and causality with philosophical debates on the causal asymmetry. I argue that causal relations are viewed as asymmetric because they are understood in temporal terms. I investigate evidence from causal learning and reasoning in both children and adults: causal perception, the temporal priority principle, and the use of temporal cues for causal inference. While this account does not suffice for correct inferences of causal structure, (...) I show it to serve as a preliminary understanding of causal concepts as asymmetric, that later incorporates other types of evidence. This approach supplies causal models with an asymmetric concept of causation that underlies hypotheses about causal structure, as I will illustrate from the framework of the knowledge-based causal induction model. I further argue for an integrating perspective, showing how the understanding of causes as preceding their effects underlies both psychological models and philosophical debates over time and the causal asymmetry, particularly regarding problem cases such as simultaneous causation or backwards causation, and the conceptual connection between causation and action. (shrink)

Research on the division of cognitive labor has found that adults and children as young as age 5 are able to find appropriate experts for different causal systems. However, little work has explored how children and adults decide when to seek out expert knowledge in the first place. We propose that children and adults rely on “mechanism metadata,” information about mechanism information. We argue that mechanism metadata is relatively consistent across individuals exposed to similar amounts of mechanism information, and it (...) is applicable to a wide range of causal systems. In three experiments, we show that adults and children as young as 5 years of age have a consistent sense of the causal complexity of different causal systems, and that this sense of complexity is related to decisions about when to seek expert knowledge, but over development there is a shift in focus from procedural information to internal mechanism information. (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)

Humans often attribute the things that happen to one or another actual cause. In this chapter, we survey some recent philosophical and psychological research on causal attribution. We pay special attention to the relation between graphical causal modeling and theories of causal attribution. We think that the study of causal attribution is one place where formal and experimental techniques nicely complement one another.

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, (...) that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory led to transitive causal judgments. On the other hand, chains schematized as multiple chunks led to intransitive judgments despite strong intermediate links. Normative accounts of causal intransitivity could not explain these intransitive judgments. (shrink)

Part I of this paper introduced a range of mechanistic theories of causality, including process theories and the complex-systems theories, and some of the problems they face. Part II argues that while there is a decisive case against a purely mechanistic analysis, a viable theory of causality must incorporate mechanisms as an ingredient, and describes one way of providing an analysis of causality which reaps the rewards of the mechanistic approach without succumbing to its pitfalls.

Empirical research has recently emerged as a key method for understanding the nature of causation, and our concept of causation. One thread of research aims to test intuitions about the nature of causation in a variety of classic cases. These experiments have principally been used to try to resolve certain debates within analytic philosophy, most notably that between proponents of transference and dependence views of causation. The other major thread of empirical research on our concept of causation has investigated the (...) role that normative considerations play in causal judgments. These experimental results suggest that philosophical accounts of our concept of causation should take a broader view of what might be relevant. For both lines of research, we describe some of the significant experiments and outline key philosophical morals that have been drawn, all while pointing out various limitations. We conclude by considering other kinds of empirical research that should be philosophically interesting for those studying the nature of causation and our concept of causation. In particular, we point towards the need for philosophical research about causal perception, causal reasoning, and causal learning, as well as ways in which this research could play a role in prescriptive metaphysics. (shrink)

This article has two goals. First, it synthesizes and critically assesses current scientific knowledge about the cognitive bases of human tool use. Second, it shows how the cognitive traits reviewed help to explain why technological accumulation evolved so markedly in humans, and so modestly in apes.

How do people understand questions about cause and prevent? Some theories propose that people affirm that A causes B if A's occurrence makes a difference to B's occurrence in one way or another. Other theories propose that A causes B if some quantity or symbol gets passed in some way from A to B. The aim of our studies is to compare these theories' ability to explain judgements of causation and prevention. We describe six experiments that compare judgements for causal (...) paths that involve a mechanism, i.e. a continuous process of transmission or exchange from cause to effect, against paths that involve no mechanism yet a change in the cause nevertheless brings about a change in the effect. Our results show that people prefer to attribute cause when a mechanism links cause to effect. In contrast, prevention is sensitive both to the presence of an interruption to a causal mechanism and to a change in the outcome in the absence of a mechanism. In this sense, ‘prevent’ means something different than ‘cause not'. We discuss the implications of our results for existing theories of causation. (shrink)

Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector”, a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine’s activation that (...) required them to use indirect evidence to make causal inferences. Critically, associative models either made no predictions, or made incorrect predictions about these inferences. In general, children were able to make these inferences, but some developmental differences between 3- and 4- year-olds were found. We suggest that children’s causal inferences are not based on recognizing associations, but rather that children develop a mechanism for Bayesian structure learning. Experiment 3 explicitly tests a prediction of this account. Children were asked to make an inference about ambiguous data based on the base-rate of certain events occurring. Fouryear-olds, but not 3-year-olds were able to make this inference. (shrink)

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...) brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

Causal queries about singular cases, which inquire whether specific events were causally connected, are prevalent in daily life and important in professional disciplines such as the law, medicine, or engineering. Because causal links cannot be directly observed, singular causation judgments require an assessment of whether a co‐occurrence of two events c and e was causal or simply coincidental. How can this decision be made? Building on previous work by Cheng and Novick (2005) and Stephan and Waldmann (2018), we propose a (...) computational model that combines information about the causal strengths of the potential causes with information about their temporal relations to derive answers to singular causation queries. The relative causal strengths of the potential cause factors are relevant because weak causes are more likely to fail to generate effects than strong causes. But even a strong cause factor does not necessarily need to be causal in a singular case because it could have been preempted by an alternative cause. We here show how information about causal strength and about two different temporal parameters, the potential causes' onset times and their causal latencies, can be formalized and integrated into a computational account of singular causation. Four experiments are presented in which we tested the validity of the model. The results showed that people integrate the different types of information as predicted by the new model. (shrink)

The aim of this dissertation is to create a naturalistic philosophical picture of creative capacities that are specific to our species, focusing on artistic ability, religious reflection, and scientific study. By integrating data from diverse domains within a philosophical anthropological framework, I have presented a cognitive and evolutionary approach to the question of why humans, but not other animals engage in such activities. Through an application of cognitive and evolutionary perspectives to the study of these behaviors, I have sought to (...) provide a more solid footing for philosophical anthropological discussions of uniquely human behavior. In particular, I have argued that art, religion and science, which are usually seen as achievements that are quite remote from ordinary modes of reasoning, are subserved by evolved cognitive processes that serve functions in everyday cognitive tasks, that arise early and spontaneously in cognitive development, that are shared cross-culturally, and that have evolved in response to selective pressures in our ancestral past. These mundane cognitive processes provide a measuring rod with which we can assess a diversity of cultural phenomena; they form a unified explanatory framework to approach human culture. I have argued that we can explain uncommon thoughts in terms of interactions between common minds. This dissertation is subdivided into four parts. Part I outlines the problem of human uniqueness, examining theories on how humans conceptualize the world, and what their mental tool box looks like. Part II discusses the evolutionary and cognitive origins of human artistic behavior. Part III focuses on the cognitive science of religion, especially on how it can be applied to the reasoning of theologians and philosophers of religion. Part IV considers the cognitive basis of scientific practice. (shrink)

A familiar adage in the philosophy of science is that general hypotheses are better supported by varied evidence than by uniform evidence. Several studies suggest that young children do not respect this principle, and thus suffer from a defect in their inductive methodology. We argue that the diversity principle does not have the normative status that psychologists attribute to it, and should be replaced by a simple rule of probability. We then report experiments designed to detect conformity to the latter (...) rule in children's inductive judgment. The results suggest that young children in both the United States and Taiwan are sensitive to the constraints imposed by the rule on judgments of probability and evidential strength. We conclude with a suggested reinterpretation of the thesis that children's inductive methodology qualifies them as “little scientists.”. (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)

The goal of the present set of studies is to explore the boundary conditions of category transfer in causal learning. Previous research has shown that people are capable of inducing categories based on causal learning input, and they often transfer these categories to new causal learning tasks. However, occasionally learners abandon the learned categories and induce new ones. Whereas previously it has been argued that transfer is only observed with essentialist categories in which the hidden properties are causally relevant for (...) the target effect in the transfer relation, we here propose an alternative explanation, the unbroken mechanism hypothesis. This hypothesis claims that categories are transferred from a previously learned causal relation to a new causal relation when learners assume a causal mechanism linking the two relations that is continuous and unbroken. The findings of two causal learning experiments support the unbroken mechanism hypothesis. (shrink)

Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference from correlation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how causal (...) mechanisms represented by causal networks can contribute to reasoning involving correlation and causation. The understanding of causation and causal mechanisms provides the basis for a presentation of the causal network instantiation model of medical explanation. (shrink)

Mental imagery (varieties of which are sometimes colloquially refered to as “visualizing,” “seeing in the mind's eye,” “hearing in the head,” “imagining the feel of,” etc.) is quasi-perceptual experience; it resembles perceptual experience, but occurs in the absence of the appropriate external stimuli. It is also generally understood to bear intentionality (i.e., mental images are always images of something or other), and thereby to function as a form of mental representation. Traditionally, visual mental imagery, the most discussed variety, was thought (...) to be caused by the presence of picturelike representations (mental images) in the mind, soul, or brain, but this is no longer universally accepted. (shrink)

We argue that current discussions of criteria for actual causation are ill-posed in several respects. (1) The methodology of current discussions is by induction from intuitions about an infinitesimal fraction of the possible examples and counterexamples; (2) cases with larger numbers of causes generate novel puzzles; (3) "neuron" and causal Bayes net diagrams are, as deployed in discussions of actual causation, almost always ambiguous; (4) actual causation is (intuitively) relative to an initial system state since state changes are relevant, but (...) most current accounts ignore state changes through time; (5) more generally, there is no reason to think that philosophical judgements about these sorts of cases are normative; but (6) there is a dearth of relevant psychological research that bears on whether various philosophical accounts are descriptive. Our skepticism is not directed towards the possibility of a correct account of actual causation; rather, we argue that standard methods will not lead to such an account. A different approach is required. (shrink)

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.

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. (...) We replicated these findings with less‐informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge. (shrink)

A substantial body of evidence shows that people tend to rely too heavily on explanations when trying to justify an opinion. Some research suggests these errors may arise from an inability to distinguish between explanations and the evidence that bears upon them. We examine an alternative account, that many people do distinguish between explanations and evidence, but rely more heavily on unsubstantiated explanations when evidence is scarce or absent. We examine the philosophical and psychological distinctions between explanation and evidence, and (...) show that participants use explanations as a substitute for missing evidence. Experiment 1 replicates the results of other researchers, but further shows that participants generate more evidence when they are not constrained by their lack of data. Merely mentioning a source of data can alter both their evaluation (Experiment 2) and their production (Experiment 3) of explanations and evidence. In Experiment 4, we show that participants can explicitly consider the availability of evidence and other pragmatic factors when evaluating arguments. Finally, we consider the implications of using explanations to replace missing evidence as a strategy in argument. (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)

We often explain by citing an absence or an omission. Apart from the problem of assigning a causal role to such apparently negative factors as absences and omissions, there is a puzzle as to why only some absences and omissions, out of indefinitely many, should figure in explanations. In this paper we solve this ’many absences problem’ by using the contrastive model of explanation. The contrastive model of explanation is developed by adapting Peter Lipton’s account. What initially appears to be (...) only a trivial amendment to Lipton’s Difference Condition enables us both to offer a much more satisfactory solution to the ’many absences problem’ than David Lewis did, and also to explain why explanation in terms of absences and omissions should be so common. (shrink)

(2013). Hierarchical Bayesian models as formal models of causal reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 36-45. doi: 10.1080/19462166.2012.700321.

Three experiments investigated reasoners' beliefs about causal powers; that is, their beliefs about the capacity of a putative cause to produce a given effect. Covariation-based theories (e.g., Cheng, 1997; Kelley, 1973; Novick & Cheng, 2004) posit that beliefs in causal power are represented in terms of the degree of covariation between the cause and its effect; covariation is defined in terms of the degree to which the effect occurs in the presence of the cause, and fails tooccur in the absence (...) of the cause. To test the degree to which beliefs incausal power are reflected in beliefs about covariation information, participants in three experiments rated their beliefs that putative causes have the capacity to produce a given effect (i.e., possess the causal power to produce an effect) as well as their beliefs regarding the degree to which the putative cause and effectcovary. A strong positive correlation was discovered between participants' beliefs in causal power and their beliefs that the effect occurs in the presence of the cause. However, no direct relationship was found between participants' beliefs in causal power and their belief that the effect will fail tooccur in the absence of the cause. These findings were replicated using bothwithin- (Experiments 1 and 3) and between-subject designs (Experiment 2). In Experiment 3, we extended these analyses to measures of familiarity, imageability, and detailedness of the representation. We found that participants' beliefs in causal power were strongly associated with familiarity, and imageability, but not the perceived detailedness of the cause and effect relationship. These data provide support for a multidimensional account of causal knowledge whereby people's representations of causation include, but are not limited to, the covariation, familiarity, and imageability of cause and effect relationships. (shrink)

We argue in favour of the general proposition that the nature of reasoning is best understood within a context of its origins and development. A major dimension of what develops in the years from childhood to adulthood, we propose, is increasing meta-level monitoring and management of cognition. Two domains are examined in presenting support for these claims—multivariable causal reasoning and argumentive reasoning.

Attribution theory has played a major role in social-psychological research. Unfortunately, the term attribution is ambiguous. According to one meaning, forming an attribution is making a dispositional (trait) inference from behavior; according to another meaning, forming an attribution is giving an explanation (especially of behavior). The focus of this paper is on the latter phenomenon of behavior explanations. In particular, I discuss a new theory of explanation that provides an alternative to classic attribution theory as it dominates the textbooks and (...) handbooks—which is typically as a version of Kelley’s (1967) model of attribution as covariation detection. I begin with a brief critique of this theory and, out of this critique, develop a list of requirements that an improved theory has to meet. I then introduce the new theory, report empirical data in its support, and apply it to a number of psychological phenomena. I finally conclude with an assessment of how much progress we have made in understanding behavior explanations and what has yet to be learned. (shrink)

I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between ‘mechanist’ and ‘associationist’ psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, (...) incorrectly let large causes ‘overshadow’ small causes, misrepresents the most likely, and warranted, causal explanation available to the subjects, in the light of which their responses were normative; (iii) showing how a recent psychological theory (due to P. Cheng) of human judgment of causal power can be considerably generalized: and (iv) suggesting a range of possible experiments comparing human and computer abilities to extract causal information from associations. (shrink)

Why does discourse so often seem shallow, with people arguing past one another more than with one another? Might contributing causes be individual and logical rather than only dialogical? We consider here whether there exist errors in reasoning that could be particularly damaging in their effects on argumentive discourse. In particular, we examine implications for discourse of two such errors – explanation as a replacement for evidence and neglecting the likelihood of multiple causes contributing to an outcome. In Studies 1 (...) and 2, we show these errors to be prevalent in a cross section of adults, as well as samples of community college students and young adolescents, with minimal age-related improvement. They also occur, although less frequently, among a sample of highly educated adults, and in Study 3, we examine their role in the discourse of college-educated adults. We point finally to evidence that these individual reasoning errors are potentially addressable through education. (shrink)

We note the development of the widely employed but loosely defined construct of critical thinking from its earliest instantiations as a measure of individual ability to its current status, marked by efforts to better connect the construct to the socially-situated thinking demands of real life. Inquiry and argument are identified as key dimensions in a process-based account of critical thinking. Argument is identified as a social practice, rather than a strictly individual competency. Yet, new empirical evidence is presented documenting a (...) role for individual reasoning competencies in supporting the effectiveness of argumentive discourse. A successful curriculum is described for employing extended engagement in dialogic argumentation as a pathway to development of individual argumentive skill. (shrink)

Most explanations are either about events (why things happen), or about properties (why objects have the enduring characteristics that they do). Explanations of events have been studied extensively in philosophy and psychology, whereas the explanations of properties have received little or no attention in the literature. The present study is an exploration of the ways in which we explain various types of properties. Ten participants provided explanations of 45 properties by completing sentences of the form: “Xs have p because…” where (...) p is a property of the concept X. When coded into three traditional kinds of explanation (i.e. causal, purposive, categorical), nearly half of the explanations did not fit into any of these categories and were classified as “other”. Different patterns of preference for explanations emerged across the various property types for each domain. A qualitative analysis of the other kind revealed that the most frequent form of explanation involved another property of the object, as in ‘Xs have property p because they have property q”. Explanations of properties showed a homeostatic character. Simply relating two properties of a concept seems sufficient as an explanation of a property. (shrink)

In everyday life, people typically observe fragments of causal networks. From this knowledge, people infer how novel combinations of causes they may never have observed together might behave. I report on 4 experiments that address the question of how people intuitively integrate multiple causes to predict a continuously varying effect. Most theories of causal induction in psychology and statistics assume a bias toward linearity and additivity. In contrast, these experiments show that people are sensitive to cues biasing various integration rules. (...) Causes that refer to intensive quantities (e.g., taste) or to preferences (e.g., liking) bias people toward averaging the causal influences, whereas extensive quantities (e.g., strength of a drug) lead to a tendency to add. However, the knowledge underlying these processes is fallible and unstable. Therefore, people are easily influenced by additional task‐related context factors. These additional factors include the way data are presented, the difficulty of the inference task, and transfer from previous tasks. The results of the experiments provide evidence for causal model and related theories, which postulate that domain‐general representations of causal knowledge are influenced by abstract domain knowledge, data‐driven task factors, and processing difficulty. (shrink)

Why do we represent the world around us using causal generalizations, rather than, say, purely statistical generalizations? Do causal representations contain useful additional information, or are they merely more efficient for inferential purposes? This paper considers the second kind of answer: it investigates some ways in which causal cognition might aid us not because of its expressive power, but because of its organizational power. Three styles of explanation are considered. The first, building on the work of Reichenbach in "The Direction (...) of Time", points to causal representation as especially efficient for predictive purposes in a world containing certain pervasive patterns of conditional independence. The second, inspired by work of Woodward and others, finds causal representation to be an excellent vehicle for representing all-important relations of manipulability. The third, based in part on my own work, locates the importance of causal cognition in the special role it reserves for information about underlying mechanisms. All three varieties of explanation show promise, but particular emphasis is placed on the third. (shrink)

Scientific cognition is a hard-won achievement, both from a historical point of view and a developmental point of view. Here, I review seven facets of lay cognition that run counter to, and often impede, scientific cognition: incompatible folk theories, missing ontologies, tolerance for shallow explanations, tolerance for contradictory explanations, privileging explanation over empirical data, privileging testimony over empirical data, and misconceiving the nature of science itself. Most of these facets have been investigated independent of the others, and I propose directions (...) for future research that might integrate them so as to explore potential commonalities among seemingly disparate obstacles to science learning, as well as potential strategies for bridging lay cognition and scientific cognition in the context of science education. (shrink)

A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are (...) directly connected by causal relationships. These expectations also included sensitivity to higher‐order feature interactions that emerge from the asymmetries inherent in causal relationships. Quantitative fits of causal‐model theory were superior to those obtained with extensions to traditional similarity‐based models that represent causal knowledge either as higher‐order relational features or “prior exemplars” stored in memory. (shrink)

We use our knowledge of causal relationships to imagine possible events. We also use these relationships to look deep into the past and infer events that were not witnessed or to infer what can not be directly seen in the present. Knowledge of causal relationships allows us to go beyond the here and now. This chapter introduces a new theoretical framework for how this very basic concept might be mentally represented. It proposes an epistemological theory of causation — that is, (...) a theory that specifies the nature of people's knowledge of causation, the notion of causation used in everyday language and reasoning. (shrink)