The human imagination remains one of the last uncharted terrains of the mind. People often imagine how events might have turned out something had been different. The of reality, those aspects more readily changed, indicate that counterfactual thoughts are guided by the same principles as rational thoughts. In the past, rationality and imagination have been viewed as opposites. But research has shown that rational thought is more imaginative than cognitive scientists had supposed. In The Rational Imagination, I argue that imaginative (...) thought is more rational than scientists have imagined. People exhibit remarkable similarities in the sorts of things they change in their mental representation of reality when they imagine how the facts could have turned out differently. For example, they tend to imagine alternatives to actions rather than inactions, events within their control rather than those beyond their control, and socially unacceptable events rather than acceptable ones. Their thoughts about how an event might have turned out differently lead them to judge that a strong causal relation exists between an antecedent event and the outcome, and their thoughts about how an event might have turned out the same lead them to judge that a weaker causal relation exists. In a simple temporal sequence, people tend to imagine alternatives to the most recent event. The central claim in the book is that counterfactual thoughts are organised along the same principles as rational thought. The idea that the counterfactual imagination is rational depends on three steps: (1) humans are capable of rational thought; (2) they make inferences by thinking about possibilities; and (3) their counterfactual thoughts rely on thinking about possibilities, just as rational thoughts do. The sorts of possibilities that people envisage explain the mutability of certain aspects of mental representations and the immutability of other aspects. (shrink)

Over the last quarter century, the dominant tendency in comparative cognitive psychology has been to emphasize the similarities between human and nonhuman minds and to downplay the differences as (Darwin 1871). In the present target article, we argue that Darwin was mistaken: the profound biological continuity between human and nonhuman animals masks an equally profound discontinuity between human and nonhuman minds. To wit, there is a significant discontinuity in the degree to which human and nonhuman animals are able to approximate (...) the higher-order, systematic, relational capabilities of a physical symbol system (PSS) (Newell 1980). We show that this symbolic-relational discontinuity pervades nearly every domain of cognition and runs much deeper than even the spectacular scaffolding provided by language or culture alone can explain. We propose a representational-level specification as to where human and nonhuman animals' abilities to approximate a PSS are similar and where they differ. We conclude by suggesting that recent symbolic-connectionist models of cognition shed new light on the mechanisms that underlie the gap between human and nonhuman minds. (shrink)

Reasoning about underlying causal relations drives responsibility judgments: agents are held responsible for the outcomes they cause through their behaviors. Two main causal reasoning approaches exist: dependence theories emphasize statistical relations between causes and effects, while transference theories emphasize mechanical transmission of energy. Recently, pluralistic or hybrid models, combining both approaches, have emerged as promising psychological frameworks. In this paper, we focus on causal reasoning as involved in third-party judgements of responsibility and on related judgments of intention and control. In (...) particular, we used a novel visual paradigm to investigate the combined effects of two well-known causal manipulations, namely omission and pre-emption, on these evaluations. Our findings support the view that people apply a pluralistic causal reasoning when evaluating individual responsibility for negative outcomes. In particular, we observed diminished responsibility when dependence, transference, or both fail, compared to when these mechanisms are upheld. Responsibility judgement involves a cognitive hybrid of multiple aspects of causal reasoning. However, important differences exist at the interindividual level, with most people weighting transference more than dependence. (shrink)

According to the mental model theory, causes and enablers differ in meaning, and therefore in their logical consequences (Goldvarg & Johnson-Laird, 2001). They are consistent with different possibilities. Recent psychological studies have argued to the contrary, and suggested that linguistic cues guide this distinction (Kuhnmünch & Beller, 2005). The issue is important because neither British nor American law recognizes this distinction (e.g., Roberts & Zuckerman, 2004). Yet, in our view, it is central to human conceptions of causality. Hence, in two (...) experiments, we examined our participants’ ability to distinguish between causes and enablers in scenarios describing the actions of two agents and a subsequent outcome, e.g.: ‘Mary threw a lighted cigarette into a bush. Just as the cigarette was going out, Laura deliberately threw petrol on it. The resulting fire burnt down her neighbor’s house.’ Here Mary enabled the fire to occur, whereas Laura caused the fire to occur. (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)

How people understand the fundamental dynamics of stock and flow is an important basic theoretical question with many practical applications. In this paper, we present a universal frame for understanding stock-flow reasoning in terms of the theory of force dynamics. This deep-level analysis is then applied to two different presentation formats of SF tasks in the context of climate change. We can explain why in a coordinate-graphic presentation misunderstandings occur, whereas in a verbal presentation a better understanding is found. We (...) end up with recommendations for presentation formats that will help people to better understand SF dynamics. Better public SF understanding might in turn also enhance corresponding public action – such as enhancing pro-environmental actions in relation to climate change. (shrink)

The authors outline a theory of conditionals of the form If A then C and If A then possibly C. The 2 sorts of conditional have separate core meanings that refer to sets of possibilities. Knowledge, pragmatics, and semantics can modulate these meanings. Modulation can add information about temporal and other relations between antecedent and consequent. It can also prevent the construction of possibilities to yield 10 distinct sets of possibilities to which conditionals can refer. The mental representation of a (...) conditional normally makes explicit only the possibilities in which its antecedent is true, yielding other possibilities implicitly. Reasoners tend to focus on the explicit possibilities. The theory predicts the major phenomena of understanding and reasoning with conditionals. (shrink)

When people want to identify the causes of an event, assign credit or blame, or learn from their mistakes, they often reflect on how things could have gone differently. In this kind of reasoning, one considers a counterfactual world in which some events are different from their real-world counterparts and considers what else would have changed. Researchers have recently proposed several probabilistic models that aim to capture how people do (or should) reason about counterfactuals. We present a new model and (...) show that it accounts better for human inferences than several alternative models. Our model builds on the work of Pearl (2000), and extends his approach in a way that accommodates backtracking inferences and that acknowledges the difference between counterfactual interventions and counterfactual observations. We present six new experiments and analyze data from four experiments carried out by Rips (2010), and the results suggest that the new model provides an accurate account of both mean human judgments and the judgments of individuals. (shrink)

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)

Traditionally, moral philosophers have distinguished between doing and allowing harm, and have normally proceeded as if this bipartite distinction can exhaustively characterize all cases of human conduct involving harm. By contrast, cognitive scientists and psychologists studying causal judgment have investigated the concept ‘enable’ as distinct from the concept ‘cause’ and other causal terms. Empirical work on ‘enable’ and its employment has generally not focused on cases where human agents enable harm. In this paper, we present new empirical evidence to support (...) the claim that some important cases in the moral philosophical literature are best viewed as instances of enabling harm rather than doing or allowing harm. We also present evidence that enabling harm is regarded as normatively distinct from doing and allowing harm when it comes to assigning compensatory responsibility. Moral philosophers should be exploring the tripartite distinction between doing harm, allowing harm, and enabling harm, rather than simply the traditional bipartite distinction. Cognitive scientists and psychologists studying moral judgment, who have so far largely adopted the bipartite distinction in this area of research, should likewise investigate the tripartite distinction. (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)

How do people evaluate causal relationships? Do they just consider what actually happened, or do they also consider what could have counterfactually happened? Using eye tracking and Gaussian process modeling, we investigated how people mentally simulated past events to judge what caused the outcomes to occur. Participants played a virtual ball‐shooting game and then—while looking at a blank screen—mentally simulated (a) what actually happened, (b) what counterfactually could have happened, or (c) what caused the outcome to happen. Our findings showed (...) that participants moved their eyes in patterns consistent with the actual or counterfactual events that they mentally simulated. When simulating what caused the outcome to occur, participants moved their eyes consistent with simulations of counterfactual possibilities. These results favor counterfactual theories of causal reasoning, demonstrate how eye movements can reflect simulation during this reasoning and provide a novel approach for investigating retrospective causal reasoning and counterfactual thinking. (shrink)

When comparing the roles of the lightning strike and the dry climate in causing the forest fire, one might think that the lightning strike is more of a cause than the dry climate, or one might think that the lightning strike completely caused the fire while the dry conditions did not cause it at all. Psychologists and philosophers have long debated whether such causal judgments are graded; that is, whether people treat some causes as stronger than others. To address this (...) debate, we first reanalyzed data from four recent studies. We found that causal judgments were actually multimodal: although most causal judgments made on a continuous scale were categorical, there was also some gradation. We then tested two competing explanations for this gradation: the confidence explanation, which states that people make graded causal judgments because they have varying degrees of belief in causal relations, and the strength explanation, which states that people make graded causal judgments because they believe that causation itself is graded. Experiment 1 tested the confidence explanation and showed that gradation in causal judgments was indeed moderated by confidence: people tended to make graded causal judgments when they were unconfident, but they tended to make more categorical causal judgments when they were confident. Experiment 2 tested the causal strength explanation and showed that although confidence still explained variation in causal judgments, it did not explain away the effects of normality, causal structure, or the number of candidate causes. Overall, we found that causal judgments were multimodal and that people make graded judgments both when they think a cause is weak and when they are uncertain about its causal role. (shrink)

When the absence of an event causes some outcome, it is an instance of omissive causation. For instance, not eating lunch may cause you to be hungry. Recent psychological proposals concur that the mind represents causal relations, including omissive causal relations, through mental simulation, but they disagree on the form of that simulation. One theory states that people represent omissive causes as force vectors; another states that omissions are representations of contrasting counterfactual simulations; a third argues that people think about (...) omissions by representing sets of iconic possibilities – mental models – in a piecemeal fashion. In this paper, we tease apart the empirical predictions of the three theories and describe experiments that run counter to two of them. Experiments 1 and 2 show that reasoners can infer temporal relations from omissive causes – a pattern that contravenes the force theory. Experiment 3 asked participants to list the possibilities consistent with an omissive cause – it found that they tended to list particular privileged possibilities first, most often, and faster than alternative possibilities. The pattern is consistent with the model theory, but inconsistent with the contrast hypothesis. We marshal the evidence and explain why it helps to solve a long-standing debate about how the mind represents omissions. (shrink)

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which (...) participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account. (shrink)

This paper offers a modeling account of episodic representation. I argue that the episodic system constructsmental models: representations that preserve the spatiotemporal structure of represented domains. In prototypical cases, these domains are events: occurrences taken by subjects to have characteristic structures, dynamics and relatively determinate beginnings and ends. Due to their simplicity and manipulability, mental event models can be used in a variety of cognitive contexts: in remembering the personal past, but also in future-oriented and counterfactual imagination. As structural representations, (...) they allow surrogative reasoning, supporting inferences about their constituents which can be used in reasoning about the represented 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)

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)

We report two Experiments to compare counterfactual thoughts about how an outcome could have been different and causal explanations about why the outcome occurred. Experiment 1 showed that people generate counterfactual thoughts more often about controllable than uncontrollable events, whereas they generate causal explanations more often about unexpected than expected events. Counterfactual thoughts focus on specific factors, whereas causal explanations focus on both general and specific factors. Experiment 2 showed that in their spontaneous counterfactual thoughts, people focus on normal events (...) just as often as exceptional events, unlike in directed counterfactual thoughts. The findings are consistent with the suggestion that counterfactual thoughts tend to focus on how a specific unwanted outcome could have been prevented, whereas causal explanations tend to provide more general causal information that enables future understanding, prediction, and intervention in a wide range of situations. (shrink)

Folk theories—untutored people’s (often implicit) theories about various features of the world—have been fashionable objects of inquiry in psychology for almost two decades now (e.g., Hirschfeld and Gelman 1994), and more recently they have been of interest in experimental philosophy (Nichols 2004). Folk theories of psy- chology, physics, biology, and ethics have all come under investigation. Folk meta- physics, however, has not been as extensively studied. That so little is known about folk metaphysics is unfortunate for (at least) two reasons. (...) First, folk metaphysics is almost certainly implicit, and it is likely to be our default way of thinking about metaphysical problems. Moreover, one’s metaphysical commitments can have pro- found consequences—in scientific, religious, and ethical contexts, for example. Thus, folk metaphysics ought to be dragged out into the open and exposed to criticism. As Peirce eloquently remarked (1994, 1.129; see also 1994, 7.579). (shrink)

The relationship between mental models and ethical decision making, along with the mechanisms through which mental models affect EDM, are not well understood. Using the sensemaking approach to EDM, we empirically tested the relationship of mental models to EDM. Participants were asked to depict their mental models in response to an ethics case to reveal their understanding of the ethical dilemma, and then provide a response, along with a rationale, to a different ethical problem. Findings indicated that complexity of respondents’ (...) mental models was related to EDM, and that this relationship was mediated by sensemaking processes. The implications of these findings for improving integrity training in organizations, as well as ultimately understanding the role of mental models in EDM, are discussed. (shrink)

Some philosophers argue that the principles of human reasoning are impeccable, and that mistakes are no more than momentary lapses in “information processing”. This article makes a case to the contrary. It shows that human reasoners commit systematic fallacies. The theory of mental models predicts these errors. It postulates that individuals construct mental models of the possibilities to which the premises of an inference refer. But, their models usually represent what is true in a possibility, not what is false. This (...) procedure reduces the load on working memory, and for the most part it yields valid inferences. However, as a computer program implementing the theory revealed, it leads to fallacious conclusions for certain inferences—those for which it is crucial to represent what is false in a possibility. Experiments demonstrate the variety of these fallacies and contrast them with control problems, which reasoners tend to get right. The fallacies can be compelling illusions, and they occur in reasoning based on sentential connectives such as “if” and “or”, quantifiers such as “all the artists” and “some of the artists”, on deontic relations such as “permitted” and “obligated”, and causal relations such as “causes” and “allows”. After we have reviewed the principal results, we consider the potential for alternative accounts to explain these illusory inferences. And we show how the illusions illuminate the nature of human rationality. (shrink)

Both philosophers and psychologists have argued for the existence of distinct kinds of explanations, including teleological explanations that cite functions or goals, and mechanistic explanations that cite causal mechanisms. Theories of causation, in contrast, have generally been unitary, with dominant theories focusing either on counterfactual dependence or on physical connections. This paper argues that both approaches to causation are psychologically real, with different modes of explanation promoting judgments more or less consistent with each approach. Two sets of experiments isolate the (...) contributions of counterfactual dependence and physical connections in causal ascriptions involving events with people, artifacts, or biological traits, and manipulate whether the events are construed teleologically or mechanistically. The findings suggest that when events are construed teleologically, causal ascriptions are sensitive to counterfactual dependence and relatively insensitive to the presence of physical connections, but when events are construed mechanistically, causal ascriptions are sensitive to both counterfactual dependence and physical connections. The conclusion introduces an account of causation, an "exportable dependence theory," that provides a way to understand the contributions of physical connections and teleology in terms of the functions of causal ascriptions. (shrink)

(2013). The processes of inference. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 4-20. doi: 10.1080/19462166.2012.674060.

According to the transitive dynamics model, people can construct causal structures by linking together configurations of force. The predictions of the model were tested in two experiments in which participants generated new causal relationships by chaining together two (Experiment 1) or three (Experiment 2) causal relations. The predictions of the transitive dynamics model were compared against those of Goldvarg and Johnson-Laird’s model theory (Goldvarg & Johnson- Laird, 2001). The transitive dynamics model consistently predicted the overall causal relationship drawn by participants (...) for both types of causal chains, and, when compared to the model theory, provided a better fit to the data. The results suggest that certain kinds of causal reasoning may depend on force dynamic—rather than on logical or purely statistical—representations. (shrink)

The current experiments examine mental health clinicians’ beliefs about biological, psychological, and environmental bases of the DSM‐IV‐TR mental disorders and the consequences of those causal beliefs for judging treatment effectiveness. Study 1 found a large negative correlation between clinicians’ beliefs about biological bases and environmental/psychological bases, suggesting that clinicians conceptualize mental disorders along a single continuum spanning from highly biological disorders (e.g., autistic disorder) to highly nonbiological disorders (e.g., adjustment disorders). Study 2 replicated this finding by having clinicians list what (...) they thought were the specific causes of nine familiar mental disorders and rate their bio–psycho–environmental bases. Study 3 further found that clinicians believe medication to be more effective for biologically based mental disorders and psychotherapy to be more effective for psychosocially based mental disorders. These results demonstrate that even expert mental health clinicians make strong distinctions between psychological and biological phenomena. (shrink)

The mental model theory of naive causal understanding and reasoning (Goldvarg & Johnson‐Laird, 2001, Cognitive Science, 25, 565–610) claims that people distinguish between causes and enabling conditions on the basis of sets of models that represent possible causal situations. In the tasks used to test this hypothesis, however, the proposed set of models was confounded with linguistic cues that frame which event to assume as given (the enabling condition) and which to consider as responsible for the effect under this assumption (...) (the cause). By disentangling these two factors, we were able to show that when identifying causes and enabling conditions in these tasks, people rely strongly on the linguistic cues but not on the proposed set of models and that this set of models does not even reflect people's typical interpretation of the tasks. We propose an alternative explanation that integrates syntactic and causal considerations. (shrink)

We report three experiments investigating whether people’s judgments about causal relationships are sensitive to the robustness or stability of such relationships across a range of background circumstances. In Experiment 1, we demonstrate that people are more willing to endorse causal and explanatory claims based on stable (as opposed to unstable) relationships, even when the overall causal strength of the relationship is held constant. In Experiment 2, we show that this effect is not driven by a causal generalization’s actual scope of (...) application. In Experiment 3, we offer evidence that stable causal relationships may be seen as better guides to action. Collectively, these experiments document a previously underappreciated factor that shapes people’s causal reasoning: the stability of the causal relationship. (shrink)

The psychology of reasoning is increasingly considering agents' values and preferences, achieving greater integration with judgment and decision making, social cognition, and moral reasoning. Some of this research investigates utility conditionals, ‘‘if p then q’’ statements where the realization of p or q or both is valued by some agents. Various approaches to utility conditionals share the assumption that reasoners make inferences from utility conditionals based on the comparison between the utility of p and the expected utility of q. This (...) article introduces a new parameter in this analysis, the underlying causal structure of the conditional. Four experiments showed that causal structure moderated utility-informed conditional reasoning. These inferences were strongly invited when the underlying structure of the conditional was causal, and significantly less so when the underlying structure of the conditional was diagnostic. This asymmetry was only observed for conditionals in which the utility of q was clear, and disappeared when the utility of q was unclear. Thus, an adequate account of utility-informed inferences conditional reasoning requires three components: utility, probability, and causal structure. (shrink)

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 (...) reason when asked counterfactual questions about causal relations that we refer to as undoing, a family of effects that derive from the claim that intervened-on variables become independent of their normal causes. Six studies support the prediction for causal arguments but not consistently for parallel conditional ones. Two of the studies show that effects are treated as diagnostic when their values are observed but nondiagnostic when they are intervened on. These results cannot be explained by theories that do not distinguish interventions from other sorts of events. (shrink)

A great need for systematic evaluation of ethics training programs exists. Those tasked with developing an ethics training program may be quick to dismiss the value of training evaluation in continuous process improvement. In the present effort, we use a case study approach to delineate how to leverage formative and summative evaluation measures to create a high-quality ethics education program. With regard to formative evaluation, information bearing on trainee reactions, qualitative data from the comments of trainees, in addition to empirical (...) findings, can ensure that the training program operates smoothly. Regarding summative evaluation, measures examining trainee cognition, behavior, and organization-level results provide information about how much trainees have changed as a result of taking the ethics training. The implications of effective training program evaluation are discussed. (shrink)

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found (...) strong evidence that learners have interindividually variable but intraindividually stable priors about causal parameters that express a preference for causal determinism. These priors predict which structure subjects preferentially select. The priors can be manipulated experimentally and appear to be domain-general. Heuristic strategies of structure induction are suggested that can be viewed as simplified implementations of the priors. (shrink)

Currently, two frameworks of causal reasoning compete: Whereas dependency theories focus on dependencies between causes and effects, dispositional theories model causation as an interaction between agents and patients endowed with intrinsic dispositions. One important finding providing a bridge between these two frameworks is that failures of causes to generate their effects tend to be differentially attributed to agents and patients regardless of their location on either the cause or the effect side. To model different types of error attribution, we augmented (...) a causal Bayes net model with separate error sources for causes and effects. In several experiments, we tested this new model using the size of Markov violations as the empirical indicator of differential assumptions about the sources of error. As predicted by the model, the size of Markov violations was influenced by the location of the agents and was moderated by the causal structure and the type of causal variables. (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)