Selecting explanations from causal chains: Do statistical principles explain preferences for voluntary causes?

We investigate whether people prefer voluntary causes to physical causes in unfolding causal chains and whether statistical (covariation, sufficiency) principles can predict how people select explanations. Experiment 1 shows that while people tend to prefer a proximal (more recent) cause in chains of unfolding physical events, causality is traced through the proximal cause to an underlying distal (less recent) cause when that cause is a human action. Experiment 2 shows that causal preference is more strongly correlated with judgements of sufficiency and conditionalised sufficiency than with covariation or conditionalised covariation. In addition, sufficiency judgements are partial mediators of the effect of type of distal cause (voluntary or physical) on causal preference. The preference for voluntary causes to physical causes corroborates findings from social psychology, cognitive neuroscience and jurisprudence that emphasise the primacy of intentions in causal attribution processes. Copyright © 2009 John Wiley & Sons, Ltd.     

Acting knowingly: effects of the agent's awareness of an opportunity on causal attributions

According to difference-based (e.g. counterfactual/covariational) models of causal judgement, the epistemic state of the agent should not affect judgements of cause. Four experiments examined opportunity chains in which a physical event (distal cause) enabled a subsequent proximal cause to produce an outcome. All four experiments showed that when the proximal cause was a human action, it was judged as more causal if the agent was aware of his opportunity than if he was not or if the proximal cause was a physical event. The first two experiments showed that these preferences could not be explained in terms of differences in perceived conditional probability (whether from the observer's or the agent's point of view), social controllability or perceptions of the causal sequence as forming a single unit. The third experiment showed that awareness affected the perceived deliberateness with which the action brought the outcome about but not its perceived voluntariness. The fourth experiment showed that when the outcome was intended, the perceived deliberateness of the agent's action was a plausible mediator of the effect of awareness of opportunity on causal preference. We conclude that awareness of the opportunity allows inferences about the deliberate production of the outcome when the action is voluntary, which in turn influence causal judgements.     

Simplicity and probability in causal explanation

What makes some explanations better than others? This paper explores the roles of simplicity and probability in evaluating competing causal explanations. Four experiments investigate the hypothesis that simpler explanations are judged both better and more likely to be true. In all experiments, simplicity is quantified as the number of causes invoked in an explanation, with fewer causes corresponding to a simpler explanation. Experiment 1 confirms that all else being equal, both simpler and more probable explanations are preferred. Experiments 2 and 3 examine how explanations are evaluated when simplicity and probability compete. The data suggest that simpler explanations are assigned a higher prior probability, with the consequence that disproportionate probabilistic evidence is required before a complex explanation will be favored over a simpler alternative. Moreover, committing to a simple but unlikely explanation can lead to systematic overestimation of the prevalence of the cause invoked in the simple explanation. Finally, Experiment 4 finds that the preference for simpler explanations can be overcome when probability information unambiguously supports a complex explanation over a simpler alternative. Collectively, these findings suggest that simplicity is used as a basis for evaluating explanations and for assigning prior probabilities when unambiguous probability information is absent. More broadly, evaluating explanations may operate as a mechanism for generating estimates of subjective probability.     

Children's Understanding of the Causal Origins of Disability

This study explores children's understanding of the causal origins of disabilities. Using a forced-choice explanation task, children's understanding of social-psychological, physical, and biological causal explanations of disabilities was considered. We presented 79 children (26 4- to 5-year-olds, 26 6- to 7-year-olds, and 27 10- to 11-year-olds) with 4 vignettes describing a child with a particular disability (physical disability, blindness, learning disability, attention deficit hyperactivity disorder). Participants rated their agreement with a variety of causal explanations of disability. Results showed significant age, disability, and causal explanation differences in children's causal understandings of disability. Children of all ages showed a preference for physical and biological causes of disability and rejected social-psychological causal explanations. These findings highlight the usefulness of employing a forced-choice methodology to examine young children's concepts of the causal origins of disabilities.     

Infant perception of causal motion produced by humans and inanimate objects

Both the movements of people and inanimate objects are intimately bound up with physical causality. Furthermore, in contrast to object movements, causal relationships between limb movements controlled by humans and their body displacements uniquely reflect agency and goal-directed actions in support of social causality. To investigate the development of sensitivity to causal movements, we examined the looking behavior of infants between 9 and 18 months of age when viewing movements of humans and objects. We also investigated whether individual differences in gender and gross motor functions may impact the development of the visual preferences for causal movements. In Experiment 1, infants were presented with walking stimuli showing either normal body translation or a “moonwalk” that reversed the horizontal motion of body translations. In Experiment 2, infants were presented with unperformable actions beyond infants’ gross motor functions (i.e., long jump) either with or without ecologically valid body displacement. In Experiment 3, infants were presented with rolling movements of inanimate objects that either complied with or violated physical causality. We found that female infants showed longer looking times to normal walking stimuli than to moonwalk stimuli, but did not differ in their looking time to movements of inanimate objects and unperformable actions. In contrast, male infants did not show sensitivity to causal movement for either category. Additionally, female infants looked longer at social stimuli of human actions than male infants. Under the tested circumstances, our findings indicate that female infants have developed a sensitivity to causal consistency between limb movements and body translations of biological motion, only for actions with previous visual and motor exposures, and demonstrate a preference toward social information.     

Judgement of two causal candidates from contingency information: II. Effects of information about one cause on judgements of the other cause

When judgements are being made about two causes there are eight possible kinds of contingency information: occurrences and nonoccurrences of the outcome when both causes are present, when Cause 1 alone is present, when Cause 2 alone is present, and when neither cause is present. It is proposed that contingency information is used to some extent to judge proportionate strength, which is the proportion of occurrences of the outcome that each cause can account for. This leads to a prediction that judgements of one cause will be influenced by information about occurrences, but not nonoccurrences, of the outcome when only the other cause is present. In six experiments consistent support was found for this prediction when the cause being judged had a positive relation with the outcome, but no consistent tendency was found when the cause being judged had a negative relation with the outcome. The effects found for causes with positive contingency cannot be explained by the Rescorla-Wagner model of causal judgement nor by the hypothesis that causal judgements are based on conditional contingencies.     

Concepts of kinship relations and inheritance in childhood and adolescence

This paper examines the development and consistency of children's (4, 7, 10, and 14 years) naïve concepts of inheritance using three tasks. A modified adoption task asked participants to distinguish between biological and social parentage in their predictions and explanations of the origins of different feature types (physical characteristics, disabilities, and personality traits). The causal mechanisms task asked participants to rate their preference for various mechanisms of inheritance for physical features. The family relatedness task required participants to provide judgements on the relatedness of family members and explain their understanding of kinship terms. Developmental trends were revealed in all three tasks. There was a trend towards increased consistency in judgements across tasks with age, but low correlations between explanations given in different task contexts. Findings are discussed with reference to the onset and variability of inheritance concepts and the implications for education and future research.     

Culpable Control and Causal Deviance

Actions that are intended to produce harmful consequences can fail to achieve their desired effects in numerous ways. We refer to action sequences in which harmful intentions are thwarted as deviant causal chains. The culpable control model of blame (CCM) is a useful tool for predicting and explaining the attributions that observers make of the actors whose harmful intentions go awry. In this paper, we describe six types of deviant causal chains; those in which: an actor’s attempt is obviated by the intervention of another person or the environment; the intended effects could not have been produced regardless of the actor’s behavior; other causes diminish the actor’s causal role; the actor brings about foreseen but undesired consequences as a result of pursuing his or her focal goal; the focal action produces a chain of increasingly remote causal events; and the actor derives unforeseen benefits from his or her nefarious actions. A basic assumption of the CCM in these cases is that attributions for the participants’ actions will depend on positive and negative evaluations of their intentions and behaviors. We describe empirical findings that are consistent with this assumption, and predict other findings for causal deviance phenomena that have not yet been investigated empirically.     

Prime and probability: causal knowledge affects inferential and predictive effects on self-agency experiences

Experiences of having caused a certain outcome may arise from motor predictions based on action–outcome probabilities and causal inferences based on pre-activated outcome representations. However, when and how both indicators combine to affect such self-agency experiences is still unclear. Based on previous research on prediction and inference effects on self-agency, we propose that their (combined) contribution crucially depends on whether people have knowledge about the causal relation between actions and outcomes that is relevant to subsequent self-agency experiences. Therefore, we manipulated causal knowledge that was either relevant or irrelevant by varying the probability of co-occurrence (50% or 80%) of specific actions and outcomes. Afterwards, we measured self-agency experiences in an action–outcome task where outcomes were primed or not. Results showed that motor prediction only affected self-agency when relevant actions and outcomes were learned to be causally related. Interestingly, however, inference effects also occurred when no relevant causal knowledge was acquired.     

Asymmetries in predictive and diagnostic reasoning

In this article, we address the apparent discrepancy between causal Bayes net theories of cognition, which posit that judgments of uncertainty are generated from causal beliefs in a way that respects the norms of probability, and evidence that probability judgments based on causal beliefs are systematically in error. One purported source of bias is the ease of reasoning forward from cause to effect (predictive reasoning) versus backward from effect to cause (diagnostic reasoning). Using causal Bayes nets, we developed a normative formulation of how predictive and diagnostic probability judgments should vary with the strength of alternative causes, causal power, and prior probability. This model was tested through two experiments that elicited predictive and diagnostic judgments as well as judgments of the causal parameters for a variety of scenarios that were designed to differ in strength of alternatives. Model predictions fit the diagnostic judgments closely, but predictive judgments displayed systematic neglect of alternative causes, yielding a relatively poor fit. Three additional experiments provided more evidence of the neglect of alternative causes in predictive reasoning and ruled out pragmatic explanations. We conclude that people use causal structure to generate probability judgments in a sophisticated but not entirely veridical way.     

Effects of other‐profitability and self‐profitability on evaluative judgements of behaviours

Behaviours are judged by their causes (i.e. the actor's intentions) as well as by their consequences. The present study focuses on the effects of the latter. Subjects judged behaviours that were either socially good, socially bad, competent, or incompetent. The consequences of the behaviours were either (a) personal consequences for the actor, (b) interpersonal consequences for others, or (c) not mentioned. Behaviours with interpersonal consequences were judged more extremely than with personal consequences. When no consequences were mentioned, the behaviours were rated similarly as in the condition with interpersonal consequences. These effects emerged regardless of behaviour valence (positive versus negative) and dimension (social versus competence‐related). The results suggest that others' behaviours are judged primarily by their interpersonal implications, and that observers take the perspective of those whose outcomes may be affected by the actor. Copyright © 1999 John Wiley & Sons, Ltd.     

What Sort of Taxonomy of Causation Do We Need for Language Understanding?*

A proposal is made concerning the introduction of the notions of cause and reason into a natural language understanding system. Its hypothesis is that one should prefer rational explanations of actions when dealing with human, or human-like, agents, if one can find them in what one is analyzing, but that in other, nonhuman, cases one should prefer causal explanations. The reader is reminded of the existing state of the preference semantics system, and then are described the changes that would have to be made to incorporate the above proposal and to find examples to test it. Finally, it is argued that there is no reason to think that a more complex taxonomy of causes (beyond the opposition of causes and reasons) is required, at least not if, as here. one seeks a procedural distinction corresponding to any taxonomic distinction among inference rules.     

Judgments of cause and blame: the effects of intentionality and foreseeability

What are the factors that influence everyday attributions of cause and blame? The current studies focus on sequences of events that lead to adverse outcomes, and examine people's cause and blame ratings for key events in these sequences. Experiment 1 manipulated the intentional status of candidate causes and their location in a causal chain. Participants rated intentional actions as more causal, and more blameworthy, than unintentional actions or physical events. There was also an overall effect of location, with later events assigned higher ratings than earlier events. Experiment 2 manipulated both intentionality and foreseeability. The preference for intentional actions was replicated, and there was a strong influence of foreseeability: actions were rated as more causal and more blameworthy when they were highly foreseeable. These findings are interpreted within two prominent theories of blame, [Shaver, K. G. (1985). The attribution of blame: Causality, responsibility, and blameworthiness. New York: Springer-Verlag] and [Alicke, M. D. (2000). Culpable control and the psychology of blame. Psychological Bulletin, 126, 556-574]. Overall, it is argued that the data are more consistent with Alicke's model of culpable control.     

Psychological distance and judgments of causal impact

When assessing causal impact, individuals have to consider two pieces of information: the magnitude of the cause that resulted in an effect, and the magnitude of the resulting effect. In the present research, participants judged the causal impact of cause–effect relationships in which the magnitude of causes and effects varied independently. Participants mainly relied on effect magnitude, rating causal impact to be much higher when strong (vs. weak) effects emerged. When participants took cause magnitude into account (which they did, but to a lesser extent), their judgments reflected a covariation rule (i.e., causal impact being maximal for strong causes generating strong effects) rather than a ratio rule (i.e., causal impact being maximal for weak causes generating strong effects). These distinct views on causal impact were moderated by psychological distance: Effect magnitude dominated judgments of proximal events, whereas cause magnitude had relatively more impact on causal judgments of distal events.     

Moving Beyond Causes: Optimality Models and Scientific Explanation

A prominent approach to scientific explanation and modeling claims that for a model to provide an explanation it must accurately represent at least some of the actual causes in the event's causal history. In this paper, I argue that many optimality explanations present a serious challenge to this causal approach. I contend that many optimality models provide highly idealized equilibrium explanations that do not accurately represent the causes of their target system(s). Furthermore, in many contexts, it is in virtue of their independence of causes that optimality models are able to provide a better explanation than competing causal models. Consequently, our account of explanation and modeling must expand beyond the causal approach.     

‘Pain Always Asks for a Cause’: Nietzsche and Explanation

Those who have emphasised Nietzsche's naturalism have often claimed that he emulates natural scientific methods by offering causal explanations of psychological, social, and moral phenomena. In order to render Nietzsche's method consistent with his methodology, such readers of Nietzsche have also claimed that his objections to the use of causal explanations are based on a limited scepticism concerning the veracity of causal explanations. My contention is that proponents of this reading are wrong about both Nietzsche's methodology and his method. I argue for this by: first, showing that Nietzsche was suspicious of causal explanations not only on sceptical grounds but also for reasons provided by his psychological analysis of our tendency to look for causes; and second, arguing for a non‐causal interpretation of Nietzsche's approach to psychological explanation.     

Gender and speech rate in the perception of competence and social attractiveness

The authors' hypotheses were that (a) listeners regard speakers whose global speech rates they judge to be similar to their own as more competent and more socially attractive than speakers whose rates are different from their own and (b) gender influences those perceptions. Participants were 17 male and 28 female listeners; they judged each of 3 male and 3 female speakers in terms of 10 unipolar adjective scales. The authors used 8 of the scales to derive 2 scores describing the extent to which the listener viewed a speaker as competent and socially attractive. The 2 scores were related by trend analyses (a) to the listeners' perceptions of the speakers' speech rates as compared with their own and (b) to comparisons of the actual speech rates of the speakers and listeners. The authors examined trend components of the data by split-plot multiple regression analyses. In general, the results supported both hypotheses. The participants judged speakers with speech rates similar to their own as more competent and socially attractive than speakers with speech rates slower or faster than their own. However, the ratings of competence were significantly influenced by the gender of the listeners, and those of social attractiveness were influenced by the gender of the listeners and the speakers.     

Nietzsche and the origin of the idea of modernism

This paper combines a phenomenological account of the types of causal transaction found in social reality with a critique of two theories, one structuralist and one Marxist, that contravene it. Part I argues that there are three types of causal transaction in social life in addition to physical causal transactions: people bringing about states of affairs by acting, states of affairs bringing about actions by inducing responses, and entities and states of affairs bringing about what makes sense to people to do by making certain factors determine this. It is also contended that social formations and structures cause actions and other social formations/structures only by way of participating in these types of transaction. The conditions under which this occurs are discussed. Part II criticizes Peter Blau's account of structural effects and Jean‐Paul Sartre's version of a materialist theory of history, two theories that either advocate or require causal transactions between social structures/ formations which do not reduce to transactions of the types described in Part I. The paper concludes by suggesting that social entities that make actions possible do not thereby cause them.     

Spontaneous counterfactual thoughts and causal explanations

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.     

Occam's rattle: children's use of simplicity and probability to constrain inference

A growing literature suggests that generating and evaluating explanations is a key mechanism for learning and inference, but little is known about how children generate and select competing explanations. This study investigates whether young children prefer explanations that are simple, where simplicity is quantified as the number of causes invoked in an explanation, and how this preference is reconciled with probability information. Both preschool-aged children and adults were asked to explain an event that could be generated by 1 or 2 causes, where the probabilities of the causes varied across conditions. In 2 experiments, it was found that children preferred explanations involving 1 cause over 2 but were also sensitive to the probability of competing explanations. Adults, in contrast, responded on the basis of probability alone. These data suggest that children employ a principle of parsimony like Occam's razor as an inductive constraint and that this constraint is employed when more reliable bases for inference are unavailable.