Singular Clues to Causality and Their Use in Human Causal Judgment

What Causes Harmful Algal Blooms? A Case Study of Causal Attributions and Conflict in a Lakeshore Community

Environmental management involves the complex interaction between identifying the causes of problems and implementing solutions. Our exploratory study draws on attribution theory to analyze the causal attributions among community members experiencing frequent and intensifying harmful algal blooms in a lake of western New York State. Our interviews (n = 21) revealed that causal attributions were grounded in observation but that scientific observations led to very different causal attributions than direct observations among a subset of the lay public. Some community members also developed causal attributions based on their social relationships. Differences in causal attributions became the basis of widespread intracommunity disagreement, which in turn hampered management efforts. Our work demonstrates the need for meaningful public engagement in water management-engagement that addresses causal beliefs within the community, even if those beliefs may not align with scientific understandings.

Child-directed speech is optimized for syntax-free semantic inference

The way infants learn language is a highly complex adaptive behavior. This behavior chiefly relies on the ability to extract information from the speech they hear and combine it with information from the external environment. Most theories assume that this ability critically hinges on the recognition of at least some syntactic structure. Here, we show that child-directed speech allows for semantic inference without relying on explicit structural information. We simulate the process of semantic inference with machine learning applied to large text collections of two different types of speech, child-directed speech versus adult-directed speech. Taking the core meaning of causality as a test case, we find that in child-directed speech causal meaning can be successfully inferred from simple co-occurrences of neighboring words. By contrast, semantic inference in adult-directed speech fundamentally requires additional access to syntactic structure. These results suggest that child-directed speech is ideally shaped for a learner who has not yet mastered syntactic structure.

Predicting responsibility judgments from dispositional inferences and causal attributions

The question of how people hold others responsible has motivated decades of theorizing and empirical work. In this paper, we develop and test a computational model that bridges the gap between broad but qualitative framework theories, and quantitative but narrow models. In our model, responsibility judgments are the result of two cognitive processes: a dispositional inference about a person's character from their action, and a causal attribution about the person's role in bringing about the outcome. We test the model in a group setting in which political committee members vote on whether or not a policy should be passed. We assessed participants' dispositional inferences and causal attributions by asking how surprising and important a committee member's vote was. Participants' answers to these questions in Experiment 1 accurately predicted responsibility judgments in Experiment 2. In Experiments 3 and 4, we show that the model also predicts moral responsibility judgments, and that importance matters more for responsibility, while surprise matters more for judgments of wrongfulness.

The Development of Spatial-Temporal, Probability, and Covariation Information to Infer Continuous Causal Processes

This paper considers how 5- to 11-year-olds’ verbal reasoning about the causality underlying extended, dynamic natural processes links to various facets of their statistical thinking. Such continuous processes typically do not provide perceptually distinct causes and effect, and previous work suggests that spatial–temporal analysis, the ability to analyze spatial configurations that change over time, is a crucial predictor of reasoning about causal mechanism in such situations. Work in the Humean tradition to causality has long emphasized on the importance of statistical thinking for inferring causal links between distinct cause and effect events, but here we assess whether this is also viable for causal thinking about continuous processes. Controlling for verbal and non-verbal ability, two studies (N = 107; N = 124) administered a battery of covariation, probability, spatial–temporal, and causal measures. Results indicated that spatial–temporal analysis was the best predictor of causal thinking across both studies, but statistical thinking supported and informed spatial–temporal analysis: covariation assessment potentially assists with the identification of variables, while simple probability judgment potentially assists with thinking about unseen mechanisms. We conclude that the ability to find out patterns in data is even more widely important for causal analysis than commonly assumed, from childhood, having a role to play not just when causally linking already distinct events but also when analyzing the causal process underlying extended dynamic events without perceptually distinct components.

The developmental profile of temporal binding: From childhood to adulthood

Temporal binding refers to a phenomenon whereby the time interval between a cause and its effect is perceived as shorter than the same interval separating two unrelated events. We examined the developmental profile of this phenomenon by comparing the performance of groups of children (aged 6–7, 7–8, and 9–10 years) and adults on a novel interval estimation task. In Experiment 1, participants made judgements about the time interval between (a) their button press and a rocket launch, and (b) a non-causal predictive signal and rocket launch. In Experiment 2, an additional causal condition was included in which participants made judgements about the interval between an experimenter’s button press and the launch of a rocket. Temporal binding was demonstrated consistently and did not change in magnitude with age: estimates of delay were shorter in causal contexts for both adults and children. In addition, the magnitude of the binding effect was greater when participants themselves were the cause of an outcome compared with when they were mere spectators. This suggests that although causality underlies the binding effect, intentional action may modulate its magnitude. Again, this was true of both adults and children. Taken together, these results are the first to suggest that the binding effect is present and developmentally constant from childhood into adulthood.

The Preference for Joint Attributions Over Contrast-Factor Attributions in Causal Contrast Situations

A current issue about causal attribution is whether people take simple contrast-factor attributions or complex joint attributions in contrast situations. For example, a stone does not dissolve in water and a piece of salt dissolves in water. That the piece of salt dissolves in water is due to: (A) the influence of the piece of salt; (B) the influence of the water; (C) the joint influence of the piece of salt and the water. We propose a mechanism-based sufficiency account for such questions. It argues that causal attributions are guided by mechanism-based explanatory sufficiency, and people prefer a mechanism-based attribution with explanatory sufficiency. This account predicts the sufficient joint attribution (the C option), whereas the conventional covariation approach predicts the contrast-factor attribution (the A option). Two experiments investigated whether contrast situations affect causal attributions for compound causation with explicit mechanism information and simple causation without explicit mechanism information, respectively. Both experiments found that in both the presence and absence of contrast situations, the majority of participants preferred sufficient joint attributions to simple contrast-factor attributions regardless of whether explicit mechanism information was present, and contrast situations did not affect causal attributions. These findings favor the mechanism-based sufficiency account rather than the covariation approach and the complexity account. In contrast situations, the predominance of joint attributions implies that explanatory complexity affects causal attributions by the modulation of explanatory sufficiency, and people prefer mechanism-based joint attributions that provide sufficient explanations for effects. The present findings are beyond the existing approaches to causal attributions.

Children's reasoning about continuous causal processes: The role of verbal and non-verbal ability

Background

Causes produce effects via underlying mechanisms that must be inferred from observable and unobservable structures. Preschoolers show sensitivity to mechanisms in machine‐like systems with perceptually distinct causes and effects, but little is known about how children extend causal reasoning to the natural continuous processes studied in elementary school science, or how other abilities impact on this.

Aims

We investigated the development of children's ability to predict, observe, and explain three causal processes, relevant to physics, biology, and chemistry, taking into account their verbal and non‐verbal ability.

Sample

Children aged 5–11 years (N = 107) from London and Oxford, with wide ethnic/linguistic variation, drawn from the middle/upper socioeconomic status (SES) range.

Methods

Children were tested individually on causal tasks focused on sinking, absorption, and dissolving, using a novel approach in which they observed contrasting instances of each, to promote attention to mechanism. Further tasks assessed verbal (expressive vocabulary) and non‐verbal (block design) ability.

Results

Reports improved with age, though with differences between tasks. Even young participants gave good descriptions of what they observed. Causal explanations were more strongly related to observation than to prediction from prior knowledge, but developed more slowly. Non‐verbal but not generic verbal ability predicted performance.

Conclusions

Reasoning about continuous processes is within the capacity of children from school entry, even using verbal reports, though they find it easier to address more rapid processes. Mechanism inference is uncommon, with non‐verbal ability an important influence on progress. Our research is the first to highlight this key factor in children's progress towards thinking about scientific phenomena.

I. INTRODUCTION: UNDERSTANDING MEDICINES AND MEDICAL INTERVENTIONS

We live in an increasingly pharmacological and medical world, where children and adults frequently encounter alleged treatments for an enormous range of illnesses. How do we come to understand what heals and why? Here, 15 studies explore how 1,414 children (ages 5-11) and 882 adults construe the efficacies of different kinds of cures. Developmental patterns in folk physics, psychology, and biology lead to predictions about which expectations about cures will remain relatively constant across development and which will change. With respect to stability, we find that even young school children (ages 5-7) distinguish between physical and psychological disorders and the treatments most effective for each. In contrast, young children reason differently about temporal properties associated with cures. They often judge that dramatic departures from prescribed schedules will continue to be effective. Young children are also less likely than older ages to differentiate between the treatment needs of acute versus chronic disorders. Young children see medicines as agent-like entities that migrate only to afflicted regions while having "cure-all" properties, views that help explain their difficulties grasping side effects. They also differ from older children and adults by judging pain and effort as reducing, instead of enhancing, a treatment's power. Finally, across all studies, optimism about treatment efficacy declines with age. Taken together, these studies show major developmental changes in how children envision the ways medicines work in the body. Moreover, these findings link to broader patterns in cognitive development and have implications for how medicines should be explained to children.

Causal reasoning with mental models

This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.

Causality in thought

Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle. Can human causal inference be captured by relations of probabilistic dependency, or does it draw on richer forms of representation? This article explores this question by reviewing research in reasoning, decision making, various forms of judgment, and attribution. We endorse causal Bayesian networks as the best normative framework and as a productive guide to theory building. However, it is incomplete as an account of causal thinking. On the basis of a range of experimental work, we identify three hallmarks of causal reasoning-the role of mechanism, narrative, and mental simulation-all of which go beyond mere probabilistic knowledge. We propose that the hallmarks are closely related. Mental simulations are representations over time of mechanisms. When multiple actors are involved, these simulations are aggregated into narratives.