People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.

A connectionist model of causal attribution is presented, emphasizing the use of domain-general principles of processing and learning previously employed in models of semantic cognition. The model categorizes objects dependent upon their observed “causal properties ” and is capable of making several types of inferences that four-year-old children have been shown to be capable of. The model gives rise to approximate conformity to normative models of causal inference and gives approximate estimates of the probability that an object presented in an ambiguous situation actually possesses a particular causal power, based on background knowledge and recent observations. It accounts for data from three sets of experimental studies of the causal inferencing abilities of young children. The model provides a base for further efforts to delineate the intuitive mechanisms of causal inference employed by children and adults, without appealing to inherent principles or mechanisms specialized for causal as opposed to other forms of reasoning.

This study investigates the interaction between preschoolers ’ initial theories and their ability to learn causal relations from patterns of data. Children observed ambiguous evidence in which sets of two candidate causes co-occurred with an effect (e.g. A&B � E, A&C � E, A&D � E, etc). In one condition, all candidate causes were from the appropriate domain (a biological cause for a biological effect); in another condition, the recurring candidate cause, A, crossed domains (a psychological cause for a biological effect). When all causes were domainappropriate, children were able to use the data to identify A as a cause. When the recurring cause crossed domains, children were less likely to endorse A. However, preschoolers were significantly more willing to accept cross-domain causes after seeing the evidence than at baseline. A Bayesian model is proposed to explain this interaction. Very young children have remarkably sophisticated causal knowledge about the world. Children reason about the causes of mental states (e.g., Meltzoff, 1995), physical systems (e.g., Bullock, Gelman, & Baillargeon, 1982; Shultz, 1982), and biological events (e.g., Gelman & Wellman, 1991; Kalish, 1996). Preschoolers can even make predictions about hidden variables and explain events in terms of unobservable causes (Schulz & Sommerville, in press). Many researchers have suggested that children’s causal knowledge can be characterized as intuitive theories: abstract, coherent, defeasible representations of causal

Dynamics and the perception of causal events To imagine possible events, we use our knowledge of causal relationships. To look deep into the past and infer events that were not witnessed, we use causal relationships as well. We also use causal knowledge to infer what can not be directly seen in the present, for instance, the existence of planets around distant stars, or the presence of subatomic particles. Knowledge of causal relationships allows us to go beyond the immediate here and now. In this chapter I introduce a new theoretical framework for how this very basic concept might be mentally represented. In effect, I propose 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. In philosophy, epistemological theories are often contrasted with metaphysical theories, theories about the nature of reality. Since people’s concepts of causation are assumed to be in error, most metaphysical theories of causation seek to reform rather than describe the concept of CAUSE in people’s heads, (see Mackie, 1974; Dowe, 2000). Theories of causation in psychology have followed suit by linking people’s representations of causation to the outward

Research in open-domain commonsense reasoning has been hindered by the lack of evaluation metrics for judging progress and comparing alternative approaches. Taking inspiration from large-scale question sets used in natural language processing research, we authored one thousand English-language questions that directly assess commonsense causal reasoning, called the Choice Of Plausible Alternatives (COPA) evaluation. Using a forcedchoice format, each question gives a premise and two plausible causes or effects, where the correct choice is the alternative that is more plausible than the other. This paper describes the authoring methodology that we used to develop a validated question set with sufficient breadth to advance open-domain commonsense reasoning research. We discuss the design decisions made during the authoring process, and explain how these decisions will affect the design of high-scoring systems. We also present the performance of multiple baseline approaches that use statistical natural language processing techniques, establishing initial benchmarks for future systems.

Causal learning requires integrating constraints provided by domain-specific theories with domaingeneral statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate causes co-occurred with an effect. Evidence was presented in the

This paper proposes a new model of causal meaning, the Vector Model, which formalizes a model of causation based on Talmys notions of force dynamics (Wolff, Song, & Driscoll, 2002). In the Vector Model, the concepts of CAUSE, ENABLE and PREVENT are distinguished from one another in terms of force vectors, their resultant and the relationship of each force vector to a target vector. The predictions of the model were tested in two experiments in which participants saw realistic 3D-animations of an inflatable boat moving through a pool of water. The boats movements were completely determined by the force vectors entered into a physics simulator. Participants linguistic descriptions of the animations were closely matched by those predicted by the model given the same force vectors as those used to produce the animations. Our model may have implications for the semantics of causal verbs as well as the perception of causal events.

Three experiments investigated the impact of delay on human causal learning. We present a new paradigm based on the presentation of continuous event streams, and use it to test two hypotheses drawn from associative learning theories of causal inference. Unlike free-operant procedures traditionally used to study temporal aspects of causal learning (Shanks, Pearson, & Dickinson, 1989; Shanks & Dickinson, 1987; Buehner & May, 2002, 2003, 2004), the procedure employed here allows full control over all aspects of stimulus delivery while at the same time overcoming the ecologically invalid notion of discrete learning trials. Results show that delays generally impair causal learning, but prior knowledge and experience mediate this detrimental effect. In accordance with associative learning theory, pre-exposure to an unreinforced background context facilitates the discovery of delayed causal relationships. However, contrary to associative learning theory, increasing the amount of experience with a delayed causal relationship does not improve discovery. Implications for associative learning and causal model theories are discussed.

In multiple‐cue learning people acquire information about cue‐outcome relations and combine these into predictions or judgments. Previous studies claim that people can achieve high levels of performance without explicit knowledge of the task structure or insight into their own judgment policies. It has also been argued that people use a variety of suboptimal strategies to solve such tasks. In two experiments we re‐examined these conclusions by introducing novel measures of task knowledge and self‐insight, and using ‘rolling regression ’ methods to analyze individual learning. Participants successfully learned a four‐cue probabilistic environment and showed accurate knowledge of both the task structure and their own judgment processes. Learning analyses suggested that the apparent use of suboptimal strategies emerges from the incremental tracking of statistical contingencies in the environment. These findings have wide repercussions for the study of multicue learning in both normal and patient populations. Insight 3

Causation by omission is instantiated when an effect occurs from an absence, as in The absence of nicotine causes withdrawal or Not watering the plant caused it to wilt. The phenomenon has been viewed as an insurmountable problem for process theories of causation, which specify causation in terms of conserved quantities, like force, but not for theories that specify causation in terms of statistical or counterfactual dependencies. A new account of causation challenges these assumptions. According to the force theory, absences are causal when the removal of a force leads to an effect. Evidence in support of this account was found in three experiments in which people classified animations of complex causal chains involving force removal, as well as chains involving virtual forces, that is, forces that were anticipated but never realized. In a fourth experiment, the force theory’s ability to predict synonymy relationships between different types of causal expressions provided further evidence for this theory over dependency theories. The findings show not only how causation by omission can be grounded in the physical world, but also why only certain absences, amongst the potentially infinite number of absences, are causal.