A Theory of Causal Learning in Children: Causal Maps and Bayes Nets (original) (raw)
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Developmental Psychology, 2001
Three studies investigated whether young children make accurate causal inferences on the basis of patterns of variation and covariation. Children were presented with a new causal relation by means of a machine called the "blicket detector." Some objects, but not others, made the machine light up and play music. In the first 2 experiments, children were told that "blickets make the machine go" and were then asked to identify which objects were "blickets." Two-, 3-, and 4-year-old children were shown various patterns of variation and covariation between two different objects and the activation of the machine. All 3 age groups took this information into account in their causal judgments about which objects were blickets. In a 3rd experiment, 3-and 4-year-old children used the information when they were asked to make the machine stop. These results are related to Bayes-net causal graphical models of causal learning.
Developmental Science, 2007
A fundamental assumption of the causal graphical model framework is the Markov assumption, which posits that learners can discriminate between two events that are dependent because of a direct causal relation between them and two events that are independent conditional on the value of another event(s). Sobel and Kirkham (2006) demonstrated that 8-month-old infants registered conditional independence information among a sequence of events; infants responded according to the Markov assumption in such a way that was inconsistent with models that rely on simple calculations of associative strength. The present experiment extends these findings to younger infants, and demonstrates that such responses potentially develop during the second half of the first year of life. These data are discussed in terms of a developmental trajectory between associative mechanisms and causal graphical models as representations of infants' causal and statistical learning.
Preschool children learn causal structure from conditional interventions
The conditional intervention principle is a formal principle that relates patterns of interventions and outcomes to causal structure. It is a central assumption of the causal Bayes net formalism. Four experiments suggest that preschoolers can use the conditional intervention principle both to learn complex causal structure from patterns of evidence and to predict patterns of evidence from knowledge of causal structure. Other theories of causal learning do not account for these results.
Using Domain-General Principles to Explain Children's Causal Reasoning Abilities
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 4-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.
Cognition, 2014
Children learn causal relationships quickly and make far-reaching causal inferences from what they observe. Acquiring abstract causal principles that allow generalization across different causal relationships could support these abilities. We examine children's ability to acquire abstract knowledge about the forms of causal relationships and show that in some cases they learn better than adults. Adults and 4-and 5-year-old children saw events suggesting that a causal relationship took one of two different forms, and their generalization to a new set of objects was then tested. One form was a more typical disjunctive relationship; the other was a more unusual conjunctive relationship. Participants were asked to both judge the causal efficacy of the objects and to design actions to generate or prevent an effect. Our results show that children can learn the abstract properties of causal relationships using only a handful of events. Moreover, children were more likely than adults to generalize the unusual conjunctive relationship, suggesting that they are less biased by prior assumptions and pay more attention to current evidence. These results are consistent with the predictions of a hierarchical Bayesian model.
2017
The present paper examines what domain-general causal knowledge reasoners need for at least some outcome-variable types to construct useable content-specific causal knowledge. In particular, it explains why it is essential to have analytic knowledge of causal-invariance integration functions: knowledge for predicting the expected outcome assuming that the empirical knowledge acquired regarding a causal relation holds across the learning context and an application context. The paper reports two studies that support the hypothesis that preschool children have such knowledge regarding binary causes and effects, enabling them to generalize across contexts rationally, favoring the causal-invariance hypothesis over alternative hypotheses, including interaction (e.g., linear) integration functions, heuristics, and biases.
European Journal of Contemporary Education
The understanding and generalisation of causality are important thinking abilities, as they form the basis for a person's activity. Researchers exploring these abilities do not have a unified opinion regarding the age of children when they develop causative understanding and its determinant factors (e.g. age, prior knowledge, the content of a task, etc.). The aim of the current research is to investigate the abilities of 4-7-year-old children to explain causative relations and independently generalise them. An original experiment using spatial figures of animals was chosen for the research. 66 preschool children participated in the research, each group being represented by 22 children (4-5-year old, 5-6-year old, and 6-7-year old respectively). The research results revealed that preschool children (4-7-year old) are able to distinguish and explain causative relations. Besides, no difference was determined between the children's abilities to explain and generalise causalities in relation to age (4-5, 5-6, and 6-7). It is assumed that the children of different age understand causal structures in the same way when the spatial figures of animals, which are close and familiar to children, are used as simulation material in the research. The obtained results of the experiment are discussed in the context of the works of other researchers.
Preschool children learn about causal structure from conditional interventions
Developmental Science, 2007
The conditional intervention principle is a formal principle that relates patterns of interventions and outcomes to causal structure. It is a central assumption of experimental design and the causal Bayes net formalism. Two studies suggest that preschoolers can use the conditional intervention principle to distinguish causal chains, common cause and interactive causal structures even in the absence of differential spatiotemporal cues and specific mechanism knowledge. Children were also able to use knowledge of causal structure to predict the patterns of evidence that would result from interventions. A third study suggests that children's spontaneous play can generate evidence that would support such accurate causal learning.
Children's use of interventions to learn causal structure
Children between 5 and 8 years of age freely intervened on a three-variable causal system, with their task being to discover whether it was a common cause structure or one of two causal chains. From 6 or 7 years of age, children were able to use information from their interventions to correctly disambiguate the structure of a causal chain. We used a Bayesian model to examine children’s interventions on the system; this showed that with development children became more efficient in producing the interventions needed to disambiguate the causal structure and that the quality of interventions, as measured by their informativeness, improved developmentally. The latter measure was a significant predictor of children’s correct inferences about the causal structure. A second experiment showed that levels of performance were not reduced in a task where children did not select and carry out interventions themselves, indicating no advantage for self-directed learning. However, children’s performance was not related to intervention quality in these circumstances, suggesting that children learn in a different way when they carry out interventions themselves.