Causal learning mechanisms in very young children: Two, three-, and four-year-olds infer causal relations from patterns of variation and covariation (original) (raw)
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A Theory of Causal Learning in Children: Causal Maps and Bayes Nets
Psychological Review, 2004
The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.
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.
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.
2000
Three studies explored whether and when children could categorize objects on the basis of a novel underlying causal power. To test this we constructed a "blicket detector," a machine that lit up and played music when certain objects were placed on it. First, 2-, 3-and 4-year-old children saw that an object labeled as a "blicket" would set off the machine. In a categorization task, other objects were demonstrated on the machine. Some set it off and some did not. Children were asked to say which objects were "blickets." In an induction task, other objects were or were not labeled as "blickets." Children had to predict which objects would have the causal power to set off the machine. The causal power could conflict with perceptual properties of the object, such as color and shape. In an association task the object was associated with the machine's lighting up but did not cause it to light up. Even the youngest children sometimes used the causal power to determine the object's name rather than using its perceptual properties and sometimes used the object's name rather than its perceptual properties to predict the object's causal powers. Children rarely categorized the object on the basis of the associated event. Young children also sometimes made interesting memory errors-they incorrectly reported that objects with the same perceptual features had had the same causal power. These studies demonstrate that even very young children will easily and swiftly learn about a new causal power of an object and spontaneously use that information in classifying and naming the object.
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.
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.
Blickets and babies: The development of causal reasoning in toddlers and infants
Developmental Psychology, 2006
Previous research has suggested that preschoolers possess a cognitive system that allows them to construct an abstract, coherent representation of causal relations among events. Such a system lets children reason retrospectively when they observe ambiguous data in a rational manner (e.g., D. M. Sobel, J. B. Tenenbaum, & A. Gopnik, 2004). However, there is little evidence that demonstrates whether younger children possess similar inferential abilities. In Experiment 1, the authors extended previous findings with older children to examine 19-and 24-month-olds' causal inferences. Twenty-four-montholds' inferences were similar to those of preschoolers, but younger children lacked the ability to make retrospective causal inferences, perhaps because of performance limitations. In Experiment 2, the authors designed an eye-tracking paradigm to test younger participants that eliminated various manual search demands. Eight-month-olds' anticipatory eye movements, in response to retrospective data, revealed inferences similar to those of 24-month-olds in Experiment 1 and preschoolers in previous research. These data are discussed in terms of associative reasoning and causal inference.