Mechanisms of theory formation in young children

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.

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.

British Journal of Educational Psychology

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. Causal cognitionthe ability to perceive and infer cause-effect relationslies at the core of scientific investigation and is equally crucial in everyday thinking. It revolves around the notion that causes produce effects by means of an underlying mechanism. While some This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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.

Cognitive Development, 2009

Three experiments investigate how self-generated explanation influences children's causal learning. Five-year-olds (N=114) observed data consistent with two hypotheses and were prompted to explain or to report each observation. In Study 1, when making novel generalizations, explainers were more likely to favor the hypothesis that accounted for more observations. In Study 2, explainers favored a hypothesis that was consistent with prior knowledge. Study 3 pitted a hypothesis that accounted for more observations against a hypothesis consistent with prior knowledge. Explainers were more likely to base generalizations on prior knowledge. Findings suggest that attempts to explain drive children to evaluate hypotheses using features of "good" explanations, or those supporting generalizations with broad scope, as informed by children's prior knowledge and observations.

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.

Developmental Psychology, 2012

Cognitive Science, 2004

Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a "blicket detector," a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine's activation that required them to use indirect evidence to make causal inferences. Critically, associative models either made no predictions, or made incorrect predictions about these inferences. In general, children were able to make these inferences, but some developmental differences between 3and 4-year-olds were found. We suggest that children's causal inferences are not based on recognizing associations, but rather that children develop a mechanism for Bayesian structure learning. Experiment 3 explicitly tests a prediction of this account. Children were asked to make an inference about ambiguous data based on the base rate of certain events occurring. Four-year-olds, but not 3-year-olds were able to make this inference.

Frontiers in Psychology, 2015

Young children, in general, appear to have a strong drive to explore the environment in ways that reveal its underlying causal structure. But are they really attuned specifically to casual information in this quest for understanding, or do they show equal interest in other types of non-obvious information about the world? To answer this question, we introduced 20 threeyear-old children to two puppets who were anxious to tell the child about a set of novel artifacts and animals. One puppet consistently described causal properties of the items while the other puppet consistently described carefully matched non-causal properties of the same items. After a familiarization period in which children learned which type of information to expect from each informant, children were given the opportunity to choose which they wanted to hear describe each of eight pictured test items. On average, children chose to hear from the informant that provided causal descriptions on 72% of the trials. This preference for causal information has important implications for explaining the role of conceptual information in supporting early learning and may suggest means for maximizing interest and motivation in young children.