Developmental research has shown the profound changes that children's causal knowledge undergoes throughout development. These changes have been identified and thoroughly studied, but the processes and mechanisms behind the changes are still unknown. To better understand the processes involved in developmental change, this proposal examines three sources of causal information - perceptual cues, contingencies, and effects of interventions - and explores how they interact and what they each contribute to the development of causal reasoning in young children. In particular, these studies seek to show that young children pay attention to all three types of information, so that when some information is missing or incomplete, children can use other information to acquire causal knowledge. The first two studies directly contrast perceptual information (spatial contiguity cues) with information about both direct and conditional contingencies in order to observe their relative effects on young children's causal inferences. The third study examines the relative effects of interventions and probabilistic contingency information on adults' causal judgments. The final study looks at the relative effects of interventions and deterministic and probabilistic contingencies on young children's causal judgments. Results of these studies will provide valuable information about the interaction between perception, contingency and intervention underlying the acquisition of causal knowledge, and will help us to understand some of the mechanisms that enable children to learn about the world. ? ?
| Kushnir, Tamar; Gopnik, Alison (2007) Conditional probability versus spatial contiguity in causal learning: Preschoolers use new contingency evidence to overcome prior spatial assumptions. Dev Psychol 43:186-96 |
| Kushnir, Tamar; Gopnik, Alison (2005) Young children infer causal strength from probabilities and interventions. Psychol Sci 16:678-83 |
| Gopnik, Alison; Glymour, Clark; Sobel, David M et al. (2004) A theory of causal learning in children: causal maps and Bayes nets. Psychol Rev 111:3-32 |
