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Learning about causes from people and about people as causes: probabilistic models and social causal reasoning
Daphna Buchsbaum1, Elizabeth Seiver, Sophie Bridgers
1Institute of Human Development, Department of Psychology, University of California at Berkeley, Tolman Hall, Berkeley, CA 94720, USA.
Insights
Children learn about the world by linking social and physical causation. They use probabilistic models to update causal theories about both environments, demonstrating a unified approach to understanding cause and effect.
Area of Science:
- Cognitive Science
- Developmental Psychology
- Causal Inference
Background:
- Children face challenges in understanding the causal structure of their physical and social worlds.
- Prior research focused on children's physical causal inference using probabilistic evidence.
- Learning social causation, including behavior causes and action-outcome links, is crucial for development.
Purpose of the Study:
- To argue that social and causal reasoning are deeply interconnected in children's minds.
- To propose that children jointly construct causal theories for social and physical environments.
- To explore how children integrate social and physical evidence for causal inference.
Main Methods:
- Review of studies on adult and child causal inference.
- Analysis of how children use social cues (pedagogy, testimony, confidence) with statistical evidence.
- Examination of probabilistic models of cognition for causal theory updating.
Main Results:
- Adults can infer causal and action structures from unsegmented human motion.
- Children use social information to infer physical causes and are influenced by demonstrator pedagogy.
- Children integrate testimony, confidence, and own observations to infer causes and informant knowledge.
Conclusions:
- Social and physical reasoning are fundamentally similar and deeply linked in children.
- Children jointly construct and update causal theories using probabilistic models.
- Psychological causation and attribution are integrated within the same causal system as physical causation.
Abstract:
A major challenge children face is uncovering the causal structure of the world around them. Previous research on children's causal inference has demonstrated their ability to learn about causal relationships in the physical environment using probabilistic evidence. However, children must also learn about causal relationships in the social environment, including discovering the causes of other people's behavior, and understanding the causal relationships between others' goal-directed actions and the outcomes of those actions. In this chapter, we argue that social reasoning and causal reasoning are deeply linked, both in the real world and in children's minds. Children use both types of information together and in fact reason about both physical and social causation in fundamentally similar ways. We suggest that children jointly construct and update causal theories about their social and physical environment and that this process is best captured by probabilistic models of cognition. We first present studies showing that adults are able to jointly infer causal structure and human action structure from videos of unsegmented human motion. Next, we describe how children use social information to make inferences about physical causes. We show that the pedagogical nature of a demonstrator influences children's choices of which actions to imitate from within a causal sequence and that this social information interacts with statistical causal evidence. We then discuss how children combine evidence from an informant's testimony and expressed confidence with evidence from their own causal observations to infer the efficacy of different potential causes. We also discuss how children use these same causal observations to make inferences about the knowledge state of the social informant. Finally, we suggest that psychological causation and attribution are part of the same causal system as physical causation. We present evidence that just as children use covariation between physical causes and their effects to learn physical causal relationships, they also use covaration between people's actions and the environment to make inferences about the causes of human behavior.
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