2014/01/01 by Samuel G. B. Johnson, Frank C. Keil · 47 citations
Mathematics · Psychology · #Artificial intelligence #Behavioral Health and Interventions #Blocking (statistics) #Causal inference #Causal reasoning #Causal structure #Causality (physics) #Child and Animal Learning Development #Cognition #Cognitive psychology #Computer science #Construal level theory #Cultural Differences and Values #Econometrics #Event (particle physics) #Hierarchical organization #Hierarchy #Inference #Matching (statistics) #Mathematics #Psychology #Similarity (geometry) #Social psychology #Superordinate goals #Variety (cybernetics)
paper · open access · doi:10.1037/a0038192
published in Journal of Experimental Psychology General 143(6), 2223-2241 (American Psychological Association)
openalex publication_date 2014/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Children and adults make rich causal inferences about the physical and social world, even in novel situations where they cannot rely on prior knowledge of causal mechanisms. We propose that this capacity is supported in part by constraints provided by event structure--the cognitive organization of experience into discrete events that are hierarchically organized. These event-structured causal inferences are guided by a level-matching principle, with events conceptualized at one level of an event hierarchy causally matched to other events at that same level, and a boundary-blocking principle, with events causally matched to other events that are parts of the same superordinate event. These principles are used to constrain inferences about plausible causal candidates in unfamiliar situations, both in diagnosing causes (Experiment 1) and predicting effects (Experiment 2). The results could not be explained by construal level (Experiment 3) or similarity-matching (Experiment 4), and were robust across a variety of physical and social causal systems. Taken together, these experiments demonstrate a novel way in which noncausal information we extract from the environment can help to constrain inferences about causal structure.