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Deon T. Benton; David Kamper; Rebecca M. Beaton; David M. Sobel – Developmental Science, 2024
Causal reasoning is a fundamental cognitive ability that enables individuals to learn about the complex interactions in the world around them. However, the mechanisms that underpin causal reasoning are not well understood. For example, it remains unresolved whether children's causal inferences are best explained by Bayesian inference or…
Descriptors: Preschool Children, Thinking Skills, Associative Learning, Abstract Reasoning
Minju Kim; Adena Schachner – Developmental Science, 2025
Listening to music activates representations of movement and social agents. Why? We test whether causal reasoning plays a role, and find that from childhood, people can intuitively reason about how musical sounds were generated, inferring the events and agents that caused the sounds. In Experiment 1 (N = 120, pre-registered), 6-year-old children…
Descriptors: Causal Models, Abstract Reasoning, Thinking Skills, Music
Verhaar, Erik; Medendorp, Wijbrand Pieter; Hunnius, Sabine; Stapel, Janny C. – Developmental Science, 2022
If cues from different sensory modalities share the same cause, their information can be integrated to improve perceptual precision. While it is well established that adults exploit sensory redundancy by integrating cues in a Bayes optimal fashion, whether children under 8 years of age combine sensory information in a similar fashion is still…
Descriptors: Bayesian Statistics, Causal Models, Statistical Inference, Visual Perception
Mani, Nivedita; Schreiner, Melanie S.; Brase, Julia; Köhler, Katrin; Strassen, Katrin; Postin, Danilo; Schultze, Thomas – Developmental Science, 2021
Developmental research, like many fields, is plagued by low sample sizes and inconclusive findings. The problem is amplified by the difficulties associated with recruiting infant participants for research as well as the increased variability in infant responses. With sequential testing designs providing a viable alternative to paradigms facing…
Descriptors: Bayesian Statistics, Infants, Language Acquisition, Vocabulary
Johnston, Angie M.; Johnson, Samuel G. B.; Koven, Marissa L.; Keil, Frank C. – Developmental Science, 2017
Like scientists, children seek ways to explain causal systems in the world. But are children scientists in the strict Bayesian tradition of maximizing posterior probability? Or do they attend to other explanatory considerations, as laypeople and scientists--such as Einstein--do? Four experiments support the latter possibility. In particular, we…
Descriptors: Young Children, Thinking Skills, Inferences, Bayesian Statistics

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