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Rodriguez Buritica, Julia M.; Eppinger, Ben; Schuck, Nicolas W.; Heekeren, Hauke R.; Li, Shu-Chen – Developmental Science, 2016
Observational learning is an important mechanism for cognitive and social development. However, the neurophysiological mechanisms underlying observational learning in children are not well understood. In this study, we used a probabilistic reward-based observational learning paradigm to compare behavioral and electrophysiological markers of…
Descriptors: Correlation, Children, Observational Learning, Reinforcement
Waismeyer, Anna; Meltzoff, Andrew N.; Gopnik, Alison – Developmental Science, 2015
How do young children learn about causal structure in an uncertain and variable world? We tested whether they can use observed probabilistic information to solve causal learning problems. In two experiments, 24-month-olds observed an adult produce a probabilistic pattern of causal evidence. The toddlers then were given an opportunity to design…
Descriptors: Toddlers, Young Children, Probability, Causal Models
Learner Performance in Multimedia Learning Arrangements: An Analysis across Instructional Approaches
Eysink, Tessa H. S.; de Jong, Ton; Berthold, Kirsten; Kolloffel, Bas; Opfermann, Maria; Wouters, Pieter – American Educational Research Journal, 2009
In this study, the authors compared four multimedia learning arrangements differing in instructional approach on effectiveness and efficiency for learning: (a) hypermedia learning, (b) observational learning, (c) self-explanation-based learning, and (d) inquiry learning. The approaches all advocate learners' active attitude toward the learning…
Descriptors: Instructional Design, Observational Learning, Learning Processes, Teaching Methods
Waldmann, Michael R.; Hagmayer, York – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2005
The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely…
Descriptors: Competence, Observational Learning, Causal Models, Probability

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