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Saylor, Megan M.; Sabbagh, Mark A.; Fortuna, Alexandra; Troseth, Georgene – Cognitive Development, 2009
In two studies, we investigated preschoolers' ability to use others' preferences to learn names for things. Two studies demonstrated that preschool children make smart use of others' preferences. In the first study, preschool children only used information about others' preferences when they were clearly linked to referential intentions. The…
Descriptors: Preschool Children, Cognitive Development, Learning Strategies, Intention
Lemaire, Patrick; Lecacheur, Mireille – Cognitive Development, 2011
Third, fifth, and seventh graders selected the best strategy (rounding up or rounding down) for estimating answers to two-digit addition problems. Executive function measures were collected for each individual. Data showed that (a) children's skill at both strategy selection and execution improved with age and (b) increased efficiency in executive…
Descriptors: Grade 3, Grade 5, Grade 7, Age Differences
Dixon, James A.; Boncoddo, Rebecca – Cognitive Development, 2009
In an accompanying study, Alibali et al. [Alibali, M. W., Ockuly, K. M., Fischer, A. D. (2009) "Learning new problem-solving strategies leads to changes in problem representation." "Cognitive Development, 24," 89-101.] present an important experimental result: introducing a new strategy can affect conceptual aspects of children's problem…
Descriptors: Cognitive Structures, Problem Solving, Cognitive Development, Learning Strategies
Alibali, Martha W.; Phillips, Karin M. O.; Fischer, Allison D. – Cognitive Development, 2009
Children sometimes solve problems incorrectly because they fail to represent key features of the problems. One potential source of improvements in children's problem representations is learning new problem-solving strategies. Ninety-one 3rd- and 4th-grade students solved mathematical equivalence problems (e.g., 3+4+6=3+__) and completed a…
Descriptors: Experimental Groups, Control Groups, Problem Solving, Learning Strategies

Mareschal, Denis; Shultz, Thomas R. – Cognitive Development, 1996
Presents a computational framework for modeling cognitive development that provides a language paradigm with which to compare and contrast different facets of children's knowledge. Describes the generative connectionist algorithm "cascade-correlation," the successful use of the algorithm to model cognitive development in various…
Descriptors: Algorithms, Children, Cognitive Development, Cognitive Measurement