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Unger, Layla; Yim, Hyungwook; Savic, Olivera; Dennis, Simon; Sloutsky, Vladimir M. – Developmental Science, 2023
Recent years have seen a flourishing of Natural Language Processing models that can mimic many aspects of human language fluency. These models harness a simple, decades-old idea: It is possible to learn a lot about word meanings just from exposure to language, because words similar in meaning are used in language in similar ways. The successes of…
Descriptors: Natural Language Processing, Language Usage, Vocabulary Development, Linguistic Input
Grzyb, Beata J.; Nagai, Yukie; Asada, Minoru; Cattani, Allegra; Floccia, Caroline; Cangelosi, Angelo – Developmental Science, 2019
Young children sometimes attempt an action on an object, which is inappropriate because of the object size--they make scale errors. Existing theories suggest that scale errors may result from immaturities in children's action planning system, which might be overpowered by increased complexity of object representations or developing teleofunctional…
Descriptors: Error Patterns, Young Children, Cognitive Processes, Semantics
Fletcher-Watson, S.; Collis, J. M.; Findlay, J. M.; Leekam, S. R. – Developmental Science, 2009
Change blindness describes the surprising difficulty of detecting large changes in visual scenes when changes occur during a visual disruption. In order to study the developmental course of this phenomenon, a modified version of the flicker paradigm, based on Rensink, O'Regan & Clark (1997), was given to three groups of children aged 6-12 years…
Descriptors: Blindness, Models, Semantics, Visual Perception
Xu, Fei; Tenenbaum, Joshua B. – Developmental Science, 2007
We report a new study testing our proposal that word learning may be best explained as an approximate form of Bayesian inference (Xu & Tenenbaum, in press). Children are capable of learning word meanings across a wide range of communicative contexts. In different contexts, learners may encounter different sampling processes generating the examples…
Descriptors: Semantics, Bayesian Statistics, Sampling, Inferences
McClelland, James L.; Thompson, Richard M. – Developmental Science, 2007
A connectionist model of causal attribution is presented, emphasizing the use of domain-general principles of processing and learning previously employed in models of semantic cognition. The model categorizes objects dependent upon their observed 'causal properties' and is capable of making several types of inferences that 4-year-old children have…
Descriptors: Semantics, Probability, Inferences, Models