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Piantadosi, Steven T.; Kidd, Celeste; Aslin, Richard – Developmental Science, 2014
Studies of infant looking times over the past 50 years have provided profound insights about cognitive development, but their dependent measures and analytic techniques are quite limited. In the context of infants' attention to discrete sequential events, we show how a Bayesian data analysis approach can be combined with a rational cognitive…
Descriptors: Infants, Eye Movements, Infant Behavior, Cognitive Development
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Hamlin, J. Kiley; Ullman, Tomer; Tenenbaum, Josh; Goodman, Noah; Baker, Chris – Developmental Science, 2013
Evaluating individuals based on their pro- and anti-social behaviors is fundamental to successful human interaction. Recent research suggests that even preverbal infants engage in social evaluation; however, it remains an open question whether infants' judgments are driven uniquely by an analysis of the mental states that motivate others' helpful…
Descriptors: Infants, Social Cognition, Bayesian Statistics, Infant Behavior
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Denison, Stephanie; Xu, Fei – Developmental Science, 2010
Previous research has revealed that infants can reason correctly about single-event probabilities with small but not large set sizes (Bonatti, 2008; Teglas "et al.", 2007). The current study asks whether infants can make predictions regarding single-event probability with large set sizes using a novel procedure. Infants completed two trials: A…
Descriptors: Prediction, Infants, Probability, Preschool Children
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Christiansen, Morten H.; Onnis, Luca; Hockema, Stephen A. – Developmental Science, 2009
When learning language, young children are faced with many seemingly formidable challenges, including discovering words embedded in a continuous stream of sounds and determining what role these words play in syntactic constructions. We suggest that knowledge of phoneme distributions may play a crucial part in helping children segment words and…
Descriptors: Phonemes, Nouns, Probability, Language Acquisition
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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
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Sobel, David M.; Kirkham, Natasha Z. – Developmental Science, 2007
A fundamental assumption of the causal graphical model framework is the Markov assumption, which posits that learners can discriminate between two events that are dependent because of a direct causal relation between them and two events that are independent conditional on the value of another event(s). Sobel and Kirkham (2006) demonstrated that…
Descriptors: Markov Processes, Infants, Metacognition, Thinking Skills