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Eva Viviani; Michael Ramscar; Elizabeth Wonnacott – Cognitive Science, 2024
Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) showed how, consistent with the predictions of error-driven learning models, the order in which stimuli are presented in training can affect category learning. Specifically, learners exposed to artificial language input where objects preceded their labels learned the discriminating features of…
Descriptors: Symbolic Learning, Learning Processes, Artificial Intelligence, Prediction
Bertenthal, Bennett I.; Scheutz, Matthias – Cognitive Science, 2013
Cooper et al. (this issue) develop an interactive activation model of spatial and imitative compatibilities that simulates the key results from Catmur and Heyes (2011) and thus conclude that both compatibilities are mediated by the same processes since their single model can predict all the results. Although the model is impressive, the…
Descriptors: Models, Test Validity, Test Reliability, Reader Response
Navarro, Daniel J.; Dry, Matthew J.; Lee, Michael D. – Cognitive Science, 2012
Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key "sampling" assumption about how the available data were generated.…
Descriptors: Logical Thinking, Generalization, Sampling, Learning
Reali, Florencia; Christiansen, Morten H. – Cognitive Science, 2005
The poverty of stimulus argument is one of the most controversial arguments in the study of language acquisition. Here we follow previous approaches challenging the assumption of impoverished primary linguistic data, focusing on the specific problem of auxiliary (AUX) fronting in complex polar interrogatives. We develop a series of corpus analyses…
Descriptors: Language Acquisition, Grammar, Sentence Structure, Stimulus Generalization

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