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Siegelman, Noam; Bogaerts, Louisa; Frost, Ram – Cognitive Science, 2019
In order to extract the regularities underlying a continuous sensory input, the individual elements constituting the stream have to be encoded and their transitional probabilities (TPs) should be learned. This suggests that variance in statistical learning (SL) performance reflects efficiency in encoding representations as well as efficiency in…
Descriptors: Sensory Experience, Cognitive Processes, Prediction, Performance
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Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco – Cognitive Science, 2016
Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…
Descriptors: Orthographic Symbols, Neurological Organization, Models, Probability