In the present contribution, we show that principles of discriminative learning of symbolic time series go a long way in accounting for these effects, thus making an important contribution to our understanding of the human lexical processor and its sensitivity to word distributions both within and across paradigms.
Paradigm Relative Entropy and Discriminative Learning
Pirrelli Vito;Marzi Claudia;Ferro Marcello;Cardillo Franco Alberto
2017
Abstract
In the present contribution, we show that principles of discriminative learning of symbolic time series go a long way in accounting for these effects, thus making an important contribution to our understanding of the human lexical processor and its sensitivity to word distributions both within and across paradigms.File in questo prodotto:
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