Machine Learning Methods for Word Learning and Perceptual
de Franca Bassani
Universidade Federal de Pernambuco
Centro de Informaática - CIn, Departamento de Sistemas de
lundi 22 mai
12h00 - 14h00
SU 1550 (Amphitheatre)
Abstract: Concept acquisition is a
central ability required for many cognitive tasks, including
word learning for language acquisition. There is a significant
amount of theoretical work suggesting that certain types of
concepts can be learned through the categorization of
perceptions. The creation of a computational model for
perception categorization, capable of dealing with real-world
data, could greatly advance research in the fields mentioned
above. It could allow us to replicate in silicon, experiments
carried out with human beings and evaluating theoretical models
and hypotheses about related phenomena. In this talk, we will
describe a perception categorization model that was developed
based on state-of-the-art machine learning methods. The proposed
model is capable of handling high-dimensional real-world inputs
such as image and audio and create categories of perceptions
that are consistent with simple concrete concepts expected to be
acquired by human subjects with the provided input data. The
model has been applied to replicate cross-situational word
learning experiments carried out with human subjects in various
situations, displaying similar word learning patterns.
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