Artículos de revistas
Minimal model of associative learning for cross-situational lexicon acquisition
Journal of Mathematical Psychology,Maryland Heights: Academic Press,v. 56, n. 6, p. 396-403, Dec. 2012
Tilles, Paulo F. C.
Fontanari, Jose Fernando
An explanation for the acquisition of word-object mappings is the associative learning in a crosssituational scenario. Here we present analytical results of the performance of a simple associative learning algorithm for acquiring a one-to-one mapping between N objects and N words based solely on the cooccurrence between objects and words. In particular, a learning trial in our learning scenario consists of the presentation of C + 1 < N objects together with a target word, which refers to one of the objects in the context. We find that the learning times are distributed exponentially and the learning rates are given by ln [N(N-1)/C+'(N-1)POT. 2'] in the case the N target words are sampled randomly and by 1N ln [N-1/C] in the case they follow a deterministic presentation sequence. This learning performance is much superior to those exhibited by humans and more realistic learning algorithms in cross-situational experiments. We show that introduction of discrimination limitations using Weber’s law and forgetting reduce the performance of the associative algorithm to the human level.