dc.creator | Tilles, Paulo F. C. | |
dc.creator | Fontanari, Jose Fernando | |
dc.date.accessioned | 2016-02-29T22:28:58Z | |
dc.date.accessioned | 2018-07-04T16:54:15Z | |
dc.date.available | 2016-02-29T22:28:58Z | |
dc.date.available | 2018-07-04T16:54:15Z | |
dc.date.created | 2016-02-29T22:28:58Z | |
dc.date.issued | 2012-12 | |
dc.identifier | Journal of Mathematical Psychology,Maryland Heights: Academic Press,v. 56, n. 6, p. 396-403, Dec. 2012 | |
dc.identifier | 0022-2496 | |
dc.identifier | http://www.producao.usp.br/handle/BDPI/49705 | |
dc.identifier | 10.1016/j.jmp.2012.11.002 | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1641951 | |
dc.description.abstract | 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. | |
dc.language | eng | |
dc.publisher | Academic Press | |
dc.publisher | Maryland Heights | |
dc.relation | Journal of Mathematical Psychology | |
dc.rights | Copyright Elsevier | |
dc.rights | restrictedAccess | |
dc.subject | Associative word learning | |
dc.subject | Cross-situational learning | |
dc.subject | Stochastic models of learning | |
dc.title | Minimal model of associative learning for cross-situational lexicon acquisition | |
dc.type | Artículos de revistas | |