Minimal model of associative learning for cross-situational lexicon acquisition

Autor(es)

Tilles, Paulo F. C. ^[1] ; Fontanari, Jose F. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Fis Sao Carlos, BR-13560970 Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF MATHEMATICAL PSYCHOLOGY; v. 56, n. 6, p. 396-403, DEC 2012.
• Citações Web of Science: 6
• Assunto(s): Processos estocásticos

Resumo

An explanation for the acquisition of word-object mappings is the associative learning in a cross-situational 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 co-occurrence 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 In {[}N(N-1)/C+(N-1)(2)] in the case the N target words are sampled randomly and by 1/N In {[}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. (C) 2012 Elsevier Inc. All rights reserved. (AU)

• Processo FAPESP: 11/11386-1 - Estudo das transições de fase de não-equilíbrio do modelo de Axelrod de disseminação cultural
• Beneficiário: Paulo Fernando Coimbra Tilles
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado