Minimal model of associative learning for cross-situational lexicon acquisition

Author(s)

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

Affiliation

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

• Document type: Journal article
• Source: JOURNAL OF MATHEMATICAL PSYCHOLOGY; v. 56, n. 6, p. 396-403, DEC 2012.
• Web of Science Citations: 6

Abstract

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)

• FAPESP's process: 11/11386-1 - Analysis of the bonequilibrium phase transition of Axelrods model for cultural dissemination
• Grantee: Paulo Fernando Coimbra Tilles
• Support Opportunities: Scholarships in Brazil - Post-Doctoral