Minimal model of associative learning for cross-situational lexicon acquisition

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 ln[N(N−1)C+(N−1)2] in the case the N target words are sampled randomly and by 1Nln[N−1C] 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.