Minimal model of associative learning for cross-situational lexicon acquisition

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.Aerospace Research and Development (SOARD) Grant No. FA9550- 10-1-0006CNPqFAPES