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 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 NN objects and NN 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

► We consider unsupervised learning in a cross-situational learning scenario.
► We model the acquisition of a lexicon using an associative algorithm.
► Learning error decreases exponentially with the number of learning trials.
► Algorithm with unlimited memory and discriminability outperforms humans’ learning.
► Learning strategies evolved to compensate humans’ imperfect discriminability.