Cross-situational learning of large lexicons with finite memory
This addresses the challenge of modeling realistic memory constraints in language acquisition for cognitive science, but it is incremental as it builds on prior models by incorporating forgetting.
The study tackled the problem of how learners can acquire a large lexicon with imperfect memory by relaxing the assumption of perfect recall in cross-situational word learning models, finding that a learner with memory decay can still achieve a human-scale lexicon by adulthood under realistic exposure and decay rates, as long as referential uncertainty is limited or mutual exclusivity is used.
Cross-situational word learning, wherein a learner combines information about possible meanings of a word across multiple exposures, has previously been shown to be a very powerful strategy to acquire a large lexicon in a short time. However, this success may derive from idealizations that are made when modeling the word-learning process. In particular, an earlier model assumed that a learner could perfectly recall all previous instances of a word's use and the inferences that were drawn about its meaning. In this work, we relax this assumption and determine the performance of a model cross-situational learner who forgets word-meaning associations over time. Our main finding is that it is possible for this learner to acquire a human-scale lexicon by adulthood with word-exposure and memory-decay rates that are consistent with empirical research on childhood word learning, as long as the degree of referential uncertainty is not too high or the learner employs a mutual exclusivity constraint. Our findings therefore suggest that successful word learning does not necessarily demand either highly accurate long-term tracking of word and meaning statistics or hypothesis-testing strategies.