Minimal model of associative learning for cross-situational lexicon acquisition
This addresses the challenge of lexicon acquisition in cognitive science, but it is incremental as it builds on existing associative learning models.
The study tackled the problem of learning word-object mappings through associative learning in cross-situational scenarios, finding that a simple algorithm achieves learning rates of ln[N(N-1)/(C+(N-1)^2)] for random sampling and (1/N)ln[(N-1)/C] for deterministic sequences, which is superior to human performance, but adding discrimination limitations and forgetting reduces it to human levels.
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 $\ln{[\frac{N(N-1)}{C + (N-1)^{2}}]}$ in the case the $N$ target words are sampled randomly and by $\frac{1}{N} \ln [\frac{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.