People learn the meaning of words in ambiguous contexts with many possible words for any referent and many referents for any word. Cross-situational learning is an approach to solve this word-to-world mapping problem based on the idea that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we investigate the performance of a simplified variant of the general-purpose Neural Modeling Fields (NMF) categorization mechanism to infer the correct word-referent mapping in a cross-situational learning scenario that simulates experiments with adult subjects. We study two learning situations: the batch-mode learning in which the processing of data requires the memorization of all training examples, and the online learning in which the data processing occurs concomitantly with the exhibition of the examples. A training example consists of a picture of a number of objects accompanied by the utterance of the same number of words. We show that the equations derived to describe the batch-mode learning situation can also be applied to the more realistic online learning situation The resulting online algorithm yields predictions which are both qualitatively and quantitatively in agreement with the experimental results.
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