| Popis: |
We present a neural network model of referent identification in a visual world task. Inputs are visual representations of item pairs unfolding with sequences of phonemes identifying the target item. The model is trained to output the semantic representation of the target and to suppress the distractor . The training set uses a 200-word lexicon typically known by toddlers. The phonological, visual and semantic representations are derived from real corpora. Successful performance requires correct association between labels and visual and semantic representations, as well as correct location identification. The model reproduces experimental evidence that phonological, perceptual and categorical relationships modulate item preferences. Referent identification is achieved with bottomup processing, suggesting the toddler behaviour need not involve top-down feedback. |
