Myunggu Kang

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Computational modeling has served a powerful tool for studying cross-situational word learning. Previous research has focused on convergence behaviors in a static environment, ignoring dynamic cognitive aspects of concept change. Here we investigate concept drift in word learning in story-telling situations. Informed by findings in cognitive neuroscience,(More)
Humans can associate vision and language modalities and thus generate mental imagery, i.e. visual images, from linguistic input in an environment of unlimited inflowing information. Inspired by human memory, we separate a text-to-image retrieval task into two steps: 1) text-to-image conversion (generating visual queries for the 2 step) and 2) image-to-image(More)
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