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the topic model can be extended to bring it closer to the richness of human language. Although we
are still far from understanding how people comprehend and acquire language, these examples
illustrate how increasingly complex structures can be learned using statistical methods, and they
show some of the potential for generative models to provide insight into the psychological
questions raised by human linguistic abilities. Across many areas of cognition, perception, and
action, probabilistic generative models have recently come to offer a unifying framework for
understanding aspects of human intelligence as rational adaptations to the statistical structure of
the environment (Anderson, 1990; Anderson & Schooler, 1991; Geisler et al., 2001; Griffiths &
Tenenbaum, 2006b, 2006a; Kemp et al., 2004; Koerding & Wolpert, 2004; Simoncelli &
Olshausen, 2001; Wolpert et al., 1995). It remains to be seen how far this approach can be carried
in the study of semantic representation and language use, but the existence of large corpora of
linguistic data and powerful statistical models for language clearly make this a direction worth
pursuing.
Topics in semantic representation 65
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