George Dyson's visit to Google

http://www.edge.org/3rd_culture/dyson05/dyson05_index.html

Human non-markov statistical language learning

Amy Perfors (or was it James Greiner?) posts at Social Sciences Statistics Blog on Human Statistical Learning:

From the modeling perspective, this result can be captured by Markov models in which the learner keeps track of the string of syllables and the transition probabilities between them, updating the transition probabilities as they hear more data. More recent work has begun to investigate whether humans are capable of statistical learning that cannot be captured by a Markov model -- that is, learning nonadjacent dependencies (dependencies between syllables that do not directly follow each other) in a stream of speech. For instance, papers by Gomez et. al. and Onnis et. al. provide evidence that discovering even nonadjacent dependencies is possible through statistical learning, as long as the variability of the intervening items is low or high enough. This has obvious implications for how statistical learning might help in acquiring grammar (in which many dependencies are nonadjacent), but it also opens up new modeling issues, since simple Markov models are no longer applicable. What more sophisticated statistical and computational tools are necessary in order to capture own unconscious, amazing abilities?