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Cross-situational learning is a mechanism for learning the meaning of words across multiple exposures, despite exposure-by-exposure uncertainty as to the word's true meaning. We present experimental evidence showing that humans learn words effectively using cross-situational learning, even at high levels of referential uncertainty. Both overall success(More)
We present a mathematical model of cross-situational learning , in which we quantify the learnability of words and vocabularies. We find that high levels of uncertainty are not an impediment to learning single words or whole vocabulary systems, as long as the level of uncertainty is somewhat lower than the total number of meanings in the system. We further(More)
We give an exact solution to the Kolmogorov equation describing genetic drift for an arbitrary number of alleles at a given locus. This is achieved by finding a change of variable which makes the equation separable, and therefore reduces the problem with an arbitrary number of alleles to the solution of a set of equations that are essentially no more(More)
Cross-situational learning is a mechanism for learning the meaning of words across multiple exposures, despite exposure-by-exposure uncertainty as to a word's true meaning. Doubts have been expressed regarding the plausibility of cross-situational learning as a mechanism for learning human-scale lexicons in reasonable timescales under the levels of(More)
We investigate a set of stochastic models of biodiversity, population genetics, language evolution, and opinion dynamics on a network within a common framework. Each node has a state 0<x(i)<1 with interactions specified by strengths m(ij). For any set of m(ij), we derive an approximate expression for the mean time to reach fixation or consensus (all x(i)=0(More)
This preliminary draft is intended to orient presenters from across the language sciences who have been invited to attend the conference celebrating the 60th Anniversary of Language Learning, to be held at the University of Michigan, on the theme Language is a Complex Adaptive System. The presenters are asked to focus upon the issues presented here when(More)
Cross-situational learning allows word learning despite exposure-by-exposure uncertainty about a word's meaning, by combining information across exposures to a word. A number of experimental studies demonstrate that humans are capable of cross-situational learning. The strongest claims here are made by Yu and Smith (2007), who provide experimental data(More)