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      Word lengths are optimized for efficient communication

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      Proceedings of the National Academy of Sciences
      Proceedings of the National Academy of Sciences

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          Abstract

          We demonstrate a substantial improvement on one of the most celebrated empirical laws in the study of language, Zipf's 75-y-old theory that word length is primarily determined by frequency of use. In accord with rational theories of communication, we show across 10 languages that average information content is a much better predictor of word length than frequency. This indicates that human lexicons are efficiently structured for communication by taking into account interword statistical dependencies. Lexical systems result from an optimization of communicative pressures, coding meanings efficiently given the complex statistics of natural language use.

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          An empirical study of smoothing techniques for language modeling

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            Eye movements reveal the on-line computation of lexical probabilities during reading.

            Skilled readers are able to derive meaning from a stream of visual input with remarkable efficiency. In this article, we present the first evidence that statistical information latent in the linguistic environment can contribute to an account of reading behavior. In two eye-tracking studies, we demonstrate that the transitional probabilities between words have a measurable influence on fixation durations, and using a simple Bayesian statistical model, we show that lexical probabilities derived by combining transitional probability with the prior probability of a word's occurrence provide the most parsimonious account of the eye movement data. We suggest that the brain is able to draw upon statistical information in order to rapidly estimate the lexical probabilities of upcoming words: a computationally inexpensive mechanism that may underlie proficient reading.
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              Effects of contextual predictability and transitional probability on eye movements during reading.

              In 2 eye-movement experiments, the authors tested whether transitional probability (the statistical likelihood that a word precedes or follows another word) affects reading times and whether this occurs independently from contextual predictability effects. Experiment 1 showed early effects of predictability, replicating S. A. McDonald and R. C. Shillcock's (2003a) finding that words with a high transitional probability (defeat following accept) are read faster than words with a low transitional probability (losses following accept). However, further analyses suggested that the transitional probability effect was likely due to differences in predictability rather than transitional probability. Experiment 2, using a better controlled set of items, again showed an effect of predictability, but no effect of transitional probability. The authors conclude that effects of transitional probability are part of regular predictability effects. Their data also show that predictability effects are detectable very early in the eye-movement record and between contexts that are weakly constraining.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                March 01 2011
                March 01 2011
                March 01 2011
                January 28 2011
                : 108
                : 9
                : 3526-3529
                Article
                10.1073/pnas.1012551108
                3048148
                21278332
                99f69f52-303f-42be-a06f-e65f18945e78
                © 2011
                History

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