Optimization models of natural communication
This work addresses the problem of understanding natural communication patterns for researchers in psycholinguistics and information theory, but it is incremental as it reviews and refines existing models.
The paper reviews an information-theoretic model family that explains Zipf's law for word frequencies and other linguistic patterns by combining mutual information maximization and form entropy minimization, linking it to compression and self-organization.
A family of information theoretic models of communication was introduced more than a decade ago to explain the origins of Zipf's law for word frequencies. The family is a based on a combination of two information theoretic principles: maximization of mutual information between forms and meanings and minimization of form entropy. The family also sheds light on the origins of three other patterns: the principle of contrast, a related vocabulary learning bias and the meaning-frequency law. Here two important components of the family, namely the information theoretic principles and the energy function that combines them linearly, are reviewed from the perspective of psycholinguistics, language learning, information theory and synergetic linguistics. The minimization of this linear function is linked to the problem of compression of standard information theory and might be tuned by self-organization.