39 research outputs found
The optimality of attaching unlinked labels to unlinked meanings
Vocabulary learning by children can be characterized by many biases. When encountering a
new word, children as well as adults, are biased towards assuming that it means something totally
different from the words that they already know. To the best of our knowledge, the 1st mathematical
proof of the optimality of this bias is presented here. First, it is shown that this bias is a particular case of the maximization of mutual information between words and meanings. Second, the optimality is proven within a more general information theoretic framework where mutual information maximization competes with other information theoretic principles. The bias is a prediction from modern information theory. The relationship between information theoretic principles and the principles of contrast and mutual exclusivity is also shown.Peer ReviewedPostprint (published version
Beyond description. Comment on "Approaching human language with complex networks" by Cong & Liu
Comment on "Approaching human language with complex networks" by Cong & Li
A commentary on "The now-or-never bottleneck: a fundamental constraint on language", by Christiansen and Chater (2016)
In a recent article, Christiansen and Chater (2016) present a fundamental
constraint on language, i.e. a now-or-never bottleneck that arises from our
fleeting memory, and explore its implications, e.g., chunk-and-pass processing,
outlining a framework that promises to unify different areas of research. Here
we explore additional support for this constraint and suggest further
connections from quantitative linguistics and information theory
The meaning-frequency law in Zipfian optimization models of communication
According to Zipf's meaning-frequency law, words that are more frequent tend
to have more meanings. Here it is shown that a linear dependency between the
frequency of a form and its number of meanings is found in a family of models
of Zipf's law for word frequencies. This is evidence for a weak version of the
meaning-frequency law. Interestingly, that weak law (a) is not an inevitable of
property of the assumptions of the family and (b) is found at least in the
narrow regime where those models exhibit Zipf's law for word frequencies
Optimization models of natural communication
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.Peer ReviewedPostprint (author's final draft
The advent and fall of a vocabulary learning bias from communicative efficiency
Biosemiosis is a process of choice-making between simultaneously alternative options. It is well-known that, when sufficiently young children encounter a new word, they tend to interpret it as pointing to a meaning that does not have a word yet in their lexicon rather than to a meaning that already has a word attached. In previous research, the strategy was shown to be optimal from an information theoretic standpoint. In that framework, interpretation is hypothesized to be driven by the minimization of a cost function: the option of least communication cost is chosen. However, the information theoretic model employed in that research neither explains the weakening of that vocabulary learning bias in older children or polylinguals nor reproduces Zipf’s meaning-frequency law, namely the non-linear relationship between the number of meanings of a word and its frequency. Here we consider a generalization of the model that is channeled to reproduce that law. The analysis of the new model reveals regions of the phase space where the bias disappears consistently with the weakening or loss of the bias in older children or polylinguals. The model is abstract enough to support future research on other levels of life that are relevant to biosemiotics. In the deep learning era, the model is a transparent low-dimensional tool for future experimental research and illustrates the predictive power of a theoretical framework originally designed to shed light on the origins of Zipf’s rank-frequency law.DCC and RFC are supported by the grant TIN2017-89244-R from MINECO (Ministerio de EconomÃa, Industria y Competitividad). RFC is also supported by the recognition 2017SGR-856 (MACDA) from AGAUR (Generalitat de Catalunya).
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Peer ReviewedPostprint (published version
Parallels of human language in the behavior of bottlenose dolphins
A short review of similarities between dolphins and humans with the help of
quantitative linguistics and information theory
Zipf's Law : Balancing signal usage cost and communication efficiency
Copyright: © 2015 Salge et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedWe propose a model that explains the reliable emergence of power laws (e.g., Zipf's law) during the development of different human languages. The model incorporates the principle of least effort in communications, minimizing a combination of the information-Theoretic communication inefficiency and direct signal cost. We prove a general relationship, for all optimal languages, between the signal cost distribution and the resulting distribution of signals. Zipf's law then emerges for logarithmic signal cost distributions, which is the cost distribution expected for words constructed from letters or phonemes. Copyright:Peer reviewedFinal Published versio