Principle of Least Effort and Sentence Length in Public Speaking

Maximization of entropy production during vibration in a suspension based on graphite and silicone oil

The electrical conductivity of a suspension of graphite and silicone oil in the presence of a small electric potential difference and vibration (40÷50Hz) is considered. The concentration of the suspension was selected near the percolation threshold. It has been found that such a nonequilibrium system evolves to increase its dissipation power, and low-energy vibrations accelerate, and high-energy ones hinder this process. Since the dissipation power of the considered system is proportional to entropy production, the results obtained are consistent with the so-called maximum entropy production principle (MEPP). This is the first experimental confirmation of MEPP in the case of a nonequilibrium process with continuous transformations under conditions for the emergence of alternative variants for the development. The possibility of choice among alternatives in a complex system is a fundamental point in the experimental discussion of MEPP, and it was previously underestimated.

Less is more in language production: an information-theoretic analysis of agrammatism in primary progressive aphasia

Agrammatism is a disorder of language production characterized by short, simplified sentences, the omission of function words, an increased use of nouns over verbs and a higher use of heavy verbs. Despite observing these phenomena for decades, the accounts of agrammatism have not converged. Here, we propose and test the hypothesis that the lexical profile of agrammatism results from a process that opts for words with a lower frequency of occurrence to increase lexical information. Furthermore, we hypothesize that this process is a compensatory response to patients' core deficit in producing long, complex sentences. In this cross-sectional study, we analysed speech samples of patients with primary progressive aphasia (n = 100) and healthy speakers (n = 65) as they described a picture. The patient cohort included 34 individuals with the non-fluent variant, 41 with the logopenic variant and 25 with the semantic variant of primary progressive aphasia. We first analysed a large corpus of spoken language and found that the word types preferred by patients with agrammatism tend to have lower frequencies of occurrence than less preferred words. We then conducted a computational simulation to examine the impact of word frequency on lexical information as measured by entropy. We found that strings of words that exclude highly frequent words have a more uniform word distribution, thereby increasing lexical entropy. To test whether the lexical profile of agrammatism results from their inability to produce long sentences, we asked healthy speakers to produce short sentences during the picture description task. We found that, under this constrained condition, a similar lexical profile of agrammatism emerged in the short sentences of healthy individuals, including fewer function words, more nouns than verbs and more heavy verbs than light verbs. This lexical profile of short sentences resulted in their lower average word frequency than unconstrained sentences. We extended this finding by showing that, in general, shorter sentences get packaged with lower-frequency words as a basic property of efficient language production, evident in the language of healthy speakers and all primary progressive aphasia variants.

Slash Truncation Positive Normal Distribution and Its Estimation Based on the EM Algorithm

In this paper, we present an extension of the truncated positive normal (TPN) distribution to model positive data with a high kurtosis. The new model is defined as the quotient between two random variables: the TPN distribution (numerator) and the power of a standard uniform distribution (denominator). The resulting model has greater kurtosis than the TPN distribution. We studied some properties of the distribution, such as moments, asymmetry, and kurtosis. Parameter estimation is based on the moments method, and maximum likelihood estimation uses the expectation-maximization algorithm. We performed some simulation studies to assess the recovery parameters and illustrate the model with a real data application related to body weight. The computational implementation of this work was included in the tpn package of the R software.