Iconicity in the evolution of language

Evolving pragmatics

There has been a long-standing controversy in the context of language evolution on whether the original function of human language was internal thought or external communication. However, given the fact that language clearly serves both functions, internalization and externalization must have been co-evolutionarily acted in the emergence of human language. This article proposes a theoretical hypothesis about this co-evolutionary relationship of internalization and externalization, which especially explains the emergence of the human lexicon. To discuss the evolution of language from a comprehensive perspective, this article proposes a promising model that integrates two approaches with different standpoints: generative grammar and cognitive linguistics. This paper also examines the definition and nature of the lexicon and lexical items based on this integrated model. The hypothesis presented here demonstrates that the co-evolutionary work of internalization and externalization has been involved in two processes in the development of the lexicon: the establishment of syntactic objects (lexical items) and the improvement of creativity responsible for the expansion of lexicon size. The main conclusion is that these processes have formed a positive feedback loop and provided our lexicon with complex and unique properties.

Accounts of the evolution of human language must, by their very nature, express claims of a historical sort: claims about why, when, where or how human language emerged and/or developed in some distant past. What is more, it is of the essence of those claims that they are put forward in the absence of direct evidence about the events and factors that may or may not have been involved in the evolution of language. In modern work on language evolution, however, scholars have come up with various strategies aimed at lessening or even solving this problem of evidential paucity. One of these approaches starts from the assumption that language evolution can be studied by examining other phenomena about which there is direct evidence. This article sets out, in informal terms, what it is to use such phenomena as windows on language evolution.

Linguistics From an Evolutionary Point of View

Although the field of bone regeneration has experienced great advancements in the last decades, integrating all the relevant, patient-specific information into a personalized diagnosis and optimal treatment remains a challenging task due to the large number of variables that affect bone regeneration. Computational models have the potential to cope with this complexity and to improve the fundamental understanding of the bone regeneration processes as well as to predict and optimize the patient-specific treatment strategies. However, the current use of computational models in daily orthopedic practice is very limited or inexistent. We have identified three key hurdles that limit the translation of computational models of bone regeneration from bench to bed side. First, there exists a clear mismatch between the scope of the existing and the clinically required models. Second, most computational models are confronted with limited quantitative information of insufficient quality thereby hampering the determination of patient-specific parameter values. Third, current computational models are only corroborated with animal models, whereas a thorough (retrospective and prospective) assessment of the computational model will be crucial to convince the health care providers of the capabilities thereof. These challenges must be addressed so that computational models of bone regeneration can reach their true potential, resulting in the advancement of individualized care and reduction of the associated health care costs.

Active-passive compositions provide a class of systems from which we can draw intuition and examples for generalizations about system emergence at the fundamental level. In this paper, we will employ the modeling and simulation framework based on system theory focusing on its concepts of iterative specification and the Discrete Event Systems Specification (DEVS) formalism. Our approach to emergence of human language formulates the problem as an instance of a set of components that must be coupled to form a system with innovative inter-component information exchange. We show that passive/active alternation interaction of a cross-coupled pair can satisfy the conditions required for well-defined systems.

Phylogenetic steps in the evolution of vocal communication have a bearing on the brain mechanisms involved in the emergence of human language and speech. A schema of the neuronal organization of voicing in a hierarchical manner is presented. At the lowest mesencephalic level the movements of the vocal apparatus are coordinated and integrated into species-specific vocal gestures. At the middle level these signals are controlled by the anterior limbic cortex, which serves this function in primates only, and only in the human species is the highest level around the cortical larynx and facial area actively involved in the vocal signalling process. This functional schema is used to explain the sequential stages in the ontogenetic process of phonemicization in the human infant, and special emphasis is placed on vocal-auditory feedback mechanisms which come into play from the lowest to the highest level of the central nervous system during maturation. Even feedback loops of the lowest level enable the distinction to be made between self-produced vocalizations and those produced by others. These mechanisms are thought to be an early means for the development of self-awareness. If one grants that the human infant possesses self-awareness, one must concede that such stages of the mind were developed before the emergence of the human species.

This important and original book examines the nature of politics from a perspective cutting across conventional academic disciplines. Returning to the traditional emphasis on human nature as the foundation of politics, Roger D. Masters links major issues in Western political philosophy with contemporary research in the life sciences. By relating evolutionary biology, social psychology, linguistics, and game theory to politics, Masters proposes a framework for the emerging field of biopolitics. Masters shows how selfishness and altruism, participation in social groups, languages and cultures, and politics can all be illuminated by empirical evidence from the life sciences. To explore the nature of the human individual, for example, he presents experimental evidence indicating that the psychology of Plato is more exact than the traditions of Hobbes, Locke, and modern behaviorism. Cultural evolution is viewed as a process based on the emergence of human language as a system of information coding and transmission similar to the genetic system but not reducible to genetic determinism. Analyzing the origin of the centralized state, Masters relates contemporary theories of natural selection to the study of social cooperation in political philosophy, game theory, and history. This ambitious work, the fruit of more than twenty years of theoretical and experimental research, seeks to overcome the gap between the natural and social sciences. In addition, it offers a reasonable basis for judging, political institutions and ethical standards. Challenging the nihilism and relativism that have predominated in the West over the last century, Masters indicates why an evolutionary approach to human nature provides reasons for preferring a constitutional regime to autocratic or tyrannical governments. By viewing politics in terms of the kinds of social participation typical within our species for millions of years, this “new naturalism” can help us to formulate decent and humane standards of social life while providing a scientific foundation for the study of political behavior.

Neurobiology of vocal communication: mechanisms for sensorimotor integration and vocal patterning

The Darwinian protolanguage hypothesis is one of the most popular theories of the evolution of human language. According to this hypothesis, language evolved through a three stage process involving general increases in intelligence, the emergence of grammatical structure as a result of sexual selection on protomusical songs, and finally the attachment of meaning to the components of those songs. The strongest evidence for the second stage of this process has been considered to be birdsong, and as a result researchers have investigated the existence of various forms of grammar in the production and comprehension of songs by birds. Here, we argue that mating dances are another relevant source of sexually-selected complexity that has until now been largely overlooked by proponents of Darwinian protolanguage, focusing especially on the dances of long-tailed manakins. We end by sketching several lines of research that should be pursued to determine the relevance of mating dances to the evolution of language.

Databases and computational methods for the identification of piRNA-related molecules: A survey

ABSTRACTIntroduction: There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches.Areas covered: This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials.Expert opinion: Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.

Striking variation exists in preferences for specific spatial linguistic strategies among different speech communities. Increasing evidence now suggests that this might not simply be a result of neutral drift, but rather a form of linguistic adaptation to the local social, cultural, or physical environment. Recent studies indicate that different factors like topography, subsistence style, and bilingualism successfully predict the choice of spatial frames of reference (FoR) on linguistic and non-linguistic tasks. However, the exact causal relationships between these variables and the cultural evolutionary mechanisms behind the selection of one FoR strategy over another are still not fully understood. In this paper, we argue that to arrive at a more mechanistic and causal understanding of the cultural evolution of spatial language, observations from descriptive fieldwork should be combined with experimental and computational methods. In the framework we present, causal relationships between linguistic and non-linguistic variables (such as FoR choice and topography) can be isolated and systematically tested in order to shed light on how sociotopographic factors motivate the variation in spatial language we observe cross-linguistically. We discuss experimental results from behavioral studies and computer simulations that illustrate how this approach can deliver empirical findings that go beyond simple correlations.

Assessment of corneal sensory function is more frequently being carried out using novel non-contact methods, which promise better stimulus control and a greater intensity range than more traditional techniques. An examination of the characteristics of the air-jet stimulus of these 'non-contact' aesthesiometers is important to enable an understanding of how these instruments stimulate the cornea. The purpose of this study was to model the air-jet stimulus of the CRCERT-Belmonte Aesthesiometer (CBA) and its interaction with the cornea. A computerised simulation was used to model the CBA stimulus and this model was then examined in the context of verbal descriptions of the supra-threshold stimulus provided by 27 human subjects with normal corneas. The computational fluid dynamics model and the subjective descriptions considered CBA airflow values between 70 and 400 mL min(-1) . The computational fluid dynamics results showed that the CBA air-jet stimulus behaves as expected in terms of force exerted and stabilises and subsides quickly during the 1 s duration for which it is turned on/off. The computer modelling demonstrated that the stimulus of the CBA consists of a central core of air flow, similar in diameter to the CBA nozzle, which exerts maximum pressure at the corneal apex. This core is surrounded by a less prominent zone of lower pressure, extending out to the corneal periphery at higher flow rates. The computer modelling results were confirmed by the subjective descriptions of intensity and corneal area stimulated, whereas relation to duration of sensation was more equivocal. The CBA probably stimulates sensory receptors over a greater area of the ocular surface compared to the traditional nylon filament aesthesiometer and may recruit neurons sensitive to additional stimulus modalities. Future computational models should also incorporate temperature as an important aspect of the corneal response. This study has effectively demonstrated that it is possible to create a virtual model of an air-jet aesthesiometer stimulus using computational methods and that this model is corroborated by subjective descriptions provided by subjects.

This paper describes the uses of computer models in studying the evolution of language. Language is a complex dynamic system that can be studied at the level of the individual and at the level of the population. Much of the dynamics of language evolution and language change occur because of the interaction of these two levels. It is argued that this interaction is too complicated to study with pen-and-paper analysis alone and that computer models, therefore, provide a useful tool for understanding language evolution. Different techniques are presented: direct optimization, genetic algorithms and agent-based models. Of each of these techniques, an example is briefly presented. Also, the importance of correctly measuring and presenting the results of computer simulations is stressed.