VII. Our Earthuman Ascent: A Major Evolutionary Transition in Twindividuality

1. A Cultural (Geonome) Code : Systems Linguistics

Mullins, Daniel, et al. The Role of Writing and Recordkeeping in the Cultural Evolution of Human Cooperation. Journal of Economic Behavior & Organization. Online March, 2013. With Harvey Whitehouse and Quentin Atkinson, Oxford University, Institute of Cognitive and Evolutionary Anthropology, researchers argue that a ramifying human literacy was a crucial, underrated factor in historical achievements of workable group identities and reciprocal empathies. See also Atkinson above for further evidence.

Efforts to account for the emergence of large-scale cooperative human societies have focused on a range of cultural advances, from the advent of agriculture to the emergence of new forms of political regulation and social identification. Little attention has been accorded to the role of writing and recordkeeping in cultural evolution. Recent insights garnered here from behavioural economics, paleography, grammatology, evolutionary psychology, and anthropology suggest that writing and recordkeeping helps to solve the problem of cooperation in large groups by transcending the severe limitations of our evolved psychology through the elaboration of four cooperative tools – (1) reciprocal behaviours, (2) reputation formation and maintenance, (3) social norms and norm enforcement, and (4) group identity and empathy. (Abstract)

Namhee, Lee, et al, eds. The Interactional Instinct: The Evolution and Acquisition of Language. Oxford: Oxford University Press, 2009. From the UCLA Neurobiology of Language Research Group, led by co-author John Schumann, a good example of the apt use of complexity principles to explain, as not possible earlier, how we hominids learned to speak and write in increasingly effective ways. In such regard, linguistic form becomes a nonlinear CAS as it generates multistrata interactive flow. (See also Larsen-Freeman and Cameron) A wider import may then accrue. By such insights, language is found to display the same recurrent dynamics that serve to self-organize genomes. By further extension, since language and its content may be considered as genetic in kind, their commonality could imply an independent source with the guise of a cosmic genetic code.

From our perspective, linguistic structure emerges as a complex adaptive system from the verbal interaction of hominids attempting to communicate with one another. Individuals organize lexical items into structures, and if the structures are efficiently producible, comprehensible, and learnable, then their use will spread throughout the community and become part of the “grammar” of the language. (4)

Newman, Stuart and Ramray Bhat. Dynamical Patterning Modules: A "Pattern Language" for Development and Evolution of Multicellular Form. International Journal of Developmental Biology. 53/5-6, 2009. From an issue on “Pattern Formation Today,” the New York Medical College biologists envision an innate textual guidance for life’s phenotypic radiation from microbe to mammal. See also in this issue articles by Robert Hazen, Ken Weiss and Anne Buchanan, and others.

Comparative anatomists have long recognized that animal bodies share a common morphological phrase book. More recently, molecular evolutionists have discovered that the metazoan share a common developmental-genetic vocabulary. Both of these findings, as we have shown, stem from the existence of a pattern language for animal development. The grammar of this language emerged abruptly more than 500 million years ago when a group of proteins and pathways of the unicellular world, by coming to operate on the mesoscale, mobilized the physical laws pertaining to soft-matter and excitable media in the construction of multicellular organisms. (702)

Ninio, Anat. Language and the Learning Curve. Oxford: Oxford University Press, 2006. In so many diverse fields, the application of complex systems science brings a novel, heretofore elusive, theoretical illumination. Compare with Eric Beinhocker’s new book on economics. And each subject area draws upon the same self-organizing, scale invariant network dynamics, which are lately found to manifestly recur everywhere. By the integral perspective of a worldwide humankind, a life and people-friendly genesis universe is revealed.

Anat Ninio is a professor of psychology at the Hebrew University of Jerusalem. In this breakthrough book she tells how children develop articulate syntax and learn language by plugging into their parent’s linguistic networks in the same way as one logs onto the identical structure of the Internet. This insight is reached after duly noting existing models of valency, lexicality, grammar, and so on. Complexity theory can then chart a middle course between Chomskyesque nativism and constructive social interactionism.

We can test this theoretical model in quite a direct way, which, as far as I know, has not been attempted before in the complexity literature. This test translates the term self-organization to a clear process of continuing to build an existing network with the same power-law features as before. If we take a group of young children just starting to produce some type of linguistic structure and track the gradual development of the syntactic network that they construct as a group, we should find that they literally recreate the global statistical features of the adult network constructed of the same type of linguistic items that they are said to (virtually) link into. (129)

Apparently, syntactic knowledge grows in a fractal manner. In a true scale-free manner, the link structure of the language network is the same at every stage, regardless of the size of the system. Such a process of propagation well suits language, the archetypical complex system. (137) Developmental data shows that children act just like Google when it searches the Web: they pick popular items, but only if their content is relevant for them. The results support a view of children as free agents exercising Preferential Attachment when they develop their minds and acquire knowledge in a social environment. (141)

Niyogi, Partha. The Computational Nature of Language Learning and Evolution. Cambridge: MIT Press, 2006. A theme that runs through this technical work by a University of Chicago computer scientist is an evolving communication due to the emergent properties of complex adaptive systems.

Noll, Hans. The Digital Origin of Human Language. BioEssays. 25/5, 2003. Exchanges of information which involve both digital encoding and analog pattern recognition and a discrete alphabet fueled the emergence of the modern human. By these views based on an array of studies, an accord is noted between the genetic code, immune system, neural networks and linguistic patterns since evolving nature uses the same script over and over. It is then implied that a child recapitulates the way that language developed in hominids.

The rapid spread, universal adoption and exclusive survival of a digital phonetic language has its parallel in the emergence of an universal genetic code since the inception of life more than three billion years ago. (491) It is therefore not surprising that structure and evolution of language are a reflection of information processing at the molecular, cellular and multicellular level. (496)

Nowak, Martin, et al. Computational and Evolutionary Aspects of Language. 417/611. 2002, . An entry to Nowak and colleagues’ frontier work on a universal dynamics and grammar which can specify the biological and cultural evolution of language.

Oudeyer, Pierre-Yves. Self-Organization in the Evolution of Speech. Oxford: Oxford University Press, 2006. A researcher at Luc Steels’ Sony Computer Science Lab in Paris situates the rise of human diction and discourse within a dynamic evolutionary process from which it springs via an interactive meld of self-organization and selection. The work contains a good introduction to these topics, along with how such phenomena can be artificially simulated.

Oudeyer, Pierre-Yves. The Self-Organization of Speech Sounds. Journal of Theoretical Biology. 233/3, 2005. Before natural selection can act, the ability to speak occurs when interacting agents (e.g., vowels) self-organize for this purpose.

Indeed, a growing number of researchers on the origins of language consider that a number of properties of language can only be explained by the dynamics of the complex interactions between the entities which are involved (…neural systems, vocal tract, the ear, but also the interactions between individuals in a real environment). (435) The self-organized mechanism of this system appears as a necessary complement to the classical neo-Darwinian account of the origins of speech sounds….in which the environment favors the replication of individuals capable of speech. (448)

Oudeyer, Pierre-Yves and Frederic Kaplan. Language Evolution as a Darwinian Process. Cognitive Processing. 8/1, 2009. A French and a Swiss linguist conclude, as Charles Darwin himself averred, that our discourse indeed manifests and expresses the nested constancy of life’s development ascent. For a continuing, 2013 update, see Oudeyer's chapter "Self-Organization: complex dynamical systems in the Evolution of Speech" in The Language Phenomenon (Springer).

Oudeyer, Pierre-Yves, et al. Computational and Robotic Models of Early Language Development. arXiv:1903.10246. Ensta Paris Tech University (see below, search) and Stanford University systems psychologists continue to finesse how self-organizing processes are inherently at work so as to bring about and advance communicative abilities by which a child can negotiate an expanding, variable world of experiences.

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. (Abstract)

I (Pierre-Yves Oudeyer) have been studying lifelong autonomous learning, and the self-organization of behavioural, cognitive and cultural structures, at the frontiers of artificial intelligence, machine learning, cognitive sciences and educational technologies.
I employ a special focus on mechanisms enabling agents to set their own goals, and how this can self-organize curriculum learning. I consider cognitive development as a complex dynamical system which needs to be understood through systemic thinking, leveraging tools and concepts from computational sciences, neuroscience and psychology. (www.pyoudeyer.com website)

Pagel, Mark. Human Language as a Culturally Transmitted Replicator. Nature Reviews Genetics. 10/6, 2009. Other pithy papers are posted herein by the University of Reading biologist. This essay goes on to highlight, after many years of cross-fertilization, the coming fruitful merger of genetics and linguistics. In such regard, both prescriptive testaments become manifestations of the same generic informational system, a grand “analogy” of biological and language evolution. Both are “complex and adaptively evolving systems” that imply not only a literate genetic code and a genome-like knowledge but a greater textual creation, if we might all imagine and avail to read.

Human languages form a distinct and largely independent class of cultural replicators with behaviour and fidelity that can rival that of genes. Parallels between biological and linguistic evolution mean that statistical methods inspired by phylogenetics and comparative biology are being increasingly applied to study language. Phylogenetic trees constructed from linguistic elements chart the history of human cultures, and comparative studies reveal surprising and general features of how languages evolve, including patterns in the rates of evolution of language elements and social factors that influence temporal trends of language evolution. For many comparative questions of anthropology and human behavioural ecology, historical processes estimated from linguistic phylogenies may be more relevant than those estimated from genes. (405)

Heterogeneity in the rates of evolution of words can be accommodated when inferring language phylogenies in the same way as correction for differing rates of substitution in genetics. (411) Already projects such as the World Atlas of Linguistic Structures or the Austronesian Basic Vocabulary Database document hundreds of thousands of observations on language and these databases need to be developed in a similar way to GenBank and other genetic databases. (414)

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