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

1. A Cultural (Geonome) Code : Systems Linguistics

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)

Pagel, Mark. The History, Rate and Pattern of World Linguistic Evolution. Chris Knight, et al, eds. The Evolutionary Emergence of Language. Cambridge: Cambridge University Press, 2000. A study of analogies between genetic programs and spoken languages. For an update see Atkinson, Q., et al. Languages Evolve in Punctuational Bursts. in Science 319/588, 2008 and check Mark Pagel's website.

A theme I wish to return to throughout the chapter is the often close analogies between the concepts and methods that can be used to investigate linguistic and biological evolution. Like genetic systems, languages have discrete units, they have mechanisms for replication and inheritance and they experience mutation and selection. (392)

Petrilli, Susan, ed. Translation Translation. Amsterdam: Rodopi, 2003. From the global community of scholars that study semiotics – what are signs that signify and how they communicate – comes a major volume about comparing and moving between languages, broadly conceived, from genetic molecules to cultural symbols. By this waxing view, the natural and human cosmos is literal and informative in kind, suffused with signals and messages ever being translated into each other. Typical chapters are Origins of Species by Natural Translation by Jesper Hoffmeyer (search), Biotranslation between Umwelten by Kalevi Kull and Peeter Torop, and Myrdene Anderson’s Ethnography as Translation.

Piattelli-Palmarini, Massimo and Juan Uriagereka. Still a Bridge Too Far? Biolinguistic Questions for Grounding Language on Brains. Physics of Life Reviews. 5/4, 2008. Can speech and physics find a common structural nature? Linguists suggest that findings of Fibonacci growth patterns in language implies a complex dynamic system, which might exemplify such a convergent basis. Google the lead author for more papers, e. g., with Cedric Boeckx “Language as a Natural Object – Linguistics as a Natural Science” in Linguistic Review (22/2-4, 2005). See also Marc Hauser and Thomas Bever “A Biolinguistic Agenda” in Science (322/1057, 2008).

Pinker, Steven. Words and Rules. New York: Basic Books, 1999. The MIT psychologist and linguist aims for a meld of an intrinsic, symbolist grammar with the brain’s associative neural networks.

Pollack, Robert. Signs of Life. Boston: Houghton Mifflin, 1994. The Columbia University biologist applies linguistic analysis to a “natural literature” of the genetic code and finds many similarities.

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