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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies

1. The Evolution of Cerebral Form and Cognizance

Donald, Merlin. The Definition of Human Nature. Rees, Dai and Steven Rose, eds. The New Brain Sciences. Cambridge: Cambridge University Press, 2004. The Canadian psychologist reiterates his highly regarded synthesis of the emergence of intelligence in mammals, primates, hominids and homo sapiens, which is the subject of previous two books noted on the website. This article makes a distinction between an earlier holistic, non-symbolic, neural net cognition in the animal kingdom and a symbol-driven, increasingly language-based mode that could form representations in memory. The great advance that defines evolving human beings is an externally stored, composite culture.

Donald’s succinct review suggests these reflections: in retrospect, the long evolution of cognitive faculties seems akin to the maturation of a singular planetary brain, for it passes through the same stages. The prior neural, “hologram-like” phase, in interaction with an environment, is more right brain, while later quantitative abilities are left hemisphere in kind. Human cerebral development proceeds in the same fashion. Moreover, it is noted that children learn to speak and remember by passing through the similar mimetic, mythic, and so on sequence as the human species did.

Cognition in humans is a collective product. The isolated brain does not come up with external symbols. Human brains collectively invent symbols in a creative dynamic process. (43) A more adequate description of human symbolic literacy would encompass all the skills needed to use every kind of permanent external symbol, from the pictograms and line drawings of the Upper Palaeolithic, to the astrolabes and alchemical diagrams of the medieval era, to the digital information codes used in modern electronic communications. (53) We might still be able to think of ourselves as ‘monads’ in the Leibnizian sense; that is, self-contained entities bounded by our skin membranes. But, as peripatetic minds plugged into a network, we are immersed in a gigantic external memory environment within which we can move around. (58)

Eichler, Katherina, et al. The Complete Connectome of a Learning and Memory Centre in an Insect Brain. Nature. 548/175, 2017. 17 researchers with postings at the Howard Hughes Medical Institute, University of Konstanz, Columbia University, Johns Hopkins University, Leibniz Institute for Neurobiology, and Cambridge University achieve the first full-scale diagram of higher order neural circuits for a Drosophila larvae mushroom body. We add to record how the connectome model can be readily applied to this early, rudimentary stage. Altogether an inkling of life’s evolution as a gestational development is suggested, which just now reaches a novel, consummative phase of its own self-reconstruction.

Ellis, Ralph and Natika Newton, eds. Consciousness & Emotion: Agency, Conscious Choice, and Selective Perception. Amsterdam: John Benjamins, 2005. Technical papers on the brain’s self-organizational dynamics with an emphasis on the “enaction” school of the late Francisco Varela and colleagues.

Estep, Myrna. Self-Organizing Natural Intelligence. Berlin: Springer, 2006. An Indiana University polymath scholar contests the vested academic view of a narrow, linear, machine-like intellectual capacity, in favor of new appreciations of a nonlinear dynamical nature trying by way of creature and cognition to discover and know itself. A good example of a revolution in our midst to recognize and explain a life and person-friendly natural genesis universe.

I have approached natural intelligence as a multi- and interdisciplinary phenomenon. I view intelligence as very much a part of the natural world and hence as a living thing, an emerging richly textured set of patterns that are highly complex, dynamic, self-organizing, and adaptive. (xxv) Self-organization refers to kinds of pattern-formation processes found in both physical and biological systems. Patterns in self-organizing systems emerge at global levels from large numbers of interactions among lower level components of those systems. (40)

Falk, Dean and Kathleen Gibson, eds. Evolutionary Anatomy of the Primary Cerebral Cortex. Cambridge: Cambridge University Press, 2001. An array of papers which recognize and extend the allometric studies of Harry Jerison on how the evolving brain acts as a single, coordinated organ which expands in overall size through the relative enlargement of its component modules. Michel Hofman notes that our hominid brain has reached structural and energetic limits and further advances in intelligence need take place in the realm of technological evolution.

Finlay, Barbara and Richard Darlington. Linked Regularities in the Development and Evolution of Mammalian Brains. Science. 268/1578, 1995. In the frequently cited paper, a consistent linear scale is seen to occur over an evolutionary time span as ten brain subdivisions steadily expand in size with total brain volume.

Analysis of data collected from 131 species of primates, bats and insectivores showed that the sizes of brain components from medulla to forebrain are highly predictable from absolute brain size by a non- linear function. The order of neurogenesis was found to be highly conserved across a wide range of mammals and to correlate with the relative enlargement of structures as brain size increases, with disproportionately large growth occurring in late-generated structures. (1578)

Freeman, Walter. Societies of Brains. Mahwah, NJ: Erlbaum, 1995. An eminent and innovative researcher in nonlinear neuroscience explains the self-organizing dynamics of cerebral evolution and cognitive performance.

Phylogenetic trees of brains in Molluscs (octopuses), Arthropods (spiders) and vertebrates (possums) show a series of increasing complexity. I think there is an equivalent tree of conscious beings ranked in order of capacity. (136)

Fuster, Joaquin. Cortex and Mind: Unifying Cognition. Oxford: Oxford University Press, 2003. A veteran neuroscientist proposes that the brain operates by integrating its modular domains into dynamic neural networks by which it represents its external locale. In so doing its “Newtonian” phase of modularity is said to be expanded to the important “Relativity” of context and relations. A tacit assumption holds throughout that individual cognition necessarily recapitulates the evolutionary formation of brain anatomy and mental abilities.

To characterize the cognitive structure of a cortical network, I use the term cognit, a generic term for any representation of knowledge in the cerebral cortex. A cognit is an item of knowledge about the world, the self, or the relations between them….In any case, a cognit is defined by its component nodes and by the relations between them. In neural terms, the cognit is made up of assemblies of neurons and the connections between them. (14)
In phylogeny as in ontogeny, the development of the receptive and productive aspects of language is correlated with the development of the cortices of association. (181) In evolution, animals have become progressively more efficient at processing more information in the pursuit of their goals. As intelligence tests show, the same thing is true for a human in his or her formative years. (215)

Gabora, Liane and Kirsty Kitto. Concept Combination and the Origins of Complex Cognition. Swan, Liz, ed. Origins of Mind. Berlin: Springer, 2012. University of British Columbia and Queensland University of Technology psychologists propose, and carefully defend, that an evolutionary advance to modern humans was aided by novel abilities to place isolated objects and episodes into a contextual ground. The cultural stages of Merlin Donald are surely drawn upon, similar to Chris Collins’ Paleopoetics, which this theory well aligns with. As a result, life’s penchant for enhanced creativity is explained through better ways to join artifacts and experiences into new combinations and perspectives. (One is reminded of Arthur Koestler’s 1970s bi-association model.) View each author’s publications for more creative insights about emergent human imaginations, often rising from quantum realms. And may we altogether lately glimpse a self-creating natural genesis that is trying to discover and choose itself?

At the core of our uniquely human cognitive abilities is the capacity to see things from different perspectives, or to place them in new context. We propose that this was made possible by two cognitive transitions. First, the large brain of Homo erectus facilitated the onset of recursive recall: the ability to string thoughts together into a stream of potentially abstractor imaginative thought. Computational modeling of recursive recall in an agent-based artificial society resulted in the agents generating more diverse and valuable cultural outputs. We propose that the capacity to see things in context was enhanced much later, following the appearance of anatomically modern humans. This second transition was brought on by the onset of contextual focus: the capacity to shift between a minimally contextual analytic mode of thought, and a highly contextual associative mode of thought, conducive to combining concepts in new ways and ‘breaking out of a rut’. We summarize how both transitions can be modeled using a theory of concepts, and how they interact and shift in meaning when they appear in different contexts. (Abstract)

Geary, David, et al, eds. Evolutionary Origins and Early Development of Number Processing. Amsterdam: Elsevier, 2015. With roots in the work of Rochel Gelman and Claude Gallistel, who write a Foreword, this retrospective study illumes across the creaturely procession from invertebrates to primates and homo sapiens a consistent ability to estimate and count, lately as arithmetic and algebra. With chapters by Giorgio Vallortigara, Irene Pepperberg, Tasha Posid and Sara Cordes, Joseph Cantlon, and other authorities, whether fish or fowl a trend evolved from an initial analog mode that could assess quantities into a later symbolic number phase. A parallel theme then notes how infants and children proceed to learn in the same way. To reflect, the whole temporal cosmos appears as a long education to learn numerical categories and alphabet letters so as to write, read, and discover.

Ginsburg, Simona and Eva Jablonka. The Evolution of Associative Learning. Journal of Theoretical Biology. Online in Press, 2010. See also by the authors: Epigenetic Learning in Non-Neural Organisms in Journal of Biosciences (34/4, 2009) and Experiencing: A Jamesian Approach in Journal of Consciousness Studies (17/5-6, 2010). These papers by the Open University of Israel, and Tel Aviv University, philosophers of science contend that life’s developmental procession is to be seen as most characterized and guided by a ramifying central nervous system with attendant sentient capabilities. As a surmise, might one then imagine, by this novel distinction, a grand evolutionary education of a self-edifying cosmos in emergent quest of its own self-witness and conception?

We have argued that the emergence of associative learning altered the life and adaptive possibilities of animals. It marked the beginning of a new stage in the history of life, with brains and learning becoming the main engines of animal evolution. The effects of associative learning are compatible with the large changes in morphology and behavior seen in Cambrian metazoans. (17)

Gollo, Leanardo, et al. Single-Neuron Criticality Optimizes Analog Dendritic Computation. Nature Scientific Reviews. 3/3222, 2013. We cite this posting as a good example of neural researchers validating how much of an exemplary nonlinear portal are human brains, in both byte digital and integral holistic aspects. The multiple affiliations of the authors listed below offers proof nowadays of science’s worldwide venue.

Leanardo Gollo: IFISC, Instituto de Fısica Interdisciplinar y Sistemas Complejos (CSIC - UIB), Campus Universitat de les Illes Balears, Spain; Systems Neuroscience Group, Queensland Institute of Medical Research, Australia; Oscar Kinouchi: Faculdade de Filosofia, Ciencias e Letras de Ribeirao Preto, Universidade de Sao Paulo, Brazil; Center for Natural and Artificial Information Processing Systems – USP; Mauro Copelli: Departamento de Fısica, Universidade Federal de Pernambuco, Brazil.

Active dendritic branchlets enable the propagation of dendritic spikes, whose computational functions remain an open question. Here we propose a concrete function to the active channels in large dendritic trees. Modelling the input-output response of large active dendritic arbors subjected to complex spatio-temporal inputs and exhibiting non-stereotyped dendritic spikes, we find that the dendritic arbor can undergo a continuous phase transition from a quiescent to an active state, thereby exhibiting spontaneous and self-sustained localized activity as suggested by experiments. Analogously to the critical brain hypothesis, which states that neuronal networks self-organize near criticality to take advantage of its specific properties, here we propose that neurons with large dendritic arbors optimize their capacity to distinguish incoming stimuli at the critical state. We suggest that ‘‘computation at the edge of a phase transition’’ is more compatible with the view that dendritic arbors perform an analog rather than a digital dendritic computation. (Abstract)

Critical systems are organized in a fractal-like pattern spanning across numerous temporal and spatial scales. The brain has recently been included amongst abundant physical and biological systems exhibiting traces of criticality. In the past decade, several phenomena suggestive of critical states have been observed in different systems: multielectrode data from cortical slices in vitro, anesthetized, awake and behaving animals; human electrocorticography, electroencephalography, magnetoencephalography, as well as functional magnetic resonance imaging recordings. Together, these experiments suggest evidence for the intrinsic pervasiveness of criticality into a wide range of spatio-temporal brain scales. (First Paragraph)

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