II. Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Actual Factual Knowledge

C. Earth Learns: Interactive Person/Planet, Self-Organizing, Daily Collaboratiions

Qian, Ning and Richard Lipkin. A Learning Style Theory of Understanding Autistic Behaviors. Frontiers in Human Neuroscience. 5/Art. 77, 2011. By a similar approach to Ari Rosenberg (2015, below), a Columbia University neuroscientist and a biophysicist open with a listing of some 18 features from prior studies such as words without grammar, or trees but no forest. As the Abstract explains, a common denominator may be a particulate, “lookup table” focus, absent abilities to join or “interpolate” into patterns and regularities. Which again appears as a (male) left brain emphasis without a holistic right complement. As noted in Rosenberg, these traits or deficits could easily be seen to apply to national societies beset by internecine, senseless violence sans any common, familial relation. Autistic America cannot perceive that the two political parties, bent on destroying each other, are reciprocal archetypes.

Understanding autism’s ever-expanding array of behaviors, from sensation to cognition, is a major challenge. We posit that autistic and typically developing brains implement different algorithms that are better suited to learn, represent, and process different tasks; consequently, they develop different interests and behaviors. Computationally, a continuum of algorithms exists, from lookup table (LUT) learning, which aims to store experiences precisely, to interpolation (INT) learning, which focuses on extracting underlying statistical structure (regularities) from experiences. We hypothesize that autistic and typical brains, respectively, are biased toward LUT and INT learning, in low- and high-dimensional feature spaces, possibly because of their narrow and broad tuning functions. The LUT style is good at learning relationships that are local, precise, rigid, and contain little regularity for generalization However, it is poor at learning relationships that are context dependent, noisy, flexible, and do contain regularities for generalization. The LUT style poorly compresses information, resulting in inefficiency, sensory overload, restricted interests, and resistance to change. It also leads to poor prediction and anticipation, frequent surprises and over-reaction, impaired attentional selection and switching, concreteness, strong local focus, weak adaptation, and superior and inferior performances on simple and complex tasks. The spectrum nature of autism can be explained by different degrees of LUT learning among different individuals, and in different systems of the same individual. (Abstract)

Unlike previous efforts that focus on small subsets of autistic behaviors, we propose a theory that appears to account for a wide range of autistic behaviors. We hypothesize that autistic brains are biased toward LUT learning, which aims to store training examples precisely without extracting their underlying statistical structure or regularities, whereas typical brains are biased toward INT learning, which does not insist on representing training examples precisely but focuses instead on discovering their underlying regularities for generalization. (12)

Rasmussen, Steen, et al. Collective Intelligence of the Artificial Life Community. Artificial Life. 9/2, 2003. A report on an exercise at their August 2000 conference to collectively access the status and future of this endeavor.

Human society evolved from small, separated hunting tribes to a huge, globally integrated society….When the full diversity of the society’s intellectual dynamics is combined with the Internet’s ability to quickly and accurately link information, large groups can quickly and efficiently pool their resources and coherently analyze complexes that were very difficult to cope with in other ways. (209)

Reagle, Joseph. Good Faith Collaboration: The Culture of Wikipedia. Cambridge: MIT Press, 2011. The author is a Berkman Center for Internet and Society, Harvard University, postdoctoral fellow, well placed to wax on this blooming appearance of a self-organizing worldwide repository informed by any millions of neuron-like contributors. A notable early chapter is The Pursuit of the Universal Encyclopedia which retraces an historical passion to gather in one place, in some venue, the totality of human knowledge. In so doing, a continuity can be discerned from 1930s H. G. Wells to 2000s Jimmy Wales, of an enveloping “World Brain” that may just now attain a consummate, palliative wisdom.

Ridley, Matt. The Rational Optimist. New York: HarperCollins, 2010. From a 21st century vantage, a British science writer looks back over the millennia to discern a broad, pervasive improvement in the human condition. While admitting its tragic trajectory, this real advance is in need of exposition today to counter an apocalyptic gloom in the media. Fraught as we are with weapons, stupidity, and greed, by many measures, a consummate horizon ought to be appreciated that bodes for a earthwide eden, if we so choose. Ridley goes on to attribute this progression and vista to a ramifying, collective brain that overcomes individual animus in favor of social well being. (A similar view would be Nonzero by Robert Wright).

Riedl, Christoph, et al. Quantifying Collective Intelligence in Human Groups. Proceedings of the National Academy of Sciences. 118/21, 2021. CI researchers from Northeastern University, MIT and Carnegie Mellon University including Anita Woolley and Thomas Malone (search each) present their latest evidence that persons who collaborate and work together will be able to attain an overall increase and higher domain of cognitive ability and knowledge productivity. Again the proportion of women in the group, mediated by average social perceptiveness of members, is directly related to how well the collective learning process succeeds.

In summary, our research suggests that groups can be characterized by a quantifiable form of CI that can yield substantial benefits in many important contexts. And building a better science of CI will enable us to more effectively advance the performance of groups working on the complex and critical issues that threaten our society the most. (5)

Riviera, Emanuela. Scientific Communities as Autopoietic Systems: The Reproductive Function of Citations. Journal of the American Society for Information Science and Technology. Online May, 2013. A University of Milano-Bicocca, Italy, sociologist evokes unique, perceptive understandings of global collaborative research in terms of life’s ubiquitous essence as self-making systems. By deft apply of articulations by Humberto Maturana, Francisco Varela and Niklas Luhmann, this developing, learning noosphere gains a sense of its own self-referential sustainability.

The increasing employment of bibliometric measures for assessing, describing, and mapping science inevitably leads to the increasing need for a citation theory constituting a theoretical frame for both citation analysis and the description of citers' behavior. In this article a theoretical model, encompassing both normative and constructivist approaches, is suggested. The conceptualization of scientific communities as autopoietic systems, the components of which are communicative events, allows us to observe the reproductive function of citations conceived as codes and media of scientific communication. Citations, thanks to their constraining and enabling properties, constitute the engine of the structuration process ensuring the reproduction of scientific communities. By referring to Giddens' structuration theory, Luhmann's theory about social systems as communicative networks, Merton's “sociology of science” and his conceptualizations about the functions of citations, as well as Small's proposal about citations as concept-symbols, a sociologically integrated approach to scientometrics is proposed. (Abstract)

Citations are assumed to play a fundamental role in the reproduction of scientific communities viewed as self-organizing and self-referential systems. In self-organization theories and in communications theories, the network metaphor has often been employed: networks of relations, networks of processes, communication networks, and so on. What is suggested here is that, starting form the consideration that science is a cognitive domain, and that citations can be considered both as “codex and medic of communication”, maps created through relational bibliometric techniques enable us to render such a metaphor concrete. (2)

Robin, Amanda, et al.. Major Evolutionary Transitions and the Roles of Facilitation and Information in Ecosystem Transformations.. Frontiers in Ecology and Evolution. December, 2021. A contribution by UCLA and Stanford University biologists to a special Social Evolution and the Major Evolutionary Transition in the History of Life issue (see Peter Nonacs for review) which provides a rare, latest extension of this emergent scale onto its global fulfillment. Such a obvious but unfamiliar perception likely had to hold off until a 2020s retrospect to admit and appreciate this evident domain which has long been the basis for our EarthWise attribution. In regard, we offer an array of quotes.

Into the 21st century, the presence of “Major Evolutionary Transitions” (METs) with novel forms of organismal complexity, information and individuality have gained increasing notice among biologists. Into these 2020s, we introduce this special collection meant to gather many findings into an overdue full scale, explanatory recognition of life’s main ascendant course. We also seek to provide this evolutionary sequence within an ecological basis, aka Major System Transitions (MSTs). In regard, important morphological adaptations are noted that spread through populations because of direct-fitness advantages for individuals. We elucidate the role of information across five levels: (I) Encoded; (II) Epigenomic; (III) Learned; (IV) Inscribed; and (V) Dark, newly due to abiotic entities rather than organisms. Level IV is then seen to engender a worldwide human phase emergence. (Abstract excerpt)

The Levels of Information: Instructional: Information is transformed into physical, symbolic formats that have vast storage capacity. An instructional corpus can far exceed the combined encoded, epigenetic, learned and iconic content previously available to any single individual. Across the tree of life, only humans are known to have ever extensively created and used instructional information. Dark: Information produced by abiotic computer programs which are so complicated that biological organisms cannot replicate or derive. Examples are: internet search engines; global climate models; bioinformatic analyses of genetic data sets; neural network simulations and genetic algorithm models. The potential reach of this information may exceed that of the species that creates it, to the extent that it may become a new ‘living species’ in and of itself. (4)

The capacity for symbolic representation of language is critical for the emergence of technological innovations that expanded the realized niche for humans exponentially and paved the path to a global MST. We proliferated across every continent and environment on Earth while substantially impacting these ecosystems. One example of inscribed language producing global-altering information and technology is the very existence of the discipline of evolutionary science and the systematic study of life itself. Humans are uniquely able
to understand how evolution works. (15)

Rodriguez, Marko. The Hyper-Cortex of Human Collective-Intelligence Systems. www.arxiv.org/abs/cs.CY/0506024. Online June 2005. A computer scientist with the Center for Evolution, Complexity, and Cognition, Vrije Universiteit, Brussels (ECCO Working Paper 06-2005) finds scientific collaboration networks and digital libraries to take on the lineaments of a true worldwide brain. The next step, I would add, (see also Barabasi, et al this section) is to perceive a new phase of earth itself learning, as an emergent complex adaptive system, as it begins to achieve its own knowledge.

A hyper-cortically supported scientific community is a self-organizing entity that constantly derives solutions to its problems by matching its present state with its past realizations via the use of its artificial neural-network. (15)

Rosenberg, Ari, et al. A Computational Perspective on Autism. Proceedings of the National Academy of Sciences. 112/9158, 2015. Again, circa 2015, we can report one more field of scientific study reaching an emergent synthesis after many years. Baylor College of Medicine neuroscientists here attribute autistic spectrum disorders to variances in how the brain’s myriad neural networks process or “normalize” excitations, information, and responses. A male majority with an autistic syndrome tends to a local, fine detail, tunnel view, without any global orientation to “Bayesian priors” of past experience. As a result, they are beset by disparate pieces out of contextual guidance, dots sans connections, unable to relate to anything or anyone else, daunted by a capricious world.

See also in this journal Autism as a Disorder of Prediction by Pawan Sinha, et al (111/15220, 2014), When the World Becomes ‘Too Real:’ A Bayesian Explanation of Autistic Perception by Elizabeth Pellicano and David Burr in Nature Reviews Neuroscience (16/10, 2012) and A Learning Style Theory of Understanding Autistic Behaviors by Ning Qian and Richard Lipkin in Frontiers in Human Neuroscience (5/Art. 77, 2011), reviewed herein. As autism becomes broadly diagnosed as due to a storm of isolate neural inputs, bytes without a program, left hemisphere minus right side integrative reality, this malady could apply on a social scale as well. An autistic America seems unable to gain release from senseless wars, excessive weaponry, impulsive political and cultural conflict.

Autism is a neurodevelopmental disorder that manifests as a heterogeneous set of social, cognitive, motor, and perceptual symptoms. This system-wide pervasiveness suggests that, rather than narrowly impacting individual systems such as affection or vision, autism may broadly alter neural computation. Here, we propose that alterations in nonlinear, canonical computations occurring throughout the brain may underlie the behavioral characteristics of autism. One such computation, called divisive normalization, balances a neuron’s net excitation with inhibition reflecting the overall activity of the neuronal population. Through neural network simulations, we investigate how alterations in divisive normalization may give rise to autism symptomatology. Our findings show that a reduction in the amount of inhibition that occurs through divisive normalization can account for perceptual consequences of autism, consistent with the hypothesis of an increased ratio of neural excitation to inhibition (E/I) in the disorder. These results thus establish a bridge between an E/I imbalance and behavioral data on autism that is currently absent. Interestingly, our findings implicate the context-dependent, neuronal milieu as a key factor in autism symptomatology, with autism reflecting a less “social” neuronal population. Through a broader discussion of perceptual data, we further examine how altered divisive normalization may contribute to a wide array of the disorder’s behavioral consequences. (Rosenberg Abstract)

A rich collection of empirical findings accumulated over the past three decades attests to the diversity of traits that constitute the autism phenotypes. It is unclear whether subsets of these traits share any underlying causality. This lack of a cohesive conceptualization of the disorder has complicated the search for broadly effective therapies, diagnostic markers, and neural/genetic correlates. In this paper, we describe how theoretical considerations and a review of empirical data lead to the hypothesis that some salient aspects of the autism phenotype may be manifestations of an underlying impairment in predictive abilities. With compromised prediction skills, an individual with autism inhabits a seemingly “magical” world wherein events occur unexpectedly and without cause. Immersion in such a capricious environment can prove overwhelming and compromise one’s ability to effectively interact with it. If validated, this hypothesis has the potential of providing unifying insights into multiple aspects of autism, with attendant benefits for improving diagnosis and therapy. (Sinha Abstract)

Rosnay, Joel de. The Symbiotic Man. New York: McGraw-Hill, 2000. This synoptic work which is cited in several places finds a close comparison of brain anatomy and function with the worldwide computer network.

In this way, amazing maps of Internet topology are created. They look like dendritic maps of neurons in the brain. Dendrites are complex ramifications of the “wires” that interconnect neurons. They are involved with neural stimuli and response. Such a dendritic visualization of the Internet sheds light on its macrobiological nature and the evolutionary process leading to a brainlike global infrastructure. (60) Our collective responsibility now is to guide it to a societal symbiosis that respects, life, humanity, and human freedom. (61)

Rosvall, Martin and Carl Bergstrom. Maps of Random Walks on Complex Networks Reveal Community Structure. Proceedings of the National Academy of Sciences. 105/1118, 2008. University of Washington system biologists distill common features of scale-free nets, now found everywhere, from their topical subject of interlinked physics, biology, and social science journal citations. A prime exemplar, one might add, of such phenomena is often the dynamic neural connections of the human brain. So an implied extension might appreciate the correspondence of their illustrated webs of cross-interactions with similar cerebral maps of thinking, remembering, and learning neuron, synapse, and axon, as a real world-wide cognitive capacity.

Roush, Wade. Second Earth. Technology Review. July/August, 2007. A grand virtual marriage of the Second Life site and Google Earth prowess portends a global imaginative noosphere that everyone anywhere can immerse in, surf through, and contribute creatively to. Like, you know, a personal planet really coming to think and learn on its own, and maybe to itself if we might so avail.

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