(logo) Natural Genesis (logo text)
A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
Table of Contents
Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Recent Additions

VII. WumanKinder: An EarthSphere Transition in Individuality

1. Systems Physiology and Psychology: Somatic and Behavioral Development

A novel “developmental systems theory” conception by the Indiana University psychologists Esther Thelen and Linda Smith, and collegial others led to findings that the same complex dynamics which self-organize ecosmos and evolution are in similar effect to guide an infant’s and child’s advance in bodily maturation, visual perception, kinetic agility, self-other behaviors and sequential stages of cognitive education.

A companion apply noted herein is to wholly revise our human anatomy and physiology. As select references introduce, by way of the expansive network revolution these medical fields are being reconceived as dynamically formed and poised metabolic and skeletal systems. We note the Bar-Nam University biophysicist Shlomo Havlin and Boston University polyscientist Plamen Ivanov as veteran contributors, along with an international bionetwork.


Bartsch, Ronny, et al. Network Physiology: How Organ Systems Dynamically Interact. PLoS One. November 10, 2015.

Cangelosi, Angelo and Matthew Schlesinger. Developmental Robotics: From Babies to Robots. Cambridge: MIT Press, 2015.

Dehaene, Stanislav. How We Learn. New York: Viking, 2020.

Farris, Sarah. Evolution of Brain Elaboration Philosophical Transactions of the Royal Society B. Vol.370/Iss.1684, 2015.

Hartenstein, Volker and Angelika Stollewerk. The Evolution of Early Neurogenesis. Developmental Cell. 32/4, 2016.

Hollenstein, Tom. Twenty Years of Dynamic Systems Approaches to Development. Child Development Perspectives. 5/4, 2011.

Ivanov, Plamen, et al. Focus on the Emerging New Fields of Network Physiology and Network Medicine. New Journal of Physics. 18/100201, 2016.

Legerstee, Maria, et al. The Infant Mind: Origins of the Social Brain. New York: Guilford Press, 2012.

Liebeskind, Benjamin, et al. Complex Homology and the Evolution of Nervous Systems. Trends in Ecology and Evolution. Online December, 2015.

Overton, Willis. Life-span Development: Concepts and Issues. Lerner, Richard, editor-in-chief. The Handbook of Life-span Development. Hoboken, NJ: Wiley, 2010.

Rolls, Edmund. Cerebral Cortex: Principles of Operation. Oxford: Oxford University Press, 2016.

Van Den Heuvel, Martijn, et al. Comparative Connectomics. Trends in Cognitive Science. Online March, 2016.

Achim, Kaia and Detlev Arendt. Structural Evolution of Cell Types by Step-Wise Assembly of Cellular Modules. Current Opinion in Genetics & Development. 27/102, 2014. European Molecular Biology Laboratory developmental biologists contribute further evidence about how brain and bodies came to evolve, develop, and diversify by way of cellular and modular repetitions. In essence, deep, constant homologies from anatomies to genes are ascertained. See also Animal Evolution: The Hard Problem of Cartilage Origins by Thibaut Brunet and Arendt in Current Biology (26/14, 2016), along with The Genetic Program for Cartilage Development has Deep Homology Within Bilateria by Oscar Tarazona, et al in Nature (533/86, 2016).

Cell types are composed of cellular modules exerting specific subfunctions. The evolutionary emergence and diversification of these modules can be tracked through the comparative analysis of genomes. Here, we survey recent advances elucidating the origin of neurons, of smooth and striated muscle cells and of the T- and B-cells of the immune system in the diverging lineages of animal evolution. Gene presence and absence analyses in various metazoan genomes allow mapping the step-wise assembly of key modules – such as the postsynaptic density characteristic for neurons or the z-disk characteristic for striated muscle – on the animal evolutionary tree. Using this approach, first insight into the structural evolution of cell types can be gained. (2014 Abstract)

Our skeletons evolved from cartilaginous tissue, but it remains a mystery how cartilage itself first arose in evolution. Characterization of cartilage in cuttlefish and horseshoe crabs reveals surprising commonalities with chordate chondrocytes, suggesting a common evolutionary origin. (2016 Abstract)

Altan-Bonnet, Gregoire, et al. Quantitative Immunology for Physicists. arXiv:1907:03891. G A-B, National Cancer Institute, USA, with Thierry Mora and Aleksandra Walczak, Sorbonne University, Paris post a 78 page, 328 reference advanced synthesis of life’s immune systems by way of generic complex network dynamics. Thus in one more candidate realm, nature’s universal nonlinear self-viabilities are found to be similarly in effect. Search Albert Tauber for prior glimpses of this manifest exemplar.

The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumours. For these reason it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behaviour of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field. (Abstract)

Assmann, Birte, et al. Self-Organization in Spontaneous Movements of Neonates Generates Self-Specifying Sensory Experiences. arXiv:1902.10169. As their extensive reference list attests, four German child psychologists based at the Free University of Berlin post a 2019 affirmation of the dynamical systems theory approach initiated in the mid 1990s by Esther Thelen, Linda Smith, (search herein) and others. This insight that infants and children learn and develop by way of complex behavioral iterations, as so does the rest of evolutionary nature and culture, is now established and practiced. Once again human and universe, child and cosmos, become one and the same.

Movement experience and the coordination of perception and action are the basis of developing body awareness, emotion, motivation and cognition and the sense of self. The four limbs play a key role in the developing sense of body ownership, agency and peripersonal space. Neonatal limb movements were investigated by way of respective processes of self-organization and developing body awareness. With increasing age a shift from configurations with proximal to distal positions suggests a role of the proximal-distal dimension in movement development. We conclude that self-organization in spontaneous movements provides neonates with perceptual body- and self-specifying stimuli involved in developing body awareness and postulate the involvement of emotional and cognitive essences. (Abstract excerpt)

Bartsch, Ronny, et al. Network Physiology: How Organ Systems Dynamically Interact. PLoS One. November 10, 2015. RB, Bar-Ilan University, Kang Liu and Plamen Ivanov, Boston University, and Amir Bashan, Harvard Medical School scope out initial realizations that along with everything else, our bodily well-being, or lack thereof is wholly graced by and dependent on active webwork geometries. Their relative robustness or breakdown can then be a good measure of health or sickness (similar models are being applied to neural and behavioral states). See Network Medicine in the Age of Biomedical Big Data by Abjijeet Soanwane, (search) et al at arXiv:1903.05449 for an example of its actual utility.

We study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. (Abstract excerpt)

Bashan, Amir, et al. Network Physiology Reveals Relations between Network Topology and Physiological Function. Nature Communications. 3/702, 2011. A team of Bashan and Shlomo Havlin, Bar-Ilan University, Israel, Jan Kantelhardt, Martin Luther University, Germany, and Ronny Bartsch and Plamen Ivanov, Harvard Medical School, who also cite the Bulgarian Academy of Sciences, contribute to 21st century realizations that bodily organismic functions, rather than a homeostasis, is actually a non-equilibrium dynamic intricacy of complex network systems. This Systems Soma section tries to document an historic revision and advance, which are here seen to have much promise for health and medicine.

The human organism is an integrated network where complex physiological systems, each with its own regulatory mechanisms, continuously interact, and where failure of one system can trigger a breakdown of the entire network. Identifying and quantifying dynamical networks of diverse systems with different types of interactions is a challenge. Here we develop a framework to probe interactions among diverse systems, and we identify a physiological network. We find that each physiological state is characterized by a specific network structure, demonstrating a robust interplay between network topology and function. Across physiological states, the network undergoes topological transitions associated with fast reorganization of physiological interactions on time scales of a few minutes, indicating high network flexibility in response to perturbations. The proposed system-wide integrative approach may facilitate the development of a new field, Network Physiology. (Abstract)

This system-wide integrative approach to individual systems and the network of their interactions may facilitate the emergence of a new dimension to the field of systems physiology that will include not only interactions within but also across physiological systems. In relation to critical clinical care, where multiple organ failure is often the reason for fatal outcome, our framework may have practical utility in assessing whether dynamical links between physiological systems remain substantially altered even when the function of specific systems is restored after treatment. While we demonstrate one specific application, the framework we develop can be applied to a broad range of complex systems where the TDS method can serve as a tool to characterize and understand the dynamics and function of real-world heterogeneous and interdependent networks. The established relationship between dynamical network topology and network function has not only significant medical and clinical implications, but is also of relevance for the general theory of complex networks. (7)

Bergman, Lars, et al, eds. Developmental Science and the Holistic Approach. Mahwah, NJ: Erlbaum, 2000. Many contributions from an integral and dynamic perspective on the formation of vision, personality, and behavior.

Bjorklund, David and Bruce Ellis. Children, Childhood, and Development in Evolutionary Perspective. Developmental Review. 34/3, 2014. We examine children, childhood, and development from an evolutionary perspective. We begin by reviewing major assumptions of evolutionary–developmental psychology, including the integration of “soft” developmental systems theory DST with ideas from mainstream evolutionary psychology. We then discuss the concept of adaptive developmental plasticity and describe the core evolutionary concept of developmental programming and some of its applications to human development, as instantiated in life history theory and environmental influence. We then discuss the concept of adaptation from an evolutionary–developmental perspective, including ontogenetic and deferred adaptations. We conclude that evolutionary theory can serve as a metatheory for developmental science. (Abstract)

At the core of developmental systems theoryis the concept of probabilistic epigenesis: “individual development is characterized by an increase in novelty and complexity of organization over time due to the sequential emergence of new structural and functional properties and competencies. In biology, epigenesis also refers to the complex biochemical system that regulates gene expression. A DST view describes ontogeny as a process of continuous, bidirectional interaction between components at all levels including the genetic, cellular, phenotypic, behavioral, ecological, and cultural. (227)

Bonzanni, Mattia, et al. On the Generalization of Habituation. BioEssays. 41/7, 2019. With a Novel Model of Habituation that is Independent of any Biological System subtitle, Tufts University, Allen Discovery Center, biomedical engineers including Michael Levin offer notices and explanations of how an entity becomes accustomed to their daily environs is a common occurrence across nature and society. A commentary, Describing Atypical Instances of Intelligence by Fred Keijzer in the same issue, appreciates its content.

Habituation, a form of non‐associative learning, is no longer studied exclusively within psychology and neuroscience. Indeed, the same stimulus–response pattern has now been observed at the molecular, cellular, and organismal scales. Hence, a more inclusive theory is required to accommodate aneural forms. Here an abstraction of the habituation process that does not rely upon particular biological pathways or substrates is presented. Its formulation can be applied to interrogate systems as they respond to several stimulation paradigms, providing new insights and supporting existing behavioral data. The results suggest that habituation serves as a general biological strategy that any system can implement to adaptively respond to harmless, repetitive stimuli. (Abstract)

Boyer, Denis, et al. Non-Random Walks in Monkeys and Humans. Journal of the Royal Society Interface. 9/842, 2011. Universidad Nacional Autónoma de México, Princeton University, and VaccinApe, Bethesda, MD, researchers find, just as in every other stage and instance, nature’s common dynamical mathematics similarly guides the gracile kinectics of prosimian, hominid, and homo sapiens steps and journeys.

Principles of self-organization play an increasingly central role in models of human activity. Notably, individual human displacements exhibit strongly recurrent patterns that are characterized by scaling laws and can be mechanistically modelled as self-attracting walks. Recurrence is not, however, unique to human displacements. Here we report that the mobility patterns of wild capuchin monkeys are not random walks, and they exhibit recurrence properties similar to those of cell phone users, suggesting spatial cognition mechanisms shared with humans. We also show that the highly uneven visitation patterns within monkey home ranges are not entirely self-generated but are forced by spatio-temporal habitat heterogeneities. If models of human mobility are to become useful tools for predictive purposes, they will need to consider the interaction between memory and environmental heterogeneities. (Abstract)

Bulf, Hermann, et al. Infants Learn Better from Left to Right. Nature Scientific Reports. 7/2437, 2017. University of Milano-Bicocca and Université Paris Descartes cognitive psychologists quantify an innate propensity of babies to visually scan from left to right, which is attributed to an early favoring of the integral right hemisphere. See also Number-Space Mapping in the Newborn Chick Resembles Humans’ Mental Number Line by Rosa Rugani, et al in Science (347/534, 2015) which reports the same proclivity, re second quote.

These early directional cues might shape the direction of infants’ spatial representation of order depending on the dominant direction of their cultural environment. Alternatively, the emergence of a left-to-right spatial organization of ordered dimensions during the first months of life might be rooted in biologically-determined neural constraints in the human brain. Indeed, the right hemisphere is dominant in visuo-spatial task, and it has recently been proposed that early temporal asymmetries in hemispheric maturation, with a temporal advantage for the right over the left hemisphere, may determine a leftward asymmetrical exploration of visual space that would constrain the structure of infant’s representational space. The possibility of a link between a right hemispheric dominance and a left-to-right representation of ordinal information is also suggested by studies with non-human animals. (Bulf 4)

Humans represent numbers along a mental number line (MNL), where smaller values are located on the left and larger on the right. The origin of the MNL and its connections with cultural experience are unclear: Pre-verbal infants and nonhuman species master a variety of numerical abilities, supporting the existence of evolutionary ancient precursor systems. In our experiments, 3-day-old domestic chicks, once familiarized with a target number (5), spontaneously associated a smaller number (2) with the left space and a larger number (8) with the right space. The same number (8), though, was associated with the left space when the target number was 20. Similarly to humans, chicks associate smaller numbers with the left space and larger numbers with the right space. (Rugani Abstract)

Cairns, Robert. The Making of Development Psychology. Richard Lerner, ed. Handbook of Child Psychology, Volume 1. New York: Wiley, 1998. A century-long history of the field of developmental psychology it grew from individual conjectures to humankind’s global collaborative endeavor.

In June 1994, a Nobel Foundation symposium comprised of noted biologists and psychologists called for an integrated unified framework for the study of development. No single source or single investigator can be credited, since it has become an interdisciplinary, international movement. (92)

Cangelosi, Angelo and Matthew Schlesinger. Developmental Robotics: From Babies to Robots. Cambridge: MIT Press, 2015. A Foreword by Linda Smith, cofounder with the late Esther Thelen of dynamical systems theory for infant and child maturation, sets the theme of the work. University of Plymouth, UK, and Southern Illinois University researchers draw upon such features of human learning as self-organization, enaction, multifaceted causes, intrinsic motivation, cognitive bootstrapping, and so on, to achieve similar robotic behaviors. The core concept is the recognition that children teach and guide themselves on a progressive individuation course. An effective robotic entity should be built with open programs capable of similar responses. Another theme is a parallel between self-ontogeny and evolutionary phylogeny. See also Developmental Process Emerges from Extended Brain-Body-Behavior Networks by Lisa Byrge, Olaf Sporns, and Linda Smith in Trends in Cognitive Sciences (18/8, 2014).

Developmental robotics is a collaborative and interdisciplinary approach to robotics that is directly inspired by the developmental principles and mechanisms observed in children's cognitive development. It builds on the idea that the robot, using a set of intrinsic developmental principles regulating the real-time interaction of its body, brain, and environment, can autonomously acquire an increasingly complex set of sensorimotor and mental capabilities. This volume, drawing on insights from psychology, computer science, linguistics, neuroscience, and robotics, offers the first comprehensive overview of a rapidly growing field.

1 | 2 | 3 | 4 | 5 | 6 | 7 | 8  Next