VII. Our Earthuman Ascent: A Major Evolutionary Transition in Individuality
1. Systems Physiology and Psychology: Somatic and Behavioral Development
Cao, Miao, et al. Developmental Connectomics from Infancy through Early Childhood. Trends in Neuroscience. 40/8, 2019. Connectome: a complete set of neural elements (neurons, brain regions, etc.) and their interconnections (synapses, fiber pathways, temporal correlations.) Beijing Normal University and Children’s Hospital of Philadelphia cognitive neuroresearchers describe novel computational neuroimaging and neurophysiological methods which are revealing the course followed by cerebral architectures as they mature over the first five years of our lives. See also Mechanisms of Connectome Development by Marcus Kaiser in Trends in Cognitive Sciences (21/9, 2017).
The human brain undergoes rapid growth in both structure and function from infancy through early childhood, which influences cognitive and behavioral development in later life. The new developmental connectomics research field provides new opportunities to study developing brain through the non-invasive mapping of structural and functional connectivity patterns. We investigate connectome formation from 20 postmenstrual weeks to 5 years of age with regard to five fundamental principles of strengthened segregation/integration balance, hierarchical order from primary to higher-order regions, structural and functional maturations, individual variability, and vulnerability to risk factors and developmental disorders. (Abstract excerpt)
Courage, Mary and Mark Howe. From Infant to Child: The Dynamics of Cognitive Change in the Second Year of Life. Psychological Bulletin. 128/2, 2002. A historical and current review of the field whose studies have ranged from the constructivism of Piaget to new nativism and modularity theories. In this transitional second year occurs self-awareness and the profusion of language.
For example, the development of behavior that appears to be discontinuous or disorderly at the performance level but which arises from underlying processes that are themselves continuous and orderly (e.g., an infant’s vocabulary acquisition or first steps) is consistent with the self-organizing properties that typify non-linear dynamic systems. (268)
Pattern Unifies Autism.
Frontiers in Psychiatry.
The Simon Fraser University, Canada clinical biopsychologist has previously sketched (search) a mental spectrum from this malady that only views dots without connections (unable to relate to anyone) all the way to schizophrenia whence only patterns exist, often misread or not there. Here is it contended that our relative aware sentience can be equated with varying degrees of “perception, recognition, maintenance, generation, seeking, and processing” of certain images, or a lack thereof.
Autism is a highly heterogeneous condition, genetically and phenotypically. These diverse causes and influences have impeded its definition, recognition, assessment, and treatment. Current autism criteria involve restricted interests, repetitive behavior (RRBs) and social deficits. I suggest that this suite of autistic traits, and more can be grouped under the single rubric of “pattern,” a term that involves consistent brain and cognitive functions. RRBs result from decreased and imbalanced pattern-related perceptions, and consequent social deficits from aberrant connections and imagery. (excerpt)
Dahmen, David, et al. Second Type of Criticality in the Brain Uncovers Rich Multiple-Neuron Dynamics. Proceedings of the National Academy of Sciences. 116/13051, 2019. Julich Research Center, Germany neuroresearchers at once confirm a cerebral tendency to settle at this optimum state, while teasing out another neural way that brains avail this productive balance.
Parallel recordings of motor cortex show weak pairwise correlations on average but a wide dispersion across cells. This observation runs counter to the prevailing notion that optimal information processing requires networks to operate at a critical point, entailing strong correlations. We here reconcile this apparent contradiction by showing that the observed structure of correlations is consistent with network models that operate close to a critical point of a different nature than previously considered: dynamics that is dominated by inhibition yet nearly unstable due to heterogeneous connectivity. Our findings provide a different perspective on criticality in neural systems: network topology and heterogeneity endow the brain with two complementary substrates for critical dynamics of largely different complexities. (Significance)
De Arcangelis, Lucilla and Hans Herrmann. Learning as a Phenomenon Occurring in a Critical State. Proceedings of the National Academy of Sciences. 107/3977, 2010. We cite this paper by University of Naples and ETH Zurich biophysicists for its earlier glimpse of how the brain’s critical poise between disorder and order serves the access and gain of new knowledge. See 2019 papers in the Integrated Information section for robust confirmations of this optimum facility.
Recent physiological measurements have provided clear evidence about scale-free avalanche brain activity and EEG spectra, addressing the classical enigma of how a chaotic system can learn or respond in a controlled and reproducible way. We propose that brain activity having features typical of systems at a critical point represents a crucial ingredient for learning. Our model is able to reproduce quantitatively the experimentally observed critical state of the brain and, at the same time, learns and remembers logical rules including the exclusive OR. Learning thus occurs via plastic adaptation of synaptic strengths and exhibits universal features. (Abstract)
Dehaene, Stanislav. How We Learn. New York: Viking, 2020. The College of France, Saclay cognitive neuroscientist and author (search) gives exposition to the latest findings about a deep, definitive capacity of human beings from a fetal stage through infancy and youth to form and hold an internal representation of their external environs. Three main parts – What is Learning?, How Our Brain Learns, and The Four Pillars: Attention, Active Engagement, Error Feedback, and Consolidation – are clearly put with an intent that an integrative neuroscience which emphasizes this activity can be availed for more appropriate teachings and schools. This knowledge-gaining process is seen to so distinguish our curious species that a new Homo Docens name is proposed as we ever educate ourselves.
Cortical folds in the fetus’s brain owe their spontaneous formation to a biochemical self-organization process that depends on both the genes and the chemical environment of the cells, requiring extremely little genetic information and no learning at all. Such self-organization isn’t nearly as paradoxical as it sounds – in fact, it is omnipresent on earth. (74)
Ellis, Bruce and David Bjorklund, eds. Origins of the Social Mind: Evolutionary Psychology and Child Development. New York: Guilford Press, 2005. An impressive volume in support of evolutionary development psychology, which blends Darwinism with epigenetic influences and complex developmental systems theory in the study of children’s behavioral and cognitive maturation. In this way both self-organization and selection can come into play.
Farris, Sarah. Evolution of Brain Elaboration. Philosophical Transactions of the Royal Society B. Vol.370/Iss.1684, 2015. In a special issue on the Origin and Evolution of the Nervous System, in these 2010s when scientific fields are reaching integral confirmations, a West Virginia University neurobiologist perceives life’s encephalization of neural anatomies as a developmental ramification from a common topology present in the earliest rudiments. See also Convergent Evolution of Complex Brains and High Intelligence by Gerhard Roth in this edition (Abstract below). Life’s emergent cerebration again appears to follow a prescribed, expansive trajectory, akin to an embryogeny, toward better cognizance of which such studies are its latest worldwide phase.
Large, complex brains have evolved independently in several lineages of protostomes and deuterostomes. Sensory centres in the brain increase in size and complexity in proportion to the importance of a particular sensory modality, yet often share circuit architecture because of constraints in processing sensory inputs. The selective pressures driving enlargement of higher, integrative brain centres has been more difficult to determine, and may differ across taxa. The capacity for flexible, innovative behaviours, including learning and memory and other cognitive abilities, is commonly observed in animals with large higher brain centres. Despite differences in the exact behaviours under selection, evolutionary increases in brain size tend to derive from common modifications in development and generate common architectural features, even when comparing widely divergent groups such as vertebrates and insects. These similarities may in part be influenced by the deep homology of the brains of all Bilateria, in which shared patterns of developmental gene expression give rise to positionally, and perhaps functionally, homologous domains. Other shared modifications of development appear to be the result of homoplasy, such as the repeated, independent expansion of neuroblast numbers through changes in genes regulating cell division. The common features of large brains in so many groups of animals suggest that given their common ancestry, a limited set of mechanisms exist for increasing structural and functional diversity, resulting in many instances of homoplasy in bilaterian nervous systems. (Farris Abstract)
Fitch, W. Tecumseh, et al. Social Cognition and the Evolution of Language. Neuron. 65/6, 2010. University of Vienna cognitive biologists argue that an expansion over the past decade of the domains and extent of cultural activities from primates across to mammalian and avian species reveals many “homologous and analogous similarities.” So once more nature is found to repeat and recapitulate, in stepwise fashion, the same native, cumulative edification.
Fogel, Alan and Andrea Garvey. Alive Communication. Infant Behavior and Development. 30/2, 2007. A systems perspective increasingly illumines child psychology, here applied to explain social discourse, especially between child and care-giver, as a self-organizing dialogue.
The concept of alive communication focuses on the dynamically changing aspects of communication using three related components: coregulation, ordinary variability and innovation. (251)
Fogel, Alan, et al, eds. Human Development in the Twenty-First Century. Cambridge: Cambridge University Press, 2008. With co-editors Barbara King and Stuart Shanker, a manifesto for a dynamical “systems psychology” (my phrase) to move the endeavor from individuals alone to the equally real connections and relationships between people. Four parts range from genetics and environments to children in families and societies and to mental health issues. Since the early 1990s, much through the efforts of Linda Smith and the late Esther Thelen, as this site documents, a revolution to reconceive child and developmental science in terms of dynamic systems theory or DST has been in process. The above volume is a sign it has reached a maturity both in concept and application. An essay book review by David Witherington and Tessa Margett can be found in Human Development (52/2, 2009) from which the quote accrues.
The dynamic systems approach is rooted in the centrality of relationship for understanding complex form, both in the real-time generation and maintenance of pattern and in the ontogenetic emergence and consolidation of pattern. In contrast to the more traditional, reductionist approach to understanding organization, which relies on a breaking down of systems in order to study their parts isolation of one another, the dynamic systems approach emphasizes the need for studying the relationships that exist among parts rather than the parts themselves. (251)
Fortrat, Jacques-Olivier. Zipf’s Law of Vasovagal Heart Rate Variability Sequences. Entropy. 22/4, 2020. This entry by a UMR CNRS, Centre Hospitalier Universitaire Angers, France systems physiologist notably proceeds to find complexity phenomena at similar effect even in active cardiac function. As the quotes say, not only does its critical poise serve an optimum viability, but by this feature, the vital heart gains an affinity with brains, other organs and widely beyond. These latest findings set aside a homeostatic equilibrium model for a 21st century dynamic self-organization. Further afield, a parallel to linguistic patterns becomes evident, with beats akin to words. In this 2020 a true unity of heart, mind and prose/poetry sensitivity can be appreciated. See also Self-Organization of Blood Pressure Regulation by Fortrat and Claude Gharib in Frontiers in Physiology (March 30, 2016), Physical Mathematic Evaluation of the Cardiac Dynamic Applying the Zipf-Mandelbrot Law by Javier Oswaldo-Rodriguez, et al in Journal of Modern Physics (6/1881, 2015) and Day and Night Changes of Cardiovascular Complexity by Paolo Castiglioni, et al in Entropy (22/4, 2020).
Cardiovascular self-organized criticality (SOC) has recently been demonstrated by studying vasovagal sequences. These sequences combine bradycardia and a decrease in blood pressure. Our primary aim was to verify whether SOC could be studied by solely observing bradycardias and by showing their distribution according to Zipf’s law. Bradycardias are distributed according to Zipf’s law, providing clear insight into cardiovascular SOC. Bradycardia distribution could provide an interesting diagnosis tool for some cardiovascular diseases. (Abstract)
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