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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

O’Connell, Lauren. Evolutionary Development of Neural Systems in Vertebrates and Beyond. Journal of Neurogenetics. 27/3, 2013. For this Harvard University, Center for Systems Biology, researcher such a comprehensive reconstruction of how creaturely neurological systems came to be and evolve is now possible. With a full scenario from urchins to us now in view, a consistent, ramifying image as an embryonic development becomes evident. The second quote notes its affinity to the popular “deep homology” model to inform this realization.

The emerging field of “neuro-evo-devo” is beginning to reveal how the molecular and neural substrates that underlie brain function are based on variations in evolutionarily ancient and conserved neurochemical and neural circuit themes. Comparative work across bilaterians is reviewed to highlight how early neural patterning specifies modularity of the embryonic brain, which lays a foundation on which manipulation of neurogenesis creates adjustments in brain size. Small variation within these developmental mechanisms contributes to the evolution of brain diversity. Comparing the specification and spatial distribution of neural phenotypes across bilaterians has also suggested some major brain evolution trends, although much more work on profiling neural connections with neurochemical specificity across a wide diversity of organisms is needed. These comparative approaches investigating the evolution of brain form and function hold great promise for facilitating a mechanistic understanding of how variation in brain morphology, neural phenotypes, and neural networks influences brain function and behavioral diversity across organisms. (Abstract)

Deep homology is a concept born of evo-devo and refers to homologous molecular mechanisms or gene modules involved in homologous phenotypes that are conserved across wide evolutionary distances (Figure 4A). A classic example is eye development across metazoans, where pax genes (especially pax6) are frequently involved in the development of the eye (Shubin et al., 2009). The concept of deep homology has also recently been discussed in the context of brain function (Scharff & Petri, 2011). In the case of deep homology, behaviors that are shared across animals, such as aggression, reproductive behavior, or vocal communication, rely on ancient gene modules that are highly conserved and promote similar behaviors. (11)

O’Connell, Lauren and Hans Hofmann. Evolution of a Vertebrate Social Decision-Making Network. Science. 336/6085, 2012. University of Texas, Austin, neuroscientists offer further insights into a “remarkable conservation” of neural substrates for animal behaviors from life’s earliest multicellular appearance. For a further survey of Decision Theory views see Genes, Hormones, and Circuits by the authors in Frontiers in Neuroendocrinology (32/320, 2011), Evolutionary Themes in the Neurobiology of Social Cognition by Hofmann and Chelsea Weitekamp in Current Opinion in Neurobiology (28/1, 2014), Decision Making: The Neuroethological Turn by John Pearson, et al, in Neuron (82/5, 2014), and a special issue The Principles of Goal-Directed Decision-Making of the Philosophical Transactions of the Royal Society B (3699/1655, 2014).

Animals evaluate and respond to their social environment with adaptive decisions. Revealing the neural mechanisms of such decisions is a major goal in biology. We analyzed expression profiles for 10 neurochemical genes across 12 brain regions important for decision-making in 88 species representing five vertebrate lineages. We found that behaviorally relevant brain regions are remarkably conserved over 450 million years of evolution. We also find evidence that different brain regions have experienced different selection pressures, because spatial distribution of neuroendocrine ligands are more flexible than their receptors across vertebrates. Our analysis suggests that the diversity of social behavior in vertebrates can be explained, in part, by variations on a theme of conserved neural and gene expression networks. (O’Connell Abstract)

Neuroeconomics applies models from economics and psychology to inform neurobiological studies of choice. Such observations have led theorists to hypothesize a single, unified decision process that mediates choice behavior via a common neural currency. In parallel, recent neuroethological studies of decision making have focused on natural behaviors like foraging, mate choice, and social interactions. These decisions strongly impact evolutionary fitness and thus are likely to have played a key role in shaping the neural circuits that mediate decision making. This approach has revealed a suite of computational motifs that appear to be shared across a wide variety of organisms. We argue that the existence of deep homologies in the neural circuits mediating choice may have profound implications for understanding human decision making in health and disease. (Pearson Abstract)

Phelps, Steven. Like Minds: Evolutionary Convergence in Nervous Systems. Trends in Ecology & Evolution. 17/4, 2002. A conference summary of how both somatic and mental development employ common solutions.

Pontes, Anselmo, et al. The Evolutionary Origin of Associative Learning. American Naturalist. 195/1, 2020. By way of clever digital simulations in Richard Lenski’s lab, Michigan State University researchers including Christoph Adami test whether this analogic edification, drawn much from Simona Ginsberg and Eva Jablonka (see definitions below), is actually in effect. Indeed, results over many generations show that life does become smarter by a constant, iterative, combinational process of trials, errors and successes for both entities and groups. From 2020, a central developmental trend of “stepwise, modular, complex behaviors” as an open-ended creativity is evidentially traced and oriented.

Learning is a widespread ability among animals and is subject to evolution. But how did learning first arise? What selection pressures and phenotypic preconditions fostered its evolution? Neither the fossil record nor phylogenetic comparative studies provide answers. Here, we study digital organisms in environments that promote the evolution of navigation and associative learning. Starting with a sessile ancestor, we evolve multiple populations in four environments, each with nutrient trails with various layouts. We find that behavior evolves modularly and in a predictable sequence. Environmental patterns that are stable across generations foster the evolution of reflexive behavior, while environmental patterns that vary across generations but remain consistent for periods within an organism’s lifetime foster the evolution of learning behavior. (Abstract excerpt)

Associative learning is a theory that states that ideas reinforce each other and can be linked to one another. Associative learning is a principle that states that ideas and experiences reinforce each other and can be linked to one another, making it a powerful teaching strategy. Associative learning, in animal behaviour, is a process in which a new response becomes associated with a particular stimulus.

Premack, David. Human and Animal Cognition. Proceedings of the National Academy of Sciences. 104/13861, 2007. The University of Pennsylvania primatologist argues that although an evolutionary continuity exists with our chimpanzee ancestors, human brains possess a greatly enhanced inter-connectivity. As a result, eight domains are cited that set us quite apart: teaching, short-term memory, causal reasoning, planning, deception, transitive inference, theory of mind, and language.

Reader, Simon and Kevin Laland. Social Intelligence, Innovation and Enhanced Brain Size in Primates. Procedings of the National Academy of Sciences. 99/4436, 2002. An extensive literature search on social learning, invention, and tool use reveals a close correlation between brain size and cognitive capacity.

Redies, Christoph and Luis Puelles. Modularity in Vertebrate Brain Development and Evolution. BioEssays. 23/12, 2001. Semi-autonomous, diverse modules are at work in both the embryonic and functional phases of cerebral formation. Here is another example of the constant modularity throughout the biological kingdom.

It is thought that modularity plays an important role in the evolutionary divergence of species, because modularity allows for adaptive modification of form and function of individual body parts while, at the same time, keeping the general developmental basic plan (Bauplan) of the organism the same. (1100)

Reid, Chris, et al. Information Integration and Multiattribute Decision Making in Non-neuronal Organisms. Animal Behavior. Vol. 100, 2015. As lately possible, Rutgers University and University of Sydney biologists advance the finding that even this most rudimentary phase of animal life and evolution is distinguished by the same individual and colonial behaviors as every other stage and kingdom. Circa 2015, a grand conclusion due to humankind altogether may accrue. Earthly biological and cognitive development does proceeds as a radiation and elaboration of a singular body, brain and societal Bauplan, which quite infers an embryonic gestation. See also Collective Sensing and Collective Responses in Quorum-Sensing Bacteria by Roman Popat in Journal of the Royal Society Interface (Vol.12/Iss.103, 2015) for a similar statement.

Decision making is a necessary process for most organisms, even for the majority of known life forms: those without a brain or neurons. The goal of this review is to highlight research dedicated to understanding complex decision making in non-neuronal organisms, and to suggest avenues for furthering this work. We review research demonstrating key aspects of complex decision making, in particular information integration and multiattribute decision making, in non-neuronal organisms when (1) utilizing adaptive search strategies when foraging, (2) choosing between resources and environmental conditions that have several contradictory attributes and necessitate a trade-off, and (3) incorporating social cues and environmental factors when living in a group or colony. We discuss potential similarities between decision making in non-neuronal organisms and other systems, such as insect colonies and the mammalian brain, and we suggest future avenues of research that use appropriate experimental design and that take advantage of emerging imaging technologies.

Retaux, Sylvie, et al. Perspectives in Evo-Devo of the Vertebrate Brain. J. Todd Streelman, ed. Advances in Evolutionary Developmental Biology. Hoboken, NJ: Wiley Blackwell, 2014. Institut Alfred Fessard, CNRS, France, neuroscientists contribute to the retrospective discovery that life’s cerebral evolution is a singular embryonic elaboration from a basic neural anatomy in place from the outset. This is strongly stated, bold added, in the opening paragraph next. From its latest global cerebration, how curious that this prodigious progeny can proceed to reconstruct from whence she and he came. Who are me and We and US?

During the last century, neuroanatomists have compared adult brains, their sizes, their forms, their structures, their neuronal compositions, and their hodology. From the Golgi impregnations of Ramon y Cajal to the introduction of modern techniques of immunocytochemistry or molecular histology, the science of comparative neuroanatomy has accumulated evidence that the brains of vertebrates constitute an infinite collection of variations on a common theme. With the advent of the evolutionary developmental approach, the so-called evo-devo, in the 1980–1990, scientists started to search for the embryonic genetic mechanisms at the origin of both the unity and the variations described between brains. It was the time to compare between embryonic brains the expression patterns of dozens of patterning and regionalization genes, and to define models or frameworks in order to interpret these patterns in diverse species. The global picture that came out of these studies was that the brains of vertebrates are built along an amazingly identical plan during embryogenesis, therefore emphasizing the unity among them. This aspect has been reviewed elsewhere and will not be dealt with here in detail. Rather, we will mainly discuss the developmental mechanisms which, within a common Bauplan, allow for variations in brain anatomy. (151)

The vertebrate forebrain has undergone an extraordinary diversification in the course of evolution. For instance, could anyone see that the mammalian cerebral cortex, with its well-known organization into 6 layers, and the so-called everted pallium of teleost fishes, are homologous brain regions? Using an evolutionary developmental approach, we aim to understand the molecular and cellular mechanisms which govern the unity (homology) and the differences (diversification) present in the forebrains of various vertebrates. To this end, we study original animal models: the naturally generated cavefish and the phylogenetically important lamprey, in addition to the conventional model, the mouse. (Sylvie Retaux website)

Richardson, Ken. The Eclipse of Heritability and the Foundations of Intelligence. New Ideas in Psychology. Online October, 2012. The emeritus Open University educator cites post-sequence inabilities over the past decade to connect cerebral features with individual genes. Much more seems to be going on both within genomes and via a multitude of epigenetic effects. As his 2011 book The Evolution of Intelligent Systems: How Molecules Became Minds, (search) well explains, life’s vectorial rise of neural cognitive acumen requires and can be better understood by a novel, broadly conceived paradigm of generative nonlinear dynamics.

It is well known that theory in human cognitive ability or ‘intelligence’ is not well developed, especially with regard to sources of trait variation. Roots of theory have been sought in biology, and it is now widely accepted, on the basis of twin studies, and statistical analysis of variance, that at least half of the normal trait variation can be attributed to genetic variation, a correlation known as the trait ‘heritability’. Since the 1990s, methods in molecular biology have been adopted to go ‘beyond’ this mere statistical attribution to the identification of individual genes responsible for trait variation. More than a decade of intense effort, however, has failed to produce unambiguous, replicable findings; explanations for the ‘missing heritability’ are now being demanded; and calls for new perspectives on the roles of genes and environments in development and trait variation are being demanded. Here, I propose a dynamic systems perspective indicating how the processes in which heritability becomes missing are the very ones that provide the roots of new intelligence theory. (Abstract)

This logic of development and metabolism, as dynamic, self-organized systems, suggests radical changes in our view of the nature and role of genes, and the nature and origins of phenotypic variation. It is now clear that offspring inherit far more than their genes from parents, rather they start life as whole developmental systems. Genes are not autonomous units that somehow turn on to initiate and direct metabolism and development, as a gene-centered command system. Rather these are centered in the dynamics emerging through the vast networks of signaling and transcription regulation. (4-5)

As Vygotsky argued, this form of intelligence (human) vastly extends and amplifies the cognitive abilities of primate intelligence. The dynamics between brains interact with those within brains – just as the dynamics of physiology interact with those within cells – emerging as hierarchies of nested attractors exhibiting reflective abstraction. The cultural tool we call science is one of the best examples: a theory is a collective model of part of nature emergent from the dialectics of scientific method, taking us beyond specific empirical experience. It is such socio-psychonomics that have driven human history across millennia so that, instead of adapting to the environment, like all other species, humans have adapted the environment to themselves. (6)

Richardson, Ken. The Evolution of Intelligent Systems: How Molecules Became Minds. New York: Palgrave Macmillan, 2011. The emeritus Open University psychologist provides a well-written revision of cerebral, cognitive and social encephalization from old reduction methods to a nonlinear, self-organizing, dynamical network approach. As chapters chronicle life’s stepwise neural development from sentient cells to human and onto group cognizances, one gets a sense of a nested, recurrent gestation getting smarter by proto-whole degrees and scales from blastosphere to noosphere.

Much of the excitement (from the systems view) has stemmed from a closer look at the nature of experience in the real world, revealing just how much dynamic structure is there to foster the evolution of complex systems. The new field of dynamic systems (DST), sometimes under other guises such as non-linear dynamics, or the dynamical approach, is also showing that, in realistically changeable environments, with which most systems in living things have to cope, we need to focus on structures, not elements, in experience, in order to understand what has evolved. This has brought exciting new outlooks on living systems generally. In this book, I hope to show how they can portray evolution as a series of bridges or cascades, each responding to the dynamics of complexity in the world. (17)

Robson, David. A Brief History of the Brain. New Scientist. September 24, 2011. Whence a 21st century worldwide Brain can now view in retrospect the entire course of its earthly evolution and development. A succinct article that takes us from rudimentary sensory cells to the ramifying course of more complex and aware cerebral faculties.

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