“All the known living beings that subsist, grow, and reproduce on this planet – the trees and the flowers, the fungi and the mushrooms, the extraordinary richness of animal life, in the waters, in the air, and on land, including human beings, together with the immensely varied world of invisible bacteria and protists – all maintain and propagate themselves by the same mechanisms, no doubt inherited from a common ancestral form. The revelation is awe-inspiring. So is the realization that the unrelenting human urge to understand has, just in our times, disclosed life’s secrets for us.”
Christian De Duve, Life Evolving.
The origin and evolution of life on earth has been described in terms of the emergence of accelerating, hierarchical orders of complexity (Pettersson, 1996). The story of life on earth and the relatively brief history of human biological and cultural evolution is a fascinating one, with profound implications for understanding all aspects of our lifespan development.
Notably, everything that lives is made of one or more cells, and every living cell evolved from cells that lived on our planet some 3.5 billion years ago (De Duve, 2002). In a fascinating account of evolution, Pettersson (1996) defines nine integrative levels of natural entities: three in the physical range (i.e., fundamental particles, atoms, molecules), three in the biological range (i.e., intermediate entities, ordinary cells, and multicellular organisms), and three in the social range (i.e., one mother families, mulifamily society, and society of sovereign states). Tracing the temporal emergence of one integrative level from the level below, and using mass estimations to trace the quantitative doubling time of innovatory entities, Pettersson concluded the following:
- Evolution has been accelerating. Specifically, the period of time before entities of a higher integrative level have emerged from the biological or social level below has, in general, decreased with the advance of time.
- The following functions of culture also show accelerated change: number of different material used by man, number of occupations involving special arts and technologies, the maximum speed of transport by mechanical means, the complexity of man-made objects and the degree of skill and knowledge required to produce them, communication speed and diversity, killing capabilities, and data processing capabilities.
Arguably, the acceleration we now observe in cultural evolution may further facilitate our adaptive success as a species, assuming we can manage the accelerating increase in complexity that this cultural evolution implies. In every realm of our life on earth, the extent to which we can manage complexity will be critical to our future success. Below, I want to talk about lifespan development and aging and a recent study where we investigated biological markers of complexity.
The story of human life on earth is a story not only of accelerating complexity, but also a story of population growth and aging. Ever since the Neanderthals were overrun by a population wave of Homo sapiens some 100,000 years ago (Mayr, 2002), the global population of human beings has been growing, ageing, and living longer. At the turn of the nineteenth century, world population was approximately one billion people. By 1900, 1.7 billion people lived on the planet. The human population exceeded 6 billion in the year 2000. By 2050, it is expected that over 9 billion people will occupy 148,939,100 km² of land. Here they will cooperate and compete to survive, adapt, and flourish. Human beings have experienced unprecedented growth in their numbers largely as a consequence of the developments in social infrastructure and culture — improved sanitation and living conditions, improved medical knowledge and facilities, changes in familial, social, economic, and political organization (Moore, 1993).
The study of unicellular and multicellular organisms tells us that life operates as a delicate energy-system, a system whose energy is partly used to maintain itself, for example, through nutrition, growth, excretion, mass movement of its parts, and reproduction (Sherrington, 1955). Within every living system there is ceaseless construction work of all sorts taking place — energy must be won such that the work of living can be sustained — work that offsets the ongoing and variable degree of decay within the system. The balance between the gain and loss of energy over time can be described as a dynamic equilibrium (Bertalanffy, 1968). Living systems support their life by exploiting external energy forms. By gathering energy and using it in ways that maintain itself, a living system can achieve requisite stability and consistency in patterns of functional relations necessary for adaptation within a changeable environment. In other words, the system can consistently pursue the variety of system goals that help keep it alive and well. In this sense, living systems can be described as self-organizing, self-regulating, dynamically stable systems (Bertalanffy, 1968; Kauffman, 1993).
In the context of pursing system goals, it is unsurpring that cognitive complexity is adaptive in many aspects of life. As part of the broader evolutionary dynamic, cognitive complexity, fluid intelligence, speed of processing, and executive control in Homo sapiensshows a normative pattern of increasing from infancy to adulthood and later decreasing in old age (Fischer, 2006; Hogan, 2004). Notably, age-related diseases like dementia are accompanied by decrements in cognitive functioning (Anderson and Craik, 2000; Grady and Craik, 2000; Hogan, 2004; Hogan et al., 2003) and some researchers have argued that this decline can be explained in part by a general loss of complexity with ageing and disease (Goldberger et al., 2002; Kaplan et al., 1991; Lipsitz, 2002). Researchers have been interested in identifying potential biological markers of complexity in an effort to better understand trajectories of lifespan development and we have recently looked at measures of EEG entropy as potential candidate markers of biological complexity.
EEG is a brain imaging method that involves placing electrodes at particular points on the scalp. These electrodes allow for the measurement the electrical activity over different regions of the brain. The entropy of EEG signals is an index of the irregularity or unpredictability characteristics of such signals. It has been argued that more complex or unpreditable biosignals are indicative of a more adaptable, flexible biological system that is healthier, more resilient, and has a great overall capacity to pursue a variety of system goals (Goldberger et al., 2002; Kaplan et al., 1991; Lipsitz, 2002).
We conducted a study where we measured EEG entropy in younger adults, older adults, and older cognitively declined adults who performed 1 SD below age- and education matched peers (Hogan et al., 2012). We measured each persons electrophysiological entropy in four experimental conditions: eyes closed (5 minutes), eyes open (5 minutes), while learning a list of words presented on a computer screen, and, later, during a memory recognition test. Entropy metrics were computed across six different cortical regions: frontal left, frontal right, temporal left, temporal right, parietal left and parietal right.
The results of the study revealed a significant increase in entropy from eyes closed to eyes open to task, consistent with the idea that entropy indices are sensitive to increases in information processing demands. There was also a trend whereby older declined adults showed lower entropy than older adults in the frontal lobe, this difference being largest in the left hemisphere during the encoding phase of the experiment. Furthermore, young adults showed greater hemispheric asymmetry, more precisely, higher entropy in the right relative to the left hemisphere in the temporal lobe and higher entropy in the left relative to the right hemisphere in the parietal lobe. Older controls also showed a borderline difference between both hemispheres in the temporal lobe in the same direction as the younger adults, again suggesting a pattern of hemispheric asymmetry in entropy measures. However, older cognitively declined adults demonstrated no significant differences between left and right hemisphere entropy. Our results suggest that cognitive decline in old age is not simply linked to lower levels of entropy in key brain regions, but rather a combination of both level and differentiated range of entropy states across the brain (see also O’Hora et al., 2013).
It is important to understand the mechanisms that help to explain age- and disease-related cognitive decline and the associated loss of adaptive functioning. We believe that measures of entropy may provide us with unique insights into the nature of age- and disease-related cognitive decline. Further research is needed to understand the dynamic links between biological complexity and our adaptive success and well-being across the lifespan. Understanding factors that promote and sustain complexity and protect against age- and disease-related cognitive decline will be an important focus for future research.
The bigger question for us all is whether or not the acceleration we now observe in cultural evolution can facilitate our global adaptive success and the sustained health and well-being of our growing older adult population. Our hope is that by 2050, not only will we see 9 billion people occupying 148,939,100 km² of land, but we will see a global community where people are living longer, happier, healthier lives – a community where people are increasingly supporting one another and working cooperatively to promote our continued survival, adaptation, and flourishing. Although human beings have experienced unprecedented growth in their numbers as a consequence of the developments in social infrastructure and culture, the next phase of our cultural evolution must surely involve greater and more balanced investment in our lifespan development and the health and well-being of our growing older adult population. Perhaps there is a tenth integrative level of natural entities beyond the nine that Pettersson defines. How do you think that tenth level should look?
Originally published Oct 31, 2014 in ‘In One Lifespan’ @ PsychologyToday.com
Some links contained within this post are external
Anderson, N.D., Craik, F.I.M., 2000. Memory in the aging brain. In: Tulving, E., Craik, F.I.M. (Eds.), The Oxford Handbook of Memory. Oxford University Press, Oxford, pp. 421–452.
Bertalanffy, L. v. (1968). General system theory: foundations, development, applications. New York: Braziller.
De Duve, C. (2002). Life evolving: molecules, mind, and meaning. Oxford: Oxford University Press.
Goldberger, A.L., Peng, C.K., Lipsitz, L.A., 2002. What is physiologic complexity and how does it change with aging and disease? Neurobiol. Aging 23, 23–26.
Grady, G.L., Craik, F.I.M., 2000. Changes in memory processing with age. Curr. Opin. Neurobiol. 10, 224–231.
Hogan, M.J., 2004. The cerebellum in thought and action: a frontocerebellar ageing hypothesis. New Ideas in Psychology 22, 97–125.
Hogan, M.J., Swanwick, G.R., Kaiser, J., Rowan, M., Lawlor, B., 2003. Memory-related EEG power and coherence reductions in mild Alzheimer’s disease. Int. J. Psychophysiol. 49 (2),147–163.
Hogan, M.J.,Kilmartin, L., Keane, M., Collins, P., Staff, R., Kaiser, J., Lai, R. & Upton, N. (2012). Electrophysiological entropy in younger adults, older controls and older cognitively declined adults. Brain Research. 1445:1-10
Jonassen, D.H., 2000. Toward a design theory of problem solving. Educational Technology Research and Development, 48 (4), 63 – 85.
Kaplan, D.T., Furman, M.I., Pincus, S.M., Ryan, S.M., Lipsitz, L.A., Goldberger, A.L., 1991. Aging and the complexity of cardiovascular dynamics. Biophys. J. 59, 945–949.
Kauffman, S. A. (1993). The origins of order: self-organization and selection in evolution. New York; Oxford: Oxford University Press.
Lipsitz, L.A., 2002. Dynamics of stability: the physiologic basis of functional health and frailty. J. Gerontol. A Biol. Sci. Med. Sci. 57, B115–B125.
Mayr, E. (2002). What evolution is. London: Weidenfeld & Nicolson.
Moore, T. J. (1993). Lifespan: who lives longer– and why. New York: Simon & Schuster.
O’Hora, D., Schinkel, S., Hogan, M.J.,Kilmartin, L., Keane, M., Lai, R. & Upton, N. (2013). Age-related task sensitivity of frontal EEG entropy during encoding predicts retrieval. Brain Topography, 26 (4), 547-557
Pettersson, M. (1996). Complexity and evolution. New York: Cambridge University Press.
Sherrington, C. S. (1955). Man on his nature. Harmondsworth: Penguin.