Tuesday, December 11, 2012

Introduction

Understanding how we learn and how we perceive gives us a greater understanding of who we are. Our massive, intricate brains and all of its capabilities are what make us uniquely human and intelligent. Incredibly, the network of billions upon billions of neurons in the brain gives rise to the mind, our thoughts and our sense of identity. Despite all the advances in neuroscience, biochemistry, cognitive science and related fields, we do not have a full understanding of the brain and how it gives rise to these capacities. We know that intelligence and consciousness take place “in the mind,” but we do not know how or where, physically, they are taking place. We have gathered a great deal of information and facts about the brain, but we lack a cohesive framework for understanding the brain and human behavior. The consequences of such a framework have far reaching implications from education and artificial intelligence to human behavior and culture.

I plan to examine and evaluate existing information and theories about the brain in hopes of adopting a framework to understand human behavior, and more specifically, the evolution of intelligence and the mind. Throughout this examination, I will be highlighting how these different theories utilize key aspects of systems theory. Examining the brain and how such complex properties have emerged draws on many different disciplines including neuroscience, psychology, cognitive science, computer science, anthropology, linguistics and philosophy. Many of these fields have even developed their own lexicon to apply their field to the brain. The lack of a generally accepted framework for understanding the brain is clear evidence that an interdisciplinary view is necessary.

The brain can be viewed as a complex, dynamic, adaptive system made up of multiple interconnected elements that have the capacity to change and learn from experience. Since the brain has sensors receiving stimuli as inputs, responses and behaviors as outputs, and a heavy reliance on feedback, a systems analysis is applicable; this approach can help illuminate the intricacies and nuances of the brain as a system. Complex systems consist of a large number of simple members, elements, or agents, which interact and exchange information with one another, and with the environment to “generate qualitatively new collective behavior” (Wadhawan, 2010). A system's development can lead to the "spontaneous creation of new spatial, temporal, or functional structures” (Wadhawan, 2010). An understanding of the architecture and constituent parts of the brain is necessary to begin an exploration of its properties. Without a clear understanding of structure, the true function and functioning of the brain cannot be realized. However, the brain is much greater than the sum of its parts. Each individual neuron is not intelligent, yet the brain is. The dynamic aspects of the brain can be observed in the formation of new neural networks and connections through time. The brain is adaptive in the sense that it can respond to environmental changes. Small changes in a system can have unexpectedly large consequences, including the emergence of new properties; in the brain, billions of neurons self-organize and create a network, which gives rise to our sense of identity as a living being and all the capacities we attribute to the brain.

Although we do not fully have a grasp on how the brain works, we have accumulated mountains of data and developed some promising (and not so promising) ideas about the brain. Often, our intuition on how we believe something works can lead us astray in our attempt to truly comprehend a phenomenon. There have been various theories dating back to ancient times attempting to gain an understanding of the elusive brain. More recently, strides in biochemistry, neuroscience and related fields along with the advent of recent technology and more advanced techniques such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET scans) have given a new perspective on the brain that scientists did not have access to decades ago. These techniques help us to hypothesize about the physical, anatomical workings of the brain. However, these technologies are limited in many ways. In most circumstances, these technologies are used in a controlled environment in which images of the brain are captured after a researcher introduces or manipulates some stimuli.

Neuroscientists hope that seeing what parts of the brain are activated during certain activities and behaviors will give us a better understanding of what is taking place. More often than not, the dynamic properties of the brain cannot really be captured in a natural environment by these technologies. Often, the element of time, which is critical to the processing of systems, is ignored in these experiments by taking a “still photo" of the brain when it is undeniable that the functioning and flow of information in the brain is anything but static. Cutting edge brain imaging techniques can see changes on the order of seconds. However, this is still not precise enough because electrochemical activity in neurons takes place on the order of milliseconds (Wadhawan, 2010).

Francis Crick (1994), although most known for his discoveries associated with DNA, had a strong interest in the brain as well. He writes in his astonishing hypothesis, “'You', your joys and your sorrows, your memories and your ambitions, your sense of personal identity and free will, are in fact no more than the [behavior] of a vast assembly of nerve cells and their associated molecules” (Crick, p. 3). This is one view an individual could take; we are physical beings made up of matter, which is true. Therefore, all behavior can be explained physically. However, a physiological explanation is just one level of analysis of human behavior. One must realize that when asking questions of the nature of “Why does this animal do that?” or more specifically, “How did complex behavior evolve?” and “Why are humans conscious?” multiple perspectives must be analyzed; a failure to do so is what leads to a lack of consensus. Often, the various levels of analysis will complement each other and may even overlap, but a solution at one level of analysis cannot supersede one at another level. Sometimes, however, there can be an explanatory gap between two levels of analysis. This conflict often leads to dissent and disagreement, but in certain cases it can illuminate the truth which may go against our intuitive sense of the matter.

Works Cited

Works Cited
Allman, J. (2000). Evolving Brains. New York: Scientific American Library.
Berke, L. (2010). Development through the lifespan – 5th ed. Boston: Allyn & Bacon.
Chalmers, D. (1996). The Conscious Mind: In Search of a Fundamental Theory.
Oxford University Press.
Chalmers, D. (2010). Lecture on Singularity Theory. Delivered at University of Pennsylvania in
November, 2010.
Clarke, A. (2001). Mindware: An Introduction to the Philosophy of Cognitive Science.
Oxford: Oxford University Press.
Crick, F. (1994). The Astonishing Hypothesis: The Scientific Search for the Soul. New York:
Charles Scribner’s Sons.
Darwin, C. (1859). On the Origin of Species by Means of Natural Selection, or the Preservation
of Favoured Races in the Struggle for Life. London: Albemarble Street.
Darwin, C. (1871). The Descent of Man, and Selection in Relation to Sex.
London: Albemarble Street.
Dawkins, R. (1976). The Selfish Gene. Oxford University Press.
Dennett, D. (1994). Consciousness Explained. Boston: Little, Brown and Company.
Dennett, D. (2006). Breaking the Spell: Religion as a Natural Phenomenon. New York:
Penguin Books.
Edelman, G. (1993). Bright Air, Brilliant Fire. New York: BasicBooks.
Edelman, G. (2001). Building a Picture of the Brain. The Brain. Edited by Edelman and Jean-
Pierre Changeux. Transaction Publishers.
Edelman, G. (2006). Second Nature: Brain Science and Human Knowledge. New Haven: Yale
University Press.
Edelman, G. (2007). From Brain Dynamics to Consciousness. Lecture for IBM Almaden Series.
Retrieved from http://scitalks.com/video.php?id=3
Gottlieb, G. (2003). On making behavioral genetics truly developmental. Human Development,
46, 337-355.
Hawkins, J. with Sandra Blakeslee. 2004. On Intelligence. New York: Times Books.
Hawkins, J. (2012). International Symposium on Computer Architecture (ICSA) Keynote
Speech. Delivered June 11th, 2012.
35
Holloway, R. (1996). Toward a synthetic theory of human brain evolution. Origins of the Human
Brain. Clarendon Press: Oxford.
Jerison, H. (1976). “Paleoneurology and the Evolution of the Mind.”
Scientific American 234(1): pp. 90-101.
Lenroot, R.K. and Giedd, J.N. (2008) The changing impact of genes and environment on brain development during childhood and adolescence: Initial findings from a neuroimaging study of pediatric twins. Dev Psychopathol.
Lloyd, S. (2006). Programming the Universe. Random House Digital, Inc.
Lowe, E. J. (2000). Introduction to the Philosophy of Mind. Cambridge: University Press.
Marr, D. (1982). “Understanding Complex Information Processing Systems.”
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. Pp. 19-31. W.H.Freeman & Co Ltd.
McGill-Franzen (2011), A. Handbook of Reading Disability Research. Taylor & Francis
Minsky, M. (1980). K-Lines: A Theory of Memory. Cognitive Science 4, pp. 117-130. M.I.T.
Minsky, M. (2007). The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and
the Future of the Human Mind. Simon & Schuster: New York.
Mountcastle, V. B. (1978). An Organizing Principle for Cerebral Function. The Mindful Brain.
MIT Press: Cambridge, Massachusetts
Nelson, P. (2002). Biological Physics: Energy, Information, Life. University of Pennsylvania.
Ornstein, R. and Thompson, R. (1984). The Amazing Brain. Illustrated by
David Macaulay. Boston: Houghton Mifflin Company.
Popper, K. (1978). “Three Worlds by Karl Popper – The Tanner Lecture on Human Values.”
Delivered by Karl Popper at the University of Michigan, April 7, 1978.
Rutter, M. (2006). Genes and behavior: Nature-nurture interplay explained. Malden, MA:
Blackwell.
Szyf, M. (2009). The early life environment and the epigenome. Biochimica Biophysica Acta
(BBA), 1790(9), 878-885.
Shonkoff, J. P., Levitt, P., Boyce, W.T. et al. (2010). Early Experiences Can Alter Gene
Expression and Affect Long-Term Development. National Scientific Council on the
Developing Child. Retrieved from: http://www.developingchild.net
Shonkoff, J. P., Boyce, W. T. , & McEwen, B S. (2009). Neuroscience, molecular biology, and
the childhood roots of health disparities: Building a new framework for health promotion and disease prevention. JAMA, 301(21), 2252-2259.
Tinbergen, N. (1963). On aims and methods of ethology. Journal of Animal Psychology.
36 University of Oxford. 20: 410-433.
Wadhawan, V. (2010). Complexity Explained, The Complete Series. Retrieved from
http://nirmukta.com/complexity-explained-the-complete-series-by-dr-vinod-wadhawan/