Complex Human Behavior and Evolution:
Exploration of the Evolution of Intelligence and Consciousness
Seth Levine
EAS 499: Senior Capstone
Advisor: Dr. Janet Monge
University of Pennsylvania
School of Engineering and Applied Science
December 2012
December 2012
Table of Contents
Introduction ...................................................................................................................................... 3
Tinbergen’s Four Questions ..................................................
Darwin and Intelligence .........................................................
Philosophy of Mind ..................................................................
Ontological View ......................................................................
Topics
Marr Levels..............................................................................................................................
Brains as Computers: Similarities and Major Differences ............................................................
Brain Structure and Function ........................................................................................................
Biological Evolution and the Human Brain ...................................................................................
Language and Cultural Evolution ..................................................................................................
Hawkins and the Evolutionary Advantage of Neocortex ..............................................................
Minsky’s Society of Mind ..............................................................................................................
Dennett’s Model of Consciousness...............................................................................................
Edelman’s Brain-Based Theory of Consciousness ........................................................................
Environmental and Genetic Interaction .......................................................................................
Conclusion .....................................................................................................................................
Acknowledgements .......................................................................................................................
Works Cited ...................................................................................................................................
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.
Tinbergen’s Four Questions
Nikolas Tinbergen who is often considered the "father of ethology" explored this conflict amongst the different levels of analysis of a system. Tinbergen (1963) breaks the exploration of animal behavior into proximate and ultimate causes. Since humans are animals, many of his cornerstones of ethology and sociobiology apply to us. The four questions that must be asked are of causation, development, phylogeny (evolution), and adaptation. These four analytic levels lead to the existence of at least four answers to any question regarding a behavior or trait. Tinbergen stressed that an explanation at one level of analysis should complement rather than compete with theories at another level (Tinbergen, pp. 410-433).
When asking about causation you are questioning the mechanisms at play that are controlling how an organism's structures work. What are the stimuli that elicit the response, and how has it been modified by recent events? How do behavior and psyche function on the molecular, physiological, neurological, cognitive and social level, and what do the relations between the levels look like? Many of the physical sciences, namely neuroscience, hope their work leads to a full understanding of the biological processes underlying the actions of the body and brain. This mechanistic explanation of how an organism’s structures work is considered a proximate view because it is observing the current, static form of the trait (Tinbergen).
However, Tinbergen (1963) explains ontogeny, phylogeny and adaptation must be explored when trying to understand behavior. A structural explanation of how a certain behavior works is not enough to fully comprehend the behavior. Questions of how the behavior changes with age and what early experiences are necessary for that behavior to be shown must be asked. One must grasp how the trait has developed from its DNA coding to its current form. How recent learning has modified the trait must be addressed, as well.
Questions of development are particularly interesting when applied to the mind, intelligence and consciousness. For example, which environmental factors affect the development of awareness, thinking and consciousness? Additionally, how can intelligence be traced back phylogenically? An understanding of the structural and mechanistic processes associated with a behavior is necessary, but does not lead to a full understanding of a complex behavior such as intelligence. Often, a more dynamic view of the evolutionary changes in a species over many generations can help explain the origin of a specific trait or behavior (Tinbergen).
Darwin and Intelligence
Intelligence and consciousness can be viewed as dynamic phenomena that have evolved over time. Darwin's theory of evolution by natural selection and the more modernly refined synthetic theory of evolution can provide a sound scientific explanation for why an animal's behavior is usually well adapted for survival and reproduction in its environment. Similarly, Tinbergen's question of adaptation addressed how a species trait evolved to solve a reproductive or survival problem in the ancestral environment. Intelligence is clearly a trait that is functional to the survival and reproductive success of the organism. In fact, it is most likely the unique ways of thinking that have allowed humans to adapt to the various environments and cultures in the world. Additionally, it has allowed for massive population growth and "dominance” of humans.
Darwin (1859) used comparative psychology and biology to compare the behavior of different species to develop his theories of evolution. He concluded that animal species changed over time with later generations displaying characteristics that helped earlier generations survive (Darwin, pp. 41-63). Organisms can be viewed as open systems, which constantly exchange matter and energy with their surroundings. The variability in members of a population leads to variety in behavior and method of coping with certain conditions (Darwin, pp. 63-81). This variation due to random mutation and differing allele frequencies could be inherited from parent to offspring and shaped by the forces of natural selection. In The Descent of Man, Darwin (1874) explains that it is "highly probable that with mankind the intellectual faculties have been mainly and gradually perfected through natural selection... undoubtedly it would be interesting to trace the development of each separate faculty [intellectual skill] from the state in which it exists in lower animals to that which exists in man” (pp. 128-129). He believed it was logical to trace the development of intelligence in different species leading to the development of intelligence in humans.
However, historically, people tend to misinterpret much of Darwin's work and ideas. Many of these misinterpretations are based upon our "human-centric" views. Many believe people are the pinnacle of billions of years of evolution when in fact evolution is a continuous process with no real end goal or destination. Additionally, people tend to anthropomorphize and project human qualities upon animals. Certain aspects of intelligence can be traced through the development of "primitive" animals to "complex" animals; however, intelligence cannot fully be traced as it moved up the phylogenic scale by looking at the current faculties of animal's mind. All existing species are current products of millions of years of evolution and many different forms of intelligence, unique to each species, have developed. There is no reason to expect mental capacities of animals to fall into a smooth, continuous progression leading to the "superior" intelligence of humans. This linear approach is too simplistic to explain the emergence of new properties in a complex system.
Philosophy of Mind
Biology and its associated fields of cognitive neuroscience do shed light upon the maze of neural networks in the brain, but a purely Physicalist or Reductionist view cannot fully explain our first-person conscious experience of life. Biology has made great leaps in understanding the physiological mechanisms underlying our behavior. Neuroscience has done a wonderful job highlighting the brain areas associated with motor and sensory functions. However, knowing the physiological pathways involved with a behavior does not imply a full understanding. Attempting to explain this first person feeling of awareness is central to the discipline known as Philosophy of Mind (Lowe, 2000).
Philosophy of Mind focuses on the connection and interaction of the mind and body and its associated questions and implications. This mind-body problem is often referred to as the hard problem of consciousness and asks how mental phenomena can be affected by and can affect the physical collection of cells and molecules that make up the brain (Lowe). The brain is defined as being physical and part of the body. The mind is more difficult to define because for many it is viewed as something outside of the brain and our bodies, although it is physically manifested in the brain, its properties are usually referred to as mental and believed to be "non-physical". Some view the brain as the hardware and the mind as the software having the ability to "run" different applications and process information (Clarke, 2001). Crick's astonishing hypothesis can be difficult for many to accept. It is not easy to peer inside the brain and decide if it is the neurons firing that lead to us learning and experiencing, or if it is something else. We have mapped out our entire genome and we are searching for the code that leads to a conscious mind. It begs the question of whether or not our thoughts, or even more generally our conscience experience, can be broken down computationally.
David Chalmers, a leader in the field of Philosophy of Mind, questions, "Why does the feeling which accompanies awareness of sensory information exist at all?" He continues to argue that there is an explanatory gap between the objective world and subjective experience (Chalmers, 1996). Philosophers and many people struggle with the question of how the mind is related to the body. There is no consensus of what properties, functions and occurrences should be regarded as mental or physical.
Ontological View
Although I am exploring intelligence and consciousness, it is outside the scope of this paper to give complete answers to metaphysical questions and define ideas such as "mental", "physical", "real" or "exist." However, when examining the human brain as a complex system it is important to offer an ontological approach that accounts for “everything” that is in existence and in our experience. I take an Interactionist approach, which states that there is a physical world and a mental world that interact. Both the physical and mental world can act upon and be influenced by one another. Sir Karl Popper (1978) proposed an interesting outlook that helps to delineate between these "worlds". Popper proclaimed himself to be a "Trialist" in which he described three worlds.
The first is that of physical objects and states. This is the physical world made up of matter and energy and the only world from the point of view of a pure Physicalist. Popper's second world consists of mental states and conscious experience. This is made up of subjective "knowledge" which consists of experiences of perception, thinking, emotions, memories, dreams, creativity, imagination and other mental abilities.
This second world is difficult to categorize but Popper explores the idea. He considers there to be outer senses which are based around the sense organs, such as hearing, touching, seeing and smelling. He then describes inner senses as being one's private world of thoughts, emotions, feelings, memories, imaginings, intentions and so on. The subjective world also includes the ego or sense of self. He includes the "basis of our unity as an experiencing being throughout our whole lifetime" as part of the subjective world (Popper).
He describes the third world being one of objective knowledge. This includes the records of intellectual efforts including all scientific knowledge, the contents of books, journals and libraries and encompasses the whole world of culture. This world is the environment that has been "created by man and in turns shapes man." These three worlds establish an ontological foundation for the "subject of experience and their status within the wider scheme of things (Popper)." Without getting lost in philosophical endeavors, it is interesting to contemplate why this "feeling which accompanies awareness of sensory information exists at all” (Chalmers, 1996) from an evolutionary standpoint.
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Marr Levels
David Marr, a British neuroscientist and psychologist, explored the complex information processing system of the brain and initially focused on vision. He integrated results from psychology, artificial intelligence and neurophysiology into new models of visual processing. Marr (1982) argued that there are "different levels at which an information-processing device must be understood before one can be said to have understood it completely” (p. 24). Although he was not focusing directly on the same types of behavior, Marr's investigatory outlook is reminiscent of that of Tinbergen. He explains that "the effort is to show that in principle the microscopic and macroscopic descriptions are consistent with one another...[and] one must be prepared to contemplate different kinds of explanation at different levels of description that are linked” (pp.19-20). He broke down his explanation into three distinct, but complementary levels that are now known to many cognitive scientists and other researchers as Marr's levels of analysis, or his tri-level hypothesis (Marr, 1982).
The first level of explanation is one at the computational level. Most basically this level asks what the system does, what the goal is and what problem it solves. Once the overarching goal of the system is known, the next computational question is why. Commonly, it is a difficult task to separate the two computational concerns of what the purpose of the system is and why it does what it does. A full computational explanation would answer why the system does these things and the logic of the strategy it carried out. Although much of Marr's work was on visual processes, many of his general hypothesizes can be applied to other mental phenomena such as information processing and intelligence. One of the "underlying tasks" of the mind "is to reliably derive properties of the world" from sensory information and decipher "constraints that are both powerful enough to allow a process to be defined and generally true of the world” (Marr, p 23). He argued that the performance of our brain, which he often refers to as an "information processor" is characterized by mapping one kind of information to another. This view has been generalized and accepted by many cognitive scientists; the role of the brain is to take in sensory information and create a reliable model of the world.
The second Marr level is at the algorithmic, or representational level. After understanding a system at the computational level, an algorithmic explanation describes how the system does what it does. A proper algorithm would address how the computational theory is implemented; specifically, what processes does it employ to build and manipulate representations. Initially, one must determine a way of defining and representing the input and output of the system. In most cases, there are various choices for the representation of the input and output. Additionally, one must define and formulate the algorithm for the transformation from input to output. Usually, there are several possible algorithms for carrying out the same process; this is especially apparent when various modes of representation of inputs and outputs can be employed (Marr, 1982). Finding an algorithm for which the transformation may actually be accomplished is not a simple task with a complex system such as the brain. Our intuition would tell us that the brain takes in sensory input and outputs behaviors, thoughts and actions. However, this focus on output may not capture the full nature of the brain.
This idea of an intelligent system being defined as exhibiting intelligent behavior has been a cornerstone of Artificial Intelligence research since Alan Turing's work in 1950 on "Computing Machinery and Intelligence." In the beginning of the second half of the 20th century, Turing developed his Turing Test, which was based upon an imitation game. Basically, if a machine were indistinguishable from a human being solely on the basis of written interactions it would be considered intelligent. Clearly, this is not a sufficient test for intelligence; an intelligent being, such as a human, can be intelligent without any output of behavior. A human with their eyes closed, locked away in a dark, silent room not interacting with anyone or anything is still intelligent. Turing's test of intelligence was based on the end result of an output behavior, which is only one part of intelligence; the test did not address the process or architecture. This interpretation of intelligence being defined strictly by the ability to exhibit complex behavior has stunted our ability to properly explore intelligence and the functioning of the brain. Systems are process-oriented and cannot be defined simply by their output.
Marr's third level of analysis is one of hardware implementation. This analysis would address the physical realization of the computational theory and algorithm associated with the system. A detailed description of the architecture, structure and mechanism would be needed to understand the system at this level. This level is similar to Tinbergen's question of causation. In a biological system, one must discover what neural structures and neuronal activities are implemented by the system. This level lends itself well to the study of neuroanatomy and neuroscience, which explore synaptic mechanisms, action potentials, and inhibitory interactions among other biological occurrences. A reliable conception of the underlying mechanisms is necessary but is not sufficient to fully comprehend the system and its associated phenomena.
Marr warns that a correct explanation of a psychophysical observation must be formulated at the appropriate level. In a simplified metaphor, Marr (1982) explains that "trying to understand perception by studying only neurons is like trying to understand bird flight by studying only feathers: it just cannot be done.” He continues in his description of vision by explaining that "we can understand how these cells and neurons behave as they do by studying their wiring and interactions, but in order to understand why the receptive fields are as they are - why they are circularly symmetrical and why their excitatory and inhibitory regions have characteristic shapes and distributions" we must draw upon the study of differential equations, filters, signals, optics, mathematics and other disciplines that at first may not seem related to anatomy (Marr, p. 27).
Additionally, some phenomena may only be explained at only one or two of the levels. However, he stresses that the different levels of description should be "linked, at least in principle, into a cohesive whole, even if the linking of the levels in complete detail is impractical," they should remain "logically and causally related." Marr advises that in attempts to relate psychophysical problems to physiology, too often there is confusion about the level at which problems should be addressed. However, "each [level] has its place in the eventual understanding of perceptual information processing” (Marr, p. 25) and the brain-mind connection.
Brains as Computers: Similarities and Major Differences
Many cognitive scientists and modern thinkers equate our brain with digital computers. The brain's comparison to a modern machine is a trend seen throughout history. This comparison has helped shed light on the unique qualities of the brain by developing what intelligence is and more clearly what it is not. There are many core functional and structural components that differentiate us from any modern technology.
Computers are designed to and do a good job computing. The ever shrinking chip has allowed micro-processor speed to explode. Modern technologies even allow digital computers to process multiple computations at a given time. The most high tech machines are performing millions of operations per second. Some scientists and developers have even created machines that can sense and intake a great deal of information and store it in its memory. This is where brains and computers structurally and functionally diverge.
Brains integrate memory and computations while computers have separate compartments for memory and computation. This differentiation makes other similarities less important. The process of how brains and modern-day computers "think", compute and process is fundamentally different. It is not the number of connections of switches or neurons. Computers can integrate at least one million times the connections of a brain. It is not about the flow of information being sequential or parallel. It is an underlying difference of basic function that differentiates brains and computers.
Traditionally, computers are designed to output behavior. They are designed to do a specific task. This is why our idea of an intelligent robot is a human-like android that walks, talks and functions like humans. The best example would be Star War’s C-3PO. However, humans are not designed to just output complex, "intelligent" behavior. Brains are in charge of our bodily functions and allow us to adapt and modify our behavior to live and survive in modern day conditions. Its primary function is to monitor and execute all life-support and survival instincts. The brain develops a certain process of obtaining information and developing a model of the world. It is the how and the why that drive human brains as opposed to what output behavior defines intelligent computers.
Additionally, brains have the quality of being plastic. This is a plasticity that computers today and in the foreseeable future cannot obtain. If a computer’s storage memory is compromised or damaged, the information is lost. However, brains have the ability to develop new connections and can deal with minor “errors.” Computers struggle to adapt to error. Brains have the capacity to change, self-repair and self-correct which are key components of complex systems, while computers tend to be more programmed. The various capacities and capabilities of brains and computers can be seen in their unique architecture.
Finally, computers are extremely superior to brains when it comes to equating and processing numbers. For example, a computer can easily handle mathematical calculations and physics equations. In tests like the Turing Test, programmers often have to slow down the speed at which a computer gives an answer because it is so much faster than humans it would not seem natural to have an answer so quickly. On the other hand, modern-day computers struggle to recognize patterns. Humans have the unique ability to recognize faces from any angle, in any light and even with part of the face missing. Computers still struggle with pattern detection and identification problems such as facial recognition where humans are far superior (Hawkins, 2004).
Brain Structure and Function
When examining the structure and function of the human brain it is necessary to account for the developed organ in the proper context. Edelman (2006) describes that an analysis of the brain must address the system “first during evolution and then during individual brain development” (p. 56). In studies of intelligence, it is often overlooked that the original purpose of the brain was to monitor all bodily functions including breathing, hunger, thirst, circadian rhythm and other basic life processes. Only a full understanding of the anatomy of the brain would allow for a comprehensive theory of higher brain functioning.
The brain, along with the spine, make up the central nervous system, the body’s communication and decision center. The spinal cord is a thick column of nerve tissue that extends from the base of the brain down the spine. The brain stem, which is the portion of the brain that is continuous with the spinal cord, controls bodily functions such as heartbeat, breathing and body temperature. At the upper end of the brain stem lies the diencephalon which regulates hunger pangs, sleeps cycles and circadian rhythms. This part of the central, inner brain before the limbic area is known as the reptilian brain. Three major structures in this area are the thalamus, hypothalamus and pineal gland. The hypothalamus is involved with aggression, fear, sexual behavior and helps regulate temperature, water, appetite and thirst. The thalamus is a central structure that organizes and sends messages to different parts of the brain. The pineal gland excretes important hormones and neurotransmitters such as melatonin which helps regulate circadian rhythm, as well as, serotonin which is involved in the reward circuitry of the brain. The reptilian brain is believed to be associated with life support and survival. The history and development of the reptilian brain offer us a great deal of insight into our own brains. The reptilian brain is associated with the instinctual region of our minds.
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The limbic region, often associated with the emotional mind, generates and controls emotions such as aggression, lust and impulses. The hippocampus and amygdala are two of the major structures in this region. The hippocampus is believed to be essential in the formation of long-term memories. The amygdala aids the hippocampus with memory formation and stimulates cortex with stimuli associated with reward, fear and social functioning. The most outer region is the neocortex which is associated with our analytic mind. Often, behavior can be viewed as a balance, or compromise, between our emotional and analytical minds.
This most outer region of our brain is known as the neocortex which literally means the “new skin,” as it is the newest part of the brain and it covers most of the inner areas of the brain with its crumpled appearance consisting of many ridges and valleys known as sulci and gyri. There are approximately 100 billion nerve cells or neurons in the human cortex (Wadhawan, 2010). Although neurons do have different shapes most of them have a pyramidal shaped central body or nucleus, an axon and a number of branching structures called dendrites. The dendrites are signal receivers and the axon is a signal emitter. A synapse between two neurons is established when the axon of one neuron connects to the dendrite of another neuron. A typical axon can be involved in several thousand synapses. An action potential traveling down the axon of the presynaptic neuron results in the release of neurotransmitters into the synaptic cleft. The neurotransmitters bind to receptors in the postsynaptic membrane and change the probability that the postsynaptic cell will fire its own action potential. Particular sequences of activity can either strengthen or weaken the synapse, changing the strength of the connection between the neurons. Although perhaps oversimplified a common principle during the development and establishment of neuroanatomy is “neurons that fire together wire together,” which form stronger connections between the two neurons. Vernon Mountcastle (1978), one of the fathers of neuroscience, explains that cortical tissue can be functionally divided into vertical units known as columns consisting of neurons that respond in a similar manner to external signals with a particular attribute. This idea is known by most neuroscientists but rarely evident in their research.
Figure 1 Dendritic Tree of a neuron which receives more than 100,000 synaptic inputs (Nelson, 2002)
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The cortex is most developed in humans. Although, we do not know fully how the brain works, current research has shown us some of the basis of brain functioning. Most psychologists and cognitive science researchers identify and delineate certain regions of the neocortex which are responsible for certain tasks. For example, the prefrontal cortex is associated with reasoning power and intelligence. When studying the brain people tend to focus on a localizationist approach in which “the goal is to identify the specific locations in the brain where discrete psychological operations occur” (McGill, 2011, p.80).
The processes studied using a localizationist approach vary across the entire spectrum from perception of sensory stimuli to abstract conceptualizations. Many people split the brain into “lobes” where visual information is processed and analyzed in the occipital lobe, auditory information and speech are processed in the temporal lobe, and touch, temperature sensation, smell and taste in the parietal lobes. The frontal lobe is often associated with a great deal of higher level functioning including language, planning, decision making, and judgment.
Then certain areas of these lobes have been delineated to even more specific tasks. For example, expressive language is believed to be found in the left frontal lobe known as Broca’s area and receptive language is found in the left temporoparietal lobe known as Wernicke’s area. These associations have been confirmed by research on cases of individuals that have strokes or incur brain damage to these areas. Other tasks have been determined to take place predominantly in certain hemispheres. For example, recognizing faces and experiencing music is believed to take place in the right hemisphere.
Somewhat in contrast to this localizationist approach, there are global processes which act on specific regions of the brain, but use similar operating principles throughout. That is, sensory receptors, including the eyes, ears and skin convert physical stimuli into neurological signals which are processed in the brain as specific sets of patterns and streams of sequences of information. Although we attempt to quantify the number of senses coming into the brain, in reality, there are an array of sensors consisting of over a million sensors in your eye, a million sensors in your skin and about 30,000 sensors in your cochlea (Hawkins, 2012). We use these sensors to process and predict properties of our environment. The most basic signals are sent to the thalamus and then to respective areas in the primary occipital, temporal or parietal cortexes of the brain. These signals connect to cortical neurons that represent more abstract properties of a given signal. For example, visual signals are compared with recognizable visual features from lines, colors and orientations; songs are broken down into melodies, sounds and various timing, and so on. There is then direct connection across cortical surface areas which create association areas where multimodal mental representations are constructed (Hawkins, 2004). For example, the experience of driving combines our visual processing with the somatosensory senses of our hands on the wheel and foot on the petal, as well as the sounds of our car and the traffic outside, and so on.
Figure 2 Areas of the brain (Wadhawan)
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Biological Evolution and the Human Brain
Biological evolution is based upon the desire to perpetuate oneself and increase reproductive fitness. In this effort, species adapt to existing structure and order of surroundings to ensure survival and reproduction. The ability and methods of adaption and adaptation have evolved as life became more complex. In simpler organisms, instinct is coded in the DNA.
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Hawkins (2004) describes this as a sort of genetic memory. For example, one-celled organisms have the ability to detect the presence of nutrients and move to an area with a higher concentration of food. This is an automatic reaction based upon chemical sensors and signals that were coded in an organism's DNA. As organisms evolved, more complex behavior emerged in the form of communication systems. For example, many plants developed a vascular system, which can detect damage in its structure. In many plants, the communication system can actually detect if the damage is caused by an insect and if so triggers a defense mechanism such as the production of a toxin to combat the predator (Hawkins, 2004).
Hundreds of millions of years ago, simple nervous systems emerged in multicellular creatures as they spread throughout the earth. The development of the brain as an organ and part of the nervous system can be seen in the most primitive animals. Perhaps the simplest example is the worm with its bilaterally symmetric body structure consisting of a head end and a tail end and a left and right side (Ornstein, 1984). Each part of the segmented body contains bundles of nerve fibers that send information from receptor cells in the skin to a group of nerve cells. These groups of nerve cells communicate with one another through larger bundles of nerve fibers that extend up and down the body forming the nerve cord. As the first vertebrates developed from invertebrate ancestors, the nerve cord became encased in a bony covering, the spinal vertebrae and this became the spinal cord. Ornstein explains, “what exists as only a few extra cells in the head of the earthworm, handling information about taste and light, has evolved in us humans into incredibly complex and sophisticated structure of the human brain” (p. 21).
With the brain being the seed of intelligence, evolutionary thinkers initially associated the size of the brain with level of intelligence. Anthropological studies have indicated a tripling in human encephalization, which is a dramatic increase over the last 3 million years (Jerison, 1976). However, simply brain size does not fully explain the explosion of complex behavior and intelligence of humans. Jerison describes that "the evolution of hearing and smell to supplement vision as a distance sense is sufficient reason for the evolution of an enlarged brain in the earliest mammals. Only an enlarged brain would allow a reptilian brain to analyze non-visual information” (pp. 11-12). New neural networks would have to evolve in order to process these new senses. Jerison explains, "the first expansion of the vertebrate brain may have been primarily a packaging problem and that it may only incidentally have resulted in the evolution of intelligence” (p. 98).
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Additionally, the emergence of bipedalism is associated with the increase in visual processing. Being on two feet gave our ancestors a greater ability and reliance on vision. Walking upright also freed our arms and hands to make and use tools, as well as, to perform more complex tasks such as writing. The increase in brain size occurred concurrently with these behavior changes.
Holloway (1996) examines the evolution of the hominid brain over the last 3 million years. Similar to Jerison, Holloway explains that brain mass alone is not a sufficient variable to explain the evolution of human behavior and the brain. He proposes a major reorganizational change from a “pongid to hominid pattern” based around the “synthesis of mass with reorganization and hierarchy.” Specifically, his anthropological examination has revealed that there was a major reorganizational change in the posterior parietal, anterior occipital and superior temporal portions of the cerebral cortex that preceded the reorganization of the frontal lobe and Broca’s area which took place 1.8 million years ago. Additionally, the doubling of brain size from roughly 750 cc in H. habilis to 1400 cc in modern H. sapiens occurred after these major reorganizational changes in the brain. Another important organizational change is based around greater hemispheric specialization exhibited by the asymmetric changes in the left and right hemispheres (Holloway). Although it is difficult to fully predict the relationship between brain volume changes integrated with reorganization and body size changes, Holloway proposes a sequence shown in Figure 3. Even though the specifics of how the brain evolved may be questioned, and the assumptions may be challenged, it is undeniable that the brain has been an ever-evolving organ.
Figure 3 Summary of reorganizational and size changes in the evolution of the hominid brain (Holloway, 1996)
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Human behavior is a manifestation of underlying neural circuitry modified by evolution. Neurons initially evolved as a quicker way of sending and receiving information to various parts of the animal. The electrochemical spikes in a neuron travel much faster than the diffusion of chemicals. However, a behavior that evolved in the past to serve one function may serve an entirely different purpose at a later time. Eventually, nervous systems had elements of both memory and learning. This was a huge evolutionary advantage, which allowed mammals to learn from experience. A plastic nervous system allows for a response to environmental changes as they are happening. This allows an individual to modify behavior to achieve better survival and reproductive rates during its own lifetime.
Language and Cultural Evolution
Along with biological evolution, humans are driven by cultural evolution as well. Cultural evolution is based on the desire to pass on ideas and ways of life. For biological, or genetic, evolution DNA and genes are seen as the transmission unit. Cultural evolution is also referred to as memetic evolution because memes, "packets of information" are the units that are passed on. Richard Dawkins (1976), author of The Selfish Gene, coined the term meme as the cultural analogs to gene; they carry information about the world, just as DNA does for the one-celled organism.
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The rapid evolution of the size and capacity of the human brain is believed to be associated with the evolution of language, speech, and more complex culture. Language is one of the major advantages humans have over the rest of the animal kingdom. Through time, humans have developed the ability to use words, nouns, verbs and other parts of speech to create sentences. Language has given humans the ability to vocalize their thoughts and share with their offspring and rest of their species. The emergence of language led to abstract thought and the abstract world.
The evolution of language allowed for the development of symbolic representation and more social interaction, which in turn led to a more complex world. Seth Lloyd (2006), a professor at M.I.T., goes as far to say, "With language, our ancestors were able to create their own environment - we now call it culture - and adapt to it without the need for genetic change." Additionally, language allows for humans to connect and bond at a different level than any other animal species. This both increased social complexity and forged stronger relationships amongst humans. As social relationships increased and became more complex, the brain’s size and other capacities increased.
Hawkins and the Evolutionary Advantage of Neocortex
Our large neocortex is the crux of intelligence and gives humans an innate ability to recognize patterns. Hawkins (2004) introduces his view on consciousness as "what it feels like to have a neocortex." Vernon Mountcastle (1978) proposed a fascinating hypothesis in the late 1970s, which stated that "there is a common algorithm that is performed by all the cortical regions." This algorithm has the ability to replicate itself and in a sense organize itself. The same types of layers, cell types, and connections exist in the entire cortex. Mountcastle explains that the “differences between areas of the neocortex reflect differences in their patterns of extrinsic connections” (p. 15). Therefore, there is nothing intrinsically different in the structure or function of various areas of the neocortex. Although there is a fissure separating the two cerebral hemispheres and a sulcus dividing the front and back, there are no clear delineations of certain areas constrained to certain functions. There is major overlap between the senses as well as between the senses and motor mechanisms.
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Jeff Hawkins attempts to outline a framework for understanding intelligence in his book On Intelligence. He discusses many topics and uses logic to deduce how the brain may work. His definition of intelligence is most interesting. He defines intelligence as the ability to predict and remember. He creates what he calls a memory-prediction framework to understand the brain and its capacities.
Within this framework, there are many important aspects. To begin, Hawkins argues to steer away from our intuitive sense that intelligence is based on output behavior. Instead, he believes that intelligence is a process centered on successful prediction, experience, memory and understanding. Hawkins’ focus is on the neocortex, which is most pronounced in humans, and he focuses on four major capabilities of the cortex.
First, he describes that the cortex stores sequences of patterns, as well as, sequences of sequences. The best example would be our memory of the alphabet. It is not something recalled all together in an instant. Rather, our memory conveniently stores as a sequence of patterns. Secondly, the cortex recalls “auto-associative” memories. He is referring to our ability to recognize patterns when only given a part of the pattern. For example, if we hear half a melody of a song we know, we can often recognize the entire song and complete the melody. Essentially, each functional region is waiting for familiar patterns or pattern-fragments to be processed. The inputs to the brain are linking to themselves auto-associatively, filling in the present and what normally flows next (Hawkins, 2004).
Thirdly, patterns are stored in an invariant form. This idea of storing ideas in an invariant form can be traced back to ancient philosophers such as Plato and Aristotle. However, Hawkins steers away from most philosophical implications and focuses on our ability to store a belief or an idea of an object regardless of its current context. For example, an object such as a table is stored in our brains in an invariant form. Although there are many individual instances of a table, i.e. coffee tables, kitchen tables, restaurant tables, we know that an object with four legs and a top is most often a table regardless of context and individual differences. We have the ability to identify a novel table without ever seeing one that looked exactly like the one being viewed. This system allows knowledge of past events to be applied to new situations that are similar but not identical to the past (Hawkins, 2004).
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Lastly, Hawkins explains that the cortex stores patterns in a hierarchy. The neocortex, if stretched flat, is about the size of a large table napkin and 2mm and consists of six layers that are roughly the thickness of a playing card (Hawkins). These six layers are separated mostly by cell type and neuronal connections. Amongst these layers there is a branching hierarchy. In a certain sense, raw sensory data is being sent “up” the hierarchy, as more and more abstract and generalized versions of information are sent “down” the hierarchical layers and compared with known patterns. While reading words on a page, the higher levels of the cortical hierarchy are sending more signals down to the primary visual cortex than your eyes receives from the page (Hawkins, 2004).
This hierarchical structure gives us our ability to store patterns of patterns. He often uses music as a metaphor and describes how a song is understood. A song is made up of melodies, which are made up of a sequence of notes that are certain intervals away from one another. The nested structure of the world can be seen in our model of the world. Another great example is a piece of writing such as a book. From the simplest element lines make up letters, letters make up words, words make up sentences, sentences make up paragraphs, paragraphs make up chapters, and multiple chapters combine to form a book. Hawkins (2004) describes in great detail how this hierarchical structure seen in the world is actually similar to how the brain processes and stores information.
Our brains use stored memories to constantly make predictions about everything we see, feel and hear. Prediction is so pervasive that way we “perceive” the world does not come solely from our senses. We perceive a combination of what we sense and of our brain’s memory-derived prediction. In this predictive process neurons involved in sensing become active in advance of them actually receiving sensory input. These predictions are not always perfect, but the probabilistic predictions are often reliable. This concept is understood at some level to be intuition (Hawkins, 2004).
A large area of our neocortex expanded dramatically only a couple millions years ago. The neocortex is built using a common repeated element so evolution led to the rapid copying of an existing structure. As the cortex got larger over evolutionary time it was able to remember more and more about the world, form memories and the ability to make more predictions. Through time, the complexity of the memories and predictions increased (Hawkins, 2004).
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As we interact with the environment, whether consciously or unconsciously, we are constantly predicting what is going to happen next. For example, as you read this sentence you are predicting what the next word will be in the sentence. While a stream of sensory information is coming into our brains we have a significant amount of more information flowing back down the hierarchical memory system. This feedback is sending predictions of what to expect next (Hawkins, 2004).
Multiple regions of the neocortex are simultaneously trying to predict what their next experience will be. Visual areas make predictions about edges, shapes, objects, locations and motions. Auditory areas make predictions about tones, directions to sources and patterns of sound. Somatosensory areas make predictions about touch, texture, contour and temperature (Hawkins, 2004). When the sensory input does arrive, it is compared with what was expected. When your predictions are met, you’ll continue without consciously knowing that your predictions were verified. For example, our auditory areas predict that background noise will continue, in continuation, moment after moment, and as long as the noise does not change our expectations are not violated. However, when a background noise ceases, this violates our prediction and attracts our attention.
Another great example of this memory-prediction framework is of our mind predicting and filling in the small blind spot in each eye where the optic nerve exits each retina. Even with one eye open, our visual system “fills in” missing information to make one coherent vision. The visual cortex is drawing on memories of similar patterns and makes a continuous stream of predictions that fill in for any missing input. We perceive clear lines and boundaries separating objects when we look at the world, but the raw data entering our eyes is often noisy and ambiguous. Our eyes saccade about three times every second. A saccade is when eyes fixate on one point and then suddenly jump to another point. We are not aware of these movements, and do not consciously control them. Each time our eyes fixate on a new point, the pattern entering your brain from the eyes changes completely from the last fixation. Yet, we are only aware of a continuous view of the world.
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Minsky’s Society of Mind
Marvin Minsky (2007), co-founder of MIT’s AI laboratory, argues that “our minds did not evolve to serve as instruments for observing themselves, but for solving such practical problems as nutrition, defense, and reproduction” (p. 109). He describes the mind as being composed of many partially autonomous “agents.” These agents self-organize to create a “society” of smaller minds. This is basically another way of viewing the brain as a complex adaptive system. Minsky views the functions of the brain as being performed by “thousands of different, specialized sub-systems.” He continues to explain that we can interpret “mental states” and “partial mental state” as subsets of the states of the parts of the mind. For example, certain divisions specialize in sensory processing, language, long-range planning, etc. Each “agent” is made up of multiple subspecialists that embody smaller elements of an individual’s knowledge, skills and methods. In an earlier work, Minsky (1980) explains that “no single one of these little agents knows very much by itself, but each recognized certain configurations of a few associates and responds by altering its state” (p. 119). This type of interrelationship among the components is characteristic of complex systems. He sees the construction of the mind as the synthesis of organization systems that can support a large enough diversity of different schemes, yet enable them to work together to exploit one another’s abilities. These agencies self-organize into larger conglomerates with the ability to perform more complex functions, and then these conglomerates combine to form higher and higher levels of self-organization and the emergence of the “abilities we attribute to minds.”
Later in his argument, Minsky (2007) claims that consciousness is “used mainly for the myth that human minds are ‘self-aware’ in the sense of perceiving what happens inside themselves” (p. 327). He believes that human consciousness can never truly represent what is happening at the present moment, but only a little of the recent past. He postulates that each “agency” has a limited capacity to represent what happened recently and the fact that it takes time for agencies to communicate with one another. Consciousness in a unique way follows the observer effect in physics; an attempt to examine temporary memories distorts the very records it is trying to inspect.
Minsky’s “society of mind” challenges the commonly accepted “single-self” concept, or the idea that there is a unitary being “inside us that does all the feeling or thinking for us (Wadhawan, 2010). Proponents claim that the “single-self” concept may be helpful and useful, but it not grounded in science. Minksy (2007) explains that unifying our idea of the mind can hide “how much we’re controlled by all sorts of conflicting unconscious goals” (p. 15). When trying to answer questions about ourselves, Minsky claims, “We are switching among a huge network of models which tries to represent some particular aspects of your mind” (p. 16). Even though we feel as if our brain represents a unified self, there are many different systems working within particular models we have created.
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Dennett’s Model of Consciousness
Daniel Dennett, one of the most highly regarded philosophers and cognitive scientists, continues along a similar line of reasoning as Minsky. They both give due respect to internal subjective experience such as feelings and emotions, but these are only evidence of how things appear to them to be, rather than direct evidence of the way things actually are. Dennett’s view of consciousness is proposed as the multiple draft model of consciousness. First off, mental processes are spread over the dimensions of both space and time. He then uses the analogy of the preparation and publication of a book. The original text undergoes a number of draftings and is sent to editors before it is finalized. There are multiple drafts but only one may get chosen in a certain situation. This process is similar to how consciousness is represented in the brain (Dennett, 1994).
Dennett, similar to Minsky, stresses that it is only an illusion that a person is conscious of what is perceived as “now.” Processes in in the brain are happening simultaneously and are at the millisecond level. Because it is working at a finite speed, it is impossible to order events in the brain below the millisecond time scale (Wadhawan). There is a choice made by the brain from among the recent events and processes occurring that make up the subjective “now.” Within this argument, Dennett rejects the idea of qualia, so called “feelings that are associated with a sensation independent of sensory input.”
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From Dennett’s view, consciousness arises from the processes associated with information exchange in the brain. Conflicting pieces of sensory information, memories and emotional cues are competing with each other at all times in the brain. Every instant, a new set of factors can dominate your awareness. Dennett also believes that a necessary prerequisite for consciousness to emerge is the acquisition of a human language. Dennett (2006) claims that without language, “there is no organized subject (yet) to be the enjoyer or sufferer, no owner of the experience as contrasted with a mere cerebral locus of effects.” As our brain organizes information exchange processes, consciousness arises.
Edelman’s Brain-Based Theory of Consciousness
Gerald Edelman, American biologist and 1972 Nobel Prize winner, offers a biological theory of consciousness founded on Darwin’s Theory of Natural Selection. His most recent book, Second Nature: Brain Science and Human Knowledge, outlines the key tenets of his theory of consciousness developed throughout his career. In Edelman’s theory of Neural Darwinism, he describes three components of his neuronal group selection. First, he explains that “the development of neuronal circuits in the brain leads to enormous microscopic anatomical variation that is a result of a process of continual selection” (Edelman, 2006, p. 27). He refers to this as developmental selection and explains “the high degree of functional plasticity and the extraordinary density of their [neurons] interconnections enables neuronal groups to self-organize into many complex and adaptable modules” (Edelman, 2006). This idea is consistent with Hawkins, Dennett and Minsky, as well as, systems theory in general.
Edelman then describes experiential selection which he defines as the continuous process of synaptic selection that occurs within the diverse repertoires of neuronal groups throughout the development of the brain. He explains that “experiential selection generates dynamic systems that can map complex spatio-temporal events from the sensory organs, body systems and other neuronal groups in the brain onto other selected neuronal groups” (Edelman, 2007). Edelman views this dynamic selective process as working analogously to the processes of selection that act on populations of individuals which leads to the name of his theory Neural Darwinism. This is based off of Darwin’s fundamental idea of population thinking in which variation in a population provides the basis for selection and survival.
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Edelman’s third tenet, perhaps the most important in understanding higher capacities of the brain, is the concept of reentrant signaling between neuronal groups. Edelman (2007) demonstrates that there is a “recursive dynamic interchange of signals that occurs in parallel between brain maps, and which continuously interrelates these maps to each other in time and space.” This reentrant circuitry appears to be unique to animal brains and he describes, “there is no other object in the known universe so completely distinguished by reentrant circuitry as the human brain” (Edelman, 2001, p. 44). Reentry is seen within the neuroanatomy of the brain as a dense meshwork of reciprocal connectivity among different cortical areas as well as between the cortex and the thalamus (Edelman, 2006). Hawkins (2006) explored these pathways and explains that in the circuitry between the neocortex and the thalamus, “the connections going backward (toward the input) exceed the connections going forward by almost a factor of ten” (p.25). This reentrant circuitry allows humans to link numerous sensory signals together, make perceptual categorization and then connect them in various combinations to memory (Edelman, 2006).
This reentrant system in influenced by value systems and by selected synaptic changes by previous experiences. Edelman (2006) explains that “from very early developmental times, signals from the body to the brain and from the brain to itself lay the grounds for the emergence of a self” (p.37). Similar to Hawkins’ idea of auto-associative memories, conscious experience relies on references to its own memories. Additionally, conscious experience enhances communication with other individuals and is deeply rooted with language. Edelman proposes that at some point in high primate evolution, “a new set of reciprocal pathways was developed” which made “reentrant connections between conceptual maps of the brain and those areas capable of symbolic or semantic reference” (p. 38). According to Neural Darwinism, this reentry in the enormously complex dynamic core was the “key integrative event that led to the emergence of conscious experience” (p. 39). The feedback and messages the brain sends to itself which is often ignored in studies of intelligence may be the decisive factor to understand human experience.
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Recent research has found that environmental factors can actually change the physical structure of the brain. The mental state of an animal can be responsible for the release or suppression of a chemical, which can influence gene expression. Environmental influences, the presence of certain chemicals and the release of hormones and neurotransmitters can modify gene expression. Gene expression can be modified without changing the underlying DNA. There are certain areas that have been identified as “switch” DNA which previously was thought to be "junk" DNA. However, the more we research the area the more we realize how important this "non-coding" DNA is. The switches do not directly lead to the synthesis of proteins. However, switches and homeobox genes, which control switches, tell the protein-synthesizing DNA when to turn on and turn off and how "intense" the synthesis should be.
Genes affect people’s behavior and experience, but their experiences and behavior also affect gene expression (Gottlieb, 2003; Rutter 2006). This bidirectional relationship between heredity and environment is known as the epigenetic framework (Gottlieb). Development takes place through ongoing, bidirectional exchanges between heredity and all levels of the environment.
The Center on the Developing Child at Harvard University have been exploring how early experiences can alter gene expression and affect long-term development. They explain that “the approximately 23,000 genes that children inherit from their parents” form what they call a “structural genome” (Shonkoff et al., 2010, p.1). This structural genome is compared to the hardware of a computer because it determines the boundaries of what’s possible, but does not work without an operating system to tell it what to do. In the genome, the operating system is called the “epigenome” and it determines which functions the genetic “hardware” does and does not perform (Shonkoff et al., 2010). Through time, positive experiences, such as rich learning opportunities, or negative influences, such as “environmental toxins” or stressful life circumstances, leave a chemical “signature” on the genes. These signatures can either temporarily or permanently affect how easily the genes are switched on or off. These experience-driven, chemical changes can play particularly key roles in brain and behavioral development (Shonkoff et al., 2010).
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Laura Berk, a leading psychologist on child development, explains that providing a baby with a healthy environment and diet increases brain growth, leading to new connections between nerve cells which influence gene expression. This allows for new “gene-environment exchanges” such as advanced exploration of objects and interaction with caregivers (Berk, 2010). This helps to further enhance brain growth and gene expression. Supportive environments and rich learning experiences generate positive epigenetic signatures that “activate” genetic potential. The stimulation that occurs in the brain through active use of learning and memory circuits can establish a foundation for more effective learning capacities in the future because it is rooted to these epigenetic changes (Shonkoff et al., 2009).
In contrast, harmful environments can dampen gene expression. At times, National Scientific Council explains, “the effect can be so profound that later experiences can do little to change certain characteristics that were initially flexible” (Shonkoff et al., 2010). Epigenetic changes can be caused by repetitive, highly stressful experiences that can damage the systems that manage one’s response to adversity later in life (Szyf, 2009). For some, this epigenome provides a molecular level explanation for why and how early experiences, whether positive or negative, can have an impact that last for life.
Effective interventions can literally alter how children’s genes work and thereby have long-lasting effects on their mental and physical health, learning and behavior. In this respect, “the epigenome is the crucial link between the external environments that shape our experiences and the genes that guide our development” (Shonkoff, 2010, p.2). Gaining a greater understanding of the environmental factors that influence the development of our brains and behavior will allow us to more effectively create policy to provide a healthy environment for developing children.
Since environmental factors can change how the components of the brain exchange information and interact, it must be accounted for in a systems analysis. This external influence of the environment makes it difficult to define clear boundaries for the system; this property is characteristic of complex, open systems.
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Conclusion
It remains difficult to define many of these concepts such as mind, intelligence and consciousness. Our intuition and fundamental beliefs about what exactly these phenomena are often lead us to misinterpreting what they truly are. Regardless of the exact definition, it is important to realize that an evolutionary and systems analysis is applicable and intelligence and complex behavior can be viewed as an emergent phenomenon. A systems analysis draws upon nearly all disciplines which attempt to answer these questions.
Future research cannot ignore fundamental truths of the brain. The importance of feedback, the cross-association of the senses as well as the deeply rooted overlap between the “motor cortex” and “sensory cortex” must be addressed when developing a framework to understand behavior and intelligence. We can no longer view each sense of the brain as independent. Rather, we must realize that our senses are deeply intertwined with our thoughts, actions and movements within a memory-prediction system.
Hawkins has offered an explanation of what the brain, specifically the neocortex, which is responsible for complex behavior, does at the computational level. He explains that the brain is a predictive modeling system. It makes predictions about what we are going to hear, see and feel. It detects anomalies and makes you aware of something that you didn’t expect. Finally, based off of these predictions, it takes actions which generates our behavior. When viewing the brain as a predictive-modeling system it is clear that this system allows human to adapt to practically any environment. By having a framework in place, we can better identify and understand the specific operating principles of the brain.
It is of the utmost importance to further examine the architecture of the brain to see how structure and function interact. Additionally, the development of the brain must be put in the proper context of epigenetics and analyzed within its environment. A deeper explanation of the evolutionary advantage of neurons and the nervous system will lead to a greater understanding of human behavior. It is the process underlying intelligent behavior that needs to be explored, not just the output.
The brain is an organ that has evolved through time. It 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. There are billions of neurons that self-organize and create a network, which gives rise to our sense of identity as a living being. The brain has sensors that receive certain stimuli (i.e. physical, emotional, spatio-temporal patterns), an output or response, as well as, a heavy reliance on feedback. Therefore, a systems analysis is applicable; this approach helps illuminate the intricacies and nuances of the brain as a system. The system is dynamic meaning that it changes through time; the dynamic aspects of the brain can be observed in the formation of new neural networks and connections through time. The system is adaptive meaning that it changes in response to the environment; this allows for an evolutionary analysis. Complexity theory discusses the emergence of complex properties from the interaction of simpler components; systems have the potential to spontaneously generate new collective behaviors and structures. A multi-disciplinary view is necessary to comprehend the amazing brain that makes us human. How these phenomena arise does not need to be a mystery. With the proper framework we can further understand the brain, mind, intelligence and consciousness.
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Many of the theories presented which discuss the brain’s capacity for consciousness and intelligence at their most basic level can be viewed using a systems analysis. Any complex system, especially the brain, is much greater than a sum of its parts. The intricate web of interrelationships of simpler components can shed light on underlying processes of the system.
The study of the true functioning of the brain is contingent on so many issues that seem small, but can really change your view of modeling human behavior. In research, we have certain choices. One approach is to compare and contrast certain models and then highlight the differences. However, to unlock the real functioning of the neocortex, the root of our complex behavior, we cannot be looking for differences among the systems, when there are so many similarities throughout. In a similar sense, we can decide to examine the brain and human behavior on the individual or group level. Psychology primarily examines the brain on the individual level, while anthropology, sociology and other related fields examine human behavior on the group level. As we continue to gain knowledge about human behavior on these different levels of analysis, we have to work towards aligning these levels into one cohesive model.
Similar to any complex adaptive system, a few simple fundamental rules or laws cannot fully explain how the human brain functions. Intelligence and consciousness emerge in a system that is powerful enough to have a self-referential, self-modeling capability. We are a part of nature, embedded in the environment, and as conscious beings we have the unique ability to think about and represent ourselves. Our ability to thrive in the future is contingent on how accurately our self-reflective cortical models reflect the true nature of the world.
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Acknowledgements
Thank you to:
Dr. Janet Monge - Advisor, Mentor, Human Evolution and Human Adaptation Professor,
http://www.sas.upenn.edu/~jmonge/
Dr. Leela Jackson – Developmental Psychology Professor
Dr. Susan Schneider - Philosophy of Mind Professor, Author
http://www.sas.upenn.edu/~sls/Schneider_site/Research.html
Dr. David A. White - Animal Behavior Professor, http://www.psych.upenn.edu/~whitedj/lab/
John A. Brown - Intelligence Engineer, http://www.nhoj.info/home
Dr. Vinod Kumar Wadhawan - Complexity Explained Author,
http://nirmukta.com/2010/03/19/complexity-explained-16-evolution-of-intelligence-and-consciousness/
Influential Thinkers
Francis Crick
Nicolas Tinbergen
David Marr
Turing
Searle
Eccles
Popper
Ludwig Wittgenstein
Dennett
Jerison
Allman
Ornstein
Sternberg
Gardner
Dawkins
Damasio
Cattelli
Darwin
Holloway
McCullough
Russell
Locke
Chalmers
Lowe
Hawkins
Edelman
Mountcastle
Minsky
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