Introduction

Original Capstone Paper (2012)

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 at the most fundamental level. 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. 

(1) The dynamic aspects of the brain can be observed in the formation of new neural networks and connections through time. 

(2) 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). 

I believe you can argue that changes are taking place on the order of nanoseconds or faster in ways that can not even be observed by humans.

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.

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Tinbergen’s Four Questions

Nikolaas Tinbergen, often considered the "father of ethology," explored the 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).

A structural explanation of how a certain behavior works is not enough to fully comprehend the behavior. Tinbergen (1963) explains ontogeny(development), phylogeny and adaptation must be explored when trying to understand behavior. Developmental questions of how the behavior changes with age and what early experiences are necessary for that behavior to be shown must be asked. Evolutionary questions such as how the trait has developed from its DNA coding to its current form must be grasped. Questions about adaptation including 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).

A full understanding of both proximate and ultimate causes is necessary for a complete explanation of intelligence and consciousness.

Nikolaas Tinbergen (1973)

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Refererence:
Tinbergen, N. (1963). On aims and methods of ethology. Journal of Animal Psychology. 36 University of Oxford. 20: 410-433.

Darwin and Intelligence / Aristotle's Scala Naturae

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.

Intelligence can viewed as the one trait that has allowed for massive population growth and the perceived "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 (1874), one of Darwin's later works 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.

We have made immense strides in science since Aristotle's Scala Naturae and History of Animals. We have to continue with open, forward thinking to properly understand the work of amazing minds such as Darwin and Aristotle. The advent of genetics and neuroscience have allowed us to theorize on human evolution in a more modern, less "human-centric" manner. As we continue to theorize on human evolution we have to properly context the great minds before us, but we cannot be blinded by some of their "ancient" observations.

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).

We have to remember to include both proximate and ultimate causes for the "trait" we are examining.

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). 

Francis 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. This lack of consensus makes the brain, mind and mind-body connection a very difficult area to navigate, but it is not reason to stop the investigation.

One Man's Ontological View

I am exploring intelligence and consciousness and it is outside the scope of this argument 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.

(1) 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. 

(2) 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).

(3) 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.

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. 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 this work, Turing introduced 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. Turing believed he could prove that machines can think if they could

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).