Book Excerpts
1. Table of Contents,
2. Preface,
3. Chapter 1
4. Chapter 2 “making associations”
5. Chapter 4 “object representation”


Chapter One - The Biological Mind

Models And Myths
The Slate, The Ghost And The Savage
The First Cognitive Revolution
The Second Cognitive Revolution
Impact On Our Daily Lives
How The Mind Really Works
Plan Of The Three Books

Chapter Two - Pattern Classification

Pattern Classification In The Wild
Examples Of Human Pattern Classification
Making Associations
Machine Pattern Classification
Training A Machine Pattern Classifier

Chapter Three - Neural Networks

Why Are Neural Networks Interesting?
A Model Of Biological Neurons
Pattern Classification With Neural Networks
Training Of Neural Networks
Feedback In Layered Networks
Modeling Biological Brains

Chapter Four - Representation In Animals And Humans

Current Ideas
Object Representation
Event Representation

Chapter Five - Modeling Primary Cognition

Languageless Thought
Primate Thinking
Closing Thoughts
Opening Thoughts


PREFACE Back to top

      I’d like to comment on what got me started writing this book, give some explanation of my background, and discuss my rationale and perspective. At a symposium on cognition and the humanities that I attended in the mid-1980s, a heated argument broke out after the screening of an independent science fiction film. The film’s plot was about removing a volunteer’s brain to a vat where it would be connected via wires and electronic communications to his head. This drastic surgical procedure was required to prevent the volunteer’s brain from being destroyed by radioactivity when he descended into the ocean depths to retrieve a damaged nuclear device that threatened to kill all life on our planet. In the movie, the brave volunteer (played by the mind science philosopher Dan Dennet) first underwent a test where he could switch over from his own brain in a vat to a back-up external computer brain. By switching back and forth, he could determine whether the external computer brain functioned as well as his own biological brain.
      Dennett himself appeared at the symposium wearing the brain switch on his belt and frequently “switched over,” to the great amusement of many people in the audience. However, a number of other people in the audience were very upset by the concept of an equivalence of a machine brain and a biological brain. My impression of the loud and acrimonious debate that followed the screening of the film was that the humanists and the scientists were unable to communicate with each other. It’s not that they simply disagreed but that neither side could clearly articulate what the disagreement was actually about.
      The humanist battle cry was essentially, “I am not a machine!” while the scientists’ response was, “Yes, you are!” But that was about as deep as the discussion got. I felt that both sides of the debate needed to deeply rethink and more clearly explain their ideas so that a superficial argument could become a fruitful debate. I wrote these books to fill that need.
      I am aware that at first glance this issue of whether the brain is computational or not may appear to be an exotic scholarly debate of little interest to a broadly educated person. To counter this view, I first point out that the people who get involved in this discussion become very emotional about it very quickly. Something very deeply ingrained in our natures is troubled by the issues raised by the computational mind debate. Moreover, the issue has deep consequences for how we view our own intelligence, how we manage our lives and how we educate ourselves.
      I Am Not a Machine addresses the deep issues that provoked the arguments I witnessed almost twenty years ago and continue to witness now in a variety of settings. Many people have very strong gut reactions against computational models of the mind. It could conceivably be the case that the humanist’s objections to these models are no more founded than the humanist’s initial objections to Galileo’s theory of a sun-centered solar system or Darwin’s theory of the evolution of man. However, the jury cannot decide this issue based on their gut reactions or based on the track record of the defendants. We must dig deeper into the issues, and my book does exactly that.
      The common negative reaction that people have to being compared to computational machines is related to some complex issues. There is something deeply wrong with the way that both the public and many scientists think about such diverse issues as truth, DNA codes, and the human mind. It’s more than the desire for simple answers to complex questions. It’s a belief in binary yes/no dichotomies supported by a computational interpretation of how the mind works.
      Since that early symposium, many other researchers have also taken great strides to meet the need to clarify the debate. One recent contribution is a well-argued book by Gary Marcus entitled The Algebraic Mind: Integrating Connectionism and Cognitive Science. In it, Marcus argues persuasively for a view that the mind is fundamentally computational—that is, it operates via symbol-manipulating algorithmic processes.
      The view that Marcus argues against is often called connectionism because its essential idea is that the mind is best modeled as a complex interconnected network of neuronlike elements that operate without symbols or algorithmic rules. Researchers who have argued with conviction for this connectionist view include Jeff Elman, James McClelland, David Rumelhart and William Bechtel.
      I’m afraid that I do not accept the arguments of either camp. First, let me say that they all make very clear, intelligent, and plausible arguments. In fact, what either side says could be true. However, I think both camps suffer from too much computer science and not enough biology. Both approaches also suffer a huge “can’t get there from here” problem because neither approach offers an evolutionary path from simple organisms to more complex non-human primates and then to humans who use grammatical language. Unfortunately, it will be a long time before we have detailed enough empirical data to conclusively resolve the issue. In trying to understand how the mind works we all do what Steven Pinker calls “reverse engineering.” We all bring to that task our own education and experiences that inform our understanding of how to design things.
      Because many of the ideas in this book may be new, even to readers of the traditional cognitive science literature, I have taken great pains to start at the beginning in all areas. Thus, I hope that humanists, scientists, and the ideologically open-minded and curious reader will find my book engrossing and provocative.
      My proposed model also bridges the evolutionary gap from animals to the early hominids that began the journey towards human spoken language. Human natural language is enormously more complex than the body language and simple calls found in most animals. Human language is syntactic; that is, it is composed of many sub elements that combine to form larger units. We combine a repertoire of syllables to form words and we combine words to form sentences. This complexity has compelled most cognitive scientists to a model of the human mind that is symbolically driven. They simply could not conceive of another alternative. As implausible as a computational model was to many scientists, they had to embrace it or face eternal puzzlement––a disastrous state of mind for most people but especially for a scientist. This computational notion of mind is not new. Although sophisticated computers are new, deep thinkers such as Descartes conceived of an “engine of reason” and even some Greeks thought in terms of idealistic mechanistic models of the world. I realize that these old views are subtly entrenched in many people’s minds and that I have a tough row to hoe if I am to entice them to consider an alternative view of the mind, but that’s the challenge I have accepted.
      I’d like to remind you that science has two phases, a hypothesis generation phase and a hypothesis validation phase. When we validate, we need to make our argument linear and clear. Checks with other concepts and checks with the real world must be formal; that is, they should be set out in a structure so that others can check the reasoning and the data. That’s the hard-work part of science and I am afraid I must leave it to the hard workers. I’ve taken on the easy job of generating some new ideas, that is, the job of hypothesis generation. When I look at the old ideas I do not think they are misguided because I see a small chink in the armor; in fact, viewed each step at a time, the computational argument appears very solid. However, I believe the computational argument is wrong because the whole pattern does not make sense. My presentation is something of the opposite. My argument has not yet been reviewed by hundreds of scholars and therefore the steps of my argument are not as solidly made as others will someday make them, but I do think that the whole model of mind I present is very solid. This book is not intended as the end of a discussion but rather as part of the conversation.

CHAPTER 1 Back to top

      The essential difference between a human mind and a nonhuman primate mind is language. To set the stage for unraveling that statement we introduce ideas associated with language, computational machines, metaphors, models and the first and second revolutions in cognitive science. We also discuss some practical applications of cognitive science.

      Can animals think? Do they have language? Can an Orca whale sing to its pod, “See you guys in the spring off San Juan Island?” I have found that many people have very strong, yet divergent, views on such questions even though they apparently have not read about or reflected on the questions before. One of the things that make such discussions difficult is that people often don’t have a foundation of concepts for understanding and describing human or animal thinking.
      My focus has almost always been with human thinking but I found that I needed to understand animal thinking first. No problem, I thought. A week’s digression is all that I needed. No such luck. I ended up writing this little book. My approach to understanding animal thinking is to ask not what animals think but rather how animals think. In particular, how do animals think without using words? Many people including myself, but by no means all people, believe that humans also can think without using words. Visual artists, architects, and musicians may head that list but athletes and car mechanics are not far behind. Consequently, characterizing thought without words is important for understanding animal and human cognition.
      This is the first book in a series of three books about thinking and the biological processes that make it possible. Thinking about thinking is a tricky business not because of the confusing play on words or some vague notion that study of the self is impossible, but because, like riding a bicycle, thinking is so automatic and effortless that it’s very difficult to tease apart its component processes.
      It’s also unnatural to think about thinking. It’s natural for boys to think about girls and vice versa. It’s also natural to think about baseball, politics, and the neighbor’s new car. However, thinking itself is something of a mystery for most of us and there is sometimes an underlying sentiment that we should just let the mystery be. Well, I can’t. I’m hooked on trying to better understand the human mind. I’m not the first and I’m sure I’m not the last person with this obsession. In fact, in some small circles, and not just academic circles either, thinking about thinking is almost considered cool.
      For better or worse, I am not part of a well-defined circle of people that thinks about thinking. Consequently my ideas may be a bit different from what you’ve been exposed to. I have no way to evaluate my own ideas. No one does. We rely on our peers to do that and I’m going to be relying on you.
      I have found it extraordinarily difficult to give a one-minute summary of my ideas—what some people call an “elevator speech.” The ideas that I find tie things together don’t seem to have much of an impact on people who have been kind enough to listen to me. Sometimes a consequence of an idea is what catches people’s attention—as for example Einstein’s claim that light always travels at the same velocity and that nothing could travel faster. Now that’s catchy because you can say it in one sentence and it’s very counterintuitive so it makes you wonder. I can’t come close to matching that but here is my attempt to catch your attention.
      The essential difference between a human mind and a nonhuman primate mind is language (French, Finnish or Farsi, whatever). Human intelligence is based on language. Language is not merely a means of communication as asserted by most scientists who study thinking. By language, I mean natural syntactic language, that is, a language made of simple pieces that combine into complex pieces. In most human languages, letters combine to make words and words combine to make sentences. The communicative acts of bees, dolphins, and cats are not based on this combinatorial process and consequently the complexity of their communication and thinking is severely limited. For the purposes of our discussions these animal communicative acts are not considered to be language.
      I suspect we all agree that nonhuman primates are very intelligent and fortunately we humans are blessed with that form of intelligence as well. I call this non–language-based intelligence primary cognition. By contrast, I call language-based cognition secondary cognition. I think you might now be ready for an example. I claim that in an afternoon you cannot teach a chimp to efficiently parallel-park a car in a tight parking spot or teach a chimp to make Coq au Vin. Please ignore some practical issues such as how the chimp touches the gas pedal or holds a knife so that we can go to the heart of the matter. To quickly parallel-park a car you need to follow a simple procedure. A possible
set of instructions is “Pull up beside the car in front of the parking space, leaving about one foot of space between the cars; back up straight until your rear wheel is even with rear of the front car, then turn your steering wheel hard right; continue backing until your front wheel is aligned with the rear wheel of the front car, then turn your steering wheel hard left; finally, straighten your steering wheel when you are parallel to the curb.” If you are feeling gutsy or rude, ask someone struggling to parallel-park a car what procedure he is following and I am sure that you will be given the answer, “Huh? What procedure?”
      Some people just avoid parking in tight spaces and others laboriously go back and forth endless times as they get into a parking space. Many of them will not even know there is a simple procedure or for one reason or another don’t like cluttering their minds with procedures. It’s cooler to wing it. A chimp might be able to wing it as well if you could get him to understand the goal of the exercise. The point is that the procedure can be expressed as just a few words and those words code a set of things to do with your body. You can learn to do a complex set of body motions by carefully watching but it is very difficult and takes a long time. Language makes almost impossible tasks easy.
      Making a complex recipe such as Coq au Vin can also conceivably be learned by a creature without any language by just watching someone do it but it would probably take a lifetime or two. Each step of the process requires specialized skills represented in complex body experiences. By using language, a creature makes the steps easily labeled and recalled when needed. Spoken language helps a great deal but written language is even better.
      Creatures that can parallel-park a car or make Coq au Vin appear to be intelligent. It has been tempting for scientists to define intelligence in terms of the kinds of tasks a creature can do. The popular SAT test we give college-bound high school students and even IQ tests tend to define intelligence
in this way, that is, by a list of things a creature can do. Although an amorphous fundamental capability may conceivably underlie the list, such test-based definitions of intelligence are inherently circular. We say a human is more intelligent than a chimp because he can make Coq au Vin but then in our next breath say the reason he can make Coq au Vin is because he is more intelligent. This is analogous to sports announcers who occasionally say that such and such a player is a better athlete because he
has more athleticism.
      Please note that I am doing two things here. I am listing the underlying capabilities that make a human more intelligent than a chimp and I am limiting that list to just one thing, natural syntactic language. I have no illusions that this is an easy sell, for if it were I wouldn’t be writing these books. The point of putting this outrageous claim upfront is to let you see just how different my ideas are from what you are used to.
      There is virtually no such thing as an isolated fact in science or in any unified body of knowledge. Consequently my little claim is not a little claim at all. It has ripples that spread throughout all aspects of theories of the human mind. That’s why we will have to deal with some fundamental issues in the science of the mind in order to understand and evaluate my simple, outrageous claim. That, by the way, is where the fun lies. In trying to understand the mind from a new perspective we will have to reexamine many cherished views of what it means to be a human and what it means to be an animal. This is not an activity that the fainthearted would enjoy but I suspect that you are up to the challenge, else you wouldn’t have started to read this book in the first place. I’d like to say something else about my claim that the significant cognitive difference between humans and chimps is language. Obviously there are lots of differences between them such as chimps have more hair. So my claim is that these other differences are not necessary to explain the differences between the minds of chimps and humans. I want to identify just those differences that are absolutely necessary and that are sufficient to explain the crucial aspects of chimp and human thinking. Many of the physical differences between chimps and humans are important and greatly contribute to our understanding of ourselves and our place in the world. Our dexterous hands, for example, allow us to do many things and this contributes to our so-called intelligent behavior. Our dexterous hands also have a decisive role to play in how language is processed in our brains. However, a creature with dexterous hands but without syntactic language would not be able to create modern civilization.
      You might well be thinking that you get the gist of what I believe but now you want to know why I believe it. That’s a fair simple question but I can’t give you a fair simple answer. I have developed a new perspective on human and animal cognition and I need to explain and justify that entire perspective. That is what these three books are about. I realize that when we teach traditional concepts in any domain we do so in a style reminiscent of a religious catechism. However, that pedagogical style only works for well-known and accepted ideas. The answers given to simple questions are not really answers at all but placeholders for more complex answers that take considerable effort to develop and understand.
      What I will do now is mention some of the other cherished beliefs that I will attempt to dissuade you of. Many readers will have heard that natural language such as English has a surface structure and a deep structure. The surface structure is the “i before e, except after c” variety we were taught in grade school and is often called the grammar of a language. The deep structure is often called the formal syntax of a language and is what linguists study. Most linguists believe that this syntax is a formal system, much like a computer’s formal programming languages. The human mind is thus also seen as a formal computational system. I claim that this is not true. I believe that the mind is inherently an analog system, more like a steam engine than a computer. Things do things to things rather than things following a procedure specified by rules as in a game of cards.
      Another arena that we will reevaluate surrounds Skinner’s stimulus response theories. It’s clear to virtually all of us that Skinner’s theories are naive when applied to humans, yet in applying them to nonhuman animals we need to be more cautious. Although many scientists have said that animals are too intelligent for stimulus response theories to be true, no one has made a proposal delineating what thinking mechanisms might be operating for the trillions of trillions of the earth’s simpler creatures. I will attempt to persuade you that stimulus response processes cover a great deal of animal cognition but we will ultimately see that they are not sufficient. I will need to posit two additional cognitive features. One feature has to do with how a creature represents what is out there and the second has to do with the ability to “plan and evaluate” the very next motor action a creature might make. I put “plan and evaluate” in quotes because how animals do it is significantly different from how you and I do it using language. The simple process I will present to you eliminates the need to endorse a theory of animal thinking based on formal computational processes. Understanding how animals think turns out to be absolutely essential to understanding how humans think and my theory fills that need. This first book in the series of three books addresses the cognition of creatures without language and that part of human cognition not supported by a natural language such as French, Finnish or Farsi.
      Moving forward at warp speed, I want to say something about Mr. Ithink- therefore-I-am. Descartes wrote obscurely about how the mind and the body are based on separate “substances.” Today most scientists find Descartes’ discussion of the soul and “mind” to be arcane and perhaps even silly. We all know that the physical brain is where it’s at and most of us avoid spiritualist theories. However, I am going to suggest an alternative interpretation of Descartes’ theories. Descartes and I share an intuition about how human cognition has two fundamentally different modes. I am going to align Descartes’ idea of the body with the eye-blink and searchfor- sex-and-food aspect of human and animal cognition and I am going to align Descartes’ idea of the mind with the build-airplanes and write-novels aspect of human language-based cognition. I’m not going to cite Descartes as an authority but rather as a friend who has been deeply misunderstood. The role of language in human cognition is addressed in the second of the three books.

CHAPTER 2 Back to top

As you know, associations are mental connections between sensations, ideas, or memories. Associations are also an important form of pattern classification. You might think that pattern classification only refers to visual forms we see in space or on a piece of paper, but I’d like you to think otherwise.
      Associations are based on our experiences and much of our non– language-based knowledge of the world is built upon them. The classic example of association is Pavlov’s experiments with dogs. In Pavlov’s famous experiment, he rang a bell when the dog was being fed. As time went by, the dog salivated and drooled when it heard the bell. The dog had come to associate the sound of the bell with the food the dog was about to be served.
      Animals make similar associations when something they eat makes them sick. The animal associates that food with its sickness and will avoid it in the future. The animal has no way of knowing for sure that eating that particular food was the cause of his getting sick. If an experimenter induces sickness by means of a drug that the animal is unaware of and timed with the eating of the unfamiliar food then the animal will erroneously avoid that food in the future. Some of us cringe when we see a flash of bright lightning because we have learned to associate it with the loud thunder that usually follows it.       Associations are not always helpful or pleasant. I have heard examples of people who have received chemotherapy for the treatment of cancer. These patients have sometimes reacted strongly, even involuntarily vomiting, when they approached the building in which they received treatment or upon seeing a stranger who they momentarily thought was their doctor.
We often think of pattern classification as operating in just one sensory domain. We recognize the visual pattern of a set of stripes on an animal as being those of a zebra or we recognize a sequence of acoustic inputs as the sound of the dinner bell. Associations, however, often cross sense modalities.
We see and then eat the food; later we feel a pain in our stomach. We see the lightning, then we hear the thunder. Learning the pattern, “lightning-thunder,” is what we mean by associating the sense input from the lightning with the sense input from the thunder. Associations often occur over time, linking something that occurs in the present with something that is likely to occur in the future. Associations provide a heads-up signal that allows one to prepare for what is coming next. If I hear the lion’s growl, I associate that with the big aggressive lions I have seen, and I seek cover as if I had actually seen the lion.
      We can think of associations as classifying patterns that exist in two different sensory modalities at two different times. I learn the pattern of the sound of the rattlers on the snake and the later pain of being bitten by the snake. I therefore think of associations as a kind of pattern classification.
       Humans use associations to make sense of the world. We learn that our typing gets better if we practice. We notice that the brownies we make are better if we don’t bake them too long. We often have explanations for why things work this way: “Practice makes perfect,” or “Baking things too long dries them out.” Such explanations often come after we have made the associations. We use mnemonic devices to help us remember our associations and to communicate them to other people. In other words, we often use experience, not reason, to arrive at insight. Our gut reactions mirror the primary cognitive processes of non–language-using animals. The secondary or language aspect of our cognition often kicks in after the fact. Of course, the experience that creates associations is not our only teacher. We do have language based models and theories and these are an entirely different aspect of human cognition. But for now we limit our attention to the primary cognitive processes that we share with many non-human creatures.
      Another example of the importance of associations is how coincidence plays such a strong role in our beliefs. If I turn a light switch on at the same time my neighbor turns on her radio, I am momentarily convinced that I caused her radio to turn on. That belief is just there, I don’t have to ponder it. In fact, I have to ponder it to dispel the belief. It is, after all, just an unlikely coincidence. This same mechanism is what convinces people that if a stranger was in town when a crime was committed then the stranger must have committed the crime. Isn’t this part of where our religious and racial prejudices come from? Many of our personal beliefs are based on the perception of patterns that in fact are only partially suggestive. Not too long ago before the rise of general education many people really did believe that bad luck would follow if a black cat crossed their path. Superstitions are often based on people’s observation of patterns. However, it sometimes only takes two crossings of a black cat and a person’s finding something bad in his life to make a connection, especially when his friends hold the same belief.
       I’d now like to consider some mental behavior that may not at first seem to be based on the ability to make associations. How much is four times five? Did you calculate it or did you just know? Most of us memorized the multiplication table in elementary school. But what goes on in your brain when you recall the answer to a simple arithmetic question? If you are familiar with simple computer procedures you might have said, “Well, maybe, it’s a table lookup.” With a table lookup procedure you do not compute the answer but find an answer that was previously computed. A table is a way of representing, presenting, or storing information. Your grade school multiplication table had rows and columns. The row starting with a “4” and the column starting with a “5”, intersected at a cell of the table with a “20” in it. A table could also be a long list with an entry beside each item in the list—like a telephone book. You list the names in alphabetical order and place the person’s telephone number next to his or her name. That is how computers store data. Each place on the list (person’s name) corresponds to a storage register in the machine, and the related item (telephone number) is the contents of the storage register. Such a computational scheme is also a possible model of how the brain stores information.
       However, there is little evidence to support the table lookup model. The simplistic computer metaphor breaks down here. Believing in that simple metaphor is tantamount to expecting to find gears inside your
arm because that is how we would build a robotic arm. It’s easy for a machinist to make gears out of metal, but it is very hard for a biologic system to grow gears. More importantly, we just haven’t found any gears in any body’s arms. Looking for storage registers in the neural tissue of a brain is a much more difficult matter. We can’t imagine how millions of registers would be organized, how they would be addressed, or how their contents would be encoded. Furthermore, we haven’t found any biological brain structures that are possible candidates. Consequently, models of the human body, brain, or mind should not be restricted to mechanisms or procedures that engineers use to build physical devices. For now at least, let’s reject the idea of a computerlike table lookup in the brain as a model for how we know our multiplication tables.
       As an alternative, I suggest that we regard the process of retrieving an entry to the multiplication table as a straightforward pattern-classifiPATTERN cation task. One’s brain instantaneously recognizes the sequence “4 x 5” and associates it with the number “20.” In fact, we can imagine a cognitive process that recognizes the following arithmetic problems as all belonging to the same class: 4 x 5, 5 x 4, 2 x 10, and 10 x 2. When this class of number patterns is recognized, the process triggers the associated “answer”: 20. Our brains handle much language-based and non–language-based cognition in the same basic manner that it handles the classification and association of patterns.
      Another area we might not immediately think of as involving pattern recognition is body motor control, such as wiggling your ears or learning to shoot a basketball through a hoop. In both cases, we tend to learn by trial and error. We try to repeat a movement that appears to work. Part of this repetition involves recognition of our own muscle and body movement and trying to duplicate the motion, perhaps with a slight variation. Even a simple motion such as walking down stairs involves keeping our balance by classifying inputs from our body’s position sensors. Thus, body awareness is a form of pattern classification. If you have ever tried to learn to walk down stairs using crutches, you might recall how much attention and care that took the first few times. In addition, watch a toddler during
his first month of toddling and you may gain an appreciation of the complexity of the task of walking.
      Pattern classification and association of sensory experiences are fundamental processes involved in animal and human cognition. This mode of cognition is quite distinct from high-level cognition involving spoken or written language. To learn more about pattern classification and association as models of human and animal cognition we need to examine some details of how classification works. The best way to do this is to study how an abstract machine might accomplish the task—not because the brain would do it the same way but because the nature of the task is the same and easier to understand with simple examples.

CHAPTER 4 Back to top

      Almost all creatures have the apparently bland ability to recognize objects. Even ants with their tiny hundred-thousand (105)-neuron brains apparently do it. (An ant’s brain is tiny relative to a human’s brain. The human brain, with about 1012 neurons, is ten million times bigger than an ant’s brain.) Sensory perception deals with how the eye and the visual cortex conspire to make objects more salient in the visual field. While this is an interesting topic, I wish to focus our attention on the higher mental processes that work with the recognized objects
      As we have discussed previously, mental representation92 is key to understanding cognition and I propose that object representation is the cornerstone that enables animal cognition. Derek Bickerton in his great book, Language and Species, refers to this ubiquitous mental capability as the primary representation system (PRS). He writes:
      It follows that what is presented to any species, not excluding our own, by its senses is not ‘reality’
      but a species-specific view of reality—not ‘what is out there,’ but what is useful for the species to       know about what is out there.93

Object Cognition Is a Big Deal
      The insight that object cognition is a big deal comes from engineers who try to make computer image processing systems that can understand twodimensional images.94 We see trees, trucks, people, and sky in a picture but the computer only sees splotches of brown, green and blue. It has taken thousands of engineers, each working tens of thousands of hours to devise partially successful computer algorithms to make sense of common visual scenes. Your brain’s only input about the visual world is through your eyes. The retina of your eye has the same splotches of brown, green and blue that a simple photograph or TV image has. We do not need to understand the details of the algorithms that engineers use to process visual scenes or the neural processing in the brain. We only need to note that the brain does this totally amazing job of image interpretation effortlessly and fast. By the way, not all biological creatures parse their images into objects. Very simple creatures can be attracted or repelled by certain smells, sounds, or light. In their simple environment, the specific sensation may be due to some object but the simple sensory cue is sufficient to allow them to make life-enhancing decisions.
      Some creatures may have very fragmentary representation of objects. For example, researchers have induced bees to attempt to pollinate from a two-dimensional unstructured yellow disk with a cup of nectar in its center. Apparently bees do see just a vaguely compact shape of color and are not fussy about what sort of thing they interpret as being a flower. Another example is the various contraptions people place in their gardens to keep the birds from eating their seeds and young plants. A shirt stuffed with straw, called, of course, a scarecrow, often scares crows, and pie tins tied to poles move in the breeze and create light and sound that ward off many birds. These last examples illustrate that mentally representing objects admits a continuum of specificity. I don’t know if the crow responds to the shape of the scarecrow, its size, its array of colors, or to all of these features.
      Let’s try to imagine a second world that does not have objects on the scale easily observed by human beings. Imagine an exploding galaxy where there are random formations of colored and luminescent gas. In this imaginary world, objects, as we normally think of them, do not exist. The visible patterns of gas continually change just as fog and clouds in our 138 I Am Not a Machine world continually change shape. Sometimes in our world, we see a cloud whose shape resembles a whale or a human face but then we blink and the cloud looks different. In such an imaginary objectless environment, a device—be it a brain or a computer—designed to recognize patterns and identify objects would be useless. The patterns would be shifting and they would not be important to survival. A person in our real world who is trying to predict the weather or fly an airplane through clouds doesn’t care if the cloud looks like a whale or a face. Shapes are only important for reasonably stable tangible objects.

Parsing Images
      Back on planet earth, we have things like tigers that want to eat us and trout that we may want to eat. It is very important that the animal brain recognizes and deals with these objects. These physical objects in the real world are the source of those splotches of color on our retina. Over millions of years, brains have evolved structures for parsing retinal images, that is, breaking them into parts that correspond to those external objects. We don’t see splotches; we see trees and houses. By comparison, very simple biological organisms, such as bacteria that respond to temperature and chemical variations in their environment, do not have the ability to represent objects.
      Let’s push this idea a bit further. Imagine walking through a jungle for the first time. You may not recognize many objects. You may not even notice that the object in front of you is not a tree limb but a snake poised to strike. There are lots of strange sounds and smells as well. This is the sensory confusion we can imagine a newborn infant experiencing. Eventually we learn to associate a certain smell with the sight of a dead animal, and a certain rattle and hiss with a deadly kind of snake. Our ability to parse scenes also works for sounds and smells as well. Furthermore, and this is astounding, a smell, sound, and image can become associated with a single object. We do not say to ourselves after some intellectual exercise that a certain rank smell, a barking sound, and the image of a shaggy four-legged medium-sized creature must all be from the same source; no, we just know it’s our dog that needs a bath and wants dinner. Our inputs from several senses are combined and automatically attributed to a single source. No big deal? Yes, big deal.

Signal Fusion
       Engineers who have been pulling their hair out trying to get machines
to do this signal combining effectively call the process signal fusion. Signal fusion simply means seamlessly merging signal streams from different sensors to produce a unified understanding of the source of the signals. Signal fusion is really a kind of association process. We learn with experience that the lightning we see is associated with the thunder we hear a few seconds later. We attribute both sensory inputs to a single electrical phenomenon, a single source. We internally represent the source of both sensory signal streams with a single “virtual object.” I am using the word virtual to convey the extended use of the word object. Our brains have evolved to represent objects and they do it very well. They do it particularly well in the natural world where they were designed to work and they did it millions of years before they evolved to handle language.

Cogjects Defined
Now that we have begun to understand the recognition of unique objects and classes of objects, as well as the parsing and fusion of sensory signals, let’s hypothesize that objects are represented as discrete entities, that is, individually distinct items in our brains. To highlight this idea of a virtual mental object I am now going to give it a name. I call these cognitive objects cogjects and present them in the same spirit that we have come to accept gravity and electromagnetic radiation as useful constructs. We know how they work, but we don’t know what they really are. In a sense that I will clarify later, the cogject is more than a “percept” (an impression of an object obtained by the senses) but is less than a “concept” (something conceived in the mind or an abstract or generic idea generalized from particular instances95).       The intent of this discussion is to show how cogjects work in various cognitive processes and how they can be used to model cognition in general. By giving something a name we can focus our attention on it and learn some of its attributes. Think of an abstract concept such as inflation, which is now a household word that we know a great deal about. Simply giving a name to something does not in itself solve any problems but it can start a learning process that leads to new ideas and solutions.



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