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Computer Models of Thought and Language : Kenneth Mark Colby :

Propelled by subsequent advances in both computer technology and brain research, an ambitious multidisciplinary effort to understand human intelligence gradually developed, firmly rooted in the idea that humans are, like computers, information processors. This effort now involves thousands of researchers, consumes billions of dollars in funding, and has generated a vast literature consisting of both technical and mainstream articles and books. The information processing IP metaphor of human intelligence now dominates human thinking, both on the street and in the sciences.

There is virtually no form of discourse about intelligent human behaviour that proceeds without employing this metaphor, just as no form of discourse about intelligent human behaviour could proceed in certain eras and cultures without reference to a spirit or deity. And like all the metaphors that preceded it, it will certainly be cast aside at some point — either replaced by another metaphor or, in the end, replaced by actual knowledge.

They saw the problem. It encumbers our thinking with language and ideas that are so powerful we have trouble thinking around them. The faulty logic of the IP metaphor is easy enough to state. It is based on a faulty syllogism — one with two reasonable premises and a faulty conclusion. Reasonable premise 1: all computers are capable of behaving intelligently. Reasonable premise 2: all computers are information processors. Faulty conclusion: all entities that are capable of behaving intelligently are information processors.

Setting aside the formal language, the idea that humans must be information processors just because computers are information processors is just plain silly, and when, some day, the IP metaphor is finally abandoned, it will almost certainly be seen that way by historians, just as we now view the hydraulic and mechanical metaphors to be silly. If the IP metaphor is so silly, why is it so sticky? What is stopping us from brushing it aside, just as we might brush aside a branch that was blocking our path? Is there a way to understand human intelligence without leaning on a flimsy intellectual crutch?

And what price have we paid for leaning so heavily on this particular crutch for so long? The IP metaphor, after all, has been guiding the writing and thinking of a large number of researchers in multiple fields for decades. At what cost? When the student has finished, I cover the drawing with a sheet of paper, remove a dollar bill from my wallet, tape it to the board, and ask the student to repeat the task. When he or she is done, I remove the cover from the first drawing, and the class comments on the differences.

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Because you might never have seen a demonstration like this, or because you might have trouble imagining the outcome, I have asked Jinny Hyun, one of the student interns at the institute where I conduct my research, to make the two drawings. Jinny was as surprised by the outcome as you probably are, but it is typical.

As you can see, the drawing made in the absence of the dollar bill is horrible compared with the drawing made from an exemplar, even though Jinny has seen a dollar bill thousands of times. What is the problem?


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Obviously not, and a thousand years of neuroscience will never locate a representation of a dollar bill stored inside the human brain for the simple reason that it is not there to be found. A wealth of brain studies tells us, in fact, that multiple and sometimes large areas of the brain are often involved in even the most mundane memory tasks. When strong emotions are involved, millions of neurons can become more active. The idea, advanced by several scientists, that specific memories are somehow stored in individual neurons is preposterous; if anything, that assertion just pushes the problem of memory to an even more challenging level: how and where, after all, is the memory stored in the cell?

So what is occurring when Jinny draws the dollar bill in its absence? If Jinny had never seen a dollar bill before, her first drawing would probably have not resembled the second drawing at all. Having seen dollar bills before, she was changed in some way. Specifically, her brain was changed in a way that allowed her to visualise a dollar bill — that is, to re-experience seeing a dollar bill, at least to some extent. The difference between the two diagrams reminds us that visualising something that is, seeing something in its absence is far less accurate than seeing something in its presence.

Perhaps you will object to this demonstration. Had she done so, you might argue, she could presumably have drawn the second image without the bill being present. She has simply become better prepared to draw it accurately, just as, through practice, a pianist becomes more skilled in playing a concerto without somehow inhaling a copy of the sheet music. As we navigate through the world, we are changed by a variety of experiences. Of special note are experiences of three types: 1 we observe what is happening around us other people behaving, sounds of music, instructions directed at us, words on pages, images on screens ; 2 we are exposed to the pairing of unimportant stimuli such as sirens with important stimuli such as the appearance of police cars ; 3 we are punished or rewarded for behaving in certain ways.

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Misleading headlines notwithstanding, no one really has the slightest idea how the brain changes after we have learned to sing a song or recite a poem. Is this really how the brain works? The brain is infinitely more powerful and flexible than the most advanced computer. Machine Reductionism Critical Evaluation. Skinner criticizes the cognitive approach as he believes that only external stimulus-response behavior should be studied as this can be scientifically measured.

Therefore, mediation processes between stimulus and response do not exist as they cannot be seen and measured. Skinner continues to find problems with cognitive research methods, namely introspection as used by Wilhelm Wundt due to its subjective and unscientific nature. Humanistic psychologist Carl Rogers believes that the use of laboratory experiments by cognitive psychology have low ecological validity and create an artificial environment due to the control over variables. Rogers emphasizes a more holistic approach to understanding behavior.

The information processing paradigm of cognitive psychology views that minds in terms of a computer when processing information. However, although there are similarities between the human mind and the operations of a computer inputs and outputs, storage systems, the use of a central processor the computer analogy has been criticised by many. Such machine reductionism simplicity ignores the influence of human emotion and motivation on the cognitive system and how this may affect our ability to process information.

Behaviorism assumes that people are born a blank slate tabula rasa and are not born with cognitive functions like schemas , memory or perception. The cognitive approach does not always recognize physical re: biological psychology and environmental re: behaviorism factors in determining behavior.

Cognitive psychology has influenced and integrated with many other approaches and areas of study to produce, for example, social learning theory , cognitive neuropsychology and artificial intelligence AI.

Another strength is that the research conducted in this area of psychology very often has application in the real world. The basis of CBT is to change the way the persons processes their thoughts to make them more rational or positive. Atkinson, R. Chapter: Human memory: A proposed system and its control processes. In Spence, K. The psychology of learning and motivation Volume 2. New York: Academic Press. Beck, A. Beck Anxiety Inventory Manual. San Antonio: Harcourt Brace and Company. Hollon, S. Cognitive and cognitive-behavioral therapies.


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Garfield Eds. New York: Wiley. An aspect of Gestalt psychology. Miller, G. While at Yale in , Schank was among the first to "capitalize on the expected boom" [8] in AI when he founded Cognitive Systems, a company that went public in Schank resigned as chairman and chief executive in for personal reasons, but stayed as a board member and advisor.

In , Schank founded Cognitive Arts Corporation [7] originally named Learning Sciences Corporation to market the software developed at ILS, and led the company until it was sold in From to , Schank was the chief learning officer of Trump University. In he founded Socratic Arts, a company that sells e-learning software to both businesses and schools.

In , Schank founded XTOL Experiential Training Online which "designs learn-by-doing experiential short courses for use by universities, corporations and professional organizations, as well as Master's programs in partnership with degree-granting universities around the world.

Computer models of thought and language

Schank believes that the educational system is fundamentally broken and that software will need to replace conventional teaching methods. In Schank introduced the conceptual dependency theory for natural language understanding.

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Other schools of CBR and closely allied fields emerged in the s, investigating such topics as CBR in legal reasoning, memory-based reasoning a way of reasoning from examples on massively parallel machines , and combinations of CBR with other reasoning methods. CBR technology has produced a number of successful deployed systems, the earliest being Lockheed's CLAVIER, [17] a system for laying out composite parts to be baked in an industrial convection oven. CBR has been used extensively in help desk applications such as the Compaq SMART system [18] and has found a major application area in the health sciences.

From Wikipedia, the free encyclopedia. Roger Schank. Carnegie Mellon University University of Texas.