The Story of Psychology (100 page)

Computer science had by far the greatest impact on psychology. This new field was the product of intense research during World War II, when Allied forces urgently needed calculating machines that could rapidly handle large sets of numbers to direct antiaircraft guns, operate navigation equipment, and the like. But even very high-speed calculating machines needed to be told by a human operator, after each calculation, what to do next, which severely limited their speed and introduced inaccuracies. By the late 1940s, mathematicians and engineers were starting to provide the machines with sets of instructions (programs) stored in their electronic memories. Now the machines could swiftly and accurately guide their own operations, carry out lengthy sequences of operations, and make decisions about what needed to be done next. The calculating machines had become computers.

At first, computers dealt only with numerical problems. But as the mathematicians John von Neumann and Claude Shannon and other computer experts soon pointed out, any symbol can represent another kind of symbol. A number can stand for a letter and a series of numbers for a word, and mathematical computations can represent relationships expressed by language. For instance, [H11005] can stand for “is the same as,” [HS11005] for “is not the same as,” > for “more than” or “too much.” Given a set of rules by which to turn words into numbers and algebraic relationships and then back into words, a computer can perform operations analogous to some kinds of human reasoning.
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In 1948 the idea that the computer might in some ways function like
a mind—at the time this seemed more like science fiction than science—was first broached by von Neumann and the neurophysiologist Warren McCulloch at a California Institute of Technology conference, “Cerebral Mechanisms in Behavior.”

That notion captivated Herbert Simon, then a young professor of political science at the Carnegie Institute (now Carnegie-Mellon University).
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“Professor of political science” hardly describes him, however. Simon, the son of an electrical engineer, was so bright that he was skipped in school and was considerably younger than his friends and classmates. Add to that his being unathletic and growing up in Wisconsin keenly aware of his Jewishness, and it is not surprising that he solaced himself by becoming an exceptional student. In college he liked to think of himself as an intellectual, but in fact his interests were freakishly wide-ranging; although he became a political scientist, he was interested and self-taught in mathematics, economics (for which he was awarded a Nobel Prize in 1978), administration, logic, psychology, and computer science.

In 1954, Simon and a brilliant young graduate student of his, Allen Newell, discovered that they shared passionate interests in computers and thinking (both men later earned degrees in psychology), and in creating a computer program that would think. For a first attempt, they chose a very limited kind of thinking, namely, proving theorems in formal logic, an entirely symbolic and almost algebraic process. Simon’s task was to work out proofs of theorems while “dissecting as minutely as possible, not only the proof steps, but the cues that led me to each one.” Then the two men together tried to incorporate this information in a flow diagram that they could turn into a computer program.

After a year and a half of work, Simon and Newell electrified the audience at a 1956 symposium on information theory at MIT with a description of their intellectual offspring, Logic Theorist. Running on JOHNNIAC, a gigantic, primitive, vacuum-tube computer, it was able to prove a number of theorems in formal logic in anywhere from under a minute to fifteen minutes per proof.
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(On a modern computer it would do the same thing in virtually the blink of an eye.) Logic Theorist, the first artificial intelligence program, wasn’t very intelligent; it could prove only logic theorems—at about the same speed as an average college student—and only if they were presented in algebra-like symbols. Still, as the first computer program that did something like thinking, it was a breathtaking achievement. (George Miller was at the presentation; he regards that day as the birthday of cognitive science, even though it took him another four years to declare his apostasy from behaviorism.
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)

By the end of the following year, 1957, Newell, Simon, and a colleague, Clifford Shaw, had created a much cleverer program, General Problem Solver (GPS), which incorporated a number of broad principles common to many intellectual tasks, including proving theorems in geometry, solving cryptarithmetic problems, and playing chess. GPS would make a first move or probe to begin determining the “problem space” (the area containing all possible moves between its initial state and the desired goal), look at the result to see whether the move had brought it closer to the goal, concoct possible next moves and test them to see which one would advance it toward the goal, back up to the last decision point if the train of reasoning veered off course, and start again in another direction. A simple problem that GPS solved easily early in its career went as follows (the problem was presented not in these words, which GPS could not understand, but in mathematical symbols):

The solution, though simple, requires a seeming retreat in order to advance. The two sons get in and row across; one debarks and the other rows back and lands; the father rows across and gets out; the son on that side rows back, picks up his brother, and returns to the far shore. GPS, in devising and testing this solution, was doing something akin to human problem solving. By means of the same heuristic—a broad stratagem of exploration and evaluation—it was able to solve similar but far more difficult problems.

Two basic features of GPS and later artificial intelligence (
AI
) programs brought about a metamorphosis in cognitive psychology by giving psychologists a more detailed and workable conception of mental processes than any they had previously had, plus a practical way to investigate them.
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The first of those features is
representation:
the use of symbols to stand for other symbols or events. In GPS, numbers stand for words or relationships, and in the hardware (the actual computer) operated by GPS, groups of transistors, acting as binary switches that are either on or off, stand for those numbers. By analogy, cognitive psychologists could conceive
of the images, words, and other symbols stored in the mind as representations of external events, and of the brain’s neural responses as representations of those images, symbols, and thoughts. A representation, in other words, corresponds to the thing it represents without being at all similar to it. But this was actually an old discovery in new form; Descartes and Fermat discovered long ago that algebraic equations can be represented by lines drawn on a graph.

The second feature is
information processing:
the transforming and manipulating of data by the program in order to achieve a goal. In the case of GPS, incoming information—the feedback of each step—was evaluated as to where it had led, used to determine the next step, stored in memory, retrieved if needed again, and so on. By analogy, cognitive psychologists could conceive of the mind as an information-processing program that transforms perceptions and other incoming data into mental representations and, step by step, evaluates them, uses them to determine what to do next in the attempt to reach its goal, adds them to memory, and retrieves them for use again as needed.

The information-processing (IP) or “computational” model of thinking has been the guiding metaphor of cognitive psychology ever since the 1960s, and has enabled researchers and theorists to explore the inner universe of the mind as never before.

One specimen of such an exploration will exemplify how the IP model enables cognitive psychologists to ascertain what takes place in the mind. In a 1967 experiment, a research team headed by Michael Posner asked its subjects to say aloud, as fast as possible, whether two letters projected on a screen had the same or different names. When the subjects saw this

AA

they almost instantly said “Same,” and when they saw this

Aa

they again almost instantly said “Same.” But the researchers, using a highly accurate timer, measured a minuscule difference. On average, subjects replied to AA in 549 milliseconds and to Aa in 623 milliseconds. A tiny difference, to be sure—but a statistically significant one.
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What could account for it?

The IP model envisions any simple cognitive process as a series of step-by-step actions performed on the data. The following simple flow diagram, typical of many drawn by cognitive psychologists, symbolizes what goes on when we see and recognize something:

FIGURE 39
A typical information-processing diagram

That accounts for the reaction-time difference in the experiment. If an image proceeds directly from the first “processing” box to “consciousness,” it does so in less time than when it must pass through two or three boxes. In order to identify the letters in AA as having the same name, subjects had to perform only visual pattern recognition on the visual image; to identify those in Aa as having the same name, they had to locate the name of each letter in memory and then see whether they were the same—additional processing that took 74 milliseconds more, a tiny but consequential difference, and strong evidence of how the mind performed this little task. In a follow-up experiment subjects had to say whether AU were both vowels, and in another whether SC were both consonants; the AU response took somewhat longer than AA or Aa had, and SC much longer (nearly a second). Again, these longer reaction times indicated that more steps of mental processing were required.
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Thus even trifling experiments based on the IP model can reveal something of what goes on in the mind.

To be sure, the finding is an inference from results, not a direct observation of the process. But contrary to behaviorist dogma, inference of an unseen process from results is considered legitimate in the “hard” sciences. Geologists infer the events of the past from sediment layers, cosmologists the formation and development of the universe from the ancient light of distant galaxies, physicists the characteristics of short-lived atomic particles from tracks they leave in a cloud chamber or emulsion, and biologists the evolutionary path that led to
Homo sapiens
from fossils. So, too, with the interior universe of the mind: psychologists cannot voyage into it, but they can deduce how it works from the track, so to speak, made by an invisible thought process.

What, another revolution so soon?

Well, not on the heels of the cognitive revolution, but not far behind it. This one, though long gathering force, would not burst forth until the 1980s, but we must look ahead to its emergence because much of what we will see happening in cognitive psychology will be affected by it. It was the cognitive neuroscience revolution.

That’s a relatively new name for an old school of thought about the mind, the biological approach to mental processes that sought to explain them in terms of neuronal processes and events. We saw a notable example of it in Hubel and Wiesel’s discoveries of retinal cells that respond only to specific shapes or directions of motion. That was recent, but the neuroscientific approach has antecedents going back at least to Descartes. Although he believed in the immateriality of mind, he conjectured, as we saw, that reflexes were caused by the flow of “animal spirits” through the nervous system, much as the movements of automata in the royal gardens were caused by the flow of water in pipes, and that memory was the result of the widening of the particular “pores of the brain” through which animal spirits had passed during learning.
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Similarly, a century ago the young Freud confidently asserted that all psychological processes could be understood as “quantitatively determined states” of the neurons, though he soon admitted with chagrin that the time was not ripe for such understanding.

The same hope, though, had continued to inspire many researchers. And during the past sixty years, and especially the past twenty-five, extraordinary advances in cognitive neuroscience have led some enthusiasts to assert that it will soon replace the psychological approach to the mind and that concepts such as needs, emotions, and thoughts will be replaced by physiological data. When such data are available, Paul Churchland, a philosopher of neuroscience, asserted in 1984,