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Roger Shepard

Roger Shepard is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Roger Shepard rather than just read about it. In short: Roger Newland Shepard (January 30, 1929 – May 30, 2022) was an American cognitive scientist and author of the "universal law of generalization" (introduced in 1987). He was considered a father of research on spatial relations.

Roger Shepard — main illustration
Roger Shepard — illustration

Key takeaways

  • Roger Shepard belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Roger Shepard to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Roger Shepard from memory before moving on to harder problems.

Reference excerpt

Roger Newland Shepard (January 30, 1929 – May 30, 2022) was an American cognitive scientist and author of the "universal law of generalization" (introduced in 1987). He was considered a father of research on spatial relations. He studied mental rotation, and was an inventor of non-metric multidimensional scaling, a method for representing certain kinds of statistical data in a graphical form that can be comprehended by humans. The optical illusion called Shepard tables and the auditory illusion called Shepard tones are named for him.

Biography Shepard was born January 30, 1929, in Palo Alto, California. His father was a professor of materials science at Stanford. As a child and teenager, he enjoyed tinkering with old clockworks, building robots, and making models of regular polyhedra. He attended Stanford as an undergraduate, eventually majoring in psychology and graduating in 1951. Shepard obtained his Ph.D. in psychology at Yale University in 1955 under Carl Hovland, and completed post-doctoral training with George Armitage Miller at Harvard. Subsequent to this, Shepard was at Bell Labs and then a professor at Harvard before joining the faculty at Stanford University. Shepard was Ray Lyman Wilbur Professor Emeritus of Social Science at Stanford University. His students include Lynn Cooper, Leda Cosmides, Rob Fish, Jennifer Freyd, George Furnas, Carol L. Krumhansl, Daniel Levitin, Michael McBeath, and Geoffrey Miller. In 1997, Shepard was one of the founders of the Kira Institute.

Research

Generalization and mental representation

Shepard began researching mechanisms of generalization while he was still a graduate student at Yale: I was now convinced that the problem of generalization was the most fundamental problem confronting learning theory. Because we never encounter exactly the same total situation twice, no theory of learning can be complete without a law governing how what is learned in one situation generalizes to another.

Shepard and collaborators "mapped" large sets of stimuli using the rank order of likelihood that a person or organism would generalize the response to Stimulus A and give the same response to Stimulus B. To use an example from Shepard's 1987 paper proposing his "Universal law of generalization": will a bird "generalize" that it can eat a worm slightly different from a previous worm that it found was edible? Shepard used geometric and spatial metaphors to map a psychological space where "distances" between different stimuli were larger or smaller depending on whether the stimuli were, respectively, less or more similar. These imaginary distances are interesting because they permit mathematical inferences: the "exponential decay" in response to stimuli based on the distance holds valid for a wide range of experiments with human beings and with other organisms.

Non-metric multidimensional scaling In 1958, Shepard took a job at Bell Labs, whose computer facilities made it possible for him to expand earlier work on generalization. He reports, "This led to the development of the methods now known as non-metric multidimensional scaling – first by me (Shepard, 1962a, 1962b) and then, with improvements, by my Bell Labs mathematical colleague Joseph Kruskal (1964a, 1964b)." According to the American Psychological Association, "nonmetric multidimensional scaling .. has provided the social sciences with a tool of enormous power for uncovering metric structures from ordinal data on similarities."

Awarding Shepard its Rumelhart Prize in 2006, the Cognitive Science Society called non-metric multidimensional scaling a "highly influential early contribution," explaining that:This method provided a new means of recovering the internal structure of mental representations from qualitative measures of similarity. This was accomplished without making any assumptions about the absolute quantitative validity of the data, but solely based on the assumption of a reproducible ordering of the similarity judgements.

Mental rotation

Inspired by a dream of three-dimensional objects rotating in space, Shepard began in 1968 to design experiments to measure mental rotation. (Mental rotation involves "imagining how a two- or three-dimensional object would look if rotated away from its original upright position.") The early experiments, in collaboration with Jacqueline Metzler, used perspective drawings of very abstract objects: "ten solid cubes attached face-to-face to form a rigid armlike structure with exactly three right-angled 'elbows,'" to quote their 1971 paper, the first report of this research. Shepard and Metzler were able to measure the speed with which subjects could imagine rotating these complicated objects. Later work by Shepard with Lynn A. Cooper illuminated the process of mental rotation further. Shepard and Cooper also collaborated on a 1982 book (revised 1986) summarizing past work on mental rotation and other transformations of mental images.

Reviewing that work in 1983, Michael Kubovy assessed its importance:Up to that day in 1968 [Shepard's dream about rotating objects], mental transformations were no more accessible to psychological experimentation than were any other so-called private experiences. Shepard transformed a compelling and familiar experience into an experimentally tractable problem by injecting it into a problem-task that admits of a correct and incorrect answer.

Optical and auditory illusions

In 1990, Shepard published a collection of his drawings called Mind Sights: Original visual illusions, ambiguities, and other anomalies, with a commentary on the play of mind in perception and art. One of these illusions ("Turning the tables," p. 48) has been widely discussed and studied as the "Shepard tabletop illusion" or "Shepard tables." Others, such as the figure-ground confusing elephant he calls "L'egs-istential quandary" (p. 79) are also widely known. Shepard is also noted for his invention of the musical illusion known as Shepard tones. He began his research on auditory illusions during his years at Bell Labs, where his colleague Max Mathews was experimenting with computerized music synthesis (Mind Sights, page 30.) Shepard tones give an illusion of constantly increasing pitch. Musicians and sound-effect designers use Shepard tones to create some special effects.

… excerpt ends here. Continue reading the full article.

Illustrations

Roger Shepard illustration
Roger Shepard: Bird with earthworm: Explaining stimulus generalization, Shepard gives the example of bird that has experience of one previous worm generalizing to decide if another worm is edible.
Bird with earthworm: Explaining stimulus generalization, Shepard gives the example of bird that has experience of one previous worm generalizing to decide if another worm is edible.
Roger Shepard: Mental rotation task. Cube assemblages based on test drawings used by Shepard and Metzler. Two-dimensional figures similar to those used in work by Shepard and Cooper.
Mental rotation task. Cube assemblages based on test drawings used by Shepard and Metzler. Two-dimensional figures similar to those used in work by Shepard and Cooper.
Roger Shepard: Shepard tables illusion: The two "tabletops" are identical parallelograms
Shepard tables illusion: The two "tabletops" are identical parallelograms

Worked examples

Example 1 — a first encounter with Roger Shepard

Start with the simplest possible case. Write down what Roger Shepard claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Roger Shepard before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Roger Shepard ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Roger Shepard

In research
Roger Shepard appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Roger Shepard in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Roger Shepard is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1929 births, 2022 deaths, APA Distinguished Scientific Award for an Early Career Contribution to Psychology recipients, so understanding it makes those chapters shorter.
In everyday life
Look for Roger Shepard outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Roger Shepard in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Roger Shepard means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Roger Shepard out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Roger Shepard in simple terms?

Roger Newland Shepard (January 30, 1929 – May 30, 2022) was an American cognitive scientist and author of the "universal law of generalization" (introduced in 1987). He was considered a father of research on spatial relations.

Why does Roger Shepard matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Roger Shepard?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Roger Shepard.

Tags

  • 1929 births
  • 2022 deaths
  • APA Distinguished Scientific Award for an Early Career Contribution to Psychology recipients
  • American cognitive scientists
  • American music psychologists
  • Fellows of the Cognitive Science Society
  • Members of the American Philosophical Society
  • Members of the United States National Academy of Sciences
  • National Medal of Science laureates
  • Rumelhart Prize laureates
  • Scientists at Bell Labs
  • Scientists from California

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