Whitman Richards Whitman Richards Whitman Richards Whitman Richards

Whitman Richards

MIT 32-G364
Computer Science & AI Lab (CSAIL)
Cambridge MA 02139
617-253-5776 (voice)
617-253-8335 (facsimile)

POSITIONS (Current):
1998- Artificial Intelligence Laboratory (now CSAIL)
1994- Professor of Cognitive Science, Media Arts and Sciences
1972- Professor, Dept. Brain & Cognitive Sciences.

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(animation courtesy of Eric Saund)

Relevant Past Positions:
1993-2000 Scientific Director, Nissan Cambridge Basic Research
1992-2000 Advisory Board, Institute for Research in Cognitive Science, Univ. Penn.
1988-93 Advisory Committee, Canadian Institute for Advanced Research, AI & Robotics
1972-78 NAS-NRC Committee on Vision (Chairman 1976-77)
1953-55 Central Intelligence Agency

Main research focus has been visual perception: mechanisms and models. Beginning first with studies of early visual processing, current work is now at a very high cognitive level, with emphasis on perception as a complex system of semi-autonomous modules -- roughly akin to Minsky's "Society of Mind."

In the mid-seventies, research activity was redirected after meeting David Marr. Rather than concentrating on mechanisms of vision, the emphasis changed to understanding the minimal conditions that should be satisfied for a vision system "to work." Computational studies that met Marr's criteria turned out to be major advances in vision understanding. My contribution, together with those of my students, appears in a book called "Natural Computation", which covers work in vision, hearing, and motor control.

Since the late eighties, we've asked what it means for a machine to perceive. This has led to the study of problems in high level vision and to the question of how perceptual knowledge is represented and structured. One proactical consequence of this work was the invention of a new scaling technique, "Trajectory Mapping", which overcomes some of the limitations of traditional multi-dimensional scaling methods, and allows one to explore the "paths" that link elements in conceptual spaces. These paths seem to reflect a modal character of natural events. Understanding these types of maps has the potential benefit of revealing how cognitive representations may be organized and manipulated, giving us insights into the design of future artifacts and meaningful interfaces between mind, brain, people and machines. These more recent studies have shown the form of knowledge structures is a key to understanding rational behavior in complex, intelligent systems.


  • MURI final report(25 Oct10)

    Models for Belief Revision, Networks & Cultural Dissonance

    THESES: (Witkin)

  • Witkin: Shape from Contour (1980)

    CURRENT PUBLICATIONS: (Reprint requests: whit@csail.mit.edu)

    1. Characteristics of Small Social Networks. W. Richards and O. Macindoe. MIT-CSAIL-Tech Report 2010-033 (2010).
    2. Decomposing Social Networks. W. Richards and O. Macindoe. Proc.IEEE SocCom10 # 205, 2010 (22Aug10 Mnpls.)
    3. Graph Comparison using Fine Structure Analysis. O. Macindoe and W. Richards. Proc. IEEE SocCom10 #244, 2010.
    4. Transparency and Imaginary Colors. W. Richards, J.J. Koenderink and A. vanDoorn. J. Opt. Soc. Am A 26, 1119-1128, 2009.
    5. Representing Small Group Evolution W. Richards, N. Wormald. Proc. IEEE SocCom09 SIN09 #232 2009; also MIT-CSAIL-TR-2009-012.
    6. Modal Inference. W. Richards. Proc. AAAI Symp on "Naturally Inspired Articicial Intelligence" Tech Report FS-08-06 Nov. 2008.
    7. Does Monocular visual space contain planes?. J. J. Koenderink et al. Acta Psychologica 2009.
    8. Multi-level Cellular Automata & Social Dynamics. J. J. Koenderink and W.. Richards. Proc. IEEE SocCom09 Sin09 #240 2009. Also Poster at Ann. Meeting Cognitive Science Society, Amsterdam, 2009.
    9. Configuration Stereopsis: a new look at the depth-disparity relation. W. Richards. Spatial Vision, 22, 91-103, 2009.
    10. Neural Voting Machines. W. Richards and S. Seung. Neural Networks 19 (2006) 1161-1167.
    11. Probability of Collective Choice with Shared Knowledge Structures. W. Richards, B.D.McKay, and D. Richards. Jrl. Math. Psych. 46,338-351,2002.
    12. Categorical Representation and Recognition of Oscillatory Motion Patterns. J. Davis, A. Bobick, and W. Richards. CVPR 2000
    13. Relating Categories of Intentional Animal Motions. J. Davis & W. Richards. Ohio State Univ. Dept. Computer Sci. Tech. Report OSU-11/00-TR25 [2000]
    14. Mapping the mental space of rectangles. J. Feldman & W. Richards, Perception 27:1191-1202, 1998.
    15. Attentional Frames, Frame Curves and Figural Boundaries: The Inside/Outside Dilemma. J. B. Subirana-Vilanova & W. Richards. Vision Research 36:10 1493-1501, 1996.
    16. In Perception as Bayesian Inference Knill, D., & W. Richards (Eds.) Cambridge University Press, 1996. Chpt. 2. "Modal Structure and reliable inference" (with Allan Jepson and David Knill). Chpt. 3, "Priors, preferences and categorical percepts" (with A. Jepson & J. Feldman).
    17. Structuring information with mental models: A tour of Boston. I. Lokuge, S.A. Gilbert, and W. Richards. Proceedings of ACM SIGCHI '96, 1996.
    18. Trajectory Mapping (TM): A new non-metric scaling technique (with J.J. Koenderink). Perception, 24:1315-1331, 1995. ; also MIT AI Memo 1468 (1994); also Proc. European Conference on Visual Perception, ECVP '93, Edinburgh, August, 1993.

    1. Anigrafs: experiments in collective consciousness
    2. Perception as Bayesian Inference, D. Knill & W. Richards (Eds.) Cambridge University Press,
    3. Natural Computation, W. Richards (Ed.), M.I.T. Press, Cambridge, Mass., 1988.
    4. Image Understanding 1989, S. Ullman & W. Richards (Eds.), Ablex, Norwood, N.J., 1990.
    5. Image Understanding 1985-86, W. Richards & S. Ullman (Eds.), Ablex, Norwood, N.J., 1987.
    6. Image Understanding 1984, S. Ullman and W. Richards (Eds.), Ablex, Norwood, N.J., 1984.
    7. Recent Progress in Perception. (A Scientific American Reader.) R. Held and W. Richards (Eds.) W.H. Freeman, San Francisco, 1976.
    8. Perception: Mechanisms and Models. (A Scientific American Reader.) R. Held and W. Richards (Eds.) W.H. Freeman, San Francisco, 1972.



  • Collective Choice and Mutual Knowledge Structures D. Richards, W. Richards and B.D.McKay. Advances in Complex Systems 1: 1999 . See also Santa Fe Institute Working Paper 98-04-032, 1998.

    An Observation About Myelination. W. Richards, R. Kalil, and C.L. Moore. Exp Brain Res (1983) 52: 219-225.

    Anomalous Stereoscopic Depth Perception. W. Richards. Journal of the Optical Society of America, Vol. 61, No. 3, pp. 410-414, March 1971.

    Oculomotor Effects upon Binolcular Rivalry. W. Richards. Psychol. Forsch. 33, 136-154 (1970).

    Playing Twenty Questions with Nature. W. Richards and A. Bobick. Computational Processes in Human Vision: An Interdisciplinary Perspective, pp. 3 - 26 (1988).

    Quantifying Sensory Channels: Generalizing Colorimetry to Orientation and Texture, Touch, and Tones. W. Richards. Sensory Processes 3, 207-229 (1979).

    Response Functions for Sine- and Square- Wave Modulations of Disparity. W. Richards. Journal of the Optical Society of America, Vol. 62, Number 7, July 1972.

    Saccadic Suppression. W. Richards, Journal of the Optical Society of America, Vol 59, No. 5 617-623, May 1969.

    Seeing shapes that are almost totally occluded: A new look at Parks's camel. Shinsuke Shimojo and Whitman Richards. Perception & Psychophysics 1986, 39 (6), 418-426.

    Spectral categoratization of materials. John M. Rubin and W.A. Richards. Image Understanding 1985-86, MIT, Ablex Publishing, New Jersey.

    Structure from stereo and motion. W. Richards. Journal of the Optical Society of America, Vol. 2, p. 343, February 1985.

    Why Rods and Cones? W. Richards. Biological Cybernetics 33, 125-135 (1979).


    Perception and Cognition
    Shape Representation
    Color Vision
    Binocular Vision
    Movement Perception
    Texture Perception
    Neuroanatomy & Neuropsychology
    Oculomotor Influences in Perception
    Graphics Psychophysics