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Dale Purves
Born (1938-03-11) 11 March 1938 (age 86)
Years active1964 – present
Known forPopular books on perception

Dale Purves (born 1938[1]) is Director of the Neuroscience and Behavioural Disorders program at Duke-NUS Graduate Medical School and Executive Director of the Neuroscience Research Partnership at A*STAR, both located in Singapore. Until 2009 he was Director of the Center for Cognitive Neuroscience and George Barth Geller Professor for Research in Neurobiology at Duke University. He received a B.A. from Yale University in 1960 (where he was a member of Manuscript Society) and an M.D. from the Harvard Medical School in 1964. After several years in clinical medicine as a surgical house officer at the Massachusetts General Hospital and as a Peace Corps Physician, he gave up medicine in favor of a career in neuroscience research. He was a postdoctoral fellow in the Department of Neurobiology at Harvard from 1968 to 1971 and in the Department of Biophysics, University College London, from 1971 to 1973. He then joined the faculty in the Department of Physiology and Biophysics at the Washington University in 1971, where he remained until 1990. During that time he studied the development of the nervous system, and was elected to the National Academy of Sciences in 1989. He arrived at Duke in 1990 as the founding chair of the Department of Neurobiology, where he became increasingly interested in cognitive neuroscience.

Purves’s work at Duke has focused on visual and auditory perception (including perception of music), exploring the hypothesis that, as a means of contending with the inverse problem in perception, percepts are generated by a neural strategy that represents the empirical significance of sensory stimuli rather than their physical features (see also empirical theories of perception).

Wholly empirical theory of perception

According to the wholly empirical theory of perception developed by Dale Purves, R. Beau Lotto and others, perception must be understood as the outcome of a neural strategy to generate appropriate behavior from sensory stimulation. Since the inverse problem in optics precludes direct analysis of image features, this strategy must take into account an entire history (on a species and individual level) of associations between sensory impressions and the success or failure of behavior.

For example, the perception of brightness as it is typically understood is confounded by the fact that an object can send more light to the eye because it’s under a stronger light, because it naturally reflects more light, or for other reasons. Because all the eye receives is the final product, our visual systems cannot logically determine what the relative contributions of each factor are. However, successful behavior depends on the ability to discriminate these different conditions. On the empirical account, perceptions are determined by the frequency with which we encountered both of these various possibilities in in the past, and are not determined by the properties of stimuli as such.

To understand this concept of visual perception, note the discrepancies in brightness between the top and bottom blocks in the picture to the right. While the top block always appears darker than the bottom block, if a finger is held over the junction between the two blocks (enclosed by the four red lines) this apparent brightness difference disappears. In terms of the empirical theory of vision, these perceptual effects arise because in the past, this kind of scene (i.e. the way the shadows are positioned, the luminance difference across the junction) would usually have signified a behaviorally important difference between the two blocks. These kinds of effects can all be explained in terms of experience but are difficult to account for in any other way.

On the wholly empirical account, this strategy determines qualities of perception in all visual domains and sensory modalities. Accumulating evidence suggests that the perception of color,[2][3] contrast,[4] distance,[5] size,[6] line orientation and angles,[7] and motion,[8][9][10] as well as pitch and consonance in music,[11][12][13] may be determined by empirically derived associations between the sensory patterns humans have always experienced and the relative success of behavior in response to those patterns.

Selected recent publications

  • Yang Z, Purves D (2003) Image/Source statistics in natural scenes. Network: Computation in Neural Systems 14: 371–390[1].
  • Yang Z, Purves D (2003) A statistical explanation of visual space. Nature Neuroscience 6: 632–640[2].
  • Schwartz D, Howe CQ, Purves D (2003) The statistical structure of human speech sounds predicts musical universals. J Neurosci 23:7160–7168[3].
  • Purves D, Lotto RB (2003) Why we see what we do: An empirical theory of vision. Sunderland, MA: Sinauer Associates[4].
  • Howe Q, Purves D (2003) Size contrast explained by the statistics of scene geometry. J Cog Neurosci 16:90–102[5].
  • Long F, Purves D (2003) Natural scene statistics as a universal basis for color context effects. Proc Natl Acad Sci 100 (25): 15190–15193.[6]
  • Purves D, Williams MS, Nundy S, Lotto RB (2004) Perceiving the intensity of light. Psychological Rev 111(1): 142–158.
  • Yang Z, Purves D (2004) The statistical structure of natural light patterns determines perceived light intensity. Proc Natl Acad Sci 101: 8745–8750[7].
  • Schwartz D, Purves D (2004) Pitch is determined by naturally occurring periodic sounds. Hearing Research 194: 31–46[8].
  • Howe CQ, Purves D (2005) Natural scene geometry predicts the perception of angles and line orientation. Proc Natl Acad Sci 102: 1228–1233[9].
  • Howe CQ, Purves D (2005) The Müller-Lyer illusion explained by the statistics of image-source relationships. Proc Natl Acad Sci 102: 1234–1239[10].
  • Howe CQ, Yang Z, Purves D (2005) The Poggendorff illusion explained by natural scene statistics of image-source relationships. Proc Natl Acad Sci 102:7707–7712 [11].
  • Long F, Yang Z, Purves D (2006) Spectral statistics in natural scene predict hue, saturation, and brightness. Proc Natl Acad Sci 103: 6013–6018[12].
  • Howe CQ, Lotto RB, Purves D (2006) Comparison of bayesian and empirical ranking approaches to visual perception. J Theor Biol 241: 866–875[13].
  • Boots B, Nundy S, Purves D (2007) Evolution of visually-guided behavior in artificial agents. Network: Computation in Neural Systems 18 (1): 1–24[14].
  • Ross D, Choi J, Purves D (2007) Musical intervals in speech. Proc Natl Acad Sci 104(23): 9852–9857[15].
  • Purves et al. (2007) Principles of Cognitive Neuroscience. Sunderland,MA: Sinauer Associates, 2007[16]
  • Purves D, Augustine GA, Fitzpatrick D, Hall W, LaMantia A-S, McNamara JO, Williams SM (2008) Neuroscience, 4th edition. Sinauer Associates: Sunderland, MA[17]..
  • Wojtach W.T., Sung K, Truong S, Purves D (2008) An empirical explanation of the flash-lag effect. Proc Natl Acad Sci 105(42): 16338–16343[18].
  • Sung K, Wojtach W.T., Purves D (2009) An empirical explanation of aperture effects. Proc Natl Acad Sci 106: 298–303[19].
  • Wojtach W. T., Sung K, Purves D (2009) An empirical explanation of the speed-distance effect. PLoS ONE 4(8): e6771[20].
  • Gill KZ and Purves D (2009) A biological rationale for musical scales. PLoS ONE 4: e8144[21].
  • Bowling DL, Gill KZ, Choi JD, Prinz J, and Purves D (2009) Major and minor music compared to excited and subdued speech. J Acoust Soc Am 127(1): 491–503.[22].
  • Purves, Dale. Brains: How they Seem to Work. Financial Times Press Science, New Jersey, 2010.

References

  1. ^ Date information sourced from Library of Congress Authorities data, via corresponding WorldCat Identities linked authority file (LAF).
  2. ^ Perceiving Colour. Lotto RB, Purves D. Review of Progress in Coloration 34:12–25. (2004)
  3. ^ Natural scene statistics as the universal basis for color context effects. Long F, Purves D. Proceedings of the National Academy of the Sciences 100(25): 15190–15193. (2003).
  4. ^ An empirical explanation of the Chubb illusion. Lotto RB, Purves D. Journal of Cognitive Neuroscience 13(5): 547–555. (2001)
  5. ^ A statistical explanation of visual space. Yang Z, Purves D. Nature Neuroscience 6:632:640 (2003).
  6. ^ Size contrast and assimilation explained by the statistics of scene geometry. Howe CQ, Purves D. Journal of Cognitive Neuroscience 16(1): 90–102. (2004).
  7. ^ Natural scene geometry predicts the perception of angles and line orientation. Howe CQ, Purves D. Proceedings of the National Academy of the Sciences 102(4): 1228–1233. (2005).
  8. ^ An empirical explanation of the flash-lag effect. Wojtach W.T., Sung K., Truong S., Purves D. (2008) Proceedings of the National Academy of the Sciences 105(2): 16338–16343.
  9. ^ An empirical explanation of aperture effects. Sung K., Wojtach W.T., Purves D. (2009) Proceedings of the National Academy of the Sciences 106:298–303.
  10. ^ An empirical explanation of the speed-distance effect. Wojtach W.T., Sung K., Purves D. (2009) PLoS ONE 4(8): e6771.
  11. ^ The statistical structure of human speech sounds predicts musical universals. Schwartz DA, Howe CQ, Purves D, Journal of Neuroscience 23(18): 7160–7168. (2003).
  12. ^ Musical intervals in speech. Ross D, Choi J, Purves D, Proceedings of the National Academy of the Sciences 104(23): 9852–9857. (2007).
  13. ^ Major and minor music compared to excited and subdued speech. Bowling D.L., K.Gill. et al. Journal of the Acoustical Society of America 127(1): 491–503. (2010).

Laboratory of Dale Purves, M.D.


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