Medicine Studies

, Volume 2, Issue 3, pp 161–173 | Cite as

Why Images?



Given that many imaging technologies in biology and medicine are non-optical and generate data that is essentially numerical, it is a striking feature of these technologies that the data generated using them are most frequently displayed in the form of semi-naturalistic, photograph-like images. In this paper, I claim that three factors underlie this: (1) historical preferences, (2) the rhetorical power of images, and (3) the cognitive accessibility of data presented in the form of images. The third of these can be argued to provide an epistemic advantage to images, but I will further argue that this is often misleading and that images can in many cases be less informative than the corresponding mathematical data.


Imaging PET fMRI Images 


  1. Abraham, T. (2003). From theory to data: Representing neurons in the 1940’s. Journal of the History of Biology, 415–426.Google Scholar
  2. Ambrose, J. 1973. Computerized transverse axial tomography scanning (tomography): Part 2. Clinical application. British Journal of Radiology 46: 1023–1047.CrossRefGoogle Scholar
  3. Berrill, N.J. 1984. The pearls of wisdom: An exposition. Perspectives in Biology and Medicine 28(1): 1–16.Google Scholar
  4. Breidbach, O. 2002. Representation of the Microcosm—The claim for objectivity in 19th century scientific microphotography. Journal of the History of Biology 35: 221–250.CrossRefGoogle Scholar
  5. Cartwright, L. 1995. Screening the body: Tracing medicine’s visual culture. Minneapolis: University of Minnesota Press.Google Scholar
  6. Crick, F. 1994. The astonishing hypothesis: The scientific search for the soul. New York: Charles Scribner’s Sons.Google Scholar
  7. Daston, L., and P. Galison. 1992. The image of objectivity. Representations 40: 81–128.CrossRefGoogle Scholar
  8. Dumit, J. 2004. Picturing personhood: Brain scans and biomedical identity. Princeton: Princeton University Press.Google Scholar
  9. Elkins, J. 1999. Pictures of the body: Pain and metamorphosis. Stanford: Stanford University Press.Google Scholar
  10. Friston, K., C. Chu, J. Mourão-Miranda, O. Hulme, G. Rees, W. Penny, and J. Ashburner. 2008. Bayesian decoding of brain images. Neuroimage 38: 181–205.CrossRefGoogle Scholar
  11. Greene, M.T. 2005. Seeing clearly is not necessarily believing. Nature 435: 143.CrossRefGoogle Scholar
  12. Hanson, N.R. 1965. Patterns of discovery. Cambridge: Cambridge University Press.Google Scholar
  13. Haxby, J.V., et al. 2001. Distributed and overlapping representations of faces and objects in ventral cerebral cortex. Science 293: 2425–2430.CrossRefGoogle Scholar
  14. Haynes, J.-D., and G. Rees. 2005. Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nature Neuroscience 8: 1–6.Google Scholar
  15. Haynes, J.-D., et al. 2007. Reading hidden intentions in the human brain. Current Biology 17: 323–328.CrossRefGoogle Scholar
  16. Hearst, J.E. 1990. Microscopy: ‘Seeing is Believing’. Nature 347(6290): 230.CrossRefGoogle Scholar
  17. Herschman, H.R., D.C. MacLaren, M. Iyer, et al. 2000. Seeing is believing: Non-invasive, quantitative and repetitive imaging of reporter gene expression in living animals using positron emission tomography. Journal of Neuroscience Research 59(6): 699–705.CrossRefGoogle Scholar
  18. Jones, C.A., and P. Galison (eds.). 1988. Picturing science, producing art. New York: Routledge.Google Scholar
  19. Kamitani, Y., and F. Tong. 2005. Decoding the visual and subjective contents of the human brain. Nature Neuroscience 8: 679–685.CrossRefGoogle Scholar
  20. Keller, E.F. 2002. Making sense of life: Explaining biological development with models, metaphors, and machines. Cambridge, MA: Harvard University Press.Google Scholar
  21. Kevles, B.H. 1996. Medical imaging in the twentieth century. Brunswick, NJ: Rutgers University Press.Google Scholar
  22. Kriegeskorte, N., et al. 2008. Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60(6): 1126–1141.CrossRefGoogle Scholar
  23. Kuhn, T. 1970. The structure of scientific revolutions, 2nd, Enlarged ed ed. Chicago: University of Chicago Press.Google Scholar
  24. Lynch, M., and S. Woolgar (eds.). 1990. Representation in scientific practice. Cambridge, MA: MIT Press.Google Scholar
  25. Monteith, G.R. 2000. Seeing is believing: Recent trends in the measurement of Ca2 in subcellular domains and intracellular organelles. Immunology and Cell Biology 78(4): 403–407.CrossRefGoogle Scholar
  26. Mur, M., et al. 2009. Revealing representational content with pattern-information Fmri—an introductory guide. SCAN 4: 101–109.Google Scholar
  27. Murphy, B. (1996). Color scales: dialing a defect. Retrieved January 16, 2005, from the World Wide Web:
  28. Orr-Weaver, T.L. 1995. Meiosis in Drosophila: Seeing is believing. Proceedings of the National Academy of Sciences USA 92(23): 10443–10449.CrossRefGoogle Scholar
  29. Ottino, J.A. 2003. Is a picture worth 1, 000 words? Nature 421: 474–476.CrossRefGoogle Scholar
  30. Pearson, H. 2005. CSI: Cell biology. Nature 434: 952–953.CrossRefGoogle Scholar
  31. Pereira, F., et al. 2009. Machine learning classifiers and fMRI: A tutorial overview. NeuroImage 45: S199–S209.CrossRefGoogle Scholar
  32. Ramsey, J., C. Hanson, S. Hanson, R. Halchenko, R. Poldrack, and C. Glymour. 2010. Six problems for causal inference from fMRI. Neuroimage 49: 1545–1548.CrossRefGoogle Scholar
  33. Rasmussen, N. 1997. Picture control: The electron microscope and the transformation of biology in America, 1940–1960. Stanford: Stanford University Press.Google Scholar
  34. Stafford, B.M. 1991. Body criticism: Imaging the unseen in Enlightenment art and medicine. Cambridge, MA: MIT Press.Google Scholar
  35. Stafford, B.M. 1994. Artful science: Enlightenment entertainment and the eclipse of visual education. Cambridge, MA: MIT Press.Google Scholar
  36. Stafford, B.M. 1996. Good looking: Essays on the virtues of images. Cambridge, MA: MIT Press.Google Scholar
  37. Supplee, C., and M. Bradford. 2004. 2004 Visualization challenge. Science 305: 1903.CrossRefGoogle Scholar
  38. Tufte, E.R. 1983. The visual display of quantitative information. Cheshire, CT: Graphics Press.Google Scholar
  39. Tufte, E.R. 1997. Visual explanations: Images and quantities, evidence and narrative. Cheshire, CT: Graphics Press.Google Scholar
  40. Wimsatt, W.C. 1991. Taming the dimensions—Visualizations in science. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science 1990: 111–135.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of CalgaryCalgaryCanada

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