Skip to main content
SpringerLink
Log in

Epistemology, Aesthetics and Pragmatics of Scientific and Other Images: Visualization, Representation and Reasoning

  • Chapter
  • First Online:
Book cover Fuzzy Pictures as Philosophical Problem and Scientific Practice

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 348))

  • 528 Accesses

Abstract

Vagueness of appearance and depiction is a property of the categorization of images. Through categorization, whatever pictures do, they may do approximately and vaguely. And what images can do, that is, what they do for us and we can do with them, depends on what we think their roles are. In general, pictures play a role in ordinary and scientific argument and in cognition more broadly. They are key to identifying, documenting, tracking and exploring visible properties of empirical systems and phenomena, also and to visualizing and communicating empirical and theoretical information; they can be emotionally compelling, aesthetically powerful, and exhibit and enforce values and biases. This is no less relevant in the study of systems, individual or generic, whose relevant properties are spatial, chromatic or structural. Relevant examples differ widely in medium, mode of production and use; they include photographs, drawings, data charts, diagrams, animations, film recordings, computer generated images, etc. Pictures in many such cases are meant to support inferences, recognition processes and carry heuristic and evidence value. We think with them and through them.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The literature is vast. In the scientific cases, see, for instance, Baigrie [1], Larkin and Simon [2], Tufte [43], Daston and Galison [3], Hentschel [4], Perini [5], Goodwin [6] and Kulvicki [7]. Some of the literature focuses of generalities or taxonomies; another draws attention to the contextual and contingent nature of particular cases.

  2. 2.

    Empirical methodology often works on the basis of contextual discrimination of (relatively) theoretical stuff. The distinction may be based on different criteria.

  3. 3.

    Arguably, relevant experience that in scientific inquiry plays the empirical role of perceptual input may not be strictly visual.

  4. 4.

    Frigg and Hartmann [8], Giere [9], Bailer-Jones [10], Weisberg [11].

  5. 5.

    Lynch [12].

  6. 6.

    Cat [13].

  7. 7.

    Giere [9].

  8. 8.

    Kulvicki [7].

  9. 9.

    Hesse [14].

  10. 10.

    Gentner et al. [15], Hofstadter and Sander [16].

  11. 11.

    Dunn and Everitt [17].

  12. 12.

    The ordinary psychology of analogical judgments presents asymmetries and other contextual features first detected by Tverski; this, as well as other chronological and cognitive factors, may be linked to the asymmetric function of analogy for instance in the generation and interpretation of metaphors, also in science; see Gentner et al. [15], Cat [18].

  13. 13.

    Set similarity is introduced to define near sets in Peters and Pal [19], 1.7. Set similarity ultimately rests on measures of indistinguishability of points relative to features defined on them, that is, the difference between values of feature probe functions, or membership functions.

  14. 14.

    For an application of set-theoretic criterion in the case of the relation between models and their target, see Weisberg [11].

  15. 15.

    Dubois and Prade [20], Klir and Yuan [21].

  16. 16.

    It is common to confuse animation and simulation mistaking one for the other.

  17. 17.

    Perini [5].

  18. 18.

    Shin [22].

  19. 19.

    Shin [22].

  20. 20.

    Klir and Yuan [21].

  21. 21.

    I discuss views and examples of scientific metaphors in Cat [18].

  22. 22.

    A range of issues related to so-called visuospatial thinking are discussed in Shah and Miyake [23].

  23. 23.

    Kosslyn [24], Taylor [25].

  24. 24.

    For an integrative and evaluative review of such models see Shah et al. [26].

  25. 25.

    Lynch [12]. In a forthcoming essay Scott Curtis distinguishes between the aesthetic of the smooth and the rough, with the focus is on what I have been calling intrinsic visual features of the images.

  26. 26.

    Shin [22].

  27. 27.

    Ibid.

  28. 28.

    See Barwise and Etchemendy [27], Barwise and Hammer [28], Shin [22].

  29. 29.

    Shin [22]; on Gestalt perception of information in diagrams see Coliva [29].

  30. 30.

    See Resnik [30], Giaquinto [31].

  31. 31.

    For a recent defense of Frege’s diagrammatic notation see Dirk Schlimm’s ‘On Frege’ Begriffsschrift notation for propositional logic’ (Univ. of McGill ms., 2016).

  32. 32.

    I have argued against such moves in the application of fuzzy set theory in Cat [32].

  33. 33.

    Perini [5], Siegel [33].

  34. 34.

    This is different from the meaninglessness of the thickness of the graph, or the size of the map.

  35. 35.

    For a treatment of the linguistic case with fuzzy inference rules, see Trillas and Uturbey [34].

  36. 36.

    Bogen and Woodward [35], Woodward [36], Leonelli [37]. See, for instance, also Cat [38] and Hentschel [4] for the case of photography.

  37. 37.

    Leonelli [37].

  38. 38.

    Leonelli [39].

  39. 39.

    See Elgin [44], Cat [13, 40].

  40. 40.

    Leonelli [39].

  41. 41.

    Leonelli [41].

  42. 42.

    Leonelli [39].

  43. 43.

    Ibid.

  44. 44.

    Bogen and Woodward [35], Woodward [36].

  45. 45.

    Woodward [36].

  46. 46.

    Tufte [42], 76.

  47. 47.

    Ibid.

References

  1. Baigrie, B. S. (Ed.). (1996). Picturing knowledge. Chicago: University of Chicago Press.

    Google Scholar 

  2. Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11, 65–99.

    Article  Google Scholar 

  3. Daston, L., & Galison, P. (2007). Objectivity. Cambridge, MA: Zone Press.

    Google Scholar 

  4. Hentschel, K. (2001). Mapping the spectrum. Oxford: Oxford University Press.

    MATH  Google Scholar 

  5. Perini, L. (2005). The truth in pictures. Philosophy of Science, 72, 262–285.

    Article  Google Scholar 

  6. Goodwin, W. (2009). Visual representation in science. Philosophy of Science, 76, 372–390.

    Article  Google Scholar 

  7. Kulvicki, J. (2014). Images. New York: Routledge.

    Google Scholar 

  8. Frigg, R., & Hartmann, S. (2010). Scientific models. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Winter 2015 Edition). http://plato.stanford.edu/archives/win2015/entries/scientificmodels/

  9. Giere, R. (2001). Science without laws. Chicago: University of Chicago Press.

    Google Scholar 

  10. Bailer-Jones, D. (2009). Scientific models in philosophy of science. Pittsburg: University of Pittsburg Press.

    Google Scholar 

  11. Weisberg, M. (2013). Simulation and similarity. New York: Oxford University Press.

    Book  Google Scholar 

  12. Lynch, M. (1985). Visibility. Social Studies of Science, 15(1), 37–66.

    Article  Google Scholar 

  13. Cat, J. (2015). An informal meditation on empiricism and approximation in fuzzy logic and fuzzy set theory: Between subjectivity and normativity. In R. Seising, E. Trillas, & J. Kacprzyk (Eds.), Fuzzy logic: Towards the future (pp. 179–234). Berlin: Springer.

    Google Scholar 

  14. Hesse, M. B. (1966). Models and analogies in science. Notre Dame, IN: University of Notre Dame Press.

    Google Scholar 

  15. Gentner, D., Holyoak, K. J., & Kokinov, B. N. (Eds.). (2001). The analogical mind. Perspectives from cognitive science. Cambridge, MA: Bradford Books.

    Google Scholar 

  16. Hofstadter, D., & Sander, E. (2013). Surfaces and essences: Analogy as the fuel and fire of thinking. New York: Basic books.

    Google Scholar 

  17. Dunn, G., & Everitt, B. S. (1982). An introduction to mathematical taxonomy. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  18. Cat, J. (2001). On understanding: Maxwell on the methods of illustration and scientific metaphor. Studies in History and Philosophy of Modern Physics, 33B(3), 395–442.

    Article  MathSciNet  MATH  Google Scholar 

  19. Peters, J. F., & Pal, S. K. (2010). Cantor, fuzzy, near, and rough sets in image analysis. In J. F. Pal & S. K. Peters (Eds.), Rough Fuzzy Image Analysis. Foundations and Methodologies (pp. 1–15). Boca Raton, FL: CRC Press.

    Google Scholar 

  20. Dubois, D., & Prade, H. (1980). Fuzzy sets and systems. Theory and applications. New York: Academic Press.

    MATH  Google Scholar 

  21. Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic. Theory and applications. Upper Saddle River, NJ: Prentice Hall.

    MATH  Google Scholar 

  22. Shin, S.-J. (2012). The forgotten individual: Diagrammatic reasoning in mathematics. Synthese, 186, 149–168.

    Article  MathSciNet  MATH  Google Scholar 

  23. Shah, P., & Miyake, A. (Eds.). (2005). The Cambridge handbook of visuospatial thinking. Cambridge: Cambridge University Press.

    Google Scholar 

  24. Kosslyn, S. M. (1994). Image and brain. Cambridge, MA: MIT Press.

    Google Scholar 

  25. Taylor, H. (2005) Mapping the understanding of understanding maps. In P. Shah & A. Miyake (Eds.), The Cambridge handbook of visuospatial thinking (pp. 295–333). Cambridge: Cambridge University Press.

    Google Scholar 

  26. Shah, P., Freedman, E. G., & Vekiri, I. (2005). The comprehension of quantitative information in graphical displays. In P. Shah & A. Miyake (Eds.), The Cambridge handbook of visuospatial thinking (pp. 426–476). Cambridge: Cambridge University Press.

    Google Scholar 

  27. Barwise, J., & Etchemendy, J. (1989). Information, infons and inference. In R. Cooper, K. Mukai, & J. Perry (Eds.), Situation theory and its applications I, 1 (pp. 33–78). Stanford, CA: CSLI.

    Google Scholar 

  28. Barwise, J., & Hammer, E. (1994). Diagrams and the concept of logical system. In D. M. Gabbay (Ed.), What is a logical system?. New York: Oxford University Press.

    Google Scholar 

  29. Coliva, A. (2012). Human diagrammatic reasoning and seeing-as. Synthese, 186, 121–148.

    Article  MathSciNet  MATH  Google Scholar 

  30. Resnik, M. D. (1997). Mathematics as a science of patterns. Oxford: Clarendon Press.

    MATH  Google Scholar 

  31. Giaquinto, M. (2007). Visual thinking in mathematics. Oxford: Oxford University Press.

    Book  MATH  Google Scholar 

  32. Cat, J. (2006). On fuzzy empiricism and fuzzy-set models of causality: What is all the fuzz about? Philosophy of Science, 73(1), 26–41.

    Article  MathSciNet  Google Scholar 

  33. Siegel, S. (2010). The contents of visual experience. New York: Oxford University Press.

    Google Scholar 

  34. Trillas, E., & Uturbey, L. A. (2011). Towards the dissolution of the Sorites paradox. Applied Soft Computing, 11(2), 1506–1510.

    Article  Google Scholar 

  35. Bogen, J., & Woodward, J. (1988). Saving the phenomena. Philosophical Review, 97, 303–352.

    Article  Google Scholar 

  36. Woodward, J. (2000). Proceedings of the 1998 biennial meetings of the philosophy of science association. Part II: Symposia papers. Philosophy of science, 67, Supplement, S163–S179.

    Google Scholar 

  37. Leonelli S. (2015). What counts as scientific data? A relational framework. Philosophy of Science, 82(5), 810–821.

    Google Scholar 

  38. Cat, J. (2013). Maxwell, Sutton and the birth of color photography. A binocular study. New York: Palgrave-Macmillan.

    Book  Google Scholar 

  39. Leonelli, S. (2013). Classificatory theory in biology. Biological Theory, 7, 338–345.

    Article  Google Scholar 

  40. Cat, J. (2016). The performative construction of natural kinds: Mathematical application as practice. In C. Kendig (Ed.), Natural kinds and classification in scientific practice (pp. 87–105). Abingdon: Routledge.

    Google Scholar 

  41. Leonelli, S. (2012). Classificatory theory in data-intensive sciences: The case of open biomedical ontologies. International Studies in the Philosophy of Science, 26(1), 47–65.

    Article  Google Scholar 

  42. Tufte, E. (2001) Visual Display of Quantitative Information. Cheshire, CT: Graphics Press.

    Google Scholar 

  43. Tufte, E. (1991). Envisioning Information. Cheshire, CT: Graphics Press.

    Google Scholar 

  44. Elgin, C. Z. (1997). Between the Absolute and the Arbitrary. Ithaca: Cornell University Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of History and Philosophy of Science, Indiana University, Bloomington, USA

    Jordi Cat

Authors
  1. Jordi Cat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jordi Cat .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Cat, J. (2017). Epistemology, Aesthetics and Pragmatics of Scientific and Other Images: Visualization, Representation and Reasoning. In: Fuzzy Pictures as Philosophical Problem and Scientific Practice. Studies in Fuzziness and Soft Computing, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-319-47190-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47190-7_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47189-1

  • Online ISBN: 978-3-319-47190-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation