Advertisement

Cognitive Load Research and Semantic Apprehension of Graphical Linguistics

  • Michael Workman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4799)

Abstract

In knowledge-work, there are increasing amounts of complex information rendered by information technology, which has led to the common term, information overload. Information visualization is one area where empirically tested semantic theory has not yet caught up with that of the underlying information storage and retrieval theory, contributing to information overload. In spite of a vast body of cognitive theory, much of the human factors research on information visualization has overlooked it. Specifically, information displays have facilitated the data gathering (ontological) aspects of human problem-solving and decision-making, but have exacerbated the meaning-making (epistemological) aspects of those activities by presenting information in linear rather than in graphical (holistic) forms. Drawing from extant empirical research, we present a thesis suggesting that cognitive load may be reduced when holistic information is imbued with transformational grammar to help alleviate the information overload problem, along with a methodological approach for investigation.

Keywords

Human–Computer Interaction Graphical Linguistics Cognitive Load Medical Informatics Decision Support Systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Killmer, K.A., Koppel, N.B.: So much information, so little time: Evaluating web resources with search engines. Technol. Horizons in Education Journal 30, 21–29 (2002)Google Scholar
  2. 2.
    Workman, M., Lesser, M.F., Kim, J.: An exploratory study of cognitive load in diagnosing patient conditions. International Journal for Quality in Health Care 19, 127–133 (2007)CrossRefGoogle Scholar
  3. 3.
    Bennett, K.B., Flach, J.M: Graphical displays: Implications for divided attention, focused attention, and problem solving. Human Factors 34, 513–533 (1992)Google Scholar
  4. 4.
    Chechile, R.A, Eggleston, R.G., Fleischman, R.N., Sasseville, A.M.: Modeling the cognitive content of displays. Human Factors 31, 31–43 (1989)Google Scholar
  5. 5.
    Lohr, L.: Creating visuals for learning and performance: Lessons in visual literacy. Prentice Hall, Upper Saddle River, NJ (2003)Google Scholar
  6. 6.
    Dyer, C.: Doctors go on trial for manslaughter after removing wrong kidney. British Medical Journal 324, 10–11 (2002)CrossRefGoogle Scholar
  7. 7.
    National Transportation and Safety Board. Collision of two Burlington Northern Santa Fe freight trains. Washington DC: NTSB Report PB2006-916302 Notation 7793A (2002)Google Scholar
  8. 8.
    CNN. Major power outage hits New York, other large cities, (Retrieved July 07, 2007) http://www.cnn.com/2003/US/08/14/power.outage
  9. 9.
    Bradshaw, L.: Information overload and the Hurricane Katrina post-disaster disaster. Information Enterprises, Fremantle, WA (2006)Google Scholar
  10. 10.
    Larkin, J.H., Simon, H.A.: Why a diagram is (sometimes) worth ten thousands words. Cognitive Science 11, 65–99 (1987)CrossRefGoogle Scholar
  11. 11.
    Healey, C., Kellogg, G., Booth, S., Enns, J.T.: High-speed visual estimation using preattentive processing. ACM Trans. on Computer-Human Interaction 14, 107–135 (1996)CrossRefGoogle Scholar
  12. 12.
    Chernoff, H.: Using faces to represent points in k dimensional space graphically. Journal of American Statistical Association 68, 361–368 (1973)CrossRefGoogle Scholar
  13. 13.
    Kondo, H., Mori, H.: A computer system applying the face method to represent multiphasic tests. Medical Information 12, 217–222 (1987)CrossRefGoogle Scholar
  14. 14.
    Morris, M.: Kiviat graphs - conventions and figures of merit. ACM SIGMETRICS Performance Evaluation Review 3, 2–8 (1974)CrossRefGoogle Scholar
  15. 15.
    Kolence, K.W., Kiviat, P.J.: Software unit profiles and Kiviat figures. ACM SIGMETRICS Performance Evaluation Review 2, 2–12 (1973)CrossRefGoogle Scholar
  16. 16.
    Pola, Pl., Cruccu, G., Dolce, G.: Star-like display of EEG spectral values. Electroencephalography and Clinical Neurophysiology 50, 527–529 (1980)CrossRefGoogle Scholar
  17. 17.
    Marcus, A.: Dashboards in your future. Communications of the ACM 13, 48–60 (2006)Google Scholar
  18. 18.
    Posner, M.I.: Chronometric explorations of mind. Erlbaum, Hillsdale, NJ (1978)Google Scholar
  19. 19.
    Chomsky, N.: Human language and other semiotic systems. Semiotica 25, 31–44 (1979)Google Scholar
  20. 20.
    Cooper, G.: Cognitive load theory as an aid for instructional design. Australian Journal of Educational Technology 6, 108–113 (1990)Google Scholar
  21. 21.
    Rehder, B., Hoffman, A.B.: Eye tracking and selective attention in category learning. Cognitive Psychology 51, 1–41 (2005)CrossRefGoogle Scholar
  22. 22.
    Komlodi, A., Rheingans, P., Ayachit, U., Goodall, J.R., Joshi, A.: A user-centered look at glyph-based security visualization. IEEE Conference Workshop on Visualization for Computer Security 26, 21–28 (2005)CrossRefGoogle Scholar
  23. 23.
    Mayer, R.E.: Multimedia learning. Cambridge University Press, Cambridge (2001)Google Scholar
  24. 24.
    Shneiderman, B.: Designing the user interface: Strategies for effective human-computer interaction. Addison-Wesley Longman Publishing Co, Boston (1992)Google Scholar
  25. 25.
    Tufte, E.R.: The visual display of quantitative information. Graphics Press, Cheshire, CT (1986)Google Scholar
  26. 26.
    Powsner, S.M., Tufte, E.R.: Graphical summary of patient status. The Lancet 344, 386–389 (1994)CrossRefGoogle Scholar
  27. 27.
    Bederson, B.B., Shneiderman, B., Wattenberg, M.: Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies. In: Bedderson, B.B., Shneiderman, B. (eds.) The craft of information visualization, pp. 257–278. Morgan Kaufmann Publishers, San Francisco (2002)Google Scholar
  28. 28.
    Bennett, K.B., Payne, M., Calcaterra, J., Nittoli, B.: An empirical comparison of alternative methodologies for the evaluation of configural displays. The Journal of the Human Factors and Ergonomics Society 42, 287–298 (2000)CrossRefGoogle Scholar
  29. 29.
    Carswell, C.M., Wickens, C.D.: The proximity compatibility principle: Its psychological foundation and relevance to display design. Human Factors 37, 473–494 (1995)CrossRefGoogle Scholar
  30. 30.
    Loft, S., Sanderson, P., Neal, A., Mooij, M.: Modeling and predicting mental workload in en route air traffic control: Critical review and broader implications. The Journal of the Human Factors and Ergonomics Society 49, 376–399 (2007)CrossRefGoogle Scholar
  31. 31.
    Johnson, M.H.: The development of visual attention: A cognitive neuroscience perspective. In: Gazzanga, M.S. (ed.) The cognitive neurosciences, pp. 735–750. MIT Press, Cambridge MA (1995)Google Scholar
  32. 32.
    Rafal, R., Robertson, L.: The neurology of visual attention. In: Gazzanga, M.S. (ed.) The cognitive neurosciences, pp. 625–648. MIT Press, Cambridge MA (1995)Google Scholar
  33. 33.
    Schacter, D.L.: Implicit memory: New frontiers for cognitive neuroscience. In: Gazzanga, M.S. (ed.) The cognitive neurosciences, pp. 824–825. MIT Press, Cambridge MA (1995)Google Scholar
  34. 34.
    Tulving, E.: Working memory: An Introduction. In: Gazzanga, M.S. (ed.) The cognitive neurosciences, pp. 751–754. MIT Press, Cambridge MA (1995)Google Scholar
  35. 35.
    Caplan, D.: The cognitive neuroscience of syntactic processing. In: Gazzanga, M.S. (ed.) The cognitive neurosciences, pp. 871–880. MIT Press, Cambridge MA (1995)Google Scholar
  36. 36.
    Garrett, M.: The structure of language processing: Neuropsychological evidence. In: Gazzanga, M.S. (ed.) The cognitive neurosciences, pp. 881–900. MIT Press, Cambridge MA (1995)Google Scholar
  37. 37.
    Gavrilova, T.A., Voinov, A.V.: The cognitive approach to the creation of ontology. Nauchno-Tekhnicheskaya Informatsiya 2, 59–64 (2007)Google Scholar
  38. 38.
    Schroeder, J., Xu, J., Chen, H., Chau, M.: Automated criminal link analysis based on domain knowledge. Journal of the American Society for Information Science and Technology 58, 842–855 (2007)CrossRefGoogle Scholar
  39. 39.
    McBride, B.: The resource description framework (RDF) and its vocabulary description language RDFS. In: Staab, S., Studer, R. (eds.) The handbook on ontologies in Information Systems, pp. 223–257. Springer, Heidelberg (2003)Google Scholar
  40. 40.
    Albers, M.J.: Information design considerations for improving situation awareness in complex problem solving. In: ACM SIG Design of Communication, Proc. of the 17th ann. Int. conference on computer documentation, New Orleans, LA, pp. 154–158. ACM Press, New York (1999)CrossRefGoogle Scholar
  41. 41.
    Endsley, M.R., Bolte, B., Jones, D.G.: Designing for situation awareness: An approach to user-centered design. Taylor & Francis, NY (2003)Google Scholar
  42. 42.
    Sweller, J.: Cognitive load during problem solving: Effects on learning. Cognitive Science 12, 257–285 (1988)CrossRefGoogle Scholar
  43. 43.
    Brunken, R., Steinbacher, S., Plass, J.L., Leutner, D.: Assessment of cognitive load in multimedia learning using dual task methodology. Journal of Experimental Psychology 49, 109–119 (2002)Google Scholar
  44. 44.
    Hazeltine, E., Ruthruff, E., Remington, R.W.: The role of input and output modality parings in dual-task performance: Evidence for content-dependent central interference. Cognitive Psychology 52, 291–345 (2006)CrossRefGoogle Scholar
  45. 45.
    Woods, D.D.: The cognitive engineering of problem representations. In: Weir, G.R.S., Alty, J.L. (eds.) Human-computer interaction and complex systems, pp. 169–188. Academic Press, London (1994)Google Scholar
  46. 46.
    Norman, D.A., Bobrow, D.J.: On data-limited and resource-limited processes. Cognitive Psychology 7, 44–64 (1975)CrossRefGoogle Scholar
  47. 47.
    Richardson-Klavvehn, A., Gardiner, J.M., Ramponi, C.: Level of processing and the process-dissociation procedure: Elusiveness of null effects on estimates of automatic retrieval. Memory 10, 349–364 (2002)CrossRefGoogle Scholar
  48. 48.
    Baddeley, A.D., Hitch, G.J.: Working Memory. In: Bower, G. (ed.) The psychology of learning and motivation: Advances in research and theory, pp. 47–90. Academic Press, New York (1974)Google Scholar
  49. 49.
    Breitmeyer, B.G.: Visual masking: past accomplishments, present status, future developments. Advances in Psychology 3, 9–20 (2007)Google Scholar
  50. 50.
    Holzinger, A., Geierhofer, R., Errath, M.: Semantic information in medical information systems - From data and information to knowledge: Facing information overload. In: Proceedings of I-MEDIA 2007 and I-SEMANTICS 2007, Graz, Austria, pp. 323–330 (2007)Google Scholar
  51. 51.
    Monsell, S., Driver, J.: Control of cognitive processes: Attention and performance XVIII. MIT Press, Cambridge MA (2000)Google Scholar
  52. 52.
    Jacoby, L.L.: A process discrimination framework: Separating automatic from intentional uses of memory. Journal of Memory and Language 30, 531–541 (1991)CrossRefGoogle Scholar
  53. 53.
    Barnhardt, T.M.: Number of solutions effects in stem decision: Support for the distinction between identification and production processes in priming. Memory 13, 725–748 (2005)CrossRefGoogle Scholar
  54. 54.
    Halford, G.S., Baker, R., McCredden, J.E., Bain, J.D.: How many variables can humans process? Psychological Science 16, 70–76 (2005)CrossRefGoogle Scholar
  55. 55.
    Smith, E.E., Jonides, J.: Working memory in humans: Neuropsychological evidence. In: Gazzanga, M.S. (ed.) The cognitive neurosciences, pp. 1009–1020. MIT Press, Cambridge MA (1995)Google Scholar
  56. 56.
    Anderson, J.R.: Cognitive psychology and its implications. Worth Publishers, New York, NY (2000)Google Scholar
  57. 57.
    Reder, L.M., Schunn, C.D.: Metacognition does not imply awareness: Strategy choice is governed by implicit learning and memory. In: Reder, L.M. (ed.) Implicit memory and metacognition, pp. 45–78. Lawrence Erlbaum, Hillsdale, NJ (1996)Google Scholar
  58. 58.
    Sternberg, R.J.: Intelligence, information processing, and analogical reasoning: The componential analysis of human abilities. Erlbaum, Hillsdale, NJ (1977)Google Scholar
  59. 59.
    Cowan, N.: The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences 24, 87–185 (2000)CrossRefGoogle Scholar
  60. 60.
    Sweller, J.: Cognitive load during problem solving: Effects on learning. Cognitive Science 12, 257–285 (1988)CrossRefGoogle Scholar
  61. 61.
    Bourke, P.A., Duncan, J.: Effect of template complexity on visual search and dual-task performance. Psychological Science 16, 208–213 (2005)CrossRefGoogle Scholar
  62. 62.
    Draycott, S.G., Kline, P.: Validation of the AGARD STRES battery of performance tests. Human Factors 38, 347–361 (1996)CrossRefGoogle Scholar
  63. 63.
    Wise, J.A., Thomas, J.J, Pennock, K., Lantrip, D., Pottier, M., Schur, A., Crow, V.: Visualizing the non-visual: spatial analysis and interaction with information for text documents. In: Card, S., Mackinlay, J. (eds.) Readings in information visualization: Using vision to think, pp. 442–450. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  64. 64.
    Langer, S.: Philosophy in a new key: A study in the symbolism of reason, rite, and art. Harvard University Press, Cambridge, MA (1957)Google Scholar
  65. 65.
    Bergeron, V.: Anatomical and functional modularity in cognitive science: Shifting the focus. Philosophical Psychology 20, 175–195 (2007)CrossRefGoogle Scholar
  66. 66.
    Miller, E.K., Chelazzi, L., Lueschow, A.: Multiple memory systems in the visual cortex. In: Gazzanga, G. (ed.) The cognitive neurosciences, pp. 475–490. MIT Press, Cambridge (1995)Google Scholar
  67. 67.
    Simon, G., Petit, L., Bernard, C., Rebaï, M.: Occipito-temporal N170 ERPs could represent a logographic processing strategy in visual word recognition. Behavioral and Brain Functions 3, 3–21 (2007)CrossRefGoogle Scholar
  68. 68.
    Legge, G.E., Gu, Y., Luebker, A.: Efficiency of graphical perception. Perception and Psychophysics 46, 365–374 (1989)Google Scholar
  69. 69.
    Tsang, M., Morris, N., Balakrishnan, R.: Temporal thumbnails: Rapid visualization of time-based viewing data. In: Proceedings of the 15th annual ACM symposium on user interface software and technology, pp. 175–178. ACM Press, New York (2002)Google Scholar
  70. 70.
    Montgomery, D.A.: Human sensitivity to variability information in detection decisions. Human Factors 41, 90–105 (1999)CrossRefGoogle Scholar
  71. 71.
    Pollatesk, A., Reichle, E.D., Rayner, K.: Tests of the EZReader model: Exploring the interface between cognition and eye movement control. Cognitive Psychology 52, 1–56 (2006)CrossRefGoogle Scholar
  72. 72.
    Shiffrin, R.M., Schneider, W.: Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychological Review 84, 127–190 (1977)CrossRefGoogle Scholar
  73. 73.
    Bransford, J.D., Franks, J.J.: The abstraction of linguistic ideas. Cognitive Psychology 2, 331–350 (1971)CrossRefGoogle Scholar
  74. 74.
    Kozma, R.B.: Learning with media. Review of Educational Research 61, 179–211 (1991)CrossRefGoogle Scholar
  75. 75.
    Trafton, G.J., Trickett, S.B.: Note-taking for self-explanation and problem solving. Human-Computer Interaction 16, 1–38 (2001)CrossRefGoogle Scholar
  76. 76.
    Kowler, E., Anderson, E., Dosher, B., Blaser, E.: The role of attention in the programming of saccades. Vision Research 35, 1897–1916 (1995)CrossRefGoogle Scholar
  77. 77.
    Lesser, M.F.: GIFIC: A graphical interface for information cognition for intensive care. In: Proceedings from the 18th Ann. Symp. on Computers in Applied Medical Care (1994)Google Scholar
  78. 78.
    Starren, J., Johnson, S.B.: An object-oriented taxonomy of medical data presentations. Journal of the American Medical Information Association 7, 1–20 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Michael Workman
    • 1
  1. 1.College of Business, Florida Institute of Technology, Melbourne, FLUSA

Personalised recommendations