Cognitive Information Theories of Psychology and Applications with Visualization and HCI Through Crowdsourcing Platforms

  • Darren J. Edwards
  • Linda T. Kaastra
  • Brian Fisher
  • Remco Chang
  • Min Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10264)

Abstract

This chapter introduces information processing perspectives from cognitive psychology, providing historical background content where it might prove useful. The hope is that this will provide readers enough of an understanding of psychology perspectives, theories, and methods that they can better apply crowdsourcing methods to understand the cognitive outcomes of interaction within visualization environments and other computer interfaces.

References

  1. 1.
    Angell, J.R.: The province of functional psychology. Psychol. Rev. 14(2), 61 (1907)CrossRefGoogle Scholar
  2. 2.
    Atkinson, R.C., Shiffrin, R.M.: Human memory: a proposed system and its control processes. Psychol. Learn. Motiv. 2, 89–195 (1968)CrossRefGoogle Scholar
  3. 3.
    Baddeley, A.D., Hitch, G.: Working memory. Psychol. Learn. Motivation 8, 47–89 (1974)CrossRefGoogle Scholar
  4. 4.
    Baron, J., Hershey, J.C.: Outcome bias in decision evaluation. J. Pers. Soc. Psychol. 54(4), 569 (1988)CrossRefGoogle Scholar
  5. 5.
    Boukhelifa, N., Bezerianos, A., Isenberg, T., Fekete, J.D.: Evaluating sketchiness as a visual variable for the depiction of qualitative uncertainty. IEEE Trans. Visual Comput. Graphics 18(12), 2769–2778 (2012)CrossRefGoogle Scholar
  6. 6.
    Brophy, J.E., Good, T.L.: Teacher-Student Relationships: Causes and Consequences. Holt, Rinehart & Winston, New York (1974)Google Scholar
  7. 7.
    Buhrmester, M., Kwang, T., Gosling, S.D.: Amazon’s mechanical turk a new source of inexpensive, yet high-quality, data? Perspect. Psychol. Sci. 6(1), 3–5 (2011)CrossRefGoogle Scholar
  8. 8.
    Carr, H.A.: Psychology: A Study of Mental Activity. American Psychological Association (1925)Google Scholar
  9. 9.
    Chen, M., Walton, S., Berger, K., Thiyagalingam, J., Duffy, B., Fang, H., Holloway, C., Trefethen, A.E.: Visual multiplexing. Comput. Graph. Forum 33(3), 241–250 (2014)CrossRefGoogle Scholar
  10. 10.
    Chomsky, N.: Syntactic Structures. Mouton & Co., The hague (1957)MATHGoogle Scholar
  11. 11.
    Chung, D.H., Archambault, D., Borgo, R., Edwards, D.J., Laramee, R.S., Chen, M.: How ordered is it? on the perceptual orderability of visual channel. In: INFOVIS (2016)Google Scholar
  12. 12.
    Cohen, J.: Statistical power analysis for the behavioral sciences (revised edn.) (1977)Google Scholar
  13. 13.
    Cooper, R., DeJong, D.V., Forsythe, R., Ross, T.W.: Cooperation without reputation: experimental evidence from prisoner’s dilemma games. Games Econ. Behav. 12(2), 187–218 (1996)CrossRefMATHMathSciNetGoogle Scholar
  14. 14.
    Craft, J.L., Simon, J.R.: Processing symbolic information from a visual display: interference from an irrelevant directional cue. J. Exp. Psychol. 83(3p1), 415 (1970)CrossRefGoogle Scholar
  15. 15.
    Creswell, J.W., Plano Clark, V.L., Gutmann, M.L., Hanson, W.E.: Advanced mixed methods research designs. In: Handbook of Mixed Methods in Social and Behavioral Research, pp. 209–240 (2003)Google Scholar
  16. 16.
    Crump, M.J., McDonnell, J.V., Gureckis, T.M.: Evaluating amazon’s mechanical turk as a tool for experimental behavioral research. PLoS ONE 8(3), e57410 (2013)CrossRefGoogle Scholar
  17. 17.
    Edwards, D.J.: Integrating Behavioural and Cognitive Psychology: A Modern Categorization Theoretical Approach. Nova Science Publishers, Hauppauge (2015)Google Scholar
  18. 18.
    Edwards, D.J.: Unsupervised categorization with a child sample: category cohesion development. Eur. J. Dev. Psychol. 14(1), 1–12 (2016)MathSciNetGoogle Scholar
  19. 19.
    Edwards, D.J., Perlman, A., Reed, P.: Unsupervised categorization in a sample of children with autism spectrum disorders. Res. Dev. Disabil. 33(4), 1264–1269 (2012)CrossRefGoogle Scholar
  20. 20.
    Edwards, D.J., Pothos, E.M., Perlman, A.: Relational versus absolute representation in categorization. Am. J. Psychol. 125(4), 481–497 (2012)CrossRefGoogle Scholar
  21. 21.
    Edwards, D.J., Wood, R.: Unsupervised categorization with individuals diagnosed as having moderate traumatic brain injury: over-selective responding. Brain Injury 30(13–14), 1–5 (2016)Google Scholar
  22. 22.
    Eriksen, C.W.: The flankers task and response competition: a useful tool for investigating a variety of cognitive problems. Visual Cogn. 2(2–3), 101–118 (1995)CrossRefGoogle Scholar
  23. 23.
    Ferster, C.B., Skinner, B.F.: Schedules of Reinforcement (1957)Google Scholar
  24. 24.
    Friedenberg, J., Silverman, G.: Cognitive Science: An Introduction to the Study of Mind. Sage, California (2011)Google Scholar
  25. 25.
    Gigerenzer, G., Porter, T.: The Empire of Chance: How Probability Changed Science and Everyday Life, vol. 12. Cambridge University Press, New York (1990)Google Scholar
  26. 26.
    Gingold, Y., Shamir, A., Cohen-Or, D.: Micro perceptual human computation for visual tasks. ACM Trans. Graph. (TOG) 31(5), 119 (2012)CrossRefGoogle Scholar
  27. 27.
    Gosling, S.D., Vazire, S., Srivastava, S., John, O.P.: Should we trust web-based studies? A comparative analysis of six preconceptions about internet questionnaires. Am. Psychol. 59(2), 93 (2004)CrossRefGoogle Scholar
  28. 28.
    Healey, C., Enns, J.: Attention and visual memory in visualization and computer graphics. IEEE Trans. Visual Comput. Graphics 18(7), 1170–1188 (2012)CrossRefGoogle Scholar
  29. 29.
    Heer, J., Bostock, M.: Crowdsourcing graphical perception: using mechanical turk to assess visualization design. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 203–212. ACM (2010)Google Scholar
  30. 30.
    Hoffman, Y., Perlman, A., Orr-Urtreger, B., Tzelgov, J., Pothos, E.M., Edwards, D.J.: Psychological Research. Springer, Heidelberg (2016)Google Scholar
  31. 31.
    Hollan, J., Hutchins, E., Kirsh, D.: Distributed cognition: toward a new foundation for human-computer interaction research. ACM Trans. Comput.-Hum. Interact. (TOCHI) 7(2), 174–196 (2000)CrossRefGoogle Scholar
  32. 32.
    Humphrey, G.: The psychology of the gestalt. J. Educ. Psychol. 15(7), 401 (1924)CrossRefGoogle Scholar
  33. 33.
    Hutchins, E.: Cognition in the Wild. MIT Press, Cambridge (1995)Google Scholar
  34. 34.
    Hutchins, E.: How a cockpit remembers its speeds. Cogn. Sci. 19(3), 265–288 (1995)CrossRefGoogle Scholar
  35. 35.
    Hutchins, E., Holder, B.: Conceptual models for understanding an encounter with a mountain wave. In: HCI-Aero 2000 (2000)Google Scholar
  36. 36.
    James, W.: The Principles of Psychology, vol. 2 (1890)Google Scholar
  37. 37.
    Kieras, D., Polson, P.G.: An approach to the formal analysis of user complexity. Int. J. Hum. Comput. Stud. 51(2), 405–434 (1999)CrossRefGoogle Scholar
  38. 38.
    Lakoff, G.: Women, Fire, and Dangerous Things: What Categories Reveal About the Mind. Cambridge University Press, Cambridge (1990)Google Scholar
  39. 39.
    Laurence, S., Margolis, E.: Concepts and cognitive science. In: Concepts: Core Readings, pp. 3–81 (1999)Google Scholar
  40. 40.
    Logan, G.D., Zbrodoff, N.J.: Stroop-type interference: congruity effects in color naming with typewritten responses. J. Exp. Psychol. Hum. Percept. Perform. 24(3), 978 (1998)CrossRefGoogle Scholar
  41. 41.
    Marr, D., Vaina, L.: Representation and recognition of the movements of shapes. Proc. Royal Soc. London B: Biol. Sci. 214(1197), 501–524 (1982)CrossRefGoogle Scholar
  42. 42.
    Mayes, J.T., Draper, S.W., McGregor, A.M., Oatley, K.: Information flow in a user interface: the effect of experience and context on the recall of macwrite screens. In: Human-computer interaction. pp. 222–234. Prentice Hall Press (1990)Google Scholar
  43. 43.
    Micallef, L., Dragicevic, P., Fekete, J.D.: Assessing the effect of visualizations on Bayesian reasoning through crowdsourcing. IEEE Trans. Visual Comput. Graph. 18(12), 2536–2545 (2012)CrossRefGoogle Scholar
  44. 44.
    Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2), 81 (1956)CrossRefGoogle Scholar
  45. 45.
    Moroney, N.: Unconstrained web-based color naming experiment. In: Electronic Imaging 2003, pp. 36–46. International Society for Optics and Photonics (2003)Google Scholar
  46. 46.
    Nardi, B.A.: Context and Consciousness: Activity Theory and Human-Computer Interaction. MIT Press, Cambridge (1996)Google Scholar
  47. 47.
    Neisser, U.: Cognitive Psychology. Appleton-Century-Crofts, New York (1967)Google Scholar
  48. 48.
    Nosofsky, R.M.: Exemplar-based accounts of relations between classification, recognition, and typicality. J. Exp. Psychol. Learn. Mem. Cogn. 14(4), 700 (1988)CrossRefGoogle Scholar
  49. 49.
    Orne, M.T.: On the social psychology of the psychological experiment: with particular reference to demand characteristics and their implications. Am. Psychol. 17(11), 776 (1962)CrossRefGoogle Scholar
  50. 50.
    Paolacci, G., Chandler, J.: Inside the turk understanding mechanical turk as a participant pool. Curr. Dir. Psychol. Sci. 23(3), 184–188 (2014)CrossRefGoogle Scholar
  51. 51.
    Pavlov, A.P.: ... Le crétacé inférieur de la Russie et sa faune, vol. 16. Typo-lithographie de la Société IN Kouchnéreff & c-ie (1901)Google Scholar
  52. 52.
    Perlman, A., Hoffman, Y., Tzelgov, J., Pothos, E.M., Edwards, D.J.: The notion of contextual locking: previously learnt items are not accessible as such when appearing in a less common context. Q. J. Exp. Psychol. 69(3), 410–431 (2016)CrossRefGoogle Scholar
  53. 53.
    Perlman, A., Pothos, E.M., Edwards, D.J., Tzelgov, J.: Task-relevant chunking in sequence learning. J. Exp. Psychol. Hum. Percept. Perform. 36(3), 649 (2010)CrossRefGoogle Scholar
  54. 54.
    Posner, M.I., Cohen, Y.: Components of visual orienting. Attention Perform. X: Control Lang. Processes 32, 531–556 (1984)Google Scholar
  55. 55.
    Pothos, E.M., Chater, N.: A simplicity principle in unsupervised human categorization. Cogn. Sci. 26(3), 303–343 (2002)CrossRefGoogle Scholar
  56. 56.
    Pothos, E.M., Edwards, D.J., Perlman, A.: Supervised versus unsupervised categorization: two sides of the same coin? Q. J. Exp. Psychol. 64(9), 1692–1713 (2011)CrossRefGoogle Scholar
  57. 57.
    Pothos, E.M., Perlman, A., Bailey, T.M., Kurtz, K., Edwards, D.J., Hines, P., McDonnell, J.V.: Measuring category intuitiveness in unconstrained categorization tasks. Cognition 121(1), 83–100 (2011)CrossRefGoogle Scholar
  58. 58.
    Pothos, E.M., Perlman, A., Edwards, D.J., Gureckis, T.M., Hines, P.M., Chater, N.: Modeling category intuitiveness. In: Proceedings of the 30th Annual Conference of the Cognitive Science Society. LEA, Mahwah (2008)Google Scholar
  59. 59.
    Quinlan, P.T., Humphreys, G.W.: Visual search for targets defined by combinations of color, shape, and size: an examination of the task constraints on feature and conjunction searches. Percept. Psychophysics 41(5), 455–472 (1987)CrossRefGoogle Scholar
  60. 60.
    Reips, U.D.: The web experiment method: advantages, disadvantages, and solutions. In: Psychological Experiments on the Internet, pp. 89–117 (2000)Google Scholar
  61. 61.
    Rosenthal, R., Fode, K.L.: The effect of experimenter bias on the performance of the albino rat. Behav. Sci. 8(3), 183–189 (1963)CrossRefGoogle Scholar
  62. 62.
    Rosenthal, R., Jacobson, L.: Pygmalion in the Classroom: Teacher Expectation and Pupils’ Intellectual Development. Holt, Rinehart & Winston, New York (1968)Google Scholar
  63. 63.
    Scaife, M., Rogers, Y.: External cognition: how do graphical representations work? Int. J. Hum. Comput. Stud. 45(2), 185–213 (1996)CrossRefGoogle Scholar
  64. 64.
    Shannon, C.E., Weaver, W.: The mathematical theory of communication (1949)Google Scholar
  65. 65.
    Shawver, Z., Griffith, J.D., Adams, L.T., Evans, J.V., Benchoff, B., Sargent, R.: An examination of the WHOQOL-BREF using four popular data collection methods. Comput. Hum. Behav. 55, 446–454 (2016)CrossRefGoogle Scholar
  66. 66.
    Shepard, R.N.: Attention and the metric structure of the stimulus space. J. Math. Psychol. 1(1), 54–87 (1964)CrossRefMathSciNetGoogle Scholar
  67. 67.
    Shepard, R.N., Hovland, C.I., Jenkins, H.M.: Learning and memorization of classifications. Psychol. Monogr. General Appl. 75(13), 1 (1961)CrossRefGoogle Scholar
  68. 68.
    Shepard, S., Metzler, D.: Mental rotation: effects of dimensionality of objects and type of task. J. Exp. Psychol. Hum. Percept. Perform. 14(1), 3 (1988)CrossRefGoogle Scholar
  69. 69.
    Smith, D.J., Minda, J.P.: Thirty categorization results in search of a model. J. Exp. Psychol. Learn. Mem. Cogn. 26(1), 3 (2000)CrossRefGoogle Scholar
  70. 70.
    Stewart, N., Brown, G.D., Chater, N.: Absolute identification by relative judgment. Psychol. Rev. 112(4), 881 (2005)CrossRefGoogle Scholar
  71. 71.
    Titchener, E.B.: The ‘type-theory’ of the simple reaction. Mind 18, 236–241 (1896)CrossRefGoogle Scholar
  72. 72.
    Tversky, A., Kahneman, D.: Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. Psychol. Rev. 90(4), 293 (1983)CrossRefGoogle Scholar
  73. 73.
    Tversky, A., Kahneman, D.: The framing of decisions and the psychology of choice. In: Wright, G. (ed.) Environmental Impact Assessment, Technology Assessment, and Risk Analysis, pp. 107–129. Springer, Boston (1985)CrossRefGoogle Scholar
  74. 74.
    Vanpaemel, W., Storms, G.: In search of abstraction: the varying abstraction model of categorization. Psychon. Bull. Rev. 15(4), 732–749 (2008)CrossRefGoogle Scholar
  75. 75.
    Wertheimer, M.: Laws of Organization in Perceptual Forms. A Source Book of Gestalt Psychology. Routledge & Kegan Paul, London (1923)Google Scholar
  76. 76.
    Williams, L.G.: The effects of target specification on objects fixated during visual search. Acta Psychol. 27, 355–360 (1967)CrossRefGoogle Scholar
  77. 77.
    Wright, P.C., Fields, R.E., Harrison, M.D.: Analyzing human-computer interaction as distributed cognition: the resources model. Hum.-Comput. Interact. 15(1), 1–41 (2000)CrossRefGoogle Scholar
  78. 78.
    Young, R.M., Howes, A., Whittington, J.: A knowledge analysis of interactivity. In: Proceedings of the IFIP TC13 Third Interational Conference on Human-Computer Interaction, pp. 115–120. North-Holland Publishing Co. (1990)Google Scholar
  79. 79.
    Zentall, T.R., Galizio, M., Critchfield, T.S.: Categorization, concept learning, and behavior analysis: an introduction. J. Exp. Anal. Behav. 78(3), 237–248 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Darren J. Edwards
    • 1
  • Linda T. Kaastra
    • 2
  • Brian Fisher
    • 2
  • Remco Chang
    • 3
  • Min Chen
    • 4
  1. 1.Swansea UniversitySwanseaUK
  2. 2.Simon Fraser UniversityBurnabyCanada
  3. 3.Tufts UniversityMedfordUSA
  4. 4.University of OxfordOxfordUK

Personalised recommendations