Wechselwirkung Mensch und autonomer Agent

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Menschen repräsentieren Wissen und Lernerfahrungen in Form von mentalen Modellen. Dieses aus der Kognitionspsychologie stammende Konzept ist eines der zentralen theoretischen Paradigmen für das Verständnis und die Gestaltung der Interaktion von Menschen mit technischen Systemen [1]. Mentale Modelle dienen in diesem Kontext einerseits der Beschreibung menschlicher Informationsverarbeitung, z. B.


  1. 1.
    Wilson, J.R., Rutherford, A.: Mental Models: Theory and Application in Human Factors. Hum. Factors J. Hum. Factors Ergon. Soc. 31, 617–634 (1989)Google Scholar
  2. 2.
    Bainbridge, L.: Ironies of automation. In: Johannsen, G. (ed.) Analysis, design and evaluation of man-machine systems. pp. 151–157. Pergamon (1982)Google Scholar
  3. 3.
    Norman, D.: The “problem” with automation: inappropriate feedback and interaction, not “over-automation.”Philos. Trans. R. Soc. B Biol. Sci. 327, 585–593 (1990)CrossRefGoogle Scholar
  4. 4.
    Sheridan, T.B.: Supervisory control. In: Salvendy, G. (ed.) Handbook of human factors. pp. 1243–1268. Wiley, New York (1987)Google Scholar
  5. 5.
    Boeing Commercial Airline Group: Statistical summary of commercial jet aircraft accidents: Worldwide operations 1959–2005.,
  6. 6.
    Dismukes, R.K., Berman, B.A., Loukopoulos, L.D.: The Limits of Expertise: Rethinking Pilot Error and the Causes of Airline Accidents. Ashgate Publishing, Ltd. Federal (2007)Google Scholar
  7. 7.
    Bainbridge, L.: Ironies of Automation. Automatica. 19, 775–779 (1983)CrossRefGoogle Scholar
  8. 8.
    Endsley, M.R., Kiris, E.O.: The Out-of-the-Loop Performance Problem and Level of Control in Automation. Hum. Factors J. Hum. Factors Ergon. Soc. 37, 381–394 (1995)Google Scholar
  9. 9.
    Madhavan, P., Wiegmann, D.A.: Similarities and differences between human – human and human – automation trust: an integrative review. Theor. Issues Ergon. Sci. 8, 277–301 (2007)Google Scholar
  10. 10.
    Onnasch, L., Wickens, C.D., Li, H., Manzey, D.: Human Performance Consequences of Stages and Levels of Automation: An Integrated Meta-Analysis. Hum. Factors J. Hum. Factors Ergon. Soc. 56, 476–488 (2013)Google Scholar
  11. 11.
    Endsley, M.R.: Situation awareness. In: Salvendy, G. (ed.) Handbook of human factors and ergonomics. pp. 528–542. Wiley, New York (2006)CrossRefGoogle Scholar
  12. 12.
    Endsley, M.R., Bolte, B., Jones, D.G.: Designing for situation awareness: An approach to human-centered design. Taylor & Francis, London (2003)CrossRefGoogle Scholar
  13. 13.
    Billings, C.E.: Aviation Automation: The Search for a Human-Centered Approach. Human Factors in Transportation. Lawrence Erlbaum Associates Publishers (1997)Google Scholar
  14. 14.
    Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and levels of human interaction with automation. Syst. Man Cybern. Part A Syst. Humans, IEEE Trans. 30, 286–297 (2000)Google Scholar
  15. 15.
    Christoffersen, K., Woods, D.D.: How to make automated systems team players. In: Salas, E. (ed.) Advances in Human Performance and Cognitive Engineering Research. pp. 1–12. Elsevier Science Ltd. (2002)Google Scholar
  16. 16.
    Kaber, D.B., Riley, J.M., Tan, K.-W., Endsley, M.R.: On the Design of Adaptive Automation for Complex Systems. Int. J. Cogn. Ergon. 5, 37–57 (2001)CrossRefGoogle Scholar
  17. 17.
    Inagaki, T.: Traffic systems as joint cognitive systems: issues to be solved for realizing human-technology coagency. Cogn. Technol. Work. 12, 153–162 (2010)CrossRefGoogle Scholar
  18. 18.
    Prevot, T., Homola, J.R., Martin, L.H., Mercer, J.S., Cabrall, C.D.: Toward Automated Air Traffic Control – Investigating a Fundamental Paradigm Shift in Human/Systems Interaction. Int. J. Hum. Comput. Interact. 28, 77–98 (2012)CrossRefGoogle Scholar
  19. 19.
    Challenger, R., Clegg, C.W., Shepherd, C.: Function allocation in complex systems: reframing an old problem. Ergonomics. 56, 1051–69 (2013)CrossRefGoogle Scholar
  20. 20.
    Grote, G., Weyer, J., Stanton, N.A.: Beyond human-centred automation – concepts for humanmachineGoogle Scholar
  21. interaction in multi-layered networks. Ergonomics. 57, 289–94 (2014)Google Scholar
  22. 21.
    Hancock, P.A.: Automation: how much is too much? Ergonomics. 57, 449–54 (2014)CrossRefGoogle Scholar
  23. 22.
    Arthur, C.: Google's driverless car: no steering wheel, two seats, 25mph,
  24. 23.
    Cummings, P.M.L., Ryan, J.: Shared Authority Concerns in Automated Driving Applications. LetzterZugriff: 28.08.2014
  25. 24.
    Merat, N., Lee, J.D.: Preface to the Special Section on Human Factors and Automation in Vehicles:Google Scholar
  26. Designing Highly Automated Vehicles With the Driver in Mind. Hum. Factors J. Hum. Factors Ergon. Soc. 54, 681–686 (2012)Google Scholar
  27. 25.
    Hoogendoorn, R., Arem, B. Van, Hoogendoorn, S.: Automated Driving, Traffic Flow Efficiency And Human Factors: A Literature Review. Transportation Research Board 93rd Annual Meeting. pp. 1–18. Transportation Research Board (2014)Google Scholar
  28. 26.
    Lee, J.D., See, K.A.: Trust in Automation: Designing for Appropriate Reliance. Hum. Factors J. Hum. Factors Ergon. Soc. 46, 50–80 (2004)MathSciNetGoogle Scholar
  29. 27.
    Moray, N., Inagaki, T.: Attention and complacency. Theor. Issues Ergon. Sci. 1, 354–365 (2000)Google Scholar
  30. 28.
    Ghazizadeh, M., Peng, Y., Lee, J.D., Boyle, L.N.: Augmenting the Technology Acceptance Model with Trust: Commercial Drivers' Attitudes towards Monitoring and Feedback. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 56, 2286–2290 (2012)Google Scholar
  31. 29.
    Kazi, T., Stanton, N., Walker, G., Young, M.: Designer driving: drivers' conceptual models and level of trust in adaptive cruise control. Int. J. Veh. Des. 45, 339–360 (2007)CrossRefGoogle Scholar
  32. 30.
    Koustanai, A., Cavallo, V., Delhomme, P., Mas, A.: Simulator Training With a Forward Collision Warning System: Effects on Driver-System Interactions and Driver Trust. Hum. Factors J. Hum. Factors Ergon. Soc. 54, 709–721 (2012)Google Scholar
  33. 31.
    Stanton, N.A., Young, M.S.: Driver behaviour with adaptive cruise control. Ergonomics. 48, 1294–313 (2005)CrossRefGoogle Scholar
  34. 32.
    Rajaonah, B., Tricot, N., Anceaux, F., Millot, P.: The role of intervening variables in driver–ACC cooperation. Int. J. Hum. Comput. Stud. 66, 185–197 (2008)CrossRefGoogle Scholar
  35. 33.
    Flemisch, F., Kelsch, J., Löper, C., Schieben, A., Schindler, J.: Automation spectrum, inner/outer compatibility and other potentially useful human factors concepts for assistance and automation. In: Waard, D. de, Flemisch, F., Lorenz, B., Oberheid, H., and Brookhuis, and K. (eds.) Human Factors for Assistance and Automation. pp. 1–16. Shaker, Maastricht (2008).Google Scholar
  36. 34.
    Beggiato, M., Krems, J.F.: The evolution of mental model, trust and acceptance of adaptive cruise control in relation to initial information. Transp. Res. Part F Traffic Psychol. Behav. 18, 47–57 (2013)Google Scholar
  37. 35.
    Verberne, F.M.F., Ham, J., Midden, C.J.H.: Trust in Smart Systems: Sharing Driving Goals and Giving Information to Increase Trustworthiness and Acceptability of Smart Systems in Cars. Hum. Factors J. Hum. Factors Ergon. Soc. 54, 799–810 (2012)Google Scholar
  38. 36.
    Waytz, A., Heafner, J., Epley, N.: The Mind in the Machine: Anthropomorphism Increases Trust in an Autonomous Vehicle. J. Exp. Soc. Psychol. 52, 113–117 (2014)CrossRefGoogle Scholar
  39. 37.
    Lee, J.D., Moray, N.: Trust, self-confidence, and operators' adaption to automation. Int. J. Human-Computer Stud. 40, 152–184 (1994)Google Scholar
  40. 38.
    Federal Aviation Administration: Safety Alert for Operators 13002. F. S. Service. Departement of Transportation, Washington DC (2013)Google Scholar
  41. 39.
    Buld, S., Krüger, H.-P., Hoffmann, S., Kaussner, A., Tietze, H., Totzke, I.: Wirkungen von Assistenz und Automation auf Fahrerzustand und Fahrsicherheit. Veröffentlichter Abschlussbericht Projekt EMPHASIS: Effort- Management und Performance- Handling in sicherheitsrelevanten Situationen. Würzburg: Interdisziplinäres Zentrum für VerkehrswissenschaftenGoogle Scholar
  42. an der Universität Würzburg (IZVW), Würzburg: (2002)Google Scholar
  43. 40.
    Ward, N.J.: Task automation and skill development in a simplified driving task. In Proceedings of the XIVth Triennial Congress of the International Ergonomics Association and 44th Annual Meeting of the Human Factors and Ergonomics Society. pp. 302–305, San Diego, CA (Santa Monica, CA: HFES) (2000)Google Scholar
  44. 41.
    Ma, R., Kaber, D.B.: Situation awareness and workload in driving while using adaptive cruise control and a cell phone. Int. J. Ind. Ergon. 35, 939–953 (2005)CrossRefGoogle Scholar
  45. 42.
    Merat, N., Jamson, A.H., Lai, F.C.H., Carsten, O.: Highly Automated Driving, Secondary Task Performance, and Driver State. Hum. Factors J. Hum. Factors Ergon. Soc. 54, 762–771 (2012).Google Scholar
  46. 43.
    Gasser, T.M., Arzt, C., Ayoubi, M., Bartels, A., Eier, J., Flemisch, F., Häcker, D., Hesse, T., Huber, W., Lotz, C., Maurer, M., Ruth-Schumacher, S., Schwarz, J., Vogt, W.: BASt-Bericht F 83: Rechtsfolgen zunehmender Fahrzeugautomatisierung. Bremerhaven. (2012)Google Scholar
  47. 44.
    Flemisch, F.O., Bengler, K., Bubb, H., Winner, H., Bruder, R.: Towards cooperative guidance and control of highly automated vehicles: H-Mode and Conduct-by-Wire., http://www.ncbi.nlm/, (2014)
  48. 45.
    Craik, K.J.W.: The nature of explanation. Cambridge University Press, Cambridge, England (1943)Google Scholar
  49. 46.
    Johnson-Lairds, P.N.: Mental Models: Towards a Cognitive Science of Language, Influence and Consciousness. Cambridge University Press, Cambridge, UK (1983)Google Scholar
  50. 47.
    Jones, N.A., Ross, H., Lynam, T., Perez, P., Leitch, A.: Mental Models: An Interdisciplinary Synthesis of Theory and Methods. Ecol. Soc. 16, 4–6 (2011)Google Scholar
  51. 48.
    Moray, N.: Models of models of – mental models. In: Moray, N. (ed.) Ergonomics: major writings. pp. 506–552. Taylor and Francis, London, UK. (2004)Google Scholar
  52. 49.
    Nersessian, N.J.: The cognitive basis of model-based reasoning in science. In: Carruthers, S.S. and Siegal, M. (eds.) The cognitive basis of science. pp. 133–153. Cambridge University Press, Cambridge, UK (2002)CrossRefGoogle Scholar
  53. 50.
    Norman, D.A.: Some observations on mental models. In: Baecker, R.M. and Buxton, W.A.S. (eds.) Human-computer Interaction. pp. 241–244. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1987)Google Scholar
  54. 51.
    Zhang, W., Xu, P.: Do I have to learn something new? Mental models and the acceptance of replacement technologies. Behav. Inf. Technol. 30, 201–211 (2011)CrossRefGoogle Scholar
  55. 52.
    d’Apollonia, S.T., Charles, E.S., Boyd, G.M.: Acquisition of Complex Systemic Thinking: Mental Models of Evolution. Educ. Res. Eval. 10, 499–521 (2004)CrossRefGoogle Scholar
  56. 53.
    Gefen, D., Karahanna, E., Straub, D.W.: Inexperience and experience with online stores: The importance of tam and trust. IEEE Trans. Eng. Manag. 50, 307–321 (2003)Google Scholar
  57. 54.
    Stanton, N., Young, M.S.: A proposed psychological model of driving automation. Theor. Issues Ergon. Sci. 1, 315–331 (2000)Google Scholar
  58. 55.
    Boer, E.R., Hoedemaeker, M.: Modeling driver behavior with different degrees of automation: A Hierarchical Decision Framework of Interacting Mental Models. In: Al., A.G.G. et (ed.) 17th European Annual Conference on Human Decision Making and Manual Control. pp. 63–72. Elsevier, Amsterdam (1989)Google Scholar
  59. 56.
    Schröder, T., Huck, J., de Haan, G.: Transfer sozialer Innovationen: Eine zukunftsorientierte Fallstudie zur nachhaltigen Siedlungsentwicklung. VS Verlag fuer Sozialwissenschaften, Wiesbaden (2011)CrossRefGoogle Scholar
  60. 57.
    Gigerenzer, G., Goldstein, D.G.: Reasoning the fast and frugal way: models of bounded rationality. Psychol. Rev. 103, 650–69 (1996)CrossRefGoogle Scholar
  61. 58.
    Kahnemann, D.: Thinking, fast and slow. Penguin, New York (2011)Google Scholar
  62. 59.
    Loewenstein, G.F., Lerner, J.S.: The role of affectt in decision making. In: Davidson, R., Scherer, K., and Goldsmith, H. (eds.) Handbook of affectivce science. pp. 619–642. Oxford University Press, New York (2003)Google Scholar
  63. 60.
    Kunda, Z.: The case for motivated reasoning. Psychol. Bull. 108, 480–498 (1990)CrossRefGoogle Scholar
  64. 61.
    MacKinnon, N.J., Heise, D.R.: Self, Identity, and Social Institutions. NY: Palgrave Macmillan, New York (2010)CrossRefGoogle Scholar
  65. 62.
    Wolf, I., Schröder, T., Neumann, J., de Haan, G.: Changing minds about electric cars: An empirically grounded agent-based modeling approach. Vorabdruck:, (2014)
  66. 63.
    Continental AG: Continental-Mobilitätsstudie 2013. (2013) Google Scholar
  67. 64.
    Infas, DLR: Mobilität in Deutschland (MiD) 2008. Infas Institut für angewandte Sozialwissenschaft GmbH, Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Bonn, Berlin (2008)Google Scholar
  68. 65.
    Osgood, C.E., Suci, G.J., Tannenbaum, P.H.: The Measurement of Meaning. Linguistic Society of America, Urbana (1957)Google Scholar
  69. 66.
    Heise, D.R.: Expressive order: Confirming sentiments in social action. Springer, New York (2007)Google Scholar
  70. 67.
    Löper, C., Kelsch, J., Flemisch, F.: Kooperative, manöverbasierte Automation und Arbitrierung als Bausteine für hochautomatisiertes Fahren. In: Gesamtzentrum für Verkehr Braunschweig (ed.) Automatisierungs-, Assistenzsysteme und eingebette Systeme für Transportmittel. pp. 215–237, Braunschweig (2008)Google Scholar
  71. 68.
    Buld, S., Tietze, H., Krüger, H.: Auswirkungen von Teilautomation auf das Fahren. In: Maurer, M.;Stiller, C. (Hrsg.): Fahrerassistenzsysteme mit maschineller Wahrnehmung. S. 161–187, Springer, Berlin (2005)Google Scholar

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Authors and Affiliations

  1. 1.Freie Universität BerlinInstitut FuturDeutschland

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