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  • Print publication year: 2008
  • Online publication date: June 2012

Chapter 16 - The Dynamic Interactions between Situations and Decisions

from Part III - Empirical Developments


It is often claimed that for any item to count as representational, it must form part of a general representational scheme or framework. Many people, though by no means all, claim that the idea of representation can be captured, in part, in terms of the concept of information. Many suppose that models of representation are subject to a teleological constraint. It is common to hold that, to be regarded as genuinely representational, a representation must be decouplable from the environment. In connection with the informational constraint, the possibility of representation is closely tied to the possibility of misrepresentation. Much recent work on cognition is characterized by an augmentation of the role of action coupled with an attenuation of the role of representation. This chapter discusses the representation and the extended mind, the first horn and the second horn.


Anzai, Y. (1984). Cognitive control of real-time event-driven systems. Cognitive Science, 8, 221–254.
Ashby, F. G. (2000). A stochastic version of general recognition theory. Journal of Mathematical Psychology, 44, 310–329.
Beardon, J. N., Murphey, R. O., Rapoport, A. (2005). A multiattribute extension of the secretary problem: Theory and experiments. Journal of Mathematical Psychology, 49, 410–422.
Brehmer, B. (1992). Dynamic decision making: Human control of complex systems. Acta Psychologica, 81, 211–241.
Brehmer, B., & Allard, R. (1991). Real-time dynamic decision making: Effects of task complexity and feedback delays. In J. Rasmussen, B. Brehmer, & J. Leplat (Eds.), Distributed decision making: Cognitive models for cooperative work (pp. 319–334). Chichester, UK: Wiley.
Brunswik, E. (1952). The conceptual framework of psychology. Chicago: University of Chicago Press.
Busemeyer, J. R. (1985). Decision making under uncertainty: A comparison of simple scalability, fixed sample, and sequential sampling models. Journal of Experimental Psychology, 11, 538–564.
Busemeyer, J. R., & Johnson, J. G. (2004). Computational models of decision making. In D. J. Koehler & N. Harvey (Eds.), Blackwell handbook of judgment and decision making. Oxford, UK: Blackwell.
Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100, 432–459.
Clemens, R. (1996). Making hard decisions: An introduction to decision analysis (2nd ed.). Boston: PWS-Kent.
Dhami, M. K., Hertwig, R., & Hoffrage, U. (2004). The role of representative design in an ecological approach to cognition. Psychological Bulletin, 130(6), 959–988.
Dhar, R., Nowlis, S. M., & Sherman, S. J. (2000). Trying hard or hardly trying: An analysis of context effects in choice. Journal of Consumer Psychology, 9, 189–200.
Diederich, A. (2003). MDFT account of decision making under time pressure. Psychonomic Bulletin and Review, 10(1), 157–166.
Dienes, Z., & Fahey, R. (1995). Role of specific instances in controlling a dynamic system. Journal of Experimental Psychology: Learning, Memory, & Cognition, 21, 848–862.
Edwards, W. (1962). Dynamic decision theory and probabilistic information processing. Human Factors, 4, 59–73.
Ericsson, K. A., & Simon, H. A. (1984). Protocol analysis: Verbal reports as data. Cambridge, MA: MIT Press.
Fischoff, B., & Slovic, P. (1978). A little learning: Confidence in multicue judgment tasks. Eugene, OR: Decision Research.
Frank, M. J. (2005). Dynamic dopamine modulation in the basal ganglia: A neurocomputational account of cognitive deficits in medicated and nonmedicated Parkinsonism. Journal of Cognitive Neuroscience, 17(1), 51–72.
Gibson, F., Fichman, M., & Plaut, D. C. (1997). Learning in dynamic decision tasks: Computational model and empirical evidence. Organizational Behavior and Human Performance, 71, 1–35.
Gilboa, I., & Schmeidler, D. (1995). Case-based theory. Quarterly Journal of Economics, 110, 607–639.
Goldstein, W. M., & Weber, E. U. (1995). Content and discontent: Indications and implications of domain specificity in preferential decision making. In J. R. Busemeyer et al. (Eds.), The psychology of learning and motivation: Decision making from a cognitive perspective (Vol. 32, pp. 82–136). San Diego, CA: Academic Press.
Gonzalez, C., Lerch, J. F., & Lebiere, C. (2003). Instance-based learning in dynamic decision making. Cognitive Science, 27, 591–635.
Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. Oxford, UK: Wiley.
Hammond, K. R., Hamm, R. M., Grassia, J., & Pearson, T. (1987). Direct comparison of the efficacy of intuitive and analytical cognition in expert judgment. IEEE Transactions on Systems, Man, & Cybernetics, 17(5), 753–770.
Hammond, K. R., Stewart, T. R., Brehmer, B., & Steinmann, D. O. (1975). Social judgment theory. In M. Kaplan & S. Schwartz (Eds.), Human judgment and decision processes (pp. 271–312). New York: Academic Press.
Hogarth, R. M. (1981). Beyond discrete biases: Functional and dysfunctional aspects of judgmental heuristics. Psychological Bulletin, 90, 197–217.
Holyoak, K. J., & Simon, D. (1999). Bidirectional reasoning in decision making by constraint satisfaction. Journal of Experimental Psychology: General, 128(1), 3–31.
Jagacinski, R. J., & Hah, S. (1988). Progression-regression effects in tracking repeated patterns. Journal of Experimental Psychology: Human Perception and Performance, 14, 77–88.
Jagacinski, R. J., & Miller, R. A. (1978). Describing the human operator's internal model of a dynamic system. Human Factors, 20, 425–433.
Kaplan, M. (1996). Decision theory as philosophy. Cambridge: Cambridge University Press.
Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value tradeoffs. New York: Wiley.
Kirlik, A., Miller, R. A., & Jagacinski, R. J. (1993). Supervisory control in a dynamic and uncertain environment: A process model of skilled human environment interaction. IEEE Transactions on Systems, Man, and Cybernetics, 23, 929–952.
Klein, G. A. (1989). Recognition primed decisions. In W. B. Rouse (Ed.), Advances in man-machine systems research (Vol. 5, pp. 47–92). Greenwich, CT: JAI Press.
LeBoeuf, R. A., & Shafir, E. (2001). Problems and methods in naturalistic decision-making research. Journal of Behavioral Decision Making, 14(5), 373–375.
Levin, I. P., Louviere, J. J., Schepanski, A. A., & Norman, K. L. (1983). External validity tests of laboratory studies of information integration. Organizational Behavior & Human Performance, 31(2), 173–193.
Lewin, K. (1936). Principles of topological psychology. New York: McGraw-Hill.
Link, S. W., & Heath, R. A. (1975). A sequential theory of psychological discrimination. Psychometrika, 40, 77–111.
Lipshitz, R., Klein, G., Orasanu, J., & Salas, E. (2001). Taking stock of naturalistic decision making. Journal of Behavioral Decision Making, 14(5), 331–352.
Loftus, E. F. (2003). Make-believe memories. American Psychologist, 58(11), 867–873.
Luce, R. D. (1986). Response times: Their role in inferring elementary mental organization. New York: Oxford University Press.
Luce, R. D. (2000). Utility of gains and losses: Measurement-theoretical and experimental approaches. Mahwah, NJ: Lawrence Erlbaum.
Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84(3), 231–259.
Nosofsky, R. M., & Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104, 226–300.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1992). Behavioral decision research: A constructive processing perspective. Annual Review of Psychology, 43, 87–131.
Port, R. F., & van Gelder, T. (Eds.). (1995). Mind as motion: Explorations in the dynamics of cognition. Cambridge, MA: MIT Press.
Rapoport, A. (1975). Research paradigms for studying dynamic decision behavior. In D. Wendt & C. Vlek (Eds.), Utility, probability, and human decision making (pp. 347–369). Dordrecht, The Netherlands: Reidel.
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59–108.
Roe, R. M., Busemeyer, J. R., & Townsend, J. T. (2001). Multi-alternative decision field theory: A dynamic connectionist model of decision-making. Psychological Review, 108, 370–392.
Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69, 99–118.
Simonson, I. (1989). Choice based on reasons: The case of attraction and compromise effects. Journal of Consumer Research, 16, 158–174.
Smith, P. L. (1995). Psychophysically principled models of visual simple reaction time. Psychological Review, 102(3), 567–593.
Sterman, J. D. (1994). Learning in and about complex systems. System Dynamics Review, 10, 291–330.
Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: A reference dependent model. Quarterly Journal of Economics, 106, 1039–1061.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representations of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.
Tversky, A., & Simonson, I. (1993). Context-dependent preferences. Management Science, 39, 1179–1189.
Usher, M., & McClelland, J. L. (2004). Loss aversion and inhibition in dynamical models of multialternative choice. Psychological Review, 111(3), 757–769.
van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences, 21, 1–14.
Vickers, D. (1979). Decision processes in visual perception. New York: Academic Press.
von Winterfeldt, D., & Edwards, W. (1986). Decision analysis and behavioral research. Cambridge: Cambridge University Press.
Wickens, J. (1997). Basal ganglia: Structure and computations. Network: Computation in Neural Systems, 8, R77–R109.
Yates, J. F. (2004, March/April). “Let's just go with it.”: The perils of decision neglect. Ivey Business Journal. Retrieved from
Yates, J. F., Veinott, E. S., & Patalano, A. L. (2003). Hard decisions, bad decisions: On decision quality and decision aiding. In S. L. Schneider & J. L. Shanteau (Eds.), Emerging perspectives in judgment and decision making research (pp. 13–63). New York: Cambridge University Press.
You, G. (1989). Disclosing the decision-maker's internal model and control policy in a dynamic decision task using a system control paradigm. Unpublished master's thesis, Purdue University.
Zsambok, C. E., & Klein, G. (1997). Naturalistic decision making: Expertise, research and applications. Hillsdale, NJ: Lawrence Erlbaum.