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Decision field theory (DFT) is a dynamic-cognitive approach to human decision making. It is a cognitive model that describes how people actually make decisions rather than a rational or normative theory that prescribes what people should or ought to do. It is also a dynamic model of decision making rather than a static model, because it describes how a person's preferences evolve across time until a decision is reached rather than assuming a fixed state of preference. The preference evolution process is mathematically represented as a stochastic process called a diffusion process. It is used to predict how humans make decisions under uncertainty, how decisions change under time pressure, and how choice context changes preferences. This model can be used to predict not only the choices that are made but also decision or response times. The paper "Decision Field Theory" was published by Jerome R. Busemeyer and James T. Townsend in 1993.〔Busemeyer, J. R., & Townsend, J. T. (1993) (Decision Field Theory: A dynamic cognition approach to decision making ). Psychological Review, 100, 432–459.〕〔Busemeyer, J. R., & Diederich, A. (2002). Survey of decision field theory. Mathematical Social Sciences, 43(3), 345-370.〕〔Busemeyer, J. R., & Johnson, J. G. (2004). Computational models of decision making. Blackwell handbook of judgment and decision making, 133-154.〕〔Busemeyer, J. R., & Johnson, J. G. (2008). Microprocess models of decision making. Cambridge handbook of computational psychology, 302-321.〕 The DFT has been shown to account for many puzzling findings regarding human choice behavior including violations of stochastic dominance, violations of strong stochastic transitivity, violations of independence between alternatives, serial position effects on preference, speed accuracy tradeoff effects, inverse relation between probability and decision time, changes in decisions under time pressure, as well as preference reversals between choices and prices. The DFT also offers a bridge to neuroscience.〔Busemeyer, J. R., Jessup, R. K., Johnson, J. G., & Townsend, J. T. (2006). Building bridges between neural models and complex decision making behaviour. Neural Networks, 19(8), 1047-1058.〕 Recently, the authors of decision field theory also have begun exploring a new theoretical direction called Quantum Cognition. ==Introduction== The name ''decision field theory'' was chosen to reflect the fact that the inspiration for this theory comes from an earlier approach - avoidance conflict model contained in Kurt Lewin's general psychological theory, which he called ''field'' theory. DFT is a member of a general class of sequential sampling models that are commonly used in a variety of fields in cognition.〔Ashby, F. G. (2000). A stochastic version of general recognition theory. Journal of Mathematical Psychology, 44, 310–329.〕〔Nosofsky, R. M., & Palmeri, T. J. (1997). An exemplar-based random walk model of speeded classification. Psychological Review, 104, 226–300.〕〔Laming, D. R. (1968). Information theory of choice-reaction times. New York: Academic Press.〕〔Link, S. W., & Heath, R. A. (1975). A sequential theory of psychological discrimination. Psychometrika, 40, 77–111.〕〔Smith, P. L. (1995). Psychophysically principled models of visual simple reaction time. Psychological Review, 102(3), 567–593.〕〔Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: the leaky, competing accumulator model. Psychological Review, 108(3), 550–592.〕〔Ratcliff, R. (1978). A theory of memory retrieval. Psychological review, 85(2), 59.〕 The basic ideas underlying the decision process for sequential sampling models is illustrated in Figure 1 below. Suppose the decision maker is initially presented with a choice between three risky prospects, A, B, C, at time t = 0. The horizontal axis on the figure represents deliberation time (in seconds), and the vertical axis represents preference strength. Each trajectory in the figure represents the preference state for one of the risky prospects at each moment in time.〔 Intuitively, at each moment in time, the decision maker thinks about various payoffs of each prospect, which produces an affective reaction, or valence, to each prospect. These valences are integrated across time to produce the preference state at each moment. In this example, during the early stages of processing (between 200 and 300 ms), attention is focused on advantages favoring prospect B, but later (after 600 ms) attention is shifted toward advantages favoring prospect A. The stopping rule for this process is controlled by a threshold (which is set equal to 1.0 in this example): the first prospect to reach the top threshold is accepted, which in this case is prospect A after about one second. Choice probability is determined by the first option to win the race and cross the upper threshold, and decision time is equal to the deliberation time required by one of the prospects to reach this threshold.〔 The threshold is an important parameter for controlling speed–accuracy tradeoffs. If the threshold is set to a lower value (about .50) in Figure 1, then prospect B would be chosen instead of prospect A (and done so earlier). Thus decisions can reverse under time pressure.〔Diederich, A. (2003). MDFT account of decision making under time pressure. Psychonomic Bulletin and Review, 10(1), 157–166.〕 High thresholds require a strong preference state to be reached, which allows more information about the prospects to be sampled, prolonging the deliberation process, and increasing accuracy. Low thresholds allow a weak preference state to determine the decision, which cuts off sampling information about the prospects, shortening the deliberation process, and decreasing accuracy. Under high time pressure, decision makers must choose a low threshold; but under low time pressure, a higher threshold can be used to increase accuracy. Very careful and deliberative decision makers tend to use a high threshold, and impulsive and careless decision makers use a low threshold.〔 To provide a bit more formal description of the theory, assume that the decision maker has a choice among three actions, and also suppose for simplicity that there are only four possible final outcomes. Thus each action is defined by a probability distribution across these four outcomes. The affective values produced by each payoff are represented by the values mj. At any moment in time, the decision maker anticipates the payoff of each action, which produces a momentary evaluation, Ui(t), for action i. This momentary evaluation is an attention-weighted average of the affective evaluation of each payoff: Ui(t) = Σ Wij(t)mj. The attention weight at time t, Wij(t), for payoff j offered by action i, is assumed to fluctuate according to a stationary stochastic process. This reflects the idea that attention is shifting from moment to moment, causing changes in the anticipated payoff of each action across time. The momentary evaluation of each action is compared with other actions to form a valence for each action at each moment, vi(t) = Ui(t) – U.(t), where U.(t) equals the average across all the momentary actions. The valence represents the momentary advantage or disadvantage of each action. The total valence balances out to zero so that all the options cannot become attractive simultaneously. Finally, the valences are the inputs to a dynamic system that integrates the valences over time to generate the output preference states. The output preference state for action i at time t is symbolized as Pi(t). The dynamic system is described by the following linear stochastic difference equation for a small time step h in the deliberation process: Pi(t+h) = Σ sijPj(t)+vi(t+h).The positive self feedback coefficient, sii = s > 0, controls the memory for past input valences for a preference state. Values of sii < 1 suggest decay in the memory or impact of previous valences over time, whereas values of sii > 1 suggest growth in impact over time (primacy effects). The negative lateral feedback coefficients, sij = sji < 0 for i not equal to j, produce competition among actions so that the strong inhibit the weak. In other words, as preference for one action grows stronger, then this moderates the preference for other actions. The magnitudes of the lateral inhibitory coefficients are assumed to be an increasing function of the similarity between choice options. These lateral inhibitory coefficients are important for explaining context effects on preference described later. Formally, this is a Markov process; matrix formulas have been mathematically derived for computing the choice probabilities and distribution of choice response times.〔 The decision field theory can also be seen as a dynamic and stochastic random walk theory of decision making, presented as a model positioned between lower-level neural activation patterns and more complex notions of decision making found in psychology and economics.〔 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Decision field theory」の詳細全文を読む スポンサード リンク
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