FIGURE 6.1 Illustration of the main ideas of sequential sampling decision models. The vertical axis represents strength of preference, whereas the horizontal axis represents time. The trajectories labeled P1, P2, and P3 represent the evolution of the preference states for actions 1, 2, and 3 over time, and the trajectory labeled theta represents the strength of the inhibitory bound over time. A decision is reached when one of the preference states exceeds the threshold bound, and the first action to exceed this bound determines the choice that is made. In this example, action 2 crosses the threshold first, and thus it is chosen. The vertical line indicated by RT is the time required to make this decision for this example.

attributes favor action 2. Under severe time pressure, the inhibitory threshold is low, and it will be exceeded after a small number of consequences have been sampled. Attention is focused initially on the most important attribute favoring action 1, and the result is a high probability of choosing action 1 under time pressure. Under no time pressure, the inhibitory threshold is higher, and it will be exceeded only after a large number of consequences have been sampled. Attention is focused initially on the most important attribute favoring action 1, but now there is sufficient time to refocus attention on all of the remaining attributes that favor action 2; the result is a high probability of choosing action 2 with no time pressure. In this way, the rank ordering of choice probabilities can change with the manipulation of time pressure.

In summary, sequential sampling models extend random-utility models to provide a dynamic description of the decision process within a single decision episode. This extension is important for explaining the effects of time pressure on decision making, as well as the relation between speed and accuracy in making a decision. However, the example presented above is limited to decisions involving a single stage. Most military decisions require adaptive planning for



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