decision episode begins, the decision maker is confronted with a choice among several immediately available actions. But this choice depends on plans for future actions and events that will follow the impending decision. The anticipated consequences of the decision are retrieved using a retrieval cue that includes the current action and a short sequence of future actions and events. The consequences associated with the traces activated by this retrieval cue are used to form an evaluation of each imminent action at that moment. These evaluations provide the evaluative input that enters into the updating of the preference state, and this evaluation process continues over time during the decision episode until one of the preference states grows sufficiently strong to exceed the threshold for taking action.

In summary, this integrated exemplar learning and sequential sampling decision model produces immediate actions based on evaluations of future plans and scenarios. Although plans and future scenarios are used to evaluate actions at a choice point, these plans are not rigidly followed and can change at a new choice point depending on recently experienced events. Furthermore, because the evaluations are based on feedback from past outcomes, this evaluation process can include adapting to recent events as well as learning from experience.

The model just described represents only one of many possible ways of integrating decision and learning models. Another possibility, for example, would be to combine rule-based learning models with probabilistic choice models (cf. Anderson, 1993) or to combine neural network learning models with sequential sampling decision models. Clearly more research is needed on this important topic.

INCORPORATING INDIVIDUAL DIFFERENCES AND MODERATING STATES

Decision makers differ in a multitude of ways, such as risk-averse versus risk-seeking attitudes, optimistic versus pessimistic opinions, passive versus aggressive inclinations, rational versus irrational thinking, impulsive versus compulsive tendencies, and expert versus novice abilities. They also differ in terms of physical, mental, and emotional states, such as rested versus fatigued, stressed versus calm, healthy versus wounded, and fearful versus fearless. One way to incorporate these individual difference factors and state factors into the decision process is by relating them to parameters of the decision models. The discussion here is focused on the effects of these factors on the decision making process; Chapter 9 examines moderator variables in more detail.

Individual Difference Factors

Risk attitude is a classic example of an individual difference factor in expected utility theory. The shape of the utility function is assumed to vary across



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