A common practice in cognitive modeling is to develop new models specific to each particular task. We question this approach and draw on an existing theory, instance-based learning theory (IBLT), to explain learning behavior in three different choice tasks. The same instance-based learning model generalizes accurately to choices in a repeated binary choice task, in a probability learning task, and in a repeated binary choice task within a changing environment. We assert that, although the three tasks are different, the source of learning is equivalent and therefore, the cognitive process elicited should be captured by one single model. This evidence supports previous findings that instance-based learning is a robust learning process that is triggered in a wide range of tasks from the simple repeated choice tasks to the most dynamic decision making tasks.

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a b s t r a c tResearch into human decision-making has often sidestepped the question of search despite its importance across a wide range of domains such as search for food, mates, allies, visual targets or information. Recently, research on decisions from experience has made progress in finding out how individual characteristics shape search for information. Surprisingly little is known, however, about how the properties of the choice ecology shape people's search. To fill this void, we analyzed how two key ecological properties influence search effort: domain of choice (gains vs. losses) and experienced variance (variance vs. no variance). Many people search longer when facing the prospect of losses relative to gains. Moreover, most people search more in options in which they experience variance relative to options they experience as invariant. We conclude that two factors that have been identified as important determinants of choice also influence search of information.Ó 2012 Elsevier B.V. All rights reserved. IntroductionFor Herbert Simon, one of the most influential economists and cognitive scientists of the second half of the 20th century, search was perhaps the key cognitive process of human life. For instance, in his work with Allen Newell, he conceptualized problem-solving as search for a path through a problem space with the goal of finding a route linking the problem's initial state to the goal state via intermediate steps and subgoals (Newell & Simon, 1972). Search also figures prominently in his notion of ''satisficing'' (Simon, 1956(Simon, , 1990. Satisficing is a method one can recruit when choosing from a set of alternatives (e.g., job candidates, mates) that one encounters sequentially and when little is known about the possible alternatives further down the road. Satisficing means that the decision maker sets an aspiration level and terminates sequential search for alternatives as soon as one is found whose value exceeds the aspiration level. Finally, in a grand metaphor of human life, Simon (1996) depicted our existence as search through a ''life's maze'' (p. 166), representing a multiplicity of needs and wants, and the need to ''survive without drawing upon superhuman powers of intelligence and computation'' (p. 175).Notwithstanding the importance of search as a behavioral and cognitive process across a wide range of domains-foraging for food, visual search for targets, memory search in recall and recognition tasks, the search for information on the web, and the search for mates in social environments (Todd, Hills, & Robbins, 2012)-research on behavioral decision-making (e.g., research on inductive and logical reasoning) and cognition (e.g., research on moral reasoning) has often sidestepped questions of search by using tasks in which summary information is conveniently displayed before the respondents. Past research on risky choice exemplifies this methodological proclivity. Decision researchers, using choice between monetary 0010-0277/$ -see front matter Ó