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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 threedoi:10.1108/dlo.2012.08126daa.003 fatcat:fuup5hjmebbmtajzggdhclq4xa