Instance-based learning: a general model of repeated binary choice

T. Lejarraga
2012 Development and Learning in Organizations: An International Journal  
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
more » ... 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. Note: The prediction problems in Myers et al. (1961) are expressed as choices between gambles in the following form: In both options, the gambles offer a high amount with probability p(high) and a low amount otherwise. Both gambles depend on the same random draw that determines the realization of high.
doi:10.1108/dlo.2012.08126daa.003 fatcat:fuup5hjmebbmtajzggdhclq4xa