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 » ... 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. 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