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

Article Type: Abstracts From: Development and Learning in Organizations, Volume 26, Issue 4

Lejarraga T., Dutt V. and Gonzalez C.Journal of Behavioral Decision Making, April 2012, Vol. 25 No. 2, Start page: 143, No. of pages: 10

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.ISSN: 0894-3257Article type: Research paperReference: 41AH888

Keywords: Instance-based learning, Model generalization,Probability learning, Repeated choice