A probabilistic model of eye movements in concept formation

It has been unclear whether optimal experimental design accounts of data selection may offer insight into evidence acquisition tasks in which the learner's beliefs change greatly during the course of learning. Data from Rehder and Hoffman's [Eyetracking and selective attention in category learning, Cognitive Psychol. 51 (2005) 1–41] eye movement version of Shepard, Horland and Jenkins’ classic concept learning task provide an opportunity to address these issues. We introduce a principled probabilistic concept-learning model that describes the development of subjects’ beliefs on that task. We use that learning model, together with a sampling function inspired by theory of optimal experimental design, to predict subjects’ eye movements on the active learning version of that task. Results show that the same rational sampling function can predict eye movements early in learning, when uncertainty is high, as well as late in learning when the learner is certain of the true category.