A probabilistic model of eye movements in concept formation
作者： Jonathan D. Nelson , Garrison W. Cottrell
DOI: 10.1016/J.NEUCOM.2006.02.026
关键词: Active learning 、 Artificial intelligence 、 Probabilistic logic 、 Eye movement 、 Multi-task learning 、 Cognitive psychology 、 Task (project management) 、 Concept learning 、 Function (engineering) 、 Computer science 、 Active learning (machine learning) 、 Cognition
摘要: 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 course learning. Data from Rehder and Hoffman's [Eyetracking selective attention category learning, Cognitive Psychol. 51 (2005) 1-41] eye movement version Shepard, Horland Jenkins' classic concept learning task provide an opportunity to address these issues. We introduce a principled probabilistic concept-learning model that describes development subjects' on task. use model, together with sampling function inspired by theory design, predict movements active Results show same rational can early when uncertainty is high, as well late learner certain true category.
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