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...
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| Published in | Neurocomputing (Amsterdam) Vol. 70; no. 13; pp. 2256 - 2272 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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Netherlands
Elsevier B.V
01.08.2007
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| ISSN | 0925-2312 1872-8286 |
| DOI | 10.1016/j.neucom.2006.02.026 |
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| Abstract | 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. |
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| AbstractList | 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 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. 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. 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 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.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 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. |
| Author | Cottrell, Garrison W. Nelson, Jonathan D. |
| AuthorAffiliation | a Computational Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Rd., La Jolla, CA 92037 1099, USA b Computer Science and Engineering Department, University of California, San Diego, 9500 Gilman Dr., Dept. 0404, La Jolla, CA 92093 0404, USA |
| AuthorAffiliation_xml | – name: b Computer Science and Engineering Department, University of California, San Diego, 9500 Gilman Dr., Dept. 0404, La Jolla, CA 92093 0404, USA – name: a Computational Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Rd., La Jolla, CA 92037 1099, USA |
| Author_xml | – sequence: 1 givenname: Jonathan D. surname: Nelson fullname: Nelson, Jonathan D. email: jnelson@salk.edu organization: Computational Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 N. Torrey Pines Rd., La Jolla, CA 92037 1099, USA – sequence: 2 givenname: Garrison W. surname: Cottrell fullname: Cottrell, Garrison W. email: gary@ucsd.edu organization: Computer Science and Engineering Department, University of California, San Diego, 9500 Gilman Dr., Dept. 0404, La Jolla, CA 92093 0404, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22787288$$D View this record in MEDLINE/PubMed |
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| Keywords | Optimal experimental design Intuitive statistics Bayesian reasoning Concept formation Saccadic eye movement Information theory |
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| Title | A probabilistic model of eye movements in concept formation |
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