Corter, James E. (jec34) | Teachers College Columbia University

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Corter, James
Professor of Statistics and Education
Human Development
212-678-3843

Office:
551-C GDodge

Educational Background

B.A. in Psychology (with highest honors), 1977, University of North Carolina - Chapel Hill

Graduate study, L. L. Thurstone Psychometric Laboratory, 1977-1979, University of North Carolina

Ph.D. in Experimental Psychology, 1983, Stanford University

Scholarly Interests

Judgment, Choice, and Decision-Making

Diagnostic Assessment

Human Categorization and Learning

Mathematics Problem Solving; Expertise in Probability and Statistics

Multidimensional Scaling and Clustering Methods

Visualization in Reasoning and Problem Solving

Evaluation of New Educational Technologies

 

My research program is interdisciplinary, including work in applied statistics, decision-making, psychometrics, and cognitive and educational psychology. Some key work is summarized below.

In applied statistics, I have made original contributions in developing new scaling/clustering methods to analyze proximity data.  For example, Amos Tversky and I (1986) described a new type of clustering algorithm and representation for non-nested structures, that we termed EXTREE.  In Corter (1998) I developed an algorithm for fitting additive trees that is order-N faster than the best previous method. A recent project, as yet unpublished, has explored representing asymmetric proximity relationships using directed trees. 

In decision making and judgment, my recent research has focused on how people think and behave when faced with repeated decisions, also how risk attitudes are shaped by experience and in turn affect financial and other decisions. With a former student, Yuh-Jia Chen, I have been investigating how people make repeated decisions, and the role of learning in shaping decision behavior in such contexts.  In other decision-making research I have examined the relationship of attitudes towards risk and uncertainty with decision behavior. With another former student, Jie Gao, I have been investigating a longstanding puzzle in decision-making research: why people fail to choose optimally in repeated decisions, and instead “probability match”.

In the area of cognitive/educational psychology, I study the acquisition of expertise in statistics and problem-solving.  The problem-solving research has been focused mainly in the area of probability and mathematics problem-solving, and has involved both laboratory studies (but grounded in a real educational context) and secondary analyses of large national databases on mathematics achievement.  In psychometrics, I am involved with work developing new "cognitively diagnostic" testing methods.  Recent/ongoing work in this area has been done in collaboration with Yun-Jin Rho and Huiyun Tseng (former students) and Prof. Matthew Johnson.  In the area of category learning, a former student, Toshihiko Matsuka, and I are writing up research that collected empirical data investigating how attention is allocated across stimulus dimensions in the learning of complex categories, and examines how well prominent neural network models account for the data.  Some of this work, with a modeling focus, has been conducted in collaboration with Art Markman of the University of Texas.   An emerging area of interest for me and my research team is collaborative problem solving in STEM domains.

Growing out of my interests in the roles of visual reasoning and visual representations in probability problem-solving, I have conducted investigations examining the role of diagrams in design and other complex tasks. Two recent NSF-funded projects, in collaboration with Barbara Tversky and Jeffrey Nickerson, examined the role of diagrams in reasoning, design creativity, and problem solving.  

Finally, I have conducted investigations of innovative uses of technology in education. Several previous NSF-funded projects looked at how science labs could be delivered at a distance via technology, and how this affects student learning. Another recent NSF-supported project explored the use of game-based simulations in science and medical education.  In these projects I had twin roles, as an expert in research design and data analysis, but also as an educational/cognitive psychologist.

Selected Publications

Gao, J., & Corter, J. E. (2015). Striving for perfection and falling short: The influence of goals on probability matching. Memory and Cognition, 43-5, 748-759.

Nickerson, J. V., Corter, J. E., Tversky, B., Rho, Y.-J., Zahner, D., Yu, L. (2013). Cognitive tools shape thought: Diagrams in design. Cognitive Processing, 14(3), 255-272. doi 10.1007/s10339-013-0547-

Voiklis, J. & Corter, J. E. (2012).  Conventional wisdom: Negotiating conventions of reference enhances category learning. Cognitive Science, 36 (4), 607-634.

Corter, J.E. (2011). Does investment risk tolerance predict emotional and behavioural reactions to market turmoil?  International Journal of Behavioural Accounting and Finance, 2(3/4), 225–237.

Corter, J. E., Esche, S. K., Chassapis, C., Ma, J., & Nickerson, J. V. (2011). Process and learning outcomes from remotely-operated, simulated, and hands-on student laboratories. Computers & Education, 57(3), 2054-2067.

Im, S., & Corter, J. E. (2011).  Statistical consequences of attribute misspecification in the Rule Space method.  Educational and Psychological Measurement, 71(4), 712-731.

Lee, J., & Corter, J. E. (2011).  Diagnosis of subtraction bugs using Bayesian networks.  Applied Psychological Measurement, 35(1), 27-47.

Zahner, D., Nickerson, J. V., Tversky, B., Corter, J. E., and Ma. J. (2010). A fix for fixation? Re-representing and abstracting as creative processes in the design of information systems. In Maher, M., Kim, Y. S., and Bonnardel, N. (Eds.), Artificial Intelligence in Engineering Design, Analysis and Manufacturing, 24(2), 231–244.

Zahner, D., & Corter, J. E. (2010). The process of probability problem solving: Use of external visual representations. Mathematical Thinking and Learning, 12(2), 177-204. doi:10.1080/10986061003654240

Matsuka, T., and Corter, J.E.  (2008).  Process tracing of attention allocation during category learning.  Quarterly Journal of Experimental Psychology, 61(7), 1067-1097.

Nickerson, J. V., Corter, J. E., Esche, S. K., & Chassapis, C. (2007).  A model for evaluating the effectiveness of remote engineering laboratories and simulations in education.  Computers and Education, 49(3), 718-725.

Corter, J. E., & Zahner, D. C.  (2007). Use of external visual representations in probability problem solving.  Statistics Education Research Journal, 6(1), 22-50, http://www.stat.aukland.ac.nz/serj.

Corter, J. E., & Chen, Y.-J. (2006). Do investment risk tolerance attitudes predict portfolio risk?  Journal of Business and Psychology, 20-3, 369-381.

Chen, Y.-J., & Corter, J. E.  (2006). When mixed options are preferred in multiple-trial decision making.  Journal of Behavioral Decision Making, 19(1), 1-26.

Tatsuoka, K., Guerrero, A., Corter, J.E., Tatsuoka, C., Yamada, T., Xin, T., Dogan, E., Dean, M., and Im, S. (2006). International comparisons of mathematical thinking skills in the TIMSS-R.  Japanese Journal for Research on Testing, 2(1), 3-39.

Corter, J. E. (2005).  Additive trees.  In B. Everitt & D. Howell (Eds.), Encyclopedia of Statistics in the Behavioral Sciences.  London: Wiley.

Tatsuoka, K. K., Corter, J. E., & Tatsuoka, C. (2004).  Patterns of diagnosed mathematical content and process skills in TIMSS-R across a sample of twenty countries.  American Educational Research Journal, 41(4), 901-926.

Matsuka, T. & Corter, J.E. (2004).  Stochastic learning algorithm for modeling human category learning.  International Journal of Computational Intelligence, 1(1), 40-48.

Corter, J.E.  (2003). Centroid method.  In M. Lewis-Beck, A. E. Bryman, & T. F. Liao (Eds.), Encyclopedia of Social Science Research Methods.  Thousand Oaks CA: Sage.

Corter, J.E.  (2003). Tree diagram.  In M. Lewis-Beck, A. E. Bryman, & T. F. Liao (Eds.), Encyclopedia of Social Science Research Methods.  Thousand Oaks CA: Sage.

Corter, J. E. (1998).  An efficient metric combinatorial algorithm for fitting additive trees.  Multivariate Behavioral Research, 33, 249-272.

Corter, J. E. (1996).  Tree Models of Similarity and Association.  (Sage University Papers series: Quantitative Applications in the Social Sciences, series no. 07-112). Thousand Oaks CA: Sage.

Carroll, J. D., & Corter, J. E. (1995).  A graph-theoretic method for organizing overlapping clusters into trees, multiple trees, or extended trees.  Journal of Classification, 12, 283-314.

Corter, J. E. (1995).  Using clustering methods to explore the structure of diagnostic tests.  In P. Nichols, S. Chipman & R. Brennan (Eds.), Cognitively Diagnostic Assessment.  Hillsdale NJ: Lawrence Erlbaum Associates, 305-326.

Corter, J. E., & Gluck, M. A. (1992).  Explaining basic categories:  Feature predictability and information.  Psychological Bulletin, 111(2), 291-303.

Corter, J. E., & Carroll, J. D. (1990).  Potential uses of three-way multidimensional scaling and related techniques to integrate knowledge from multiple experts.  Annals of Mathematics and Artificial Intelligence, 2(1-4), 77‑92.

Corter, J. E. (1989).  Extended tree representation of relationships among languages.  In N.X. Luong (Ed.), Analyse Arborée des Données Textuelles [special issue].  Cahiers des Utilisateurs de Machines lectronique ê des Fins d'Information et de Documentation (CUMFID), 16[Juin], 139-155.

Corter, J. E. (1988).  Testing the density hypothesis:  Reply to Krumhansl.  Journal of Experimental Psychology: General, 117, 105‑106.

Corter, J., Gluck, M. A., & Bower, G. H. (1988).  Basic levels in hierarchically structured categories.  In Proceedings of the Tenth Annual Conference of the Cognitive Science Society.  Hillsdale NJ:  Lawrence Erlbaum Associates.

Corter, J. E. (1987).  Similarity, confusability, and the density hypothesis.  Journal of Experimental Psychology: General, 116, 238‑249.

Butler, K. A., & Corter, J. E. (1986).  Use of psychometric methods in knowledge acquisition:  A case study.  In W.A. Gale (Ed.), Artificial Intelligence and Statistics.  Reading MA: Addison-Wesley.

Corter, J. E., & Tversky, A.  (1986).  Extended similarity trees.  Psychometrika, 51, 429‑451.

Gluck, M. A., & Corter, J. E. (1985).  Information, uncertainty and the utility of categories.  In Proceedings of the Seventh Annual Conference of the Cognitive Science Society.  Hillsdale NJ: Lawrence Erlbaum.

Corter, J. E., & Gluck, M. A. (1985).  Machine generalization and human categorization:  An information-theoretic view.  In Proceedings of the Workshop on Uncertainty and Probability in Artificial Intelligence, Los Angeles: AAAI/RCA.

Corter, J. E. (1983).  Psychological similarity and the density hypothesis.  Unpublished Ph.D. dissertation, Stanford University.

Corter, J. E. (1982).  ADDTREE/P:  A PASCAL program for fitting additive trees based on Sattath & Tversky's ADDTREE algorithm.  Behavior Research Methods and Instrumentation, 14, 353‑354.

My research program includes work in cognitive and educational psychology, decision-making, psychometrics, and applied statistics.  In the area of cognitive/educational psychology, I study categorization, judgment , decision-making, and problem-solving.  The problem-solving research has been focused mainly in the area of probability and mathematics problem-solving, and has involved both laboratory studies (but grounded in a real educational context) and secondary analyses of large national databases on mathematics achievement.  In psychometrics, I am involved with work exploring new "cognitively diagnostic" testing methods.  I also continue to work in the cross-disciplinary field of quantitative methods, mainly in developing new scaling/clustering methods to analyze proximity data.  

Several recent / current projects have combined several of these interests. With Kikumi Tatsuoka, I conducted an NSF-funded project involving statistical and empirical studies of mathematics problem solving, aimed at better understanding student performance on the Third International Math and Science Study - Revised (TIMSS-R).  This work explored applications of "cognitively diagnostic" psychometric methods to the study of mathematics problem solving.  A recent NSF grant, in collaboration with a group of researchers from Stevens Institute of Technology, examined the effectiveness of remotely-operated student labs and computer simulations, relative to traditional hands-on labs, in engineering courses.  Two more recent NSF-funded projects, in collaboration with Barbara Tversky and Jeffrey Nickerson, examine the role of diagrams in reasoning, design, and problem solving.  Finally, another current NSF-supported project explores the use of game-based simulations in science and medical education. In collaboration with several current and former doctoral students, I have been studying how students acquire skill in probability problem solving and what role external visual inscriptions play in these skills; other collaborative work has focused on developing and applying new measurement models to better understand problem-solving.
 
One tech report on TIMSS:
 

In the area of category learning, a former student, Toshihiko Matsuka, and I are writing up research that collected empirical data investigating how attention is allocated across stimulus dimensions in the learning of complex categories, and examines how well prominent neural network models account for the data.  Some of this work, with a modeling focus, has been conducted in collaboration with Art Markman of the University of Texas.  With another former student, Yuh-Jia Chen, I have been investigating how people make repeated decisions, and the role of learning in shaping decision behavior in such contexts.  In other decision-making research I have examined the relationship of attitudes towards risk and uncertainty with decision behavior.  With a former student, Yi-Chun Chen, I have recently been studying how students make and use budgets to manage their expenditures while achieving goals.  With Yun-Jin Rho and Huiyun Tseng (former students) and Prof. Matthew Johnson, I have been working on new cognitively diagnostic measurement models.

In the area of statistical methodology, I have been working on a long-term project on representing asymmetric proximity relationships using directed trees.

 

Member, Technical Advisory Council for periodic assessments program, NYC Board of Education, Aug. 2015 -

Editorial Board, International Journal of Behavioral Accounting and Finance, 2013 -

Best Paper award, Diagrams 2012 Conference, July 1-5, Canterbury UK, for Tversky, B., Corter, J. E., Yu, L., Mason, D. L., & Nickerson, J. V.  (2012). Representing category and continuum: Visualizing thought. 

Distinguished Research Paper Award (August 2007), Japanese Association for Research in Testing (JART), for Tatsuoka, K. K., Corter, J. E., & Tatsuoka, C. (2004).  Patterns of diagnosed mathematical content and process skills in TIMSS-R across a sample of twenty countries.  American Educational Research Journal, 41(4), 901-926.

Appointed member of Graduate Faculty of College of Arts and Sciences, Columbia University, April 1994

Teachers College Research Professorship Award, 1992-1993

Secretary/Treasurer, Classification Society of North America, 1985‑1987

National Science Foundation Pre-Doctoral Fellowship

John Motley Morehead Foundation Graduate Fellowship

Phi Beta Kappa

 
Professor of Statistics and Education, Teachers College, Columbia University, September 2007-.
Program Coordinator, Applied Statistics, May 2009-January 2010
Chair, Department of Human Development, Teachers College, Columbia University, July 2000-August 2007
Acting Chair, Department of Human Development, Summer 1999
Program Coordinator, Cognitive Studies in Education, May 1998-August 2000
Associate Professor of Statistics and Education, Teachers College, Columbia University, 1989-August 2007.
Assistant Professor of Statistics and Education, Teachers College, Columbia University, 1983-1989.
Resident Visitor, AT&T Bell Laboratories, 1983-1988.
Consultant, Xerox Palo Alto Research Center (PARC), 1981-1983.

Teaching Experience:
 
Probability and Statistical Inference                       Psychological Scaling
Applied Regression Analysis                                  Individual Decision Making
Experimental Design                                                Cognition and Computers
Multivariate Statistics                                                 Factor Analysis
Multidimensional Scaling and Clustering            Statistical Treatment of Mass Data
 
 

Professional Affiliations and Activities:
 
American Educational Research Association     Cognitive Science Society
Psychometric Society                                                Psychonomic Society
American Psychological Society                            Society for Mathematical Psychology
Judgment and Decision-Making Society              Classification Society of North America

Ad-hoc reviewing for: The Behavioral and Brain Sciences, Behavioral Research Methods, Instruments, and Computers, British Journal of Mathematical and Statistical Psychology, Cognitive Science, Journal of Experimental Psychology: General, Journal of Experimental Psychology: Learning, Memory, and Cognition, Journal of Mathematical Psychology, Memory and Cognition, Multivariate Behavioral Research, Perception and Psychophysics, Psychological Bulletin, Psychological Review, Psychometrika, Review of Educational Research, and the National Science Foundation.

National Science Foundation (NSF) grant review panelist, Spring 2003. Directorate: Behavioral and Cognitive Sciences, Program in Perception, Action, and Cognition.

National Science Foundation (NSF) site review team, Pittsburgh Science of Learning Center. Spring 2007, Spring 2008, Spring 2009.

National Science Foundation (NSF) grant review panelist, September 2008. Machine Learning program, CAREER Awards.

Service to Field, Profession, and Society:
 
Statistics/psychometric consultant to AIR (Washington, DC) on NAEP initiative
Statistics/psychometric consultant to Ivy League Athletic Association
 
Service to Teachers College (partial list):
 
Psychology Ph.D. Research Methods Exam Committee (1993-present)
Chair, Human Development (Summer 2000-August 2007)
Coordinator, Cognitive Studies in Education program (1998-2000)
Medical Benefits Committee (2005-2006)
Intellectual Property Committee
Research Literacy Task Force
Area A Ph.D committee
Interviews for Human Resources Director
Middle States Reaccreditation Committee – Evaluation Standards Subcommittee
Faculty Executive Committee
Faculty Advisory Committee
Presidential Search Committee (AY 1994-1995)

Gao, J., & Corter, J. E. (2015). Striving for perfection and falling short: The influence of goals on probability matching. Memory and Cognition, 43-5, 748-759.

Gugga, S. S., & Corter, J. E. (2014). Some effects of pragmatic schemas on solution of conditional probability problems.  In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (628-633). Austin, TX: Cognitive Science Society.

Chen, Y. J., & Corter, J. E. (2014). Learning or framing?: Effects of outcome feedback on repeated decisions from description.  In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (397-402). Austin, TX: Cognitive Science Society.

Gao, J. & Corter, J. E. (2014). Effects of problem schema on successful maximizing in repeated choices. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (933-938). Austin, TX: Cognitive Science Society.

Tversky, B., Corter, J. E., Gao, J., Tanaka, Y., & Nickerson, J. V. (2013). People, place, and time: Inferences from diagrams.  In M. Knauff, M., Pauen, N., Sebanz, & I. Wachsmuth (Eds.) Proceedings of the 35th Annual Conference of the Cognitive Science Society (pp. 3593-3597). Austin TX: Cognitive Science Society.

Nickerson, J. V., Corter, J. E., Tversky, B., Rho, Y.-J., Zahner, D., Yu, L. (2013). Cognitive tools shape thought: Diagrams in design. Cognitive Processing, 14(3), 255-272. doi 10.1007/s10339-013-0547-3.

Tversky, B., Corter, J. E., Yu, L., Mason, D. L., & Nickerson, J. V.  (2012).  Representing category and continuum: Visualizing thought. In P. Cox, P. Rodgers, and B. Plimmer (Eds.): Diagrams 2012, Lecture Notes in Computer Science 7352, pp. 23-34. Springer, Heidelberg. 

Mason, D. L., Corter, J. E., Tversky, B., & Nickerson, J. V. (2012).  Structure, space and time: Some ways that diagrams affect inferences in a planning task. In P. Cox, P. Rodgers, and B. Plimmer (Eds.): Diagrams 2012, Lecture Notes in Computer Science 7352, pp. 277-290. Springer, Heidelberg.

Voiklis, J. & Corter, J. E. (2012).  Conventional wisdom: Negotiating conventions of reference enhances category learning. Cognitive Science, 36 (4), 607-634.

Corter, J.E. (2011). Does investment risk tolerance predict emotional and behavioural reactions to market turmoil?  International Journal of Behavioural Accounting and Finance, 2(3/4), 225–237.

Corter, J. E., Esche, S. K., Chassapis, C., Ma, J., & Nickerson, J. V. (2011). Process and learning outcomes from remotely-operated, simulated, and hands-on student laboratories. Computers & Education, 57(3), 2054-2067.

Corter, J. E., Mason, D. L., Tversky, B., & Nickerson, J. V. (2011). Identifying causal pathways with and without diagrams. In C. Hoelscher, T. F. Shipley, and L. Carlson (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 2715-2720). Austin, TX: Cognitive Science Society.

Tversky, B., Corter, J. E., Yu, L., Mason, D. L., & Nickerson, J. V. (2011). Visualizing thought: Mapping category and continuum. In C. Hoelscher, T. F. Shipley, and L. Carlson (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 1577-1582). Austin, TX: Cognitive Science Society.

Im, S., & Corter, J. E. (2011).  Statistical consequences of attribute misspecification in the Rule Space method.  Educational and Psychological Measurement, 71(4), 712-731.

Lee, J., & Corter, J. E. (2011).  Diagnosis of subtraction bugs using Bayesian networks.  Applied Psychological Measurement, 35(1), 27-47.

Zahner, D., Nickerson, J. V., Tversky, B., Corter, J. E., and Ma. J. (2010). A fix for fixation? Re-representing and abstracting as creative processes in the design of information systems. In Maher, M., Kim, Y. S., and Bonnardel, N. (Eds.), Artificial Intelligence in Engineering Design, Analysis and Manufacturing, 24(2), 231–244.

Bobek, E. J., & Corter, J. E. (2010). Effects of problem difficulty and student expertise on the utility of provided diagrams in probability problem solving. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2650-2655). Austin, TX: Cognitive Science Society.

Nickerson, J. V., Tversky, B., Corter, J. E., Yu, L., Rho, Y. J., & Mason, D. L. (2010). Thinking with networks  In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2662-2667). Austin, TX: Cognitive Science Society.

Zahner, D., & Corter, J. E. (2010). The process of probability problem solving: Use of external visual representations. Mathematical Thinking and Learning, 12(2), 177-204. doi:10.1080/10986061003654240

Corter, J.E., Rho, Y., Zahner, D., Nickerson, J.V., & Tversky, B. (2009). Bugs and biases: Diagnosing misconceptions in the understanding of diagrams. In N. A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 756-761). Austin, TX: Cognitive Science Society.

Nickerson, J.V., Zahner, D., Corter, J.E., Tversky, B., Yu, L., and Rho, Y.J. (2009). Matching mechanisms to situations through the wisdom of the crowd, ICIS 2009 Proceedings, Paper 41,
http://aisel.aisnet.org/icis2009/41.

Nickerson, J.V. & Corter, J.E. (2009) Clarity from confusion:  Using intended interactions to design information systems. In Proceedings of the Fifteenth Americas Conference on Information Systems.

Matsuka, T., and Corter, J.E.  (2008).  Process tracing of attention allocation during category learning.  Quarterly Journal of Experimental Psychology, 61(7), 1067-1097.

Corter, J. E., Nickerson, J.V., Tversky, B., Zahner, D., & Rho, Y. (2008).  Using diagrams to design information systems.  In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society, (pp.  2259.2264). Austin, TX: Cognitive Science Society.

Nickerson, J.V., Corter, J.E., Tversky, B., Zahner, D., & Rho, Y. (2008a).  Diagrams as tools in the design of information systems.  In J.S. Gero & A. Goel (Eds.), Design Computing and Cognition ‘08.   Dordrecht, Netherlands:  Springer-Verlag.

Nickerson, J. V., Corter, J. E., Tversky, B., Zahner, D., and Rho, Y. (2008b). The spatial nature of thought: Understanding information systems design through diagrams, in Boland, R., Limayem, M., and Pentland B., (Eds), Proceedings of the 29th International Conference on Information Systems, Paper 216. http://aisel.aisnet.org/icis2008/216

Tversky, B., Corter, J. E., Nickerson, J.V., Zahner, D., & Rho, Y. (2008).  Transforming descriptions and diagrams to sketches in information system design.  In G. Stapelton, J. Howse, & J. Lee (Eds.), Proceedings of the 5th International Conference on the Theory and Application of Diagrams 2008. Berlin:  Springer-Verlag.

Corter, J. E., Nickerson, J. V., Esche, S. K., Chassapis, C., Im, S. & Ma, J. (2007). Constructing reality: A study of remote, hands-on and simulated laboratories.  ACM Transactions on Computer-Human Interaction (TOCHI), 14(2), 7:1-27.

Corter, J. E., Matuska, T., & Markman, A. B. (2007).  Attention allocation in learning an XOR classification task. Proceedings of the Second European Cognitive Science Conference, 935. 

Nickerson, J. V., Corter, J. E., Esche, S. K., & Chassapis, C. (2007).  A model for evaluating the effectiveness of remote engineering laboratories and simulations in education.  Computers and Education, 49(3), 718-725.

Corter, J. E., & Zahner, D. C.  (2007). Use of external visual representations in probability problem solving.  Statistics Education Research Journal, 6(1), 22-50, http://www.stat.aukland.ac.nz/serj.

Corter, J. E., & Chen, Y.-J. (2006). Do investment risk tolerance attitudes predict portfolio risk?  Journal of Business and Psychology, 20-3, 369-381.

Chen, Y.-J., & Corter, J. E.  (2006). When mixed options are preferred in multiple-trial decision making.  Journal of Behavioral Decision Making, 19(1), 1-26.

Tatsuoka, K., Guerrero, A., Corter, J.E., Tatsuoka, C., Yamada, T., Xin, T., Dogan, E., Dean, M., and Im, S. (2006). International comparisons of mathematical thinking skills in the TIMSS-R.  Japanese Journal for Research on Testing, 2(1), 3-39.

Corter, J. E. (2005).  Additive trees.  In B. Everitt & D. Howell (Eds.), Encyclopedia of Statistics in the Behavioral Sciences.  London: Wiley.

Tatsuoka, K. K., Corter, J. E., & Tatsuoka, C. (2004).  Patterns of diagnosed mathematical content and process skills in TIMSS-R across a sample of twenty countries.  American Educational Research Journal, 41(4), 901-926.

Matsuka, T. & Corter, J.E. (2004).  Stochastic learning algorithm for modeling human category learning.  International Journal of Computational Intelligence, 1(1), 40-48.

Corter, J. E., Nickerson, J. V., Esche, S. K., & Chassapis, C. (2004). Remote vs. hands-on labs: A comparative study.  In Proceedings of the 34th ASEE/IEEE Frontiers in Education Conference. Piscataway NJ: IEEE.

Matsuka, T., & Corter, J.E. (2004).  Modeling human category learning with stochastic optimization methods.  Proceedings of the Sixth International Conference on Cognitive Modeling (pp. 196-201). Mahwah NJ: Lawrence Erlbaum Associates.

Matsuka, T., Corter, J. E., & Hanson, S. J. (2004). Irresistibly attractive fruitless feature dimensions.  Proceedings of the Sixth International Conference on Cognitive Modeling (pp. 370-371). Mahwah NJ: Lawrence Erlbaum Associates.

Matsuka, T., & Corter, J. E. (2003).  Stochastic learning in neural network models of categorization.  In Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.  Hillsdale NJ:  Lawrence Erlbaum Associates.

Corter, J.E.  (2003). Centroid method.  In M. Lewis-Beck, A. E. Bryman, & T. F. Liao (Eds.), Encyclopedia of Social Science Research Methods.  Thousand Oaks CA: Sage.

Corter, J.E.  (2003). Tree diagram.  In M. Lewis-Beck, A. E. Bryman, & T. F. Liao (Eds.), Encyclopedia of Social Science Research Methods.  Thousand Oaks CA: Sage.

Matsuka, T., Corter, J. E., & Markman, A. B. (2002).  Allocation of attention in neural network models of categorization. In Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society.  Hillsdale NJ: Lawrence Erlbaum Associates.

Corter, J. E. (1998).  An efficient metric combinatorial algorithm for fitting additive trees.  Multivariate Behavioral Research, 33, 249-272.

Corter, J. E. (1997).  [Review of Classification and Cognition].  Journal of Classification, 14(1), 171-173.

Corter, J. E. (1997).  GTREE: A PASCAL program to fit additive trees to proximity data.  Public domain software, published on Lucent Technologies Bell Labs NETLIB node (http://www.netlib.com/~mds).

Corter, J. E. (1997).  User's manual for the GTREE program to fit additive trees.  Documentation for software, published on Lucent Technologies Bell Labs NETLIB node (http://www.netlib.com/~mds).

Corter, J. E. (1996).  Tree Models of Similarity and Association.  (Sage University Papers series: Quantitative Applications in the Social Sciences, series no. 07-112). Thousand Oaks CA: Sage.

Stein, H., Corter, J. E., & Hull, J. (1996).  Impact of therapist vacations on inpatients with borderline personality disorder.  Psychoanalytic Psychology, 13, 513-530.

Carroll, J. D., & Corter, J. E. (1995).  A graph-theoretic method for organizing overlapping clusters into trees, multiple trees, or extended trees.  Journal of Classification, 12, 283-314.

Corter, J. E. (1995).  Using clustering methods to explore the structure of diagnostic tests.  In P. Nichols, S. Chipman & R. Brennan (Eds.), Cognitively Diagnostic Assessment.  Hillsdale NJ: Lawrence Erlbaum Associates, 305-326.

Corter, J. E. (1994).  The statistical analysis of sequences [Review of Sequential Analysis:  A Guide for Behavioral Researchers].  New Ideas in Psychology, 12, 95-97.

Corter, J. E., & Gluck, M. A. (1992).  Explaining basic categories:  Feature predictability and information.  Psychological Bulletin, 111(2), 291-303.

Corter, J. E. (1991).  Normative theories of categorization.  [Commentary on J.R. Anderson, Is human cognition adaptive?The Behavioral and Brain Sciences, 14, 491-492.

Corter, J. E. (1991).  [Review of  Data Theory and Dimensional Analysis].  Applied Psychological Measurement, 15, 423-424.

Corter, J. E., & Carroll, J. D. (1990).  Potential uses of three-way multidimensional scaling and related techniques to integrate knowledge from multiple experts.  Annals of Mathematics and Artificial Intelligence, 2(1-4), 77‑92.

Corter, J. E. (1989).  Extended tree representation of relationships among languages.  In N.X. Luong (Ed.), Analyse Arborée des Données Textuelles [special issue].  Cahiers des Utilisateurs de Machines lectronique ê des Fins d'Information et de Documentation (CUMFID), 16[Juin], 139-155.

Corter, J. E. (1988).  Testing the density hypothesis:  Reply to Krumhansl.  Journal of Experimental Psychology: General, 117, 105‑106.

Corter, J., Gluck, M. A., & Bower, G. H. (1988).  Basic levels in hierarchically structured categories.  In Proceedings of the Tenth Annual Conference of the Cognitive Science Society.  Hillsdale NJ:  Lawrence Erlbaum Associates.

Corter, J. E. (1987).  Similarity, confusability, and the density hypothesis.  Journal of Experimental Psychology: General, 116, 238‑249.

Butler, K. A., & Corter, J. E. (1986).  Use of psychometric methods in knowledge acquisition:  A case study.  In W.A. Gale (Ed.), Artificial Intelligence and Statistics.  Reading MA: Addison-Wesley.

Corter, J. E. (1986).  Relevant features and statistical theories of generalization.  [Commentary on R.C. Schank, G.C. Collins & L.E. Hunter, Transcending inductive category formation in learning.]  The Behavioral and Brain Sciences, 9, 653‑654.

Corter, J. E., & Tversky, A.  (1986).  Extended similarity trees.  Psychometrika, 51, 429‑451.

Gluck, M. A., & Corter, J. E. (1985).  Information, uncertainty and the utility of categories.  In Proceedings of the Seventh Annual Conference of the Cognitive Science Society.  Hillsdale NJ: Lawrence Erlbaum.

Corter, J. E., & Gluck, M. A. (1985).  Machine generalization and human categorization:  An information-theoretic view.  In Proceedings of the Workshop on Uncertainty and Probability in Artificial Intelligence, Los Angeles: AAAI/RCA.

Corter, J. E. (1983).  Psychological similarity and the density hypothesis.  Unpublished Ph.D. dissertation, Stanford University.

Corter, J. E. (1982).  ADDTREE/P:  A PASCAL program for fitting additive trees based on Sattath & Tversky's ADDTREE algorithm.  Behavior Research Methods and Instrumentation, 14, 353‑354.

 

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