Jim Corter's Home Page (James E. Corter)

James E. Corter
Professor of Statistics and Education
Department of Human Development
453 Grace Dodge Hall
Teachers College, Columbia University
525 W. 120th St., New York, NY 10027

Center for Decision Sciences   at Columbia Business School
Applied Statistics Center  at CU Dept. of Statistics

E-mail: jec34@columbia.edu


Family: pic1 pic2 pic3

(w/ T Fleisher, A. Miranda) :
Blue in Green
Bright Size Life

(w/ Haruko Nara, A. Miranda):


Ph.D. in Experimental Psychology, 1983, Stanford University
Graduate study, L. L. Thurstone Psychometric Laboratory, 1977-1979, University of North Carolina
B.A. in Psychology (highest honors) 1977, University of North Carolina

Computational models of human learning and categorization.
Judgment and decision-making.
Cognitively diagnostic testing models/methods
Statistics expertise and probability problem-solving.

Clustering and scaling methods for multivariate data.


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.

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

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.

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.

Corter, J. E., & Matsuka, T.  (2008).  Observed attention allocation processes in category learning. Quarterly Journal of Experimental Psychology, 61(7), 1067-1097. (view citation and abstract)

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), article 7. (view citation and abstract)

Corter, J. E., & Zahner, D. C.  (2007). Use of external visual representations in probability problem solving.  Statistics Education Research Journal, 6(1), 22-50. (view pdf)

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

Chen, Y.-J., & Corter, J. E.  (2006). When mixed options are preferred in multiple-trial decision making.  Journal of Behavioral Decision Making, 19(1), 17-42. (view citation and abstract)

Corter, J. E. (2005).  Additive trees.  In B. Everitt & D. Howell (Eds.), Encyclopedia of Statistics in the Behavi ora l 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. (view citation and abstract)

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

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

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, 291-303. (corter_gluck_1992)

Corter, J.E., 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.

The ADDTREE/P (Corter, 1982) and GTREE (Corter, 1998) programs for fitting additive trees and the EXTREE program for fitting extended trees (Corter & Tversky, 1986) are available below. Please note that the ADDTREE/P program (Sattath & Tversky, 1977; Corter, 1982) has been superceded by the GTREE program, which gives equivalent fits and is order-n faster. The "generalized triples" algorithm used by GTREE is described in Corter, J.E. (1998).

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

I. source files (PACAL) and documentation for GTREE, ADDTREE/P, & EXTREE are available from the netlib resource now maintained by Sandia National Labs.

II. PASCAL source, documentation & DOS-executable versions available on this page.


1) The GTREE program for fitting ADDITIVE TREES. This program uses the "generalized triples" algorithm introduced by Corter (1998). It gives equivalent fits to the improved Sattath & Tversky ADDTREE algorithm implemented in the ADDTREE/P program (Corter, 1982), but does so order-N faster, which enables the analysis of larger data sets with more objects.  VERSION 12: posted 03/17/2014:   source code implements dynamic memory management to increase allowed maximum size of matrix to n=248.  The executable code below (link “gtree.exe”) is compiled for 64-bit machines.

gtree.pas: PASCAL source code (v12)
gtree.doc: documentation (text)
gtree-v11.exe: DOS-executable code (v11: 32-bit)
gtree.exe:  DOS-executable code (v12: 64-bit)

REFERENCE: Corter, J.E. (1998). An efficient metric combinatorial algorithm for fitting additive trees. Multivariate Behavioral Research, 33(2), 249-272. (download abstract from LEA)

This algorithm ADDTREE/P (Corter, 1982) incorporates improvements to the original Sattath & Tversky (1977) algorithm, resulting in slightly improved fits about 10% of the time.
addtree.pas: PASCAL source code (text)
addtree.doc: documentation (text)
addtree.exe: DOS-executable (64-bit)
addtree_32bit.exe: DOS-executable (32-bit)
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.

Sattath, S., & Tversky, A. (1977). Additive similarity trees. Psychometrika, 42, 319-345.

3) The EXTREE program for fitting EXTENDED TREES: Extended trees (Corter & Tversky, 1986) extend the hierarchical structure of a tree by adding marked segments to the tree branches representing cross-cutting features or "overlapping clusters".
extree.pas: PASCAL source code (text)
extree.doc: documentation (text)
extree.exe: DOS-executable (64-bit)
extree_32bit.exe: DOS-executable (32-bit)
REFERENCE: Corter, J.E. & Tversky, A. (1986). Extended similarity trees. Psychometrika, 51, 429-451. (download abstract from SpringerLink)