Ph.D. - Experimental Psychology
Brown University - 1987
M.S. - Electrical Engineering
Brown University - 1986
B.S. - Electrical Engineering and Psychology
University of California at San Diego - 1982
My research interests include mathematical models of how humans understand text as well as the mathematical analysis of connectionist and artificial neural network models. A long-term goal of my research program is the development of advanced methods for assessing reading comprehension in children. My research involves and integrates work from the fields of artificial intelligence, mathematical psychology, computational linguistics, and cognitive psychology.
Goldman, S. R., Golden, R. M., and van den Broek, P. (in press). Why are computational models of text comprehension useful? In. F. Schmalhofer and C. A. Perfetti (eds.) Higher Level Language Processes in the Brain: Inference and Comprehension Processes, Mahwah: New Jersey, Erlbaum. ? - Publication
Paik, D., Golden, R. M., Tolak, M., and Dowling, E. M. (in press). Blind Adaptive CDMA Processing for Smart Antennas Using the Block Shanno Constant Modulus Algorithm. IEEE Transactions on Signal Processing. ? - Publication
Kashner, T.M., Hinson, Holland, G.J., Mickey, D.D., Hoffman, K., Lind, L., Johnson, L.D., Chang, B.K., Golden, R.M., and Henley, S.S. (2007). A data accounting system for clinical investigators. Journal of American Medical Informatics Association, 14: 394-396. 2007 - Publication
Golden, R. M. (2003). Discrepancy risk model selection test theory for comparing possibly misspecified or nonnested models. Psychometrika, 68, 229-249. 2003 - Publication
Jaynes, C. and Golden, R. M. (2003). Statistical detection of local coherence relations in narrative recall and summarization data. In R. Alterman and D. Kirsh (eds.) Proceedings of the 25th Annual Conference of the Cognitive Science Society, Boston, MA: Cognitive Science Society, 3-8. 2003 - Publication
Golden, R. M. (2001). Artificial Neural Networks: Neurocomputation. In N. J. Smelser and P. B. Baltes (eds.) International Encyclopedia of the Social and Behavioral Sciences, Elsevier, Oxford, UK, Vol. 2, pp. 806-811. 2001 - Publication
Niederberger, C. S., and R. M. Golden (2001). Artificial neural networks in aurology: applications, feature extraction and user implementations, In R. Dybowski and V. Gant (eds.) Clinical Applications of Artificial Neural Networks, Cambridge, MA: Cambridge University Press, 120-142. 2001 - Publication
Golden, R. M. (2001). Statistical pattern recognition. In N. J. Smelser and P. B. Baltes (eds.) International Encyclopedia of the Social and Behavioral Sciences, Elsevier, Oxford, UK, Vol. 22, pp. 15040-15044. 2001 - Publication
Durbin, M. A., Earwood, J., & Golden, R. M. (2000). Hidden Markov models for coding story recall data In Proceedings of the 22nd Annual Cognitive Science Society Conference. Mahwah, New Jersey, 113-118. 2000 - Publication
Golden, R. M. (2000). Kirchoff law Markov fields for analog circuit design. In S. A. Solla, T. K. Leen, and K. R. Muller (eds.) Neural Information Processing Systems Proceedings, 12, MIT Press, Cambridge, pp. 907-913. 2000 - Publication
Professor of Cognitive Science
University of Texas at Dallas [2004–Present]
Program Head, Applied Cognition and Neuroscience Graduate Program,
University of Texas at Dallas [2000–Present]
Program Head, Undergraduate Cognitive Science Program
University of Texas at Dallas [2000–Present]
Participating Faculty Member in Electrical Engineering
University of Texas at Dallas [1999–Present]
Associate Professor of Cognitive Science
University of Texas at Dallas [1996–2004]
Assistant Professor of Cognitive Science
University of Texas at Dallas [1990–1996]
National Institute of Health Research Fellow
Stanford University [1987–1989]
Andrew Mellon Research Fellow
University of Pittsburgh [1986–1987]
Relating neural networks to traditional engineering approaches
1988–1988 Golden, R. M., Invited talk. AI West 1988. Long Beach, CA.
Markov random fields for text knowledge schema modeling
1996–1996 Golden, R. M., Invited talk. Symposium on High-Performance Computing in the Behavioral Sciences.University of Minnesota Supercomputer Institute.
Statistical tests for deciding which neural network architecture ''best-fits'' a given statistical environment
1998–1998 Golden, R. M., Invited talk. Institute of Neural Computation. November 17, 1998. University of California at San Diego, La Jolla, CA.
Automatic semantic annotation of domain-specific free response data.
2003–2003 Golden, R. M., 2nd Annual Research Symposium of the Human Language Technology Research Institute. University of Texas at Dallas.
Model selection statistical tests for comparing non-nested and misspecified models
1997–1997 Golden, R. M., Invited talk. Model Selection Conference (sponsored by the Society for Mathematical Psychology), Bloomington, Indiana.
- Dae-Hyun Paik (Electrical Engineering doctoral student). Graduated 12/2004. Space-time Processing with Block Shanno Algorithm for Smart Antennas in DS-CDMASystems.
- Shahram Ghiasinejad (Psychology doctoral student). Expected graduation 2005.
- Cynthia Jaynes (Psychology doctoral student. On leave of absence.)
- Rebecca Horn (part-time Psychology doctoral student).
- Greg Talkington (part-time Psychology doctoral student).
- Perwaiz Ismaili (Psychology doctoral student). Expected graduation 2008.
My research interests may be broadly characterized in terms of the development, extension, and understanding of formal mathematical models of perceptual and cognitive processes. My specific research interests can be conveniently divided into two areas of work: (1) mathematical analysis and design of artificial neural networks, and (2) mathematical models of human language and human text comprehension.
Mathematical Analysis and Design of Artificial Neural Networks:
The underlying psychological assumptions of most artificial neural network models of cognitive and neural processes are often obscured by how such models are constructed, presented, discussed, and evaluated. A common thread throughout my research program over the past 15 years has been to ''rebuild'' the neural network modeling paradigm so that neural network modeling assumptions are interpretable, theoretically well-grounded, empirically identifiable, and testable. My methodology for approaching this problem draws heavily upon classical engineering mathematics such as nonlinear dynamical systems theory, nonlinear optimization theory, and statistical pattern recognition. Examples of my work in this area include my book entitled Mathematical Methods for Neural Network Analysis and Design (MIT Press, 1996), my analysis of the BSB neural net model published in the Journal of Mathematical Psychology (Golden, 1993), and publication of a recent Psychometrika article (Golden, 2003) which describes the recent development of a new statistical test for comparing competing models which may be possibly misspecified or nonnested.
Mathematical Models of Human Language and Text Comprehension:
During the past decade, I have focused my attention on developing a new confirmatory constrained categorical time-series data analysis methodology for testing specific hypotheses about knowledge digraphs (i.e., a general class of semantic networks) which is called KDC (Knowledge Digraph Contribution) analysis. KDC analysis uses the order in which propositions appear in recall, summarization, question-answering, and other types of free response data to obtain a more revealing picture of the nature of the by-products of human comprehension processes. Golden (1998) provides the best summary of the current version of this statistical methodology. Durbin, Earwood, and Golden (2000) show how a simple probabilistic computational linguistics model based upon hidden Markov models can be trained to automatically and consistently semantically annotate human protocol data in order to support KDC analysis. The mathematical foundations of KDC theory are based largely upon the mathematical tools and techniques from asymptotic statistical theory and nonlinear optimization theory which I have exploited and developed in my investigations of artificial neural network models.
Currently, research in this area is being funded by an Information Technology Research (ITR) Award (in the area of Educational Technology) from the National Science Foundation to develop the ARCADE (Automated Reading Comprehension Assessment and Diagnostic Evaluation) system. The long-term goal of the ARCADE system is to develop a nation-wide web based system where grade school, middle school, and high school student answers to essay questions are automatically semantically analyzed and then used to make suggestions to classroom teachers in order to enhance student learning experiences in the classroom. The project involves research in the areas of: cognitive psychology, computer science, electrical engineering, educational technology, and computational linguistics.
Annals of Math and AI (2003), Applied Intelligence (1997-2000), Behavioral and Brain Sciences (1988, 1989, 1993, 1994), Cognitive Psychology (2003), Cognitive Science (1988, 1991, 1994, 2002, 2003, 2005), Computational Statistics and Data Analysis (2003), Connection Science (1990-1993), Council of Physical Sciences of the Netherlands Organization for Scientific Research (NOW) (2005), Discourse Processes (1996-1999, 2005), IEEE Transactions on Circuits and Systems (1997, 1998), IEEE Transactions on Neural Networks(1991, 1997, 1998, 2000-2003, 2005), International Journal of Neural Systems (1998), Journal of Artificial Intelligence Research (1994), Journal of Computer and System Sciences (2001), Journal of Mathematical Psychology (1991-1998, 2005), Lawrence Erlbaum Book Reviewer (1990, 1994), Machine Learning (1989), MIT Press Book Reviewer (1995, 1996, 1998), Motivation and Emotion (1996), National Science Foundation (1999), Neural Computation (1993, 1995, 1998-2000), Neural Networks (1989, 1991, 1994, 1995), Pattern Recognition Letters (1995), Proceedings of the Fourteenth Annual Cognitive Science Society Conference (1992), Psychological Review (2000), Society for Industrial and Applied Mathematics (1989).
RICHARDSON, Texas (Dec. 10, 2001) - Dr. Richard Golden, associate professor of psychology and cognitive science in the School of Human Development at The University of Texas at Dallas (UTD), has been awarded a grant by the National Science Foundation (NSF) to study artificial intelligence as a means for testing the reading comprehension of children at the elementary and junior high school level.
With the award, which is worth nearly $400,000 and falls under the NSF’s Information Technology Research (ITR) Program, Golden and a team of researchers will work to develop a Web-based artificial intelligence system called ARCADE (Automatized Reading Comprehension and Diagnostic Evaluation). Through ARCADE, children will log on to a Web site and be asked to read narratives and science texts and type essay-style answers to questions about the stories. ARCADE will then automatically group together children with similar thinking styles and provide educators with suggested teaching strategies designed to improve the quality of instruction for all children in a particular classroom.