Program Head, Applied Cognition and Neuroscience MS
PhD, Brown University
Quantitative Models in Cognition
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.
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.
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.
Golden, R. M. (2003). Discrepancy risk model selection test theory for comparing possibly misspecified or nonnested models. Psychometrika, 68: 229-249.
Golden, R. M. (1998). Knowledge digraph contribution analysis of protocol data. Discourse Processes, 25,:179-210.