Model Evaluation, Testing and Selection

[1]  Browne,et al.  Cross-Validation Methods. , 2000, Journal of mathematical psychology.

[2]  Karl R. Popper.  The Logic of Scientific Discovery. , 1977 .

[3]  R. Shiffrin,et al.  A model for recognition memory: REM—retrieving effectively from memory , 1997, Psychonomic bulletin & review.

[4]  S C McKinley,et al.  Investigations of exemplar and decision bound models in large, ill-defined category structures. , 1995, Journal of experimental psychology. Human perception and performance.

[5]  Bin Yu,et al.  Model Selection and the Principle of Minimum Description Length , 2001 .

[6]  R M Nosofsky,et al.  Similarity-scaling studies of dot-pattern classification and recognition. , 1992, Journal of experimental psychology. General.

[7]  Stephen K. Reed,et al.  Pattern recognition and categorization , 1972 .

[8]  M. Kendall,et al.  The Logic of Scientific Discovery. , 1959 .

[9]  M. Gluck.  Stimulus Generalization and Representation in Adaptive Network Models of Category Learning , 1991 .

[10]  M. Stone,et al.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[11]  Ming Li,et al.  An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.

[12]  R. Nosofsky.  Attention, similarity, and the identification-categorization relationship. , 1986, Journal of experimental psychology. General.

[13]  R. Duncan Luce,et al.  Individual Choice Behavior , 1959 .

[14]  Walter Zucchini,et al.  Model Selection , 2011, International Encyclopedia of Statistical Science.

[15]  B. Knowlton.  What can neuropsychology tell us about category learning? , 1999, Trends in Cognitive Sciences.

[16]  James L. McClelland,et al.  The TRACE model of speech perception , 1986, Cognitive Psychology.

[17]  Robert M. Nosofsky,et al.  Dissociations Between Categorization and Recognition in Amnesic and Normal Individuals: An Exemplar-Based Interpretation , 1998 .

[18]  Shun-ichi Amari,et al.  Network information criterion-determining the number of hidden units for an artificial neural network model , 1994, IEEE Trans. Neural Networks.

[19]  G. Gescheider.  Psychophysics : method, theory, and application , 1985 .

[20]  Jorma Rissanen,et al.  Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.

[21]  H Pashler,et al.  How persuasive is a good fit? A comment on theory testing. , 2000, Psychological review.

[22]  J. V. Santen,et al.  How many parameters can a model have and still be testable , 1985 .

[23]  Peter Grünwald.  Viewing all models as “probabilistic” , 1999, COLT '99.

[24]  M. Brent.  Speech segmentation and word discovery: a computational perspective , 1999, Trends in Cognitive Sciences.

[25]  J. Busemeyer,et al.  Model Comparisons and Model Selections Based on Generalization Criterion Methodology. , 2000, Journal of mathematical psychology.

[26]  Richard M. Shiffrin,et al.  The art of model development and testing , 1997 .

[27]  A. Jacobs,et al.  Localist connectionist approaches to human cognition , 1998 .

[28]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[29]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[30]  G. Karabatsos,et al.  The Rasch model, additive conjoint measurement, and new models of probabilistic measurement theory. , 2001, Journal of applied measurement.

[31]  L. Squire,et al.  The learning of categories: parallel brain systems for item memory and category knowledge. , 1993, Science.

[32]  J. Rissanen.  A UNIVERSAL PRIOR FOR INTEGERS AND ESTIMATION BY MINIMUM DESCRIPTION LENGTH , 1983 .

[33]  Mark S. Seidenberg,et al.  Impairments in verb morphology after brain injury: a connectionist model. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[35]  J. Wixted,et al.  On the Form of Forgetting , 1991 .

[36]  P. Grünwald.  The Minimum Description Length Principle (Adaptive Computation and Machine Learning) , 2007 .

[37]  R. Nosofsky,et al.  An exemplar-based random walk model of speeded classification. , 1997, Psychological review.

[38]  Jorma Rissanen,et al.  Fisher information and stochastic complexity , 1996, IEEE Trans. Inf. Theory.

[39]  Mark A. Pitt,et al.  Toward a method of selecting among computational models of cognition. , 2002 .

[40]  A. Jacobs,et al.  Models of visual word recognition: Sampling the state of the art. , 1994 .

[41]  J. Elman,et al.  Rethinking Innateness: A Connectionist Perspective on Development , 1996 .

[42]  R. Ratcliff,et al.  Explicitly modeling the effects of aging on response time , 2000, Psychonomic bulletin & review.

[43]  W. T. Maddox,et al.  Relations between prototype, exemplar, and decision bound models of categorization , 1993 .

[44]  David R. Anderson,et al.  Model selection and inference : a practical information-theoretic approach , 2000 .

[45]  J. Kruschke,et al.  ALCOVE: an exemplar-based connectionist model of category learning. , 1992, Psychological review.

[46]  I. J. Myung,et al.  Counting probability distributions: Differential geometry and model selection , 2000, Proc. Natl. Acad. Sci. USA.

[47]  Mark M. Meerschaert,et al.  Mathematical Modeling , 2014, Encyclopedia of Social Network Analysis and Mining.