Computationally Modeling an Incremental Learning Account of Semantic Interference through Phonological Influe nce

[1]  Wolfgang Cramer,et al.  A simulation model for the transient effects of climate change on forest landscapes , 1993 .

[2]  I. J. Myung,et al.  Applying Occam’s razor in modeling cognition: A Bayesian approach , 1997 .

[3]  M. Coltheart,et al.  Cumulative semantic inhibition in picture naming: experimental and computational studies , 2006, Cognition.

[4]  Børge Obel,et al.  The validity of computational models in organization science: From model realism to purpose of the model , 1995, Comput. Math. Organ. Theory.

[5]  James L. McClelland,et al.  Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. , 1995, Psychological review.

[6]  W. Levelt,et al.  Effects of semantic context in the naming of pictures and words , 2001, Cognition.

[7]  David Haussler,et al.  Occam's Razor , 1987, Inf. Process. Lett..

[8]  Gary M. Oppenheim,et al.  The dark side of incremental learning: A model of cumulative semantic interference during lexical access in speech production , 2010, Cognition.

[9]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[10]  Markus F Damian,et al.  Long-lasting semantic context effects in the spoken production of object names. , 2005, Journal of experimental psychology. Learning, memory, and cognition.

[11]  Heiko Rieger,et al.  Practical Guide to Computer Simulations , 2009 .

[12]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[13]  Elizabeth A. Hirshorn,et al.  Localizing interference during naming: Convergent neuroimaging and neuropsychological evidence for the function of Broca's area , 2009, Proceedings of the National Academy of Sciences.

[14]  Pedro M. Domingos The Role of Occam's Razor in Knowledge Discovery , 1999, Data Mining and Knowledge Discovery.

[15]  G. Regehr,et al.  Intuition in the context of discovery , 1990, Cognitive Psychology.

[16]  Jerome Sacks,et al.  Computer Experiments for Quality Control by Parameter Design , 1990 .

[17]  P. Young,et al.  Simplicity out of complexity in environmental modelling: Occam's razor revisited. , 1996 .

[18]  David G. Stork,et al.  Pattern Classification , 1973 .

[19]  James R. Larus,et al.  Wisconsin Wind Tunnel II: a fast, portable parallel architecture simulator , 2000, IEEE Concurr..

[20]  Guoqiang Peter Zhang,et al.  Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[21]  A. Meyer,et al.  Refractory effects in picture naming as assessed in a semantic blocking paradigm , 2005, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[22]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[23]  Gary M. Oppenheim,et al.  Cumulative semantic interference as learning , 2007, Brain and Language.