Perspectives on Modeling in Cognitive Science

This commentary gives a personal perspective on modeling and modeling developments in cognitive science, starting in the 1950s, but focusing on the author's personal views of modeling since training in the late 1960s, and particularly focusing on advances since the official founding of the Cognitive Science Society. The range and variety of modeling approaches in use today are remarkable, and for many, bewildering. Yet to come to anything approaching adequate insights into the infinitely complex fields of mind, brain, and intelligent systems, an extremely wide array of modeling approaches is vital and necessary.

[1]  A. Newell Unified Theories of Cognition , 1990 .

[2]  Michael N Jones,et al.  Representing word meaning and order information in a composite holographic lexicon. , 2007, Psychological review.

[3]  Charles Kemp,et al.  The discovery of structural form , 2008, Proceedings of the National Academy of Sciences.

[4]  R. Shepard The analysis of proximities: Multidimensional scaling with an unknown distance function. II , 1962 .

[5]  J. Tenenbaum,et al.  Bayesian Special Section Learning Overhypotheses with Hierarchical Bayesian Models , 2022 .

[6]  David E. Kieras,et al.  A computational theory of executive cognitive processes and multiple-task performance: Part 2. Accounts of psychological refractory-period phenomena. , 1997 .

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

[8]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: I. An account of basic findings. , 1981 .

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

[10]  James T. Townsend,et al.  Quantum dynamics of human decision-making , 2006 .

[11]  Mark A. Pitt,et al.  Measuring Model Flexibility With Parameter Space Partitioning: An Introduction and Application Example , 2008, Cogn. Sci..

[12]  G. Bower,et al.  Human Associative Memory , 1973 .

[13]  Stephen Grossberg,et al.  Embedding fields: A theory of learning with physiological implications , 1969 .

[14]  S Grossberg,et al.  Some nonlinear networks capable of learning a spatial pattern of arbitrary complexity. , 1968, Proceedings of the National Academy of Sciences of the United States of America.

[15]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[16]  Mark Steyvers,et al.  Topics in semantic representation. , 2007, Psychological review.

[17]  Stefan Pollmann,et al.  Neuroinformatics Original Research Article Pymvpa: a Unifying Approach to the Analysis of Neuroscientifi C Data , 2022 .

[18]  D. C. Mccarthy,et al.  Hippocampal and neocortical gamma oscillations predict memory formation in humans. , 2006, Cerebral cortex.

[19]  Joseph L. Zinnes,et al.  Theory and Methods of Scaling. , 1958 .

[20]  Ehtibar N. Dzhafarov,et al.  Dissimilarity cumulation theory in smoothly connected spaces , 2008 .

[21]  Michael D. Lee,et al.  A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods , 2008, Cogn. Sci..

[22]  J. Townsend,et al.  A theory of interactive parallel processing: new capacity measures and predictions for a response time inequality series. , 2004, Psychological review.

[23]  Richard C. Atkinson,et al.  Human Memory: A Proposed System and its Control Processes , 1968, Psychology of Learning and Motivation.

[24]  A. Yuille,et al.  Opinion TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Vision as Bayesian inference: analysis by synthesis? , 2022 .

[25]  R. Nosofsky American Psychological Association, Inc. Choice, Similarity, and the Context Theory of Classification , 2022 .

[26]  Spatio-temporal linear decoding of brain state : Application to performance augmentation in high-throughput tasks , .

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

[28]  S. Link,et al.  A sequential theory of psychological discrimination , 1975 .

[29]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: part 1.: an account of basic findings , 1988 .

[30]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[31]  Robert A. Bjork,et al.  All-or-none subprocesses in the learning of complex sequences☆ , 1968 .

[32]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

[33]  Paul Sajda,et al.  Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG , 2009, Proceedings of the National Academy of Sciences.

[34]  B. Dosher,et al.  Characterizing observers using external noise and observer models: assessing internal representations with external noise. , 2008, Psychological review.

[35]  Jeffrey S. Perry,et al.  Edge co-occurrence in natural images predicts contour grouping performance , 2001, Vision Research.

[36]  James L. McClelland,et al.  An interactive activation model of context effects in letter perception: Part 2. The contextual enhancement effect and some tests and extensions of the model. , 1982, Psychological review.

[37]  Richard M. Shiffrin,et al.  12 – Memory Search1 , 1970 .

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

[39]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[40]  R. Shepard The analysis of proximities: Multidimensional scaling with an unknown distance function. I. , 1962 .

[41]  R. Duncan Luce,et al.  Individual Choice Behavior: A Theoretical Analysis , 1979 .

[42]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[43]  Gordon H. Bower,et al.  Application of a model to paired-associate learning , 1961 .

[44]  W S Geisler,et al.  Physical limits of acuity and hyperacuity. , 1984, Journal of the Optical Society of America. A, Optics and image science.

[45]  N. Birbaumer,et al.  fMRI Brain-Computer Interfaces , 2008, IEEE Signal Processing Magazine.

[46]  Roger Ratcliff,et al.  A Theory of Memory Retrieval. , 1978 .

[47]  John R. Anderson,et al.  Using fMRI to Test Models of Complex Cognition , 2008, Cogn. Sci..

[48]  R. Rescorla A theory of pavlovian conditioning: The effectiveness of reinforcement and non-reinforcement , 1972 .

[49]  James A. Anderson,et al.  A theory for the recognition of items from short memorized lists , 1973 .

[50]  David M. Riefer,et al.  Theoretical and empirical review of multinomial process tree modeling , 1999, Psychonomic bulletin & review.

[51]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .

[52]  Richard M. Shiffrin,et al.  SAM: A theory of probabilistic search in associative memory. , 1980 .

[53]  W. Estes Toward a Statistical Theory of Learning. , 1994 .

[54]  D E Kieras,et al.  A computational theory of executive cognitive processes and multiple-task performance: Part 1. Basic mechanisms. , 1997, Psychological review.

[55]  John R. Anderson Language, Memory, and Thought , 1976 .

[56]  R. Luce,et al.  Individual Choice Behavior: A Theoretical Analysis. , 1960 .

[57]  R. Shiffrin,et al.  Search of associative memory. , 1981 .

[58]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[59]  Douglas L. Medin,et al.  Context theory of classification learning. , 1978 .

[60]  Jay I. Myung,et al.  Global model analysis by parameter space partitioning. , 2019, Psychological review.

[61]  P. Sajda,et al.  Spatiotemporal Linear Decoding of Brain State , 2008, IEEE Signal Processing Magazine.

[62]  John R. Anderson,et al.  Rules of the Mind , 1993 .

[63]  J. Chang,et al.  Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .

[64]  J. Busemeyer,et al.  A quantum probability explanation for violations of ‘rational’ decision theory , 2009, Proceedings of the Royal Society B: Biological Sciences.