Connectionist models of cognition.

Book synopsis: This book is a definitive reference source for the growing, increasingly more important, and interdisciplinary field of computational cognitive modeling, that is, computational psychology. It combines breadth of coverage with definitive statements by leading scientists in this field. Research in computational cognitive modeling explores the essence of cognition through developing detailed, process-based understanding by specifying computational mechanisms, structures, and processes. Computational models provide both conceptual clarity and precision at the same time. This book substantiates this approach through overviews and many examples.

[1]  D. Spalding.  The Principles of Psychology , 1873, Nature.

[2]  S. Freud.  Project for a Scientific Psychology , 1895 .

[3]  K. Lashley.  Brain Mechanisms and Intelligence: A Quantitative Study of Injuries to the Brain , 1965 .

[4]  Samuel W. Fernberger,et al.  Brain Mechanisms and Intelligence: A Quantitative Study of Injuries to the Brain. , 1931 .

[5]  N. Rashevsky.  Outline of a Physico-Mathematical Theory of the Brain , 1935 .

[6]  S. Freud.  The Standard Edition of the Complete Psychological Works of Sigmund Freud , 1953 .

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

[8]  J. Berko.  The Child's Learning of English Morphology , 1958 .

[9]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[10]  Eric H. Lenneberg,et al.  New directions in the study of language , 1964 .

[11]  Joe H. Brown.  New Directions in the Study of Language , 1965, Neurology.

[12]  G. M. Reicher.  Perceptual recognition as a function of meaninfulness of stimulus material. , 1969, Journal of experimental psychology.

[13]  John J. L. Morton,et al.  Interaction of information in word recognition. , 1969 .

[14]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[15]  D Marr,et al.  Cooperative computation of stereo disparity. , 1976, Science.

[16]  S. Kuczaj.  The acquisition of regular and irregular past tense forms , 1977 .

[17]  Chapter I – An Introduction to the Anatomy, Physiology, and Chemistry of the Brain , 1980 .

[18]  G. Miller,et al.  Cognitive science. , 1981, Science.

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

[20]  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.

[21]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Geoffrey E. Hinton,et al.  OPTIMAL PERCEPTUAL INFERENCE , 1983 .

[23]  C. Pollard,et al.  Center for the Study of Language and Information , 2022 .

[24]  Steven Pinker,et al.  Language learnability and language development , 1985 .

[25]  James L. McClelland,et al.  Levels indeed! A response to Broadbent , 1985 .

[26]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

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

[28]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[29]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[30]  James L. McClelland,et al.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition : Psychological and Biological Models , 1986 .

[31]  Geoffrey E. Hinton,et al.  Schemata and Sequential Thought Processes in PDP Models , 1986 .

[32]  D. Rumelhart.  Learning Internal Representations by Error Propagation, Parallel Distributed Processing , 1986 .

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

[34]  Carsten Peterson,et al.  A Mean Field Theory Learning Algorithm for Neural Networks , 1987, Complex Syst..

[35]  S. Grossberg,et al.  ART 2: self-organization of stable category recognition codes for analog input patterns. , 1987, Applied optics.

[36]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[37]  S. Pinker,et al.  On language and connectionism: Analysis of a parallel distributed processing model of language acquisition , 1988, Cognition.

[38]  James L. McClelland.  Parallel Distributed Processing: Implications for Cognition and Development , 1988 .

[39]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[40]  James A. Anderson,et al.  Neurocomputing: Foundations of Research , 1988 .

[41]  J. Fodor,et al.  Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.

[42]  O. G. Selfridge,et al.  Pandemonium: a paradigm for learning , 1988 .

[43]  P. Smolensky.  On the proper treatment of connectionism , 1988, Behavioral and Brain Sciences.

[44]  Geoffrey E. Hinton,et al.  A Distributed Connectionist Production System , 1988, Cogn. Sci..

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

[46]  Stephen Grossberg,et al.  Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns , 1988, Other Conferences.

[47]  Geoffrey E. Hinton.  Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space , 1989, Neural Computation.

[48]  Richard J. Brown.  Neuropsychology Mental Structure , 1989 .

[49]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[50]  Geoffrey E. Hinton,et al.  Parallel Models of Associative Memory , 1989 .

[51]  B. MacWhinney,et al.  Functionalism and the competition model , 1989 .

[52]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[53]  R A Johnston,et al.  Understanding face recognition with an interactive activation model. , 1990, British journal of psychology.

[54]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[55]  R. Morris.  Parallel Distributed Processing: Implications for Psychology and Neurobiology , 1990 .

[56]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[57]  Michael I. Jordan.  Attractor dynamics and parallelism in a connectionist sequential machine , 1990 .

[58]  James L. McClelland,et al.  On the control of automatic processes: a parallel distributed processing account of the Stroop effect. , 1990, Psychological review.

[59]  V. Marchman,et al.  U-shaped learning and frequency effects in a multi-layered perception: Implications for child language acquisition , 1991, Cognition.

[60]  T. Gelder,et al.  Classical Questions, Radical Answers: Connectionism and the Structure of Mental Representations , 1991 .

[61]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[62]  W. Bechtel,et al.  Connectionism and the Mind , 1991 .

[63]  T. Horgan,et al.  Connectionism and the Philosophy of Mind , 1991 .

[64]  Jeffrey L. Elman,et al.  A PDP Approach to Processing Center-Embedded Sentences , 1992 .

[65]  J. Cohen,et al.  Context, cortex, and dopamine: a connectionist approach to behavior and biology in schizophrenia. , 1992, Psychological review.

[66]  N. Chater,et al.  Proceedings of the fourteenth annual conference of the cognitive science society , 1992 .

[67]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[68]  S Pinker,et al.  Overregularization in language acquisition. , 1992, Monographs of the Society for Research in Child Development.

[69]  T. Shallice,et al.  Deep Dyslexia: A Case Study of , 1993 .

[70]  James L. McClelland,et al.  Can a perceptual processing deficit explain the impairment of inflectional morphology in developmental dysphasia? A computational investigation. , 1993 .

[71]  James L. McClelland,et al.  Generalization with Componential Attractors: Word and Nonword Reading in an Attractor Network , 1993 .

[72]  J. Elman.  Learning and development in neural networks: the importance of starting small , 1993, Cognition.

[73]  S. Quartz.  Neural networks, nativism, and the plausibility of constructivism , 1993, Cognition.

[74]  V. Marchman,et al.  From rote learning to system building: acquiring verb morphology in children and connectionist nets , 1993, Cognition.

[75]  Javier R. Movellan,et al.  Learning Continuous Probability Distributions with Symmetric Diffusion Networks , 1993, Cogn. Sci..

[76]  M. Owen,et al.  The genetic basis of complex human behaviors. , 1994, Science.

[77]  X. LingCharles.  Learning the past tense of English verbs , 1994 .

[78]  G. Kane.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[79]  Yves Chauvin,et al.  Back-Propagation: Theory, Architecture, and Applications , 1995 .

[80]  S Pinker,et al.  Weird past tense forms , 1995, Journal of Child Language.

[81]  Ronald J. Williams,et al.  Gradient-based learning algorithms for recurrent networks and their computational complexity , 1995 .

[82]  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.

[83]  Ron Sun,et al.  Robust Reasoning: Integrating Rule-Based and Similarity-Based Reasoning , 1995, Artif. Intell..

[84]  Gary F. Marcus,et al.  German Inflection: The Exception That Proves the Rule , 1995, Cognitive Psychology.

[85]  James L. McClelland,et al.  Understanding normal and impaired word reading: computational principles in quasi-regular domains. , 1996, Psychological review.

[86]  V. Marchman,et al.  Learning from a connectionist model of the acquisition of the English past tense , 1996, Cognition.

[87]  T. Shultz,et al.  Generative connectionist networks and constructivist cognitive development , 1996 .

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

[89]  E. Newport,et al.  WORD SEGMENTATION : THE ROLE OF DISTRIBUTIONAL CUES , 1996 .

[90]  Randall C. O'Reilly,et al.  Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm , 1996, Neural Computation.

[91]  D. Bishop,et al.  Cognitive Neuropsychology and Developmental Disorders: Uncomfortable Bedfellows , 1997, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[92]  Kim Plunkett,et al.  A Connectionist Model of the Arabic Plural System , 1997 .

[93]  T. Sejnowski,et al.  Irresistible environment meets immovable neurons , 1997, Behavioral and Brain Sciences.

[94]  A. Karmiloff-Smith.  Development itself is the key to understanding developmental disorders , 1998, Trends in Cognitive Sciences.

[95]  C. Lebiere,et al.  The Atomic Components of Thought , 1998 .

[96]  Steven Pinker,et al.  Words and rules , 1998 .

[97]  Gert Westermann,et al.  Emergent modularity and U-shaped learning in a constructivist neural network learning the English past tense , 1998 .

[98]  Mark S. Seidenberg,et al.  Category-Specific Semantic Deficits in Focal and Widespread Brain Damage: A Computational Account , 1998, Journal of Cognitive Neuroscience.

[99]  Stefan Wermter,et al.  A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning , 1998, Hybrid Neural Systems.

[100]  Edmund T. Rolls,et al.  Introduction to Connectionist Modelling of Cognitive Processes , 1998 .

[101]  R. O’Reilly.  Six principles for biologically based computational models of cortical cognition , 1998, Trends in Cognitive Sciences.

[102]  Ron Sun,et al.  Autonomous learning of sequential tasks: experiments and analyses , 1998, IEEE Trans. Neural Networks.

[103]  Y. Munakata.  Infant perseveration and implications for object permanence theories: A PDP model of the AB task , 1998 .

[104]  C. D. Green,et al.  Are Connectionist Models Theories of Cognition , 1998 .

[105]  Jeffrey L. Elman,et al.  A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations , 1998, Hybrid Neural Systems.

[106]  David C. Plaut,et al.  The emergence of phonology from the interplay of speech comprehension and production ; A distributed connectionist approach , 1998 .

[107]  Eytan Ruppin,et al.  Disorders of Brain, Behavior, and Cognition: The Neurocomputational Perspective , 1999 .

[108]  Jonathan D. Cohen,et al.  A Biologically Based Computational Model of Working Memory , 1999 .

[109]  Douglas L. T. Rohde,et al.  Language acquisition in the absence of explicit negative evidence: how important is starting small? , 1999, Cognition.

[110]  R. Jacobs.  Computational studies of the development of functionally specialized neural modules , 1999, Trends in Cognitive Sciences.

[111]  A. Miyake,et al.  Models of Working Memory: Mechanisms of Active Maintenance and Executive Control , 1999 .

[112]  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.

[113]  Garrison W. Cottrell,et al.  Organization of face and object recognition in modular neural network models , 1999, Neural Networks.

[114]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[115]  Risto Miikkulainen and Marshall R. Mayberry,et al.  Disambiguation And Grammar As Emergent Soft Constraints , 1999 .

[116]  William F. Brewer,et al.  Perceptual symbols: The power and limitations of a theory of dynamic imagery and structured frames , 1999, Behavioral and Brain Sciences.

[117]  Patrick Juola,et al.  Why double dissociations don't mean much. , 2000 .

[118]  Robert M. French,et al.  Pseudopatterns and dual-network memory models: Advantages and shortcomings , 2000, NCPW.

[119]  R. O’Reilly,et al.  Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain , 2000 .

[120]  N. Chater,et al.  Rational models of cognition , 1998 .

[121]  Exploring cognition : damaged brains and neural networks : readings in cognitive neuropsychology and connectionist modelling , 2000 .

[122]  James L. McClelland,et al.  The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.

[123]  Robert M. French,et al.  Connectionist Models of Learning, Development and Evolution , 2001, Perspectives in Neural Computing.

[124]  Peter Broeder,et al.  Models of Language Acquisition: Inductive and Deductive Approaches , 2001 .

[125]  F. Dick,et al.  Language deficits, localization, and grammar: evidence for a distributive model of language breakdown in aphasic patients and neurologically intact individuals. , 2001, Psychological review.

[126]  Marius Usher,et al.  Maintenance of semantic information in capacity-limited item short-term memory , 2001, Psychonomic bulletin & review.

[127]  G. Marcus.  The Algebraic Mind: Integrating Connectionism and Cognitive Science , 2001 .

[128]  Refractor.  Vision , 2000, The Lancet.

[129]  David C. Plaut,et al.  A connectionist model of sentence comprehension and production , 2002 .

[130]  A. Karmiloff-Smith,et al.  Modelling typical and atypical cognitive development , 2002 .

[131]  Michael S. C. Thomas,et al.  Connectionist models of development, developmental disorders and individual differences , 2002 .

[132]  Eddy J. Davelaar,et al.  AN ACTIVATION-BASED THEORY OF IMMEDIATE ITEM MEMORY , 2002 .

[133]  Y. Munakata,et al.  Active versus latent representations: a neural network model of perseveration, dissociation, and decalage. , 2002, Developmental psychobiology.

[134]  Morten H. Christiansen,et al.  Reassessing Working Memory: Comment on Just and Carpenter (1992) and Waters and Caplan (1996) , 2002 .

[135]  Michael S. C. Thomas,et al.  Are developmental disorders like cases of adult brain damage? Implications from connectionist modelling , 2002, Behavioral and Brain Sciences.

[136]  Todd Lubart,et al.  Models of Intelligence: International Perspectives , 2003 .

[137]  Mark S. Seidenberg,et al.  Phonology and syntax in specific language impairment: Evidence from a connectionist model , 2003, Brain and Language.

[138]  Michael S. C. Thomas,et al.  Modeling language acquisition in atypical phenotypes. , 2003, Psychological review.

[139]  Xiaohui Xie,et al.  Equivalence of Backpropagation and Contrastive Hebbian Learning in a Layered Network , 2003, Neural Computation.

[140]  T. Shultz.  Computational Developmental Psychology , 2003 .

[141]  James L. McClelland,et al.  Connectionist models of development , 2003 .

[142]  James L. McClelland,et al.  Developing a domain-general framework for cognition: What is the best approach? , 2003, Behavioral and Brain Sciences.

[143]  Martin Redington,et al.  Modelling Atypical Syntax Processing , 2004 .

[144]  S. Grossberg,et al.  Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors , 1976, Biological Cybernetics.

[145]  David M. Sobel,et al.  A theory of causal learning in children: causal maps and Bayes nets. , 2004, Psychological review.

[146]  George Houghton,et al.  Connectionist models in cognitive psychology , 2004 .

[147]  Elizabeth Bates,et al.  The development of complex sentence interpretation in typically developing children compared with children with specific language impairments or early unilateral focal lesions. , 2004, Developmental science.

[148]  D. Plaut,et al.  Doing without schema hierarchies: a recurrent connectionist approach to normal and impaired routine sequential action. , 2004, Psychological review.

[149]  D. Medin,et al.  SUSTAIN: a network model of category learning. , 2004, Psychological review.

[150]  J. Kroll,et al.  Handbook of bilingualism : psycholinguistic approaches , 2005 .

[151]  Michael S. C. Thomas,et al.  Characterising Compensation (Commentary on Ullman and Pierpont, “Specific Language Impairment is not Specific to Language: The Procedural Deficit Hypothesis”) , 2005, Cortex.

[152]  W.J.B. van Heuven,et al.  Computationals models of bilingual comprehension , 2005 .

[153]  J. Elman.  Distributed representations, simple recurrent networks, and grammatical structure , 1991, Machine Learning.

[154]  Michael S. C. Thomas,et al.  Can Developmental Disorders Reveal the Component Parts of the Human Language Faculty? , 2005 .

[155]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[156]  James L. McClelland,et al.  Alternatives to the combinatorial paradigm of linguistic theory based on domain general principles of human cognition , 2005 .

[157]  J. Elman.  Connectionist models of cognitive development: where next? , 2005, Trends in Cognitive Sciences.

[158]  Marshall R. Mayberry,et al.  A Connectionist Model of Sentence Comprehension in Visual Worlds , 2005 .

[159]  J. Tenenbaum,et al.  Opinion TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Theory-based Bayesian models of inductive learning and reasoning , 2022 .

[160]  Michael S. C. Thomas,et al.  Atypical representational change: conditions for theemergence of atypical modularity , 2006 .

[161]  What is Modularity Good For , 2006 .

[162]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[163]  Mark H. Johnson,et al.  The computational modelling of sensitive periods , 2006 .

[164]  J. Tenenbaum,et al.  Special issue on “Probabilistic models of cognition , 2022 .

[165]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[166]  Michael S C Thomas,et al.  The computational modeling of sensitive periods. , 2006, Developmental psychobiology.

[167]  D. Bishop.  Developmental cognitive genetics: How psychology can inform genetics and vice versa , 2006, Quarterly journal of experimental psychology.

[168]  K. Plunkett,et al.  Stochastic approaches to understanding dissociations in inflectional morphology , 2006, Brain and Language.

[169]  Denis Mareschal,et al.  Computational Modeling in Developmental Psychology , 2007, IEEE Transactions on Evolutionary Computation.

[170]  Michael W. Spratling,et al.  Neuroconstructivism - I: How the Brain Constructs Cognition , 2007 .

[171]  Michael S. C. Thomas,et al.  Neuroconstructivism. , 2020, Developmental science.

[172]  W. N. Schoenfeld,et al.  Principles of Psychology , 2007 .

[173]  L. Steels,et al.  Social dynamics: Emergence of language , 2007 .

[174]  K. Jellinger.  Processes of Change in Brain and Cognitive Development Attention and Performance XXI , 2007 .

[175]  James L. McClelland,et al.  Using domain-general principles to explain children's causal reasoning abilities. , 2007, Developmental science.

[176]  Frank Jackson,et al.  The Oxford handbook of contemporary philosophy , 2007 .

[177]  S. Carey,et al.  The Development of Human Conceptual Representations: A Case Study , 2008 .