The Past, Present, and Future of Cognitive Architectures

Cognitive architectures are theories of cognition that try to capture the essential representations and mechanisms that underlie cognition. Research in cognitive architectures has gradually moved from a focus on the functional capabilities of architectures to the ability to model the details of human behavior, and, more recently, brain activity. Although there are many different architectures, they share many identical or similar mechanisms, permitting possible future convergence. In judging the quality of a particular cognitive model, it is pertinent to not just judge its fit to the experimental data but also its simplicity and ability to make predictions.

[1]  Randolph M. Jones,et al.  Automated Intelligent Pilots for Combat Flight Simulation , 1998, AI Mag..

[2]  John R. Anderson,et al.  The SAL Integrated Cognitive Architecture , 2008, AAAI Fall Symposium: Biologically Inspired Cognitive Architectures.

[3]  A. Newell,et al.  The Soar papers (vol. II): research on integrated intelligence , 1993 .

[4]  John R. Anderson,et al.  A hybrid model of categorization , 2001, Psychonomic bulletin & review.

[5]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[6]  Richard Alterman,et al.  Convention in joint activity , 2001 .

[7]  D. Dennett.  The Intentional Stance. , 1987 .

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

[9]  Richard S. Sutton,et al.  Associative search network: A reinforcement learning associative memory , 1981, Biological Cybernetics.

[10]  Christian Lebiere,et al.  Implicit and explicit learning in ACT-R , 1998 .

[11]  Werner Buchholz,et al.  Planning a Computer System: Project Stretch , 1962 .

[12]  John R. Anderson,et al.  The Adaptive Character of Thought , 1990 .

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

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

[15]  John R. Anderson,et al.  Why do children learn to say “Broke”? A model of learning the past tense without feedback , 2002, Cognition.

[16]  John R. Anderson.  How Can the Human Mind Occur in the Physical Universe , 2007 .

[17]  Frank J. Lee,et al.  Production Compilation: A Simple Mechanism to Model Complex Skill Acquisition , 2003, Hum. Factors.

[18]  C. P. Dolan,et al.  Neuro-Soar: a neural-network architecture for goal-oriented behavior , 1993 .

[19]  D. Broadbent,et al.  On the Relationship between Task Performance and Associated Verbalizable Knowledge , 1984 .

[20]  John E. Laird,et al.  Soar-RL: integrating reinforcement learning with Soar , 2005, Cognitive Systems Research.

[21]  De Vries.  Book review: R.C. O'Reilly and Y. Munakata: Computational explorations in cognitive neuroscience: understanding the mind by stimulating the brain. Cambridge, Mass: The MIT Press. , 2002 .

[22]  John R. Anderson,et al.  Serial modules in parallel: the psychological refractory period and perfect time-sharing. , 2001, Psychological review.

[23]  Dario D. Salvucci,et al.  Threaded cognition: an integrated theory of concurrent multitasking. , 2008, Psychological review.

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

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

[26]  C. Lebiere,et al.  Stochastic Resonance in Human Cognition: ACT-R Versus Game Theory, Associative Neural Networks, Recursive Neural Networks, Q-Learning, and Humans , 2005 .

[27]  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 .

[28]  John R. Anderson,et al.  Human Symbol Manipulation Within an Integrated Cognitive Architecture , 2005, Cogn. Sci..

[29]  R. Weale.  Vision. A Computational Investigation Into the Human Representation and Processing of Visual Information. David Marr , 1983 .

[30]  M. Just,et al.  From the Selectedworks of Marcel Adam Just the Organization of Thinking: What Functional Brain Imaging Reveals about the Neuroarchitecture of Complex Cognition , 2022 .

[31]  In K. Frankish.  Cognitive Architectures , 2019, Intelligent Systems, Control and Automation: Science and Engineering.

[32]  John R. Anderson,et al.  Tower of Hanoi: evidence for the cost of goal retrieval. , 2001, Journal of experimental psychology. Learning, memory, and cognition.

[33]  John R. Anderson,et al.  The acquisition of robust and flexible cognitive skills. , 2008, Journal of experimental psychology. General.

[34]  N. Taatgen,et al.  Too much control can hurt: A threaded cognition model of the attentional blink , 2009, Cognitive Psychology.

[35]  John R. Anderson,et al.  From recurrent choice to skill learning: a reinforcement-learning model. , 2006, Journal of experimental psychology. General.

[36]  D. Fum.  Instance vs . Rule Based Learning in Controlling a Dynamic System , 2003 .

[37]  J. Gregory Trafton,et al.  Memory for goals: an activation-based model , 2002, Cogn. Sci..

[38]  E. Feigenbaum,et al.  Computers and Thought , 1963 .

[39]  Ron Sun,et al.  From implicit skills to explicit knowledge: a bottom-up model of skill learning , 2001, Cogn. Sci..

[40]  J. Gregory Trafton,et al.  Memory for Goals: An Architectural Perspective , 2020, Proceedings of the Twenty First Annual Conference of the Cognitive Science Society.

[41]  David J. Jilk,et al.  Attentional Blink: An Internal Traffic Jam , 2007 .

[42]  H. M. Ernst,et al.  Planning a Computer System , 1964 .

[43]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

[44]  Allen Newell,et al.  GPS, a program that simulates human thought , 1995 .

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

[46]  John R. Anderson,et al.  A Connectionist Implementation of the ACT-R Production System , 2008 .