59 found
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  1.  46
    John R. Anderson (2007). How Can the Human Mind Occur in the Physical Universe? OUP Usa.
    The human cognitive architecture consists of a set of largely independent modules associated with different brain regions. This book discusses in detail how these various modules can combine to produce behaviours as varied as driving a car and solving an algebraic equation.
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  2. John R. Anderson (1991). Is Human Cognition Adaptive? Behavioral and Brain Sciences 14 (3):471-485.
  3.  8
    John R. Anderson (2005). Human Symbol Manipulation Within an Integrated Cognitive Architecture. Cognitive Science 29 (3):313-341.
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  4. John R. Anderson, Qiong Zhang, Jelmer P. Borst & Matthew M. Walsh (forthcoming). The Discovery of Processing Stages: Extension of Sternberg’s Method. Psychological Review.
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  5.  61
    John R. Anderson (1978). Arguments Concerning Representations for Mental Imagery. Psychological Review (4):249-277.
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  6.  2
    Niels A. Taatgen & John R. Anderson (2002). Why Do Children Learn to Say “Broke”? A Model of Learning the Past Tense Without Feedback. Cognition 86 (2):123-155.
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  7.  7
    John R. Anderson (1987). Methodologies for Studying Human Knowledge. Behavioral and Brain Sciences 10 (3):467.
  8.  15
    John R. Anderson, Jon M. Fincham, Yulin Qin & Andrea Stocco (2008). A Central Circuit of the Mind. Trends in Cognitive Sciences 12 (4):136-143.
  9. John R. Anderson, Daniel Bothell, Michael D. Byrne, Scott Douglass, Christian Lebiere & Yulin Qin (2004). An Integrated Theory of the Mind. Psychological Review 111 (4):1036-1060.
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  10.  16
    John R. Anderson & Jon M. Fincham (2014). Discovering the Sequential Structure of Thought. Cognitive Science 38 (2):322-352.
    Multi-voxel pattern recognition techniques combined with Hidden Markov models can be used to discover the mental states that people go through in performing a task. The combined method identifies both the mental states and how their durations vary with experimental conditions. We apply this method to a task where participants solve novel mathematical problems. We identify four states in the solution of these problems: Encoding, Planning, Solving, and Respond. The method allows us to interpret what participants are doing on individual (...)
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  11.  8
    John R. Anderson & Lynne M. Reder (1999). The Fan Effect: New Results and New Theories. Journal of Experimental Psychology: General 128 (2):186.
  12.  7
    Lynne M. Reder, John R. Anderson & Robert A. Bjork (1974). A Semantic Interpretation of Encoding Specificity. Journal of Experimental Psychology 102 (4):648-656.
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  13.  7
    John R. Anderson, Cameron S. Carter, Jon M. Fincham, Yulin Qin, Susan M. Ravizza & Miriam Rosenberg‐Lee (2008). Using fMRI to Test Models of Complex Cognition. Cognitive Science 32 (8):1323-1348.
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  14.  3
    John R. Anderson (2002). Spanning Seven Orders of Magnitude: A Challenge for Cognitive Modeling. Cognitive Science 26 (1):85-112.
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  15.  33
    John R. Anderson & Christian Lebiere (2003). The Newell Test for a Theory of Cognition. Behavioral and Brain Sciences 26 (5):587-601.
    Newell proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization. There would be greater theoretical progress if we evaluated theories by a (...)
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  16.  31
    Niels Taatgen & John R. Anderson (2010). The Past, Present, and Future of Cognitive Architectures. Topics in Cognitive Science 2 (4):693-704.
    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 (...)
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  17.  4
    Raluca Budiu & John R. Anderson (2004). Interpretation‐Based Processing: A Unified Theory of Semantic Sentence Comprehension. Cognitive Science 28 (1):1-44.
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  18. John R. Anderson (1982). Acquisition of Cognitive Skill. Psychological Review 89 (4):369-406.
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  19.  8
    Myeong-Ho Sohn & John R. Anderson (2001). Task Preparation and Task Repetition: Two-Component Model of Task Switching. Journal of Experimental Psychology: General 130 (4):764.
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  20. John R. Anderson (1991). The Adaptive Nature of Human Categorization. Psychological Review 98 (3):409-429.
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  21.  2
    Hee Seung Lee, Shawn Betts & John R. Anderson (2016). Learning Problem‐Solving Rules as Search Through a Hypothesis Space. Cognitive Science 40 (5):1036-1079.
    Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem property such as computational difficulty of the rules biased the search process and so affected learning. Experiment 2 examined the impact of examples as instructional tools (...)
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  22.  3
    Kenneth R. Koedinger & John R. Anderson (1990). Abstract Planning and Perceptual Chunks: Elements of Expertise in Geometry. Cognitive Science 14 (4):511-550.
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  23. John R. Anderson & Michael Matessa (1997). A Production System Theory of Serial Memory. Psychological Review 104 (4):728-748.
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  24.  9
    Marsha C. Lovett & John R. Anderson (2005). Thinking as a Production System. In K. Holyoak & B. Morrison (eds.), The Cambridge Handbook of Thinking and Reasoning. Cambridge Univ Pr 401--429.
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  25.  3
    Philip I. Pavlik & John R. Anderson (2005). Practice and Forgetting Effects on Vocabulary Memory: An Activation‐Based Model of the Spacing Effect. Cognitive Science 29 (4):559-586.
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  26.  3
    Dario D. Salvucci & John R. Anderson (2001). Integrating Analogical Mapping and General Problem Solving: The Path‐Mapping Theory. Cognitive Science 25 (1):67-110.
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  27.  3
    John R. Anderson, Robert Farrell & Ron Sauers (1984). Learning to Program in LISP1. Cognitive Science 8 (2):87-129.
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  28.  5
    John R. Anderson (1977). Induction of Augmented Transition Networks. Cognitive Science 1 (2):125-157.
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  29.  69
    John R. Anderson (1984). The Development of Self-Recognition: A Review. Developmental Psychobiology 17:35-49.
  30. John R. Anderson & Robert Milson (1989). Human Memory: An Adaptive Perspective. Psychological Review 96 (4):703-719.
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  31.  7
    John R. Anderson, Frederick G. Conrad & Albert T. Corbett (1989). Skill Acquisition and the LISP Tutor. Cognitive Science 13 (4):467-505.
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  32.  35
    John R. Anderson, Christian Lebiere, Marsha Lovett & Lynne Reder (1998). ACT-R: A Higher-Level Account of Processing Capacity. Behavioral and Brain Sciences 21 (6):831-832.
    We present an account of processing capacity in the ACT-R theory. At the symbolic level, the number of chunks in the current goal provides a measure of relational complexity. At the subsymbolic level, limits on spreading activation, measured by the attentional parameter W, provide a theory of processing capacity, which has been applied to performance, learning, and individual differences data.
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  33. Alexander A. Petrov & John R. Anderson (2005). The Dynamics of Scaling: A Memory-Based Anchor Model of Category Rating and Absolute Identification. Psychological Review 112 (2):383-416.
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  34.  10
    Christian Lebière, John R. Anderson & Lynne M. Reder (1994). Error Modeling in the ACT-R Production System. In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum 555--559.
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  35. Andrea Stocco, Christian Lebiere & John R. Anderson (2010). Conditional Routing of Information to the Cortex: A Model of the Basal Ganglia’s Role in Cognitive Coordination. Psychological Review 117 (2):541-574.
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  36. John R. Anderson (1987). Skill Acquisition: Compilation of Weak-Method Problem Situations. Psychological Review 94 (2):192-210.
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  37. Philip I. Pavlik & John R. Anderson (2008). Using a Model to Compute the Optimal Schedule of Practice. Journal of Experimental Psychology: Applied 14 (2):101-117.
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  38.  4
    Robert L. Goldstone, John R. Anderson, Nick Chater, Andy Clark, Shimon Edelman, Kenneth Forbus, Dedre Gentner, Raymond W. Gibbs Jr, James Greeno & Robert A. Jacobs (2004). Journal of The Cognitive Science Society. Cognitive Science 28 (3).
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  39. Wai-Tat Fu & John R. Anderson (2006). From Recurrent Choice to Skill Learning: A Reinforcement-Learning Model. Journal of Experimental Psychology: General 135 (2):184-206.
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  40. John R. Anderson & Gordon H. Bower (1972). Recognition and Retrieval Processes in Free Recall. Psychological Review 79 (2):97-123.
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  41.  10
    Scott A. Douglass & John R. Anderson (2008). A Model of Language Processing and Spatial Reasoning Using Skill Acquisition to Situate Action. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society 2281--2286.
  42.  2
    Christian D. Schunn & John R. Anderson (1999). The Generality/Specificity of Expertise in Scientific Reasoning. Cognitive Science 23 (3):337-370.
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  43.  4
    John R. Anderson (1987). Implementations, Algorithms, and More. Behavioral and Brain Sciences 10 (3):498.
  44. Michael D. Byrne & John R. Anderson (2001). Serial Modules in Parallel: The Psychological Refractory Period and Perfect Time-Sharing. Psychological Review 108 (4):847-869.
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  45.  3
    John R. Anderson (1986). Category Learning: Things Aren't so Black and White. Behavioral and Brain Sciences 9 (4):651.
  46.  6
    John R. Anderson (1983). Representation Without Process? Behavioral and Brain Sciences 6 (1):137.
  47.  6
    John R. Anderson (1991). Optimality and Human Memory. Behavioral and Brain Sciences 14 (2):215-216.
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  48.  2
    Hee Seung Lee, Shawn Betts & John R. Anderson (2015). Learning Problem‐Solving Rules as Search Through a Hypothesis Space. Cognitive Science 40 (1).
    Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem property such as computational difficulty of the rules biased the search process and so affected learning. Experiment 2 examined the impact of examples as instructional tools (...)
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  49.  4
    John R. Anderson & Alison Gopnik, Marshall M. Weinberg Conference: The Future of Cognitive Science - Thursday Afternoon (Oct. 16, 2008) Session: John R. Anderson and Alison Gopnik. [REVIEW]
    Six leading experts speak about the future of cognitive science.
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  50.  4
    John R. Anderson, Deb K. Roy, Alex P. Pentland, Vincent Awmm Aleven, Kenneth R. Koedinger, Yafen Lo, Ashley Sides, Joseph Rozelle, Daniel Osherson & Bruno Laeng (2002). Regular Articles Perceiving Temporal Regularity in Music* 1 Edward W. Large, Caroline Palmer Memory for Goals: An Activation-Based Model* 39 Erik M. Altmann, J. Gregory Trafton. [REVIEW] Cognitive Science 26 (837):839.
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