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  1. Analogical Symbols: The Role of Visual Cues in Long-Term Transfer.Zhe Chen, Lei Mo, Ryan Honomichl & Myeong-Ho Sohn - 2010 - Metaphor and Symbol 25 (2):93-113.
    We are reminded of relevant stories, tales, or symbols from long-term memory when facing a novel problem our daily lives. Visual cues are 1 tool known to facilitate reminding. In 2 experiments, Chinese students, who had experienced a folk tale many years ago during childhood, were asked to solve an analogous problem. We tested the hypothesis that a visual cue can help bridge the gap between a novel problem and a source analogy experienced in the distant past. Different types of (...)
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  • Are there really two types of learning?Yorick Wilks - 1986 - Behavioral and Brain Sciences 9 (4):671-671.
  • Causal model progressions as a foundation for intelligent learning environments.Barbara Y. White & John R. Frederiksen - 1990 - Artificial Intelligence 42 (1):99-157.
  • The hard questions about noninductive learning remain unanswered.Eric Wanner - 1986 - Behavioral and Brain Sciences 9 (4):670-670.
  • Rule acquisition events in the discovery of problem‐solving strategies.Kurt VanLehn - 1991 - Cognitive Science 15 (1):1-47.
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  • Non‐LIFO Execution of Cognitive Procedures.Kurt VanLehn, William Ball & Bernadette Kowalski - 1989 - Cognitive Science 13 (3):415-465.
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  • Analogy Events: How Examples are Used During Problem Solving.Kurt VanLehn - 1998 - Cognitive Science 22 (3):347-388.
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  • Learning is critical, not implementation versus algorithm.James T. Townsend - 1987 - Behavioral and Brain Sciences 10 (3):497-497.
  • Connectionist models are also algorithmic.David S. Touretzky - 1987 - Behavioral and Brain Sciences 10 (3):496-497.
  • Rejecting induction: Using occam's razor too soon.J. T. Tolliver - 1986 - Behavioral and Brain Sciences 9 (4):669-670.
  • The pragmatics of induction.Paul Thagard - 1986 - Behavioral and Brain Sciences 9 (4):668-669.
  • What is the algorithmic level?M. M. Taylor & R. A. Pigeau - 1987 - Behavioral and Brain Sciences 10 (3):495-496.
  • Applying Marr to memory.Keith Stenning - 1987 - Behavioral and Brain Sciences 10 (3):494-495.
  • Interactive instructional systems and models of human problem solving.Edward P. Stabler - 1987 - Behavioral and Brain Sciences 10 (3):493-494.
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  • Salvaging parts of the “classical theory” of categorization.Dan Sperber - 1986 - Behavioral and Brain Sciences 9 (4):668-668.
  • Connectionism and implementation.Paul Smolensky - 1987 - Behavioral and Brain Sciences 10 (3):492-493.
  • Category differences/automaticity.Edward E. Smith - 1986 - Behavioral and Brain Sciences 9 (4):667-667.
  • Theory-laden concepts: Great, but what is the next step?Charles P. Shimp - 1986 - Behavioral and Brain Sciences 9 (4):666-667.
  • Levels of research.Colleen Seifert & Donald A. Norman - 1987 - Behavioral and Brain Sciences 10 (3):490-492.
  • The learning of function and the function of learning.Roger C. Schank, Gregg C. Collins & Lawrence E. Hunter - 1986 - Behavioral and Brain Sciences 9 (4):672-686.
  • Transcending inductive category formation in learning.Roger C. Schank, Gregg C. Collins & Lawrence E. Hunter - 1986 - Behavioral and Brain Sciences 9 (4):639-651.
    The inductive category formation framework, an influential set of theories of learning in psychology and artificial intelligence, is deeply flawed. In this framework a set of necessary and sufficient features is taken to define a category. Such definitions are not functionally justified, are not used by people, and are not inducible by a learning system. Inductive theories depend on having access to all and only relevant features, which is not only impossible but begs a key question in learning. The crucial (...)
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  • Weak versus strong claims about the algorithmic level.Paul S. Rosenbloom - 1987 - Behavioral and Brain Sciences 10 (3):490-490.
  • Schema Creation in Programming.Robert S. Rist - 1989 - Cognitive Science 13 (3):389-414.
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  • Program Structure and Design.Robert S. Rist - 1995 - Cognitive Science 19 (4):507-562.
    Most models of computer programming explain the programmer's behaviour by a single design strategy. This article presents a cognitive architecture that uses cue‐based search to model multiple design strategies including procedural, functional, means‐end or focal, and opportunistic design. The model has been implemented in an artificial intelligence (AI) system that generates Pascal programs from English specifications.Knowledge is represented as nodes that reside in internal or external memory, where a node encodes an action that may range from a line of code (...)
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  • Learning from worked‐out examples: A study on individual differences.Alexander Renkl - 1997 - Cognitive Science 21 (1):1-29.
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  • Is there more than one type of mental algorithm?Ronan G. Reilly - 1987 - Behavioral and Brain Sciences 10 (3):489-490.
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  • Ways and means.Adam V. Reed - 1987 - Behavioral and Brain Sciences 10 (3):488-489.
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  • Approaches, assumptions, and goals in modeling cognitive behavior.Richard E. Pastore & David G. Payne - 1986 - Behavioral and Brain Sciences 9 (4):665-666.
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  • The psychology of category learning: Current status and future prospect.Gregory L. Murphy - 1986 - Behavioral and Brain Sciences 9 (4):664-665.
  • Nonverbal knowledge as algorithms.Chris Mortensen - 1987 - Behavioral and Brain Sciences 10 (3):487-488.
  • Of what use categories?Ruth Garrett Millikan - 1986 - Behavioral and Brain Sciences 9 (4):663-664.
  • Connectionism and motivation are compatible.Daniel S. Levine - 1987 - Behavioral and Brain Sciences 10 (3):487-487.
  • When explanation is too hard (or understanding hijacking for novices).Michael Lebowitz - 1986 - Behavioral and Brain Sciences 9 (4):662-663.
  • Generality and applications.Jill H. Larkin - 1987 - Behavioral and Brain Sciences 10 (3):486-487.
  • New failures to learn.Barbara Landau - 1986 - Behavioral and Brain Sciences 9 (4):660-661.
  • Induction and explanation: Complementary models of learning.Pat Langley - 1986 - Behavioral and Brain Sciences 9 (4):661-662.
  • Induction and probability.Henry E. Kyburg - 1986 - Behavioral and Brain Sciences 9 (4):660-660.
  • Underestimating the importance of the implementational level.Michael Van Kleeck - 1987 - Behavioral and Brain Sciences 10 (3):497-498.
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  • Second-generation AI theories of learning.David Kirsh - 1986 - Behavioral and Brain Sciences 9 (4):658-659.
  • Clarity, generality, and efficiency in models of learning: Wringing the MOP.Kevin T. Kelly - 1986 - Behavioral and Brain Sciences 9 (4):657-658.
  • A flawed analogy?James Hendler - 1987 - Behavioral and Brain Sciences 10 (3):485-486.
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  • Transcending “transcending…”.Stephen Jośe Hanson - 1986 - Behavioral and Brain Sciences 9 (4):656-657.
  • Computational approaches to analogical reasoning.Rogers P. Hall - 1989 - Artificial Intelligence 39 (1):39-120.
  • Ambiguities in “the algorithmic level”.Alvin I. Goldman - 1987 - Behavioral and Brain Sciences 10 (3):484-485.
  • Complementing explanation with induction.Clark Glymour - 1986 - Behavioral and Brain Sciences 9 (4):655-656.
  • The study of cognition and instructional design: Mutual nurturance.Robert Glaser - 1987 - Behavioral and Brain Sciences 10 (3):483-484.
  • The evolutionary aspect of cognitive functions.J. -P. Ewert - 1987 - Behavioral and Brain Sciences 10 (3):481-483.
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  • The scientific induction problem: A case for case studies.K. Anders Ericsson - 1987 - Behavioral and Brain Sciences 10 (3):480-481.
  • Induction: Weak but essential.Thomas G. Dietterich - 1986 - Behavioral and Brain Sciences 9 (4):654-655.
  • The Cognitive Costs of Context: The Effects of Concreteness and Immersiveness in Instructional Examples.Samuel B. Day, Benjamin A. Motz & Robert L. Goldstone - 2015 - Frontiers in Psychology 6.
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