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  1. Qualitative Quantitative and Experimental Concept Possession, Criteria for Identifying Conceptual Change in Science Education.Otto Lappi - 2013 - Science & Education 22 (6):1347-1359.
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  • New failures to learn.Barbara Landau - 1986 - Behavioral and Brain Sciences 9 (4):660-661.
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  • Induction and explanation: Complementary models of learning.Pat Langley - 1986 - Behavioral and Brain Sciences 9 (4):661-662.
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  • Induction and probability.Henry E. Kyburg - 1986 - Behavioral and Brain Sciences 9 (4):660-660.
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  • Detecting anomalous features in complex stimuli: The role of structured comparison.Kenneth J. Kurtz & Dedre Gentner - 2013 - Journal of Experimental Psychology: Applied 19 (3):219.
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  • Experience in a Climate Microworld: Influence of Surface and Structure Learning, Problem Difficulty, and Decision Aids in Reducing Stock-Flow Misconceptions.Medha Kumar & Varun Dutt - 2018 - Frontiers in Psychology 9.
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  • Qualitative simulation.Benjamin Kuipers - 1986 - Artificial Intelligence 29 (3):289-338.
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  • Commonsense reasoning about causality: Deriving behavior from structure.Benjamin Kuipers - 1984 - Artificial Intelligence 24 (1-3):169-203.
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  • Causal Reasoning in Medicine: Analysis of a Protocol.Benjamin Kuipers & Jerome P. Kassirer - 1984 - Cognitive Science 8 (4):363-385.
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  • The role of similarity in categorization: providing a groundwork.Robert L. Goldstone - 1994 - Cognition 52 (2):125-157.
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  • Coherent Knowledge Structures of Physics Represented as Concept Networks in Teacher Education.Ismo T. Koponen & Maija Pehkonen - 2010 - Science & Education 19 (3):259-282.
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  • Models as Relational Categories.Tommi Kokkonen - 2017 - Science & Education 26 (7-9):777-798.
    Model-based learning has an established position within science education. It has been found to enhance conceptual understanding and provide a way for engaging students in authentic scientific activity. Despite ample research, few studies have examined the cognitive processes regarding learning scientific concepts within MBL. On the other hand, recent research within cognitive science has examined the learning of so-called relational categories. Relational categories are categories whose membership is determined on the basis of the common relational structure. In this theoretical paper, (...)
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  • The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning.Kenneth R. Koedinger, Albert T. Corbett & Charles Perfetti - 2012 - Cognitive Science 36 (5):757-798.
    Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of high potential for generality, while explicitly identifying constraints of and opportunities for detailed analysis of the knowledge students may acquire in courses. Drawing on research across domains of science, math, and language learning, we illustrate the (...)
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  • 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.
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  • Inaugurating Understanding or Repackaging Explanation?Kareem Khalifa - 2012 - Philosophy of Science 79 (1):15-37.
    Recently, several authors have argued that scientific understanding should be a new topic of philosophical research. In this article, I argue that the three most developed accounts of understanding--Grimm's, de Regt's, and de Regt and Dieks's--can be replaced by earlier accounts of scientific explanation without loss. Indeed, in some cases, such replacements have clear benefits.
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  • Perceptual Learning Modules in Mathematics: Enhancing Students' Pattern Recognition, Structure Extraction, and Fluency.Philip J. Kellman, Christine M. Massey & Ji Y. Son - 2010 - Topics in Cognitive Science 2 (2):285-305.
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  • Clarity, generality, and efficiency in models of learning: Wringing the MOP.Kevin T. Kelly - 1986 - Behavioral and Brain Sciences 9 (4):657-658.
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  • Two dogmas of conceptual empiricism: implications for hybrid models of the structure of knowledge.Frank Keil - 1998 - Cognition 65 (2-3):103-135.
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  • Discerning the Division of Cognitive Labor: An Emerging Understanding of How Knowledge Is Clustered in Other Minds.Frank C. Keil, Courtney Stein, Lisa Webb, Van Dyke Billings & Leonid Rozenblit - 2008 - Cognitive Science 32 (2):259-300.
    The division of cognitive labor is fundamental to all cultures. Adults have a strong sense of how knowledge is clustered in the world around them and use that sense to access additional information, defer to relevant experts, and ground their own incomplete understandings. One prominent way of clustering knowledge is by disciplines similar to those that comprise the natural and social sciences. Seven studies explored an emerging sense of these discipline‐based ways of clustering of knowledge. Even 5‐year‐olds could cluster knowledge (...)
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  • Enhancing Students’ Conceptual Understanding by Engaging Science Text with Reflective Writing as a Hermeneutical Circle.Calvin S. Kalman - 2011 - Science & Education 20 (2):159-172.
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  • Intuitions about mathematical beauty: A case study in the aesthetic experience of ideas.Samuel G. B. Johnson & Stefan Steinerberger - 2019 - Cognition 189 (C):242-259.
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  • Comprehension and computation in Bayesian problem solving.Eric D. Johnson & Elisabet Tubau - 2015 - Frontiers in Psychology 6:137658.
    Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian reasoning relative to normalized formats (e.g. probabilities, percentages), both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on “transparent” Bayesian problems varies widely, and generally remains rather unimpressive. (...)
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  • Influencing Mechanism of Justice Sensitivity on Knowledge Hiding in the Chinese Context.Zhang Jin-Song, Huang Hua, Ruan Dan-Yang & Jin Ya-nan - 2022 - Frontiers in Psychology 12.
    Good knowledge management is important for enterprises to maintain competitive advantage; however, the knowledge hiding behavior may hinder this process. Based on the conservation of resources and psychological ownership theories, using a chain intermediary model, this study investigates the effect of justice sensitivity on knowledge hiding through perceived time pressure and territoriality, and further tests the moderating role of territoriality. For the study, we collected 436 questionnaires from China through the Wenjuanxing Sample Service, of which 391 were valid. We then (...)
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  • Relational labeling unlocks inert knowledge.Anja Jamrozik & Dedre Gentner - 2020 - Cognition 196 (C):104146.
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  • Getting to Best: Efficiency versus Optimality in Negotiation.Elaine B. Hyder, Michael J. Prietula & Laurie R. Weingart - 2000 - Cognitive Science 24 (2):169-204.
    Negotiation between two individuals is a common task that typically involves two goals: maximize individual outcomes and obtain an agreement. However, research on the simplest negotiation tasks demonstrates that although naive subjects can be induced to improve their performance, they are often no more likely to achieve fully optimal solutions. The present study tested the prediction that a decrease in a particular type of argumentative behavior, substantiation, would result in an increase in optimal agreements. As substantiation behaviors depend primarily on (...)
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  • Expertise and categorization.Richard P. Honeck, Michael Firment & Tammy J. S. Case - 1987 - Bulletin of the Psychonomic Society 25 (6):431-434.
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  • Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions.Cindy E. Hmelo-Silver & Merav Green Pfeffer - 2004 - Cognitive Science 28 (1):127-138.
    Complex systems are pervasive in the world around us. Making sense of a complex system should require that a person construct a network of concepts and principles about some domain that represents key (often dynamic) phenomena and their interrelationships. This raises the question of how expert understanding of complex systems differs from novice understanding. In this study we examined individuals' representations of an aquatic system from the perspective of structural (elements of a system), behavioral (mechanisms), and functional aspects of a (...)
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  • A flawed analogy?James Hendler - 1987 - Behavioral and Brain Sciences 10 (3):485-486.
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  • Anomalous Evidence, Confidence Change, and Theory Change.Joshua A. Hemmerich, Kellie Van Voorhis & Jennifer Wiley - 2016 - Cognitive Science 40 (6):1534-1560.
    A novel experimental paradigm that measured theory change and confidence in participants' theories was used in three experiments to test the effects of anomalous evidence. Experiment 1 varied the amount of anomalous evidence to see if “dose size” made incremental changes in confidence toward theory change. Experiment 2 varied whether anomalous evidence was convergent or replicating. Experiment 3 varied whether participants were provided with an alternative theory that explained the anomalous evidence. All experiments showed that participants' confidence changes were commensurate (...)
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  • Digit memory of grand experts in abacus-derived mental calculation.Giyoo Hatano & Keiko Osawa - 1983 - Cognition 15 (1-3):95-110.
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  • Structuring Case-Based Ethics Training: How Comparing Cases and Structured Prompts Influence Training Effectiveness.Lauren Harkrider, Alexandra E. MacDougall, Zhanna Bagdasarov, James F. Johnson, Chase E. Thiel, Michael D. Mumford, Shane Connelly & Lynn D. Devenport - forthcoming - Ethics and Behavior:150527093230007.
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  • Structuring Case-Based Ethics Training: How Comparing Cases and Structured Prompts Influence Training Effectiveness.Lauren N. Harkrider, Alexandra E. MacDougall, Zhanna Bagdasarov, James F. Johnson, Chase E. Thiel, Michael D. Mumford, Shane Connelly & Lynn D. Devenport - 2013 - Ethics and Behavior 23 (3):179-198.
    This study examined how structuring case-based ethics training, either through (a) case presentation or (b) prompt questions, influences training outcomes. Results revealed an interaction between case presentation and prompt questions such that some form of structure improved effectiveness. Specifically, comparing cases led to greater sensemaking strategy use and decision-ethicality when trainees considered unstructured rather than structured prompts. When cases were presented sequentially, structuring prompts improved training effectiveness. Too much structure, however, decreased future ethical decision making, suggesting that there can be (...)
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  • Transcending “transcending…”.Stephen Jośe Hanson - 1986 - Behavioral and Brain Sciences 9 (4):656-657.
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  • What we count dictates how we count: A tale of two encodings.Hippolyte Gros, Jean-Pierre Thibaut & Emmanuel Sander - 2021 - Cognition 212 (C):104665.
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  • Introduction to Michelene Chi's Rumelhart Paper.Wayne D. Gray - 2021 - Topics in Cognitive Science 13 (3):438-440.
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  • The Education of Perception.Robert L. Goldstone, David H. Landy & Ji Y. Son - 2010 - Topics in Cognitive Science 2 (2):265-284.
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  • The empirical case for role-governed categories.Micah B. Goldwater, Arthur B. Markman & C. Hunt Stilwell - 2011 - Cognition 118 (3):359-376.
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  • Reuniting perception and conception.Robert L. Goldstone & Lawrence W. Barsalou - 1998 - Cognition 65 (2-3):231-262.
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  • On the acquisition of abstract knowledge: Structural alignment and explication in learning causal system categories.Micah B. Goldwater & Dedre Gentner - 2015 - Cognition 137 (C):137-153.
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  • Licensing Novel Role-Governed Categories: An ERP Analysis.Micah B. Goldwater, Arthur B. Markman, Logan T. Trujillo & David M. Schnyer - 2015 - Frontiers in Human Neuroscience 9.
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  • Ambiguities in “the algorithmic level”.Alvin I. Goldman - 1987 - Behavioral and Brain Sciences 10 (3):484-485.
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  • Analogy Generation in Science Experts and Novices.Micah B. Goldwater, Dedre Gentner, Nicole D. LaDue & Julie C. Libarkin - 2021 - Cognitive Science 45 (9):e13036.
    There is a critical inconsistency in the literature on analogical retrieval. On the one hand, a vast set of laboratory studies has found that people often fail to retrieve past experiences that share deep relational commonalities, even when they would be useful for reasoning about a current problem. On the other hand, historical studies and naturalistic research show clear evidence of remindings based on deep relational commonalities. Here, we examine a possible explanation for this inconsistency—namely, that remindings based on relational (...)
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  • Expertise and intuition: A tale of three theories. [REVIEW]Fernand Gobet & Philippe Chassy - 2009 - Minds and Machines 19 (2):151-180.
    Several authors have hailed intuition as one of the defining features of expertise. In particular, while disagreeing on almost anything that touches on human cognition and artificial intelligence, Hubert Dreyfus and Herbert Simon agreed on this point. However, the highly influential theories of intuition they proposed differed in major ways, especially with respect to the role given to search and as to whether intuition is holistic or analytic. Both theories suffer from empirical weaknesses. In this paper, we show how, with (...)
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  • Complementing explanation with induction.Clark Glymour - 1986 - Behavioral and Brain Sciences 9 (4):655-656.
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  • The study of cognition and instructional design: Mutual nurturance.Robert Glaser - 1987 - Behavioral and Brain Sciences 10 (3):483-484.
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  • Introduction domains, paradigms, and methods in the study of expertise.Robert Hoffman Ken Gilhooly - 1997 - Thinking and Reasoning 3 (4):241 – 246.
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  • The cognitive structure of scientific theories.Ronald N. Giere - 1994 - Philosophy of Science 61 (2):276-296.
    This paper explores a new reason for preferring a model-theoretic approach to understanding the nature of scientific theories. Identifying the models in philosophers' model-theoretic accounts of theories with the concepts in cognitive scientists' accounts of categorization suggests a structure to families of models far richer than has commonly been assumed. Using classical mechanics as an example, it is argued that families of models may be "mapped" as an array with "horizontal" graded structures, multiply hierarchical "vertical" structures, and local "radial" structures. (...)
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  • Toward a cognitive psychology of science.Barry Gholson & Arthur Houts - 1989 - Social Epistemology 3 (2):107 – 127.
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  • A claw is like my hand: Comparison supports goal analysis in infants.Sarah A. Gerson & Amanda L. Woodward - 2012 - Cognition 122 (2):181-192.
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