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  1. Improving student success in chemistry through cognitive science.JudithAnn R. Hartman, Eric A. Nelson & Paul A. Kirschner - 2022 - Foundations of Chemistry 24 (2):239-261.
    Chemistry educator Alex H. Johnstone is perhaps best known for his insight that chemistry is best explained using macroscopic, submicroscopic, and symbolic perspectives. But in his writings, he stressed a broader thesis, namely that teaching should be guided by scientific research on how the brain learns: cognitive science. Since Johnstone’s retirement, science’s understanding of learning has progressed rapidly. A surprising discovery has been when solving chemistry problems of any complexity, reasoning does not work: students must apply very-well-memorized facts and algorithms. (...)
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  • Automatic Generation of Cognitive Theories Using Genetic Programming.Enrique Frias-Martinez & Fernand Gobet - 2007 - Minds and Machines 17 (3):287-309.
    Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming (GP). Our approach evolves from experimental data cognitive theories that explain “the mental (...)
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  • Book: Cognitive Design for Artificial Minds.Antonio Lieto - 2021 - London, UK: Routledge, Taylor & Francis Ltd.
    Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...)
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  • Action Selection and Execution in Everyday Activities: A Cognitive Robotics and Situation Model Perspective.David Vernon, Josefine Albert, Michael Beetz, Shiau-Chuen Chiou, Helge Ritter & Werner X. Schneider - 2022 - Wiley: Topics in Cognitive Science 14 (2):344-362.
    Topics in Cognitive Science, Volume 14, Issue 2, Page 344-362, April 2022.
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  • A Computational Investigation of Sources of Variability in Sentence Comprehension Difficulty in Aphasia.Paul Mätzig, Shravan Vasishth, Felix Engelmann, David Caplan & Frank Burchert - 2018 - Topics in Cognitive Science 10 (1):161-174.
    We present a computational evaluation of three hypotheses about sources of deficit in sentence comprehension in aphasia: slowed processing, intermittent deficiency, and resource reduction. The ACT-R based Lewis and Vasishth model is used to implement these three proposals. Slowed processing is implemented as slowed execution time of parse steps; intermittent deficiency as increased random noise in activation of elements in memory; and resource reduction as reduced spreading activation. As data, we considered subject vs. object relative sentences, presented in a self-paced (...)
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  • Cognitive architectures for artificial intelligence ethics.Steve J. Bickley & Benno Torgler - forthcoming - AI and Society:1-19.
    As artificial intelligence thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite human involvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its “black box” characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by their creators (...)
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  • A Computational Cognitive Model of Syntactic Priming.David Reitter, Frank Keller & Johanna D. Moore - 2011 - Cognitive Science 35 (4):587-637.
    The psycholinguistic literature has identified two syntactic adaptation effects in language production: rapidly decaying short-term priming and long-lasting adaptation. To explain both effects, we present an ACT-R model of syntactic priming based on a wide-coverage, lexicalized syntactic theory that explains priming as facilitation of lexical access. In this model, two well-established ACT-R mechanisms, base-level learning and spreading activation, account for long-term adaptation and short-term priming, respectively. Our model simulates incremental language production and in a series of modeling studies, we show (...)
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  • Linguistic Skill and Stimulus-Driven Attention: A Case for Linguistic Relativity.Ulrich Ansorge, Diane Baier & Soonja Choi - 2022 - Frontiers in Psychology 13.
    How does the language we speak affect our perception? Here, we argue for linguistic relativity and present an explanation through “language-induced automatized stimulus-driven attention” : Our respective mother tongue automatically influences our attention and, hence, perception, and in this sense determines what we see. As LASA is highly practiced throughout life, it is difficult to suppress, and even shows in language-independent non-linguistic tasks. We argue that attention is involved in language-dependent processing and point out that automatic or stimulus-driven forms of (...)
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  • Queuing Network Modeling of the Psychological Refractory Period.Changxu Wu & Yili Liu - 2008 - Psychological Review 115 (4):913-954.
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  • The Leabra Architecture: Specialization Without Modularity.Alexander A. Petrov, David J. Jilk, Randall C. O'Reilly & Michael L. Anderson - 2010 - Behavioral and Brain Sciences 33 (4):286.
    The posterior cortex, hippocampus, and prefrontal cortex in the Leabra architecture are specialized in terms of various neural parameters, and thus are predilections for learning and processing, but domain-general in terms of cognitive functions such as face recognition. Also, these areas are not encapsulated and violate Fodorian criteria for modularity. Anderson's terminology obscures these important points, but we applaud his overall message.
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  • A Computational Evaluation of Two Models of Retrieval Processes in Sentence Processing in Aphasia.Paula Lissón, Dorothea Pregla, Bruno Nicenboim, Dario Paape, Mick L. het Nederend, Frank Burchert, Nicole Stadie, David Caplan & Shravan Vasishth - 2021 - Cognitive Science 45 (4).
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  • Sleep Deprivation and Sustained Attention Performance: Integrating Mathematical and Cognitive Modeling.Glenn Gunzelmann, Joshua B. Gross, Kevin A. Gluck & David F. Dinges - 2009 - Cognitive Science 33 (5):880-910.
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  • Formal Ontologies and Semantic Technologies: A “Dual Process” Proposal for Concept Representation.Marcello Frixione & Antonio Lieto - 2014 - Philosophia Scientae 18:139-152.
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  • Rational Task Analysis: A Methodology to Benchmark Bounded Rationality.Hansjörg Neth, Chris R. Sims & Wayne D. Gray - 2016 - Minds and Machines 26 (1-2):125-148.
    How can we study bounded rationality? We answer this question by proposing rational task analysis —a systematic approach that prevents experimental researchers from drawing premature conclusions regarding the rationality of agents. RTA is a methodology and perspective that is anchored in the notion of bounded rationality and aids in the unbiased interpretation of results and the design of more conclusive experimental paradigms. RTA focuses on concrete tasks as the primary interface between agents and environments and requires explicating essential task elements, (...)
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  • Cognitive Principles for Information Management: The Principles of Mnemonic Associative Knowledge (P-MAK).Michael Huggett, Holger Hoos & Ronald A. Rensink - 2007 - Minds and Machines 17 (4):445-485.
    Information management systems improve the retention of information in large collections. As such they act as memory prostheses, implying an ideal basis in human memory models. Since humans process information by association, and situate it in the context of space and time, systems should maximize their effectiveness by mimicking these functions. Since human attentional capacity is limited, systems should scaffold cognitive efforts in a comprehensible manner. We propose the Principles of Mnemonic Associative Knowledge (P-MAK), which describes a framework for semantically (...)
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  • Embodiment in Cognitive Systems: On the Mutual Dependence of Cognition & Robotics.David Vernon, Giorgio Metta & Giulio Sandini - unknown
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  • Left‐Corner Parsing With Distributed Associative Memory Produces Surprisal and Locality Effects.Nathan E. Rasmussen & William Schuler - 2018 - Cognitive Science 42 (S4):1009-1042.
    This article describes a left-corner parser implemented within a cognitively and neurologically motivated distributed model of memory. This parser's approach to syntactic ambiguity points toward a tidy account both of surprisal effects and of locality effects, such as the parsing breakdowns caused by center embedding. The model provides an algorithmic-level account of these breakdowns: The structure of the parser's memory and the nature of incremental parsing produce a smooth degradation of processing accuracy for longer center embeddings, and a steeper degradation (...)
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  • A Neuroadaptive Cognitive Model for Dealing With Uncertainty in Tracing Pilots' Cognitive State.Oliver W. Klaproth, Marc Halbrügge, Laurens R. Krol, Christoph Vernaleken, Thorsten O. Zander & Nele Russwinkel - 2020 - Topics in Cognitive Science 12 (3):1012-1029.
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  • A Skill‐Based Approach to Modeling the Attentional Blink.Corné Hoekstra, Sander Martens & Niels A. Taatgen - 2020 - Topics in Cognitive Science 12 (3):1030-1045.
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  • Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models.Edward A. Cranford, Cleotilde Gonzalez, Palvi Aggarwal, Sarah Cooney, Milind Tambe & Christian Lebiere - 2020 - Topics in Cognitive Science 12 (3):992-1011.
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  • Reward Is Enough.David Silver, Satinder Singh, Doina Precup & Richard S. Sutton - forthcoming - Artificial Intelligence:103535.
  • Interactive Grounding and Inference in Learning by Instruction.Dario D. Salvucci - 2021 - Topics in Cognitive Science 13 (3):488-498.
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  • Parameter Inference for Computational Cognitive Models with Approximate Bayesian Computation.Antti Kangasrääsiö, Jussi P. P. Jokinen, Antti Oulasvirta, Andrew Howes & Samuel Kaski - 2019 - Cognitive Science 43 (6):e12738.
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  • Individual Differences in Reward‐Based Learning Predict Fluid Reasoning Abilities.Andrea Stocco, Chantel S. Prat & Lauren K. Graham - 2021 - Cognitive Science 45 (2):e12941.
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  • A Multitasking General Executive for Compound Continuous Tasks.Dario D. Salvucci - 2005 - Cognitive Science 29 (3):457-492.
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  • Rational Analyses of Information Foraging on the Web.Peter Pirolli - 2005 - Cognitive Science 29 (3):343-373.
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  • Human Symbol Manipulation Within an Integrated Cognitive Architecture.John R. Anderson - 2005 - Cognitive Science 29 (3):313-341.
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  • Modeling Parallelization and Flexibility Improvements in Skill Acquisition: From Dual Tasks to Complex Dynamic Skills.Niels Taatgen - 2005 - Cognitive Science 29 (3):421-455.
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  • Comparison of Decision Learning Models Using the Generalization Criterion Method.Woo-Young Ahn, Jerome R. Busemeyer, Eric-Jan Wagenmakers & Julie C. Stout - 2008 - Cognitive Science 32 (8):1376-1402.
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  • Functional Equivalence of Sleep Loss and Time on Task Effects in Sustained Attention.Bella Z. Veksler & Glenn Gunzelmann - 2018 - Cognitive Science 42 (2):600-632.
    Research on sleep loss and vigilance both focus on declines in cognitive performance, but theoretical accounts have developed largely in parallel in these two areas. In addition, computational instantiations of theoretical accounts are rare. The current work uses computational modeling to explore whether the same mechanisms can account for the effects of both sleep loss and time on task on performance. A classic task used in the sleep deprivation literature, the Psychomotor Vigilance Test, was extended from the typical 10-min duration (...)
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  • On Quantum Models of the Human Mind.Hongbin Wang & Yanlong Sun - 2014 - Topics in Cognitive Science 6 (1):98-103.
    Recent years have witnessed rapidly increasing interests in developing quantum theoretical models of human cognition. Quantum mechanisms have been taken seriously to describe how the mind reasons and decides. Papers in this special issue report the newest results in the field. Here we discuss why the two levels of commitment, treating the human brain as a quantum computer and merely adopting abstract quantum probability principles to model human cognition, should be integrated. We speculate that quantum cognition models gain greater modeling (...)
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  • Modeling How, When, and What Is Learned in a Simple Fault‐Finding Task.Frank E. Ritter & Peter A. Bibby - 2008 - Cognitive Science 32 (5):862-892.
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  • The Role of Falsification in the Development of Cognitive Architectures: Insights From a Lakatosian Analysis.Richard P. Cooper - 2007 - Cognitive Science 31 (3):509-533.
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  • Instance‐Based Models of Metacognition in the Prisoner's Dilemma.Christopher A. Stevens, Niels A. Taatgen & Fokie Cnossen - 2016 - Topics in Cognitive Science 8 (1):322-334.
    In this article, we examine the advantages of simple metacognitive capabilities in a repeated social dilemma. Two types of metacognitive agent were developed and compared with a non-metacognitive agent and two fixed-strategy agents. The first type of metacognitive agent takes the perspective of the opponent to anticipate the opponent's future actions and respond accordingly. The other metacognitive agent predicts the opponent's next move based on the previous moves of the agent and the opponent. The modeler agent achieves better individual outcomes (...)
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  • A Computational and Empirical Investigation of Graphemes in Reading.Conrad Perry, Johannes C. Ziegler & Marco Zorzi - 2013 - Cognitive Science 37 (5):800-828.
    It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for strings (...)
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  • A Strategy‐Based Interpretation of Stroop.Marsha C. Lovett - 2005 - Cognitive Science 29 (3):493-524.
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  • Soft Constraints in Interactive Behavior: The Case of Ignoring Perfect Knowledge in‐the‐World for Imperfect Knowledge in‐the‐Head*,*.Wayne D. Gray & Wai-Tat Fu - 2004 - Cognitive Science 28 (3):359-382.
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  • Encoding and Accessing Linguistic Representations in a Dynamically Structured Holographic Memory System.Dan Parker & Daniel Lantz - 2017 - Topics in Cognitive Science 9 (1):51-68.
    This paper presents a computational model that integrates a dynamically structured holographic memory system into the ACT-R cognitive architecture to explain how linguistic representations are encoded and accessed in memory. ACT-R currently serves as the most precise expression of the moment-by-moment working memory retrievals that support sentence comprehension. The ACT-R model of sentence comprehension is able to capture a range of linguistic phenomena, but there are cases where the model makes the wrong predictions, such as the over-prediction of retrieval interference (...)
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  • Encoding and Accessing Linguistic Representations in a Dynamically Structured Holographic Memory System.Dan Parker & Daniel Lantz - 2016 - Topics in Cognitive Science 8 (4).
    This paper presents a computational model that integrates a dynamically structured holographic memory system into the ACT-R cognitive architecture to explain how linguistic representations are encoded and accessed in memory. ACT-R currently serves as the most precise expression of the moment-by-moment working memory retrievals that support sentence comprehension. The ACT-R model of sentence comprehension is able to capture a range of linguistic phenomena, but there are cases where the model makes the wrong predictions, such as the over-prediction of retrieval interference (...)
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  • Integration and Reuse in Cognitive Skill Acquisition.Dario D. Salvucci - 2013 - Cognitive Science 37 (5):829-860.
    Previous accounts of cognitive skill acquisition have demonstrated how procedural knowledge can be obtained and transformed over time into skilled task performance. This article focuses on a complementary aspect of skill acquisition, namely the integration and reuse of previously known component skills. The article posits that, in addition to mechanisms that proceduralize knowledge into more efficient forms, skill acquisition requires tight integration of newly acquired knowledge and previously learned knowledge. Skill acquisition also benefits from reuse of existing knowledge across disparate (...)
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  • Cognitive Control: Componential or Emergent?Richard P. Cooper - 2010 - Topics in Cognitive Science 2 (4):598-613.
    The past 25 years have witnessed an increasing awareness of the importance of cognitive control in the regulation of complex behavior. It now sits alongside attention, memory, language, and thinking as a distinct domain within cognitive psychology. At the same time it permeates each of these sibling domains. This introduction reviews recent work on cognitive control in an attempt to provide a context for the fundamental question addressed within this topic: Is cognitive control to be understood as resulting from the (...)
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  • Building an ACT‐R Reader for Eye‐Tracking Corpus Data.Jakub Dotlačil - 2018 - Topics in Cognitive Science 10 (1):144-160.
    Cognitive architectures have often been applied to data from individual experiments. In this paper, I develop an ACT-R reader that can model a much larger set of data, eye-tracking corpus data. It is shown that the resulting model has a good fit to the data for the considered low-level processes. Unlike previous related works, the model achieves the fit by estimating free parameters of ACT-R using Bayesian estimation and Markov-Chain Monte Carlo techniques, rather than by relying on the mix of (...)
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  • The Evolution of a Goal-Directed Exploration Model: Effects of Information Scent and GoBack Utility on Successful Exploration.Leonghwee Teo & Bonnie E. John - 2011 - Topics in Cognitive Science 3 (1):154-165.
    We explore the match of a computational information foraging model to participant data on multi-page web search tasks and find its correlation on several important metrics to be too low to be used with confidence in the evaluation of user-interface designs. We examine the points of mismatch to inspire changes to the model in how it calculates information scent scores and how it assesses the utility of backing up from a lower-level page to a higher-level page. The outcome is a (...)
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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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  • Modeling Mental Spatial Reasoning About Cardinal Directions.Holger Schultheis, Sven Bertel & Thomas Barkowsky - 2014 - Cognitive Science 38 (8):1521-1561.
    This article presents research into human mental spatial reasoning with orientation knowledge. In particular, we look at reasoning problems about cardinal directions that possess multiple valid solutions , at human preferences for some of these solutions, and at representational and procedural factors that lead to such preferences. The article presents, first, a discussion of existing, related conceptual and computational approaches; second, results of empirical research into the solution preferences that human reasoners actually have; and, third, a novel computational model that (...)
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  • A Framework for Modeling the Interaction of Syntactic Processing and Eye Movement Control.Felix Engelmann, Shravan Vasishth, Ralf Engbert & Reinhold Kliegl - 2013 - Topics in Cognitive Science 5 (3):452-474.
    We explore the interaction between oculomotor control and language comprehension on the sentence level using two well-tested computational accounts of parsing difficulty. Previous work (Boston, Hale, Vasishth, & Kliegl, 2011) has shown that surprisal (Hale, 2001; Levy, 2008) and cue-based memory retrieval (Lewis & Vasishth, 2005) are significant and complementary predictors of reading time in an eyetracking corpus. It remains an open question how the sentence processor interacts with oculomotor control. Using a simple linking hypothesis proposed in Reichle, Warren, and (...)
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  • Towards a Cognitive Theory of Cyber Deception.Edward A. Cranford, Cleotilde Gonzalez, Palvi Aggarwal, Milind Tambe, Sarah Cooney & Christian Lebiere - 2021 - Cognitive Science 45 (7):e13013.
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  • Editorial: Emergentist Approaches to Language.Brian MacWhinney, Vera Kempe, Patricia J. Brooks & Ping Li - 2022 - Frontiers in Psychology 12.
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  • Locality and Expectation Effects in Hindi Preverbal Constituent Ordering.Sidharth Ranjan, Rajakrishnan Rajkumar & Sumeet Agarwal - 2022 - Cognition 223:104959.
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  • Augmenting Cognitive Architectures to Support Diagrammatic Imagination.Balakrishnan Chandrasekaran, Bonny Banerjee, Unmesh Kurup & Omkar Lele - 2011 - Topics in Cognitive Science 3 (4):760-777.
    Diagrams are a form of spatial representation that supports reasoning and problem solving. Even when diagrams are external, not to mention when there are no external representations, problem solving often calls for internal representations, that is, representations in cognition, of diagrammatic elements and internal perceptions on them. General cognitive architectures—Soar and ACT-R, to name the most prominent—do not have representations and operations to support diagrammatic reasoning. In this article, we examine some requirements for such internal representations and processes in cognitive (...)
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