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  1. Explanation: a mechanist alternative.William Bechtel & Adele Abrahamsen - 2005 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 36 (2):421-441.
    Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ diagrams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations are (...)
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  • Modelling and representing: An artefactual approach to model-based representation.Tarja Knuuttila - 2011 - Studies in History and Philosophy of Science Part A 42 (2):262-271.
    The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on the (...)
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  • How to improve Bayesian reasoning without instruction: Frequency formats.Gerd Gigerenzer & Ulrich Hoffrage - 1995 - Psychological Review 102 (4):684-704.
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  • The Formats of Cognitive Representation: A Computational Account.Dimitri Coelho Mollo & Alfredo Vernazzani - 2023 - Philosophy of Science (3):682-701.
    Cognitive representations are typically analysed in terms of content, vehicle and format. While current work on formats appeals to intuitions about external representations, such as words and maps, in this paper we develop a computational view of formats that does not rely on intuitions. In our view, formats are individuated by the computational profiles of vehicles, i.e., the set of constraints that fix the computational transformations vehicles can undergo. The resulting picture is strongly pluralistic, it makes space for a variety (...)
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  • The intelligent use of space.David Kirsh - 1995 - Artificial Intelligence 73 (1--2):31-68.
    The objective of this essay is to provide the beginning of a principled classification of some of the ways space is intelligently used. Studies of planning have typically focused on the temporal ordering of action, leaving as unaddressed questions of where to lay down instruments, ingredients, work-in-progress, and the like. But, in having a body, we are spatially located creatures: we must always be facing some direction, have only certain objects in view, be within reach of certain others. How we (...)
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  • Thinking with maps.Elizabeth Camp - 2007 - Philosophical Perspectives 21 (1):145–182.
    Most of us create and use a panoply of non-sentential representations throughout our ordinary lives: we regularly use maps to navigate, charts to keep track of complex patterns of data, and diagrams to visualize logical and causal relations among states of affairs. But philosophers typically pay little attention to such representations, focusing almost exclusively on language instead. In particular, when theorizing about the mind, many philosophers assume that there is a very tight mapping between language and thought. Some analyze utterances (...)
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  • World and Logic.Jens Lemanski - 2021 - London, Vereinigtes Königreich: College Publications.
    What is the relationship between the world and logic, between intuition and language, between objects and their quantitative determinations? Rationalists, on the one hand, hold that the world is structured in a rational way. Representationalists, on the other hand, assume that language, logic, and mathematics are only the means to order and describe the intuitively given world. In World and Logic, Jens Lemanski takes up three surprising arguments from Arthur Schopenhauer’s hitherto undiscovered Berlin Lectures, which concern the philosophy of language, (...)
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  • The Iconic-Symbolic Spectrum.Gabriel Greenberg - 2023 - Philosophical Review 132 (4):579-627.
    It is common to distinguish two great families of representation. Symbolic representations include logical and mathematical symbols, words, and complex linguistic expressions. Iconic representations include dials, diagrams, maps, pictures, 3-dimensional models, and depictive gestures. This essay describes and motivates a new way of distinguishing iconic from symbolic representation. It locates the difference not in the signs themselves, nor in the contents they express, but in the semantic rules by which signs are associated with contents. The two kinds of rule have (...)
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  • Analyzing vision at the complexity level.John K. Tsotsos - 1990 - Behavioral and Brain Sciences 13 (3):423-445.
    The general problem of visual search can be shown to be computationally intractable in a formal, complexity-theoretic sense, yet visual search is extensively involved in everyday perception, and biological systems manage to perform it remarkably well. Complexity level analysis may resolve this contradiction. Visual search can be reshaped into tractability through approximations and by optimizing the resources devoted to visual processing. Architectural constraints can be derived using the minimum cost principle to rule out a large class of potential solutions. The (...)
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  • ‘Chasing’ the diagram—the use of visualizations in algebraic reasoning.Silvia de Toffoli - 2017 - Review of Symbolic Logic 10 (1):158-186.
    The aim of this article is to investigate the roles of commutative diagrams (CDs) in a specific mathematical domain, and to unveil the reasons underlying their effectiveness as a mathematical notation; this will be done through a case study. It will be shown that CDs do not depict spatial relations, but represent mathematical structures. CDs will be interpreted as a hybrid notation that goes beyond the traditional bipartition of mathematical representations into diagrammatic and linguistic. It will be argued that one (...)
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  • Reliability of mathematical inference.Jeremy Avigad - 2020 - Synthese 198 (8):7377-7399.
    Of all the demands that mathematics imposes on its practitioners, one of the most fundamental is that proofs ought to be correct. It has been common since the turn of the twentieth century to take correctness to be underwritten by the existence of formal derivations in a suitable axiomatic foundation, but then it is hard to see how this normative standard can be met, given the differences between informal proofs and formal derivations, and given the inherent fragility and complexity of (...)
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  • Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems.Michelene T. H. Chi, Miriam Bassok, Matthew W. Lewis, Peter Reimann & Robert Glaser - 1989 - Cognitive Science 13 (2):145-182.
    The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such (...)
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  • The soft constraints hypothesis: A rational analysis approach to resource allocation for interactive behavior.Wayne D. Gray, Chris R. Sims, Wai-Tat Fu & Michael J. Schoelles - 2006 - Psychological Review 113 (3):461-482.
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  • Representations in Distributed Cognitive Tasks.Jiaje Zhang & Donald A. Norman - 1994 - Cognitive Science 18 (1):87-122.
    In this article we propose a theoretical framework of distributed representations and a methodology of representational analysis for the study of distributed cognitive tasks—tasks that require the processing of information distributed across the internal mind and the external environment. The basic principle of distributed representations Is that the representational system of a distributed cognitive task is a set of internal and external representations, which together represent the abstract structure of the task. The basic strategy of representational analysis is to decompose (...)
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  • Information, possible worlds and the cooptation of scepticism.Luciano Floridi - 2010 - Synthese 175 (1):63 - 88.
    The article investigates the sceptical challenge from an informationtheoretic perspective. Its main goal is to articulate and defend the view that either informational scepticism is radical, but then it is epistemologically innocuous because redundant; or it is moderate, but then epistemologically beneficial because useful. In order to pursue this cooptation strategy, the article is divided into seven sections. Section 1 sets up the problem. Section 2 introduces Borei numbers as a convenient way to refer uniformly to (the data that individuate) (...)
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  • The Nature of External Representations in Problem Solving.Jiajie Zhang - 1997 - Cognitive Science 21 (2):179-217.
    This article proposes a theoretical framework for external representation based problem solving. The Tic‐Tac‐Toe and its isomorphs are used to illustrate the procedures of the framework as a methodology and test the predictions of the framework as a functional model. Experimental results show that the behavior in the Tic‐Tac‐Toe is determined by the directly available information in external and internal representations in terms of perceptual and cognitive biases, regardless of whether the biases are consistent with, inconsistent with, or irrelevant to (...)
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  • How do models give us knowledge? The case of Carnot’s ideal heat engine.Tarja Knuuttila & Mieke Boon - 2011 - European Journal for Philosophy of Science 1 (3):309-334.
    Our concern is in explaining how and why models give us useful knowledge. We argue that if we are to understand how models function in the actual scientific practice the representational approach to models proves either misleading or too minimal. We propose turning from the representational approach to the artefactual, which implies also a new unit of analysis: the activity of modelling. Modelling, we suggest, could be approached as a specific practice in which concrete artefacts, i.e., models, are constructed with (...)
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  • Visualizing Thought.Barbara Tversky - 2011 - Topics in Cognitive Science 3 (3):499-535.
    Depictive expressions of thought predate written language by thousands of years. They have evolved in communities through a kind of informal user testing that has refined them. Analyzing common visual communications reveals consistencies that illuminate how people think as well as guide design; the process can be brought into the laboratory and accelerated. Like language, visual communications abstract and schematize; unlike language, they use properties of the page (e.g., proximity and place: center, horizontal/up–down, vertical/left–right) and the marks on it (e.g., (...)
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  • Picturing Knowledge: Historical and Philosophical Problems Concerning the Use of Art in Science.Brian Scott Baigrie (ed.) - 1996 - University of Toronto Press.
    List of Illustrations Introduction 1 The Didactic and the Elegant: Some Thoughts on Scientific and Technological Illustrations in the Middle Ages and Renaissance 3 2 Temples of the Body and Temples of the Cosmos: Vision and Visualization in the Vesalian and Copernican Revolutions 40 3 Descartes’s Scientific Illustrations and ’la grande mecanique de la nature’ 86 4 Illustrating Chemistry 135 5 Representations of the Natural System in the Nineteenth Century 164 6 Visual Representation in Archaeology: Depicting the Missing-Link in Human (...)
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  • Constructing a concept of number.Karenleigh Overmann - 2018 - Journal of Numerical Cognition 2 (4):464–493.
    Numbers are concepts whose content, structure, and organization are influenced by the material forms used to represent and manipulate them. Indeed, as argued here, it is the inclusion of multiple forms (distributed objects, fingers, single- and two-dimensional forms like pebbles and abaci, and written notations) that is the mechanism of numerical elaboration. Further, variety in employed forms explains at least part of the synchronic and diachronic variability that exists between and within cultural number systems. Material forms also impart characteristics like (...)
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  • A Cognitive Theory of Graphical and Linguistic Reasoning: Logic and Implementation.Keith Stenning & Jon Oberlander - 1995 - Cognitive Science 19 (1):97-140.
    We discuss external and internal graphical and linguistic representational systems. We argue that a cognitive theory of peoples' reasoning performance must account for (a) the logical equivalence of inferences expressed in graphical and linguistic form, and (b) the implementational differences that affect facility of inference. Our theory proposes that graphical representation limit abstraction and thereby aid “processibility”. We discuss the ideas of specificity and abstraction, and their cognitive relevance. Empirical support both comes from tasks which involve the manipulation of external (...)
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  • Representations in distributed cognitive tasks.Jianhui Zhang & Donald A. Norman - 1994 - Cognitive Science 18 (1):87-122.
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  • Building Cognition: The Construction of Computational Representations for Scientific Discovery.Sanjay Chandrasekharan & Nancy J. Nersessian - 2015 - Cognitive Science 39 (8):1727-1763.
    Novel computational representations, such as simulation models of complex systems and video games for scientific discovery, are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a theoretical analysis of the cognitive roles such representations play, based on an ethnographic study of the building of computational models in a systems biology laboratory. Specifically, we focus on a case of model-building by an engineer that (...)
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  • The Role of Culture and Evolution for Human Cognition.Andrea Bender - 2020 - Topics in Cognitive Science 12 (4):1403-1420.
    Since the emergence of our species at least, natural selection based on genetic variation has been replaced by culture as the major driving force in human evolution. It has made us what we are today, by ratcheting up cultural innovations, promoting new cognitive skills, rewiring brain networks, and even shifting gene distributions. Adopting an evolutionary perspective can therefore be highly informative for cognitive science in several ways: It encourages us to ask grand questions about the origins and ramifications of our (...)
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  • Rules for the Inquiring Mind: A Unified Framework of Norms of Inquiry.Luis Rosa - 2024 - New York, NY: Routledge.
    This book concerns the nature and the norms of inquiry. It tackles not only philosophical issues regarding what inquiry is, but also issues regarding how it should and should not be executed. Roughly put, inquiry is the activity of searching for the true answers to questions of our interest. But what is the difference between empirical and armchair inquiry? And what are the right and the wrong ways to inquire? Under what conditions should one start inquiring? Which questions are such (...)
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  • Should physicists preach what they practice?Nancy J. Nersessian - 1995 - Science & Education 4 (3):203-226.
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  • Knowing with images: Medium and message.John Kulvicki - 2010 - Philosophy of Science 77 (2):295-313.
    Problems concerning scientists’ uses of representations have received quite a bit of attention recently. The focus has been on how such representations get their contents and on just what those contents are. Less attention has been paid to what makes certain kinds of scientific representations different from one another and thus well suited to this or that epistemic end. This article considers the latter question with particular focus on the distinction between images and graphs on the one hand and descriptions (...)
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  • Introduction: Varieties of Iconicity.Valeria Giardino & Gabriel Greenberg - 2015 - Review of Philosophy and Psychology 6 (1):1-25.
    This introduction aims to familiarize readers with basic dimensions of variation among pictorial and diagrammatic representations, as we understand them, in order to serve as a backdrop to the articles in this volume. Instead of trying to canvas the vast range of representational kinds, we focus on a few important axes of difference, and a small handful of illustrative examples. We begin in Section 1 with background: the distinction between pictures and diagrams, the concept of systems of representation, and that (...)
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  • Representing with imaginary models: Formats matter.Marion Vorms - 2011 - Studies in History and Philosophy of Science Part A 42 (2):287-295.
    Models such as the simple pendulum, isolated populations, and perfectly rational agents, play a central role in theorising. It is now widely acknowledged that a study of scientific representation should focus on the role of such imaginary entities in scientists’ reasoning. However, the question is most of the time cast as follows: How can fictional or abstract entities represent the phenomena? In this paper, I show that this question is not well posed. First, I clarify the notion of representation, and (...)
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  • (1 other version)Diagrams as Tools for Scientific Reasoning.Adele Abrahamsen & William Bechtel - 2015 - Review of Philosophy and Psychology 6 (1):117-131.
    We contend that diagrams are tools not only for communication but also for supporting the reasoning of biologists. In the mechanistic research that is characteristic of biology, diagrams delineate the phenomenon to be explained, display explanatory relations, and show the organized parts and operations of the mechanism proposed as responsible for the phenomenon. Both phenomenon diagrams and explanatory relations diagrams, employing graphs or other formats, facilitate applying visual processing to the detection of relevant patterns. Mechanism diagrams guide reasoning about how (...)
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  • Theorizing in sociology and social science: turning to the context of discovery.Richard Swedberg - 2012 - Theory and Society 41 (1):1-40.
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  • Informational Equivalence but Computational Differences? Herbert Simon on Representations in Scientific Practice.David Waszek - 2024 - Minds and Machines 34 (1):93-116.
    To explain why, in scientific problem solving, a diagram can be “worth ten thousand words,” Jill Larkin and Herbert Simon (1987) relied on a computer model: two representations can be “informationally” equivalent but differ “computationally,” just as the same data can be encoded in a computer in multiple ways, more or less suited to different kinds of processing. The roots of this proposal lay in cognitive psychology, more precisely in the “imagery debate” of the 1970s on whether there are image-like (...)
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  • Societies of minds: Science as distributed computing.Paul Thagard - 1991 - Studies in History and Philosophy of Science Part A 24 (1):49-67.
    Science is studied in very different ways by historians, philosophers, psychologists, and sociologists. Not only do researchers from different fields apply markedly different methods, they also tend to focus on apparently disparate aspects of science. At the farthest extremes, we find on one side some philosophers attempting logical analyses of scientific knowledge, and on the other some sociologists maintaining that all knowledge is socially constructed. This paper is an attempt to view history, philosophy, psychology, and sociology of science from a (...)
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  • Affect biases memory of location: Evidence for the spatial representation of affect.L. Elizabeth Crawford, Skye M. Margolies, John T. Drake & Meghan E. Murphy - 2006 - Cognition and Emotion 20 (8):1153-1169.
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  • An effect of spatial–temporal association of response codes: Understanding the cognitive representations of time.Antonino Vallesi, Malcolm A. Binns & Tim Shallice - 2008 - Cognition 107 (2):501-527.
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  • Abstract Planning and Perceptual Chunks: Elements of Expertise in Geometry.Kenneth R. Koedinger & John R. Anderson - 1990 - Cognitive Science 14 (4):511-550.
    We present a new model of skilled performance in geometry proof problem solving called the Diagram Configuration model (DC). While previous models plan proofs in a step‐by‐step fashion, we observed that experts plan at a more abstract level: They focus on the key steps and skip the less important ones. DC models this abstract planning behavior by parsing geometry problem diagrams into perceptual chunks, called diagram configurations, which cue relevant schematic knowledge. We provide verbal protocol evidence that DC's schemas correspond (...)
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  • Soft constraints in interactive behavior: the case of ignoring perfect knowledge in-the-world for imperfect knowledge in-the-head*1, *2.Wayne D. Gray & Wai-Tat Fu - 2004 - Cognitive Science 28 (3):359-382.
    Constraints and dependencies among the elements of embodied cognition form patterns or microstrategies of interactive behavior. Hard constraints determine which microstrategies are possible. Soft constraints determine which of the possible microstrategies are most likely to be selected. When selection is non-deliberate or automatic the least effort microstrategy is chosen. In calculating the effort required to execute a microstrategy each of the three types of operations, memory retrieval, perception, and action, are given equal weight; that is, perceptual-motor activity does not have (...)
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  • Computational Imagery.Janice Glasgow & Dimitri Papadias - 1992 - Cognitive Science 16 (3):355-394.
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  • Cognitive Artifacts and Their Virtues in Scientific Practice.Marcin Miłkowski - 2022 - Studies in Logic, Grammar and Rhetoric 67 (1):219-246.
    One of the critical issues in the philosophy of science is to understand scientific knowledge. This paper proposes a novel approach to the study of reflection on science, called “cognitive metascience”. In particular, it offers a new understanding of scientific knowledge as constituted by various kinds of scientific representations, framed as cognitive artifacts. It introduces a novel functional taxonomy of cognitive artifacts prevalent in scientific practice, covering a huge diversity of their formats, vehicles, and functions. As a consequence, toolboxes, conceptual (...)
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  • Concept mapping, mind mapping argument mapping: What are the differences and do they matter?W. Martin Davies - 2011 - Higher Education 62 (3):279–301.
    In recent years, academics and educators have begun to use software mapping tools for a number of education-related purposes. Typically, the tools are used to help impart critical and analytical skills to students, to enable students to see relationships between concepts, and also as a method of assessment. The common feature of all these tools is the use of diagrammatic relationships of various kinds in preference to written or verbal descriptions. Pictures and structured diagrams are thought to be more comprehensible (...)
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  • Expert memory: a comparison of four theories.Fernand Gobet - 1998 - Cognition 66 (2):115-152.
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  • Must scientific diagrams be eliminable? The case of path analysis.James R. Griesemer - 1991 - Biology and Philosophy 6 (2):155-180.
    Scientists use a variety of modes of representation in their work, but philosophers have studied mainly sentences expressing propositions. I ask whether diagrams are mere conveniences in expressing propositions or whether they are a distinct, ineliminable mode of representation in scientific texts. The case of path analysis, a statistical method for quantitatively assessing the relative degree of causal determination of variation as expressed in a causal path diagram, is discussed. Path analysis presents a worst case for arguments against eliminability since (...)
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  • Qualitative spatial reasoning: The CLOCK project.Kenneth D. Forbus, Paul Nielsen & Boi Faltings - 1991 - Artificial Intelligence 51 (1-3):417-471.
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  • Fodor on imagistic mental representations.Daniel C. Burnston - 2020 - Rivista Internazionale di Filosofia e Psicologia 11 (1):71-94.
    : Fodor’s view of the mind is thoroughly computational. This means that the basic kind of mental entity is a “discursive” mental representation and operations over this kind of mental representation have broad architectural scope, extending out to the edges of perception and the motor system. However, in multiple epochs of his work, Fodor attempted to define a functional role for non-discursive, imagistic representation. I describe and critique his two considered proposals. The first view says that images play a particular (...)
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  • Images and inference.Robert K. Lindsay - 1988 - Cognition 29 (3):229-250.
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  • How Experts Solve a Novel Problem in Experimental Design.Jan Maarten Schraagen - 1993 - Cognitive Science 17 (2):285-309.
    Research on expert‐novice differences has mainly focused on how experts solve familiar problems. We know far less about the skills and knowledge used by experts when they are confronted with novel problems within their area of expertise. This article discusses a study in which verbal protocols were taken from subjects of various expertise designing an experiment in an area with which they were unfamiliar. The results showed that even when domain knowledge is lacking, experts solve a novel problem within their (...)
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  • What Makes an Effective Representation of Information: A Formal Account of Observational Advantages.Gem Stapleton, Mateja Jamnik & Atsushi Shimojima - 2017 - Journal of Logic, Language and Information 26 (2):143-177.
    In order to effectively communicate information, the choice of representation is important. Ideally, a chosen representation will aid readers in making desired inferences. In this paper, we develop the theory of observation: what it means for one statement to be observable from another. Using observability, we give a formal characterization of the observational advantages of one representation of information over another. By considering observational advantages, people will be able to make better informed choices of representations of information. To demonstrate the (...)
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  • The Cognitive Advantages of Counting Specifically: A Representational Analysis of Verbal Numeration Systems in Oceanic Languages.Andrea Bender, Dirk Schlimm & Sieghard Beller - 2015 - Topics in Cognitive Science 7 (4):552-569.
    The domain of numbers provides a paradigmatic case for investigating interactions of culture, language, and cognition: Numerical competencies are considered a core domain of knowledge, and yet the development of specifically human abilities presupposes cultural and linguistic input by way of counting sequences. These sequences constitute systems with distinct structural properties, the cross-linguistic variability of which has implications for number representation and processing. Such representational effects are scrutinized for two types of verbal numeration systems—general and object-specific ones—that were in parallel (...)
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  • Electrifying diagrams for learning: principles for complex representational systems.Peter C.-H. Cheng - 2002 - Cognitive Science 26 (6):685-736.
    Six characteristics of effective representational systems for conceptual learning in complex domains have been identified. Such representations should: (1) integrate levels of abstraction; (2) combine globally homogeneous with locally heterogeneous representation of concepts; (3) integrate alternative perspectives of the domain; (4) support malleable manipulation of expressions; (5) possess compact procedures; and (6) have uniform procedures. The characteristics were discovered by analysing and evaluating a novel diagrammatic representation that has been invented to support students' comprehension of electricity—AVOW diagrams (Amps, Volts, Ohms, (...)
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  • No computer program required: Even pencil-and-paper argument mapping improves critical thinking skills.Mara Harrell - 2008 - Teaching Philosophy 31 (4):351-374.
    Argument-mapping software abounds, and one of the reasons is that using the software has been shown to teach/promote/improve critical thinking skills. These positive results are very encouraging, but they also raise the question of whether the computer tutorial environment is producing these results, or whether learning argument mapping, even with just paper and pencil, is sufficient. Based on the results of two empirical studies, I argue that the basic skill of being able to represent an argument diagrammatically plays an important (...)
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