Search results for 'Analogical Cognition' (try it on Scholar)

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  1. Theodore Bach (2012). Analogical Cognition: Applications in Epistemology and the Philosophy of Mind and Language. Philosophy Compass 7 (5):348-360.score: 240.0
    Analogical cognition refers to the ability to detect, process, and learn from relational similarities. The study of analogical and similarity cognition is widely considered one of the ‘success stories’ of cognitive science, exhibiting convergence across many disciplines on foundational questions. Given the centrality of analogy to mind and knowledge, it would benefit philosophers investigating topics in epistemology and the philosophies of mind and language to become familiar with empirical models of analogical cognition. The goal (...)
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  2. Marcello Guarini (2010). Particularism, Analogy, and Moral Cognition. Minds and Machines 20 (3):385-422.score: 108.0
    ‘Particularism’ and ‘generalism’ refer to families of positions in the philosophy of moral reasoning, with the former playing down the importance of principles, rules or standards, and the latter stressing their importance. Part of the debate has taken an empirical turn, and this turn has implications for AI research and the philosophy of cognitive modeling. In this paper, Jonathan Dancy’s approach to particularism (arguably one of the best known and most radical approaches) is questioned both on logical and empirical grounds. (...)
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  3. Dedre Gentner (2010). Bootstrapping the Mind: Analogical Processes and Symbol Systems. Cognitive Science 34 (5):752-775.score: 104.0
    Human cognition is striking in its brilliance and its adaptability. How do we get that way? How do we move from the nearly helpless state of infants to the cognitive proficiency that characterizes adults? In this paper I argue, first, that analogical ability is the key factor in our prodigious capacity, and, second, that possession of a symbol system is crucial to the full expression of analogical ability.
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  4. Alex Watson (2014). Light as an Analogy for Cognition in Buddhist Idealism (Vijñānavāda). Journal of Indian Philosophy 42 (2-3):401-421.score: 68.0
    In Sect. 1 an argument for Yogācāra Buddhist Idealism, here understood as the view that everything in the universe is of the nature of consciousness / cognition, is laid out. The prior history of the argument is also recounted. In Sect. 2 the role played in this argument by light as an analogy for cognition is analyzed. Four separate aspects of the light analogy are discerned. In Sect. 3, I argue that although light is in some ways a (...)
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  5. Theodore Bach (2011). Structure-Mapping: Directions From Simulation to Theory. Philosophical Psychology 24 (1):23-51.score: 66.0
    The theory of mind debate has reached a “hybrid consensus” concerning the status of theory-theory and simulation-theory. Extant hybrid models either specify co-dependency and implementation relations, or distribute mentalizing tasks according to folk-psychological categories. By relying on a non-developmental framework these models fail to capture the central connection between simulation and theory. I propose a “dynamic” hybrid that is informed by recent work on the nature of similarity cognition. I claim that Gentner’s model of structure-mapping allows us to understand (...)
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  6. John M. Mikhail (2011). Elements of Moral Cognition: Rawls' Linguistic Analogy and the Cognitive Science of Moral and Legal Judgment. Cambridge University Press.score: 60.0
    Is the science of moral cognition usefully modeled on aspects of Universal Grammar? Are human beings born with an innate "moral grammar" that causes them to analyze human action in terms of its moral structure, with just as little awareness as they analyze human speech in terms of its grammatical structure? Questions like these have been at the forefront of moral psychology ever since John Mikhail revived them in his influential work on the linguistic analogy and its implications for (...)
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  7. Paul Bouissac (2008). The Evolution of Priming in Cognitive Competencies: To What Extent is Analogical Reasoning Adaptive? Behavioral and Brain Sciences 31 (4):380-381.score: 60.0
    This commentary questions the general assumptions concerning the cognitive value of analogical reasoning on which the argument developed by Leech et al. appears to rest. In order to better assess the findings of their meta-analysis, it shifts the perspective from development to evolution, and frames their concern within a broader issue.
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  8. Anna Chuderska & Adam Chuderski (2013). Two Facets of Cognitive Control in Analogical Mapping: The Role of Semantic Interference Resolution Andgoal-Driven Structure Selection. Thinking and Reasoning 20 (3):352-371.score: 60.0
    (2013). Two facets of cognitive control in analogical mapping: The role of semantic interference resolution andgoal-driven structure selection. Thinking & Reasoning. ???aop.label???
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  9. Joseph M. Notterman (2000). Note on Reductionism in Cognitive Psychology: Reification of Cognitive Processes Into Mind, Mind-Brain Equivalence, and Brain-Computer Analogy. Journal of Theoretical and Philosophical Psychology 20 (2):116-121.score: 56.0
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  10. Paul Thagard & Cameron Shelley, Emotional Analogies and Analogical Inference.score: 54.0
    Despite the growing appreciation of the relevance of affect to cognition, analogy researchers have paid remarkably little attention to emotion. This paper discusses three general classes of analogy that involve emotions. The most straightforward are analogies and metaphors about emotions, for example "Love is a rose and you better not pick it." Much more interesting are analogies that involve the transfer of emotions, for example in empathy in which people understand the emotions of others by imagining their own emotional (...)
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  11. Edward A. Wasserman (2008). Development and Evolution of Cognition: One Doth Not Fly Into Flying! Behavioral and Brain Sciences 31 (4):400-401.score: 54.0
    thought, in general, and – reasoning by analogy, in particular, have been said to reside at the very summit of human cognition. Leech et al. endeavor to comprehend the development of analogous thinking in human beings. Applying Leech et al.'s general approach to the evolution of analogical behavior in animals might also prove to be of considerable value.
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  12. Samantha B. Wright, Bryan J. Matlen, Carol L. Baym, Emilio Ferrer & Silvia A. Bunge (2007). Neural Correlates of Fluid Reasoning in Children and Adults. Frontiers in Human Neuroscience 2:8.score: 52.0
    Fluid reasoning, or the capacity to think logically and solve novel problems, is central to the development of human cognition, but little is known about the underlying neural changes. During the acquisition of event-related fMRI data, children aged 6-13 (N = 16) and young adults (N = 17) performed a task in which they were asked to identify semantic relationships between drawings of common objects. On semantic problems, participants indicated which of fi ve objects was most closely semantically related (...)
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  13. K. Mitch Hodge (2006). What Myths Reveal About How Humans Think: A Cognitive Approach to Myth. Dissertation, University of Texas Arlingtonscore: 50.0
    This thesis has two main goals: (1) to argue that myths are natural products of human cognition; and (2) that structuralism, as introduced by Claude Levi-Strauss, provides an over-arching theory of myth when supplemented and supported by current research in philosophy of mind, cognitive psychology, and cognitive anthropology. With regard to (1), we argue that myths are naturally produced by the human mind through individuals’ interaction with their natural and social environments. This interaction is constrained by both the type (...)
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  14. Mauricio Suárez & Albert Solé (2006). On the Analogy Between Cognitive Representation and Truth. Theoria 21 (1):39-48.score: 48.0
    In this paper we claim that the notion of cognitive representation (and scientific representation in particular) is irreducibly plural. By means of an analogy with the minimalist conception of truth, we show thatthis pluralism is compatible with a generally deflationary attitude towards representation. We then explore the extent and nature of representational pluralism by discussing the positive and negative analogies between the inferential conception of representation advocated by one of us and the minimalist conception of truth.
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  15. John P. O'Callaghan (2002). Aquinas, Cognitive Theory, and Analogy. American Catholic Philosophical Quarterly 76 (3):451-482.score: 48.0
    Is it the case that God, human beings, and air all share the same capacity for cognition, differing only in the degree to which they engage in cognitive acts? Robert Pasnau has recently argued that according to St. Thomas Aquinas they do, a conclusion that for Pasnau follows straightforwardly from Aquinas’s discussion of God’s cognition in the first part of the Summa theologiae. Further, Pasnau holds that Aquinas’s relation to contemporary cognitive theory should be understood in light of (...)
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  16. Robert Leech, Denis Mareschal & Richard P. Cooper (2008). Analogy as Relational Priming: A Developmental and Computational Perspective on the Origins of a Complex Cognitive Skill. Behavioral and Brain Sciences 31 (4):357-378.score: 48.0
    The development of analogical reasoning has traditionally been understood in terms of theories of adult competence. This approach emphasizes structured representations and structure mapping. In contrast, we argue that by taking a developmental perspective, analogical reasoning can be viewed as the product of a substantially different cognitive ability – relational priming. To illustrate this, we present a computational (here connectionist) account where analogy arises gradually as a by-product of pattern completion in a recurrent network. Initial exposure to a (...)
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  17. Eric Steinhart (1994). Beyond Proportional Analogy: A Structural Model of Analogical Mapping. Pragmatics and Cognition 2 (1):95-129.score: 46.0
    A model of analogical mapping is proposed that uses five principles to generate consistent and conflicting hypotheses regarding assignments of elements of a source domain to analogous elements of a target domain. The principles follow the fine conceptual structure of the domains. The principles are: (1) the principle of proportional analogy; (2) the principle of mereological analogy, (3) the principle of chain reinforcement; (4) the principle of transitive reinforcement; and (5) the principle of mutual inconsistency. A constraint-satisfaction network is (...)
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  18. Allison Barnes & Paul R. Thagard (1997). Empathy and Analogy. Dialogue 36 (4):705-720.score: 44.0
    We contend that empathy is best viewed as a kind of analogical thinking of the sort described in the multiconstraint theory of analogy proposed by Keith Holyoak and Paul Thagard (1995). Our account of empathy reveals the Theory-theory/Simulation theory debate to be based on a false assumption and formulated in terms too simple to capture the nature of mental state ascription. Empathy is always simulation, but may simultaneously include theory-application. By properly specifying the analogical processes of empathy and (...)
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  19. Paul Thagard (2006). Hot Thought: Mechanisms and Applications of Emotional Cognition. Cambridge MA: Bradford Book/MIT Press.score: 44.0
    A description of mental mechanisms that explain how emotions influence thought, from everyday decision making to scientific discovery and religious belief, and an analysis of when emotion can contribute to good reasoning.
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  20. Kevin Dunbar & Isabelle Blanchette (2001). The in Vivo/in Vitro Approach to Cognition: The Case of Analogy. Trends in Cognitive Sciences 5 (8):334-339.score: 42.0
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  21. Arthur B. Markman (2011). Can Developmental Psychology Provide a Blueprint for the Study of Adult Cognition? Behavioral and Brain Sciences 34 (3):140-141.score: 42.0
    In order to develop sophisticated models of the core domains of knowledge that support complex cognitive processing in infants and children, developmental psychologists have mapped out the content of these knowledge domains. This research strategy may provide a blueprint for advancing research on adult cognitive processing. I illustrate this suggestion with examples from analogical reasoning and decision making.
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  22. Robert Leech, Denis Mareschal & Richard P. Cooper (2008). Growing Cognition From Recycled Parts. Behavioral and Brain Sciences 31 (4):401-414.score: 42.0
    In this response, we reiterate the importance of development (both ontogenetic and phylogenetic) in the understanding of a complex cognitive skill – analogical reasoning. Four key questions structure the response: Does relational priming exist, and is it sufficient for analogy? What do we mean by relations as transformations? Could all or any relations be represented as transformations? And what about the challenge of more complex analogies? In addressing these questions we bring together a number of supportive commentaries, strengthening our (...)
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  23. M. Ramscar & H. Pain (1996). Can a Real Distinction Be Made Between Cognitive Theories of Analogy and Categorisation. In. In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum. 346--351.score: 42.0
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  24. Iris van Rooij, Patricia Evans, Moritz Müller, Jason Gedge & Todd Wareham (2008). Identifying Sources of Intractability in Cognitive Models: An Illustration Using Analogical Structure Mapping. In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society.score: 42.0
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  25. Bryan B. Whaley, Lisa Smith Wagner, Kathleen E. Cook & Natalie Jeha (2002). Rebuttal Analogy and Need for Cognition Individual Differences and Rebuttal Analogy in Persuasive Messages: Effect of Need for Cognition. Communication and Cognition. Monographies 35 (3-4):193-209.score: 42.0
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  26. James F. Ross (1970). Analogy and the Resolution of Some Cognitivity Problems. Journal of Philosophy 67 (20):725-746.score: 40.0
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  27. Hartmut Haberland (1996). Cognitive Technology and Pragmatics: Analogies and (Non-)Alignments. [REVIEW] AI and Society 10 (3-4):303-308.score: 40.0
    This paper presents some considerations about the relationship between languages and computer systems from a pragmatic, user-centered point of view.
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  28. Adam Albright & Bruce Hayes (2003). Rules Vs. Analogy in English Past Tenses: A Computational/Experimental Study. Cognition 90 (2):119-161.score: 40.0
    Are morphological patterns learned in the form of rules? Some models deny this, attributing all morphology to analogical mechanisms. The dual mechanism model (Pinker, S., & Prince, A. (1998). On language and connectionism: analysis of a parallel distributed processing model of language acquisition. Cognition, 28, 73-193) posits that speakers do internalize rules, but that these rules are few and cover only regular processes; the remaining patterns are attributed to analogy. This article advocates a third approach, which uses multiple (...)
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  29. Luis Emilio Bruni (2008). Hierarchical Categorical Perception in Sensing and Cognitive Processes. Biosemiotics 1 (1):113-130.score: 40.0
    This article considers categorical perception (CP) as a crucial process involved in all sort of communication throughout the biological hierarchy, i.e. in all of biosemiosis. Until now, there has been consideration of CP exclusively within the functional cycle of perception–cognition–action and it has not been considered the possibility to extend this kind of phenomena to the mere physiological level. To generalise the notion of CP in this sense, I have proposed to distinguish between categorical perception (CP) and categorical sensing (...)
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  30. Lynn A. Cooper & Margaret P. Munger (1999). Extrapolating and Remembering Positions Along Cognitive Trajectories: Uses and Limitations of Analogies to Physical Motion. In Naomi Eilan, Rosaleen McCarthy & Bill Brewer (eds.), Spatial Representation: Problems in Philosophy and Psychology. Clarendon Press.score: 40.0
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  31. Paulo Abrantes (1999). Analogical Reasoning and Modeling in the Sciences. Foundations of Science 4 (3):237-270.score: 38.0
    This paper aims at integrating the work onanalogical reasoning in Cognitive Science into thelong trend of philosophical interest, in this century,in analogical reasoning as a basis for scientificmodeling. In the first part of the paper, threesimulations of analogical reasoning, proposed incognitive science, are presented: Gentner''s StructureMatching Engine, Mitchel''s and Hofstadter''s COPYCATand the Analogical Constraint Mapping Engine, proposedby Holyoak and Thagard. The differences andcontroversial points in these simulations arehighlighted in order to make explicit theirpresuppositions concerning the nature of (...)
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  32. Jim Davies, Nancy J. Nersessian & Ashok K. Goel (2005). Visual Models in Analogical Problem Solving. Foundations of Science 10 (1):133-152.score: 38.0
    Visual analogy is believed to be important in human problem solving. Yet, there are few computational models of visual analogy. In this paper, we present a preliminary computational model of visual analogy in problem solving. The model is instantiated in a computer program, called Galatea, which uses a language for representing and transferring visual information called Privlan. We describe how the computational model can account for a small slice of a cognitive-historical analysis of Maxwell’s reasoning about electromagnetism.
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  33. Chris Baber (2010). Distributed Cognition at the Crime Scene. AI and Society 25 (4):423-432.score: 38.0
    The examination of a scene of crime provides both an interesting case study and analogy for consideration of Distributed Cognition. In this paper, Distribution is defined by the number of agents involved in the criminal justice process, and in terms of the relationship between a Crime Scene Examiner and the environment being searched.
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  34. Henri Prade & Gilles Richard (2013). From Analogical Proportion to Logical Proportions. Logica Universalis 7 (4):441-505.score: 38.0
    Given a 4-tuple of Boolean variables (a, b, c, d), logical proportions are modeled by a pair of equivalences relating similarity indicators ( \({a \wedge b}\) and \({\overline{a} \wedge \overline{b}}\) ), or dissimilarity indicators ( \({a \wedge \overline{b}}\) and \({\overline{a} \wedge b}\) ) pertaining to the pair (a, b), to the ones associated with the pair (c, d). There are 120 semantically distinct logical proportions. One of them models the analogical proportion which corresponds to a statement of the form (...)
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  35. Franziska Preusse, Elke Van Der Meer, Gopikrishna Deshpande, Frank Krueger & Isabell Wartenburger (2011). Fluid Intelligence Allows Flexible Recruitment of the Parieto-Frontal Network in Analogical Reasoning. Frontiers in Human Neuroscience 5.score: 38.0
    Fluid intelligence is the ability to think flexibly and to understand abstract relations. People with high fluid intelligence (hi-fluIQ) perform better in analogical reasoning tasks than people with average fluid intelligence (ave-fluIQ). Although previous neuroimaging studies reported involvement of parietal and frontal brain regions in geometric analogical reasoning (which is a prototypical task for fluid intelligence), however, neuroimaging findings on geometric analogical reasoning in hi-fluIQ are sparse. Furthermore, evidence on the relation between brain activation and intelligence while (...)
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  36. Steven Lehar (forthcoming). The Function of Conscious Experience: An Analogical Paradigm of Perception and Behavior. Consciousness and Cognition.score: 36.0
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  37. G. Thomas Goodnight (2013). The Virtues of Reason and the Problem of Other Minds: Reflections on Argumentation in a New Century. Informal Logic 33 (4):510-530.score: 36.0
    From early modernity, philosophers have engaged in skeptical discussions concerning knowledge of the existence, state, and standing of other minds. The analogical move from self to other unfolds as controversy. This paper reposes the problem as an argumentation predicament and examines analogy as an opening to the study of rhetorical cognition. Rhetorical cognition is identified as a productive process coming to terms with an other through testing sustainable risk. The paper explains how self-sustaining risk is theorized by (...)
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  38. Daniel Corral & Matt Jones (2014). The Effects of Relational Structure on Analogical Learning. Cognition 132 (3):280-300.score: 36.0
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  39. Usha Goswami & Ann L. Brown (1990). Higher-Order Structure and Relational Reasoning: Contrasting Analogical and Thematic Relations. Cognition 36 (3):207-226.score: 36.0
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  40. U. Goswami (1990). Melting Chocolate and Melting Snowmen: Analogical Reasoning and Causal Relations. Cognition 35 (1):69-95.score: 36.0
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  41. David J. Chalmers, Robert M. French & Douglas R. Hofstadter (1992). High-Level Perception, Representation, and Analogy:A Critique of Artificial Intelligence Methodology. Journal of Experimental and Theoretical Artificial Intellige 4 (3):185 - 211.score: 34.0
    High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing (...)
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  42. John E. Opfer & Leonidas A. A. Doumas (2008). Analogy and Conceptual Change in Childhood. Behavioral and Brain Sciences 31 (6):723-723.score: 34.0
    Analogical inferences are an important consequence of the way semantic knowledge is represented, that is, with relations as explicit structures that can take arguments. We review evidence that this feature of semantic cognition successfully predicts how quickly and broadly children's concepts change with experience and show that Rogers & McClelland's (R&M's) parallel distributed processing (PDP) model fails to simulate these cognitive changes due to its handling of relational information.
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  43. Paavo Pylkkänen (2014). Can Quantum Analogies Help Us to Understand the Process of Thought? [2nd Ed]. Mind and Matter 12 (1):61-91.score: 34.0
    A number of researchers today make an appeal to quantum physics when trying to develop a satisfactory account of the mind, an appeal still felt to be controversial by many. Often these "quantum approaches" try to explain some well-known features of conscious experience (or mental processes more generally), thus using quantum physics to enrich the explanatory framework or explanans used in consciousness studies and cognitive science. This paper considers the less studied question of whether quantum physical intuitions could help us (...)
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  44. N. B. Turk-Browne, B. J. Scholl & M. M. Chun (2007). Babies and Brains: Habituation in Infant Cognition and Functional Neuroimaging. Frontiers in Human Neuroscience 2:16-16.score: 34.0
    Many prominent studies of infant cognition over the past two decades have relied on the fact that infants habituate to repeated stimuli — i.e. that their looking times tend to decline upon repeated stimulus presentations. This phenomenon had been exploited to reveal a great deal about the minds of preverbal infants. Many prominent studies of the neural bases of adult cognition over the past decade have relied on the fact that brain regions habituate to repeated stimuli — i.e. (...)
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  45. Pavlos Kollias & James L. McClelland (2013). Context, Cortex, and Associations: A Connectionist Developmental Approach to Verbal Analogies. Frontiers in Psychology 4.score: 34.0
    We present a PDP model of binary choice verbal analogy problems (A:B as C:[D1|D2], where D1 and D2 represent choice alternatives). We train a recurrent neural network in item-relation- item triples and use this network to test performance on analogy questions. Without training on analogy problems per se, the model explains the developmental shift from associative to relational responding as an emergent consequence of learning upon the environment’s statistics. Such learning allows gradual, item-specific acquisition of relational knowledge to overcome the (...)
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  46. Joseph Glicksohn (2001). Temporal Cognition and the Phenomenology of Time: A Multiplicative Function for Apparent Duration. Consciousness and Cognition 10 (1):1-25.score: 32.0
    The literature on time perception is discussed. This is done with reference both to the ''cognitive-timer'' model for time estimation and to the subjective experience of apparent duration. Three assumptions underlying the model are scrutinized. I stress the strong interplay among attention, arousal, and time perception, which is at the base of the cognitive-timer model. It is suggested that a multiplicative function of two key components (the number of subjective time units and their size) should predict apparent duration. Implications for (...)
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  47. Eric Dietrich (2010). Analogical Insight: Toward Unifying Categorization and Analogy. Cognitive Processing 11 (4):331-.score: 30.0
    The purpose of this paper is to present two kinds of analogical representational change, both occurring early in the analogy-making process, and then, using these two kinds of change, to present a model unifying one sort of analogy-making and categorization. The proposed unification rests on three key claims: (1) a certain type of rapid representational abstraction is crucial to making the relevant analogies (this is the first kind of representational change; a computer model is presented that demonstrates this kind (...)
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  48. Robert S. Root-Bernstein (2002). Aesthetic Cognition. International Studies in the Philosophy of Science 16 (1):61 – 77.score: 30.0
    The purpose of this article is to integrate two outstanding problems within the philosophy of science. The first concerns what role aesthetics plays in scientific thinking. The second is the problem of how logically testable ideas are generated (the so-called "psychology of research" versus "logic of (dis)proof" problem). I argue that aesthetic sensibility is the basis for what scientists often call intuition, and that intuition in turn embodies (in a literal physiological sense) ways of thinking that have their own meta-logic. (...)
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  49. Kenneth Forbus, Jeffrey Usher, Andrew Lovett, Kate Lockwood & Jon Wetzel (2011). CogSketch: Sketch Understanding for Cognitive Science Research and for Education. Topics in Cognitive Science 3 (4):648-666.score: 30.0
    Sketching is a powerful means of working out and communicating ideas. Sketch understanding involves a combination of visual, spatial, and conceptual knowledge and reasoning, which makes it both challenging to model and potentially illuminating for cognitive science. This paper describes CogSketch, an ongoing effort of the NSF-funded Spatial Intelligence and Learning Center, which is being developed both as a research instrument for cognitive science and as a platform for sketch-based educational software. We describe the idea of open-domain sketch understanding, the (...)
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