64 found
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  1. Darwin's Mistake: Explaining the Discontinuity Between Human and Nonhuman Minds.Derek C. Penn, Keith J. Holyoak & Daniel J. Povinelli - 2008 - Behavioral and Brain Sciences 31 (2):109-130.
    Over the last quarter century, the dominant tendency in comparative cognitive psychology has been to emphasize the similarities between human and nonhuman minds and to downplay the differences as (Darwin 1871). In the present target article, we argue that Darwin was mistaken: the profound biological continuity between human and nonhuman animals masks an equally profound discontinuity between human and nonhuman minds. To wit, there is a significant discontinuity in the degree to which human and nonhuman animals are able to approximate (...)
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  2.  3
    Pragmatic Reasoning Schemas.Patricia W. Cheng & Keith J. Holyoak - 1985 - Cognitive Psychology 17 (4):391-416.
    We propose that people typically reason about realistic situations using neither content-free syntactic inference rules nor representations of specific experiences. Rather, people reason using knowledge structures that we term pragmatic reasoning schemas, which are generalized sets of rules defined in relation to classes of goals. Three experiments examined the impact of a “permission schema” on deductive reasoning. Experiment 1 demonstrated that by evoking the permission schema it is possible to facilitate performance in Wason's selection paradigm for subjects who have had (...)
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  3.  80
    Mental Leaps: Analogy in Creative Thought.Keith J. Holyoak & Paul Thagard - 1995 - MIT Press.
    Keith Holyoak and Paul Thagard provide a unified, comprehensive account of the diverse operations and applications of analogy, including problem solving, ...
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  4.  18
    Analogical Mapping by Constraint Satisfaction.Keith J. Holyoak & Paul Thagard - 1989 - Cognitive Science 13 (3):295-355.
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  5.  1
    Distributed Representations of Structure: A Theory of Analogical Access and Mapping.John E. Hummel & Keith J. Holyoak - 1997 - Psychological Review 104 (3):427-466.
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  6.  2
    A Symbolic-Connectionist Theory of Relational Inference and Generalization.John E. Hummel & Keith J. Holyoak - 2003 - Psychological Review 110 (2):220-264.
  7.  5
    Bayesian Generic Priors for Causal Learning.Hongjing Lu, Alan L. Yuille, Mimi Liljeholm, Patricia W. Cheng & Keith J. Holyoak - 2008 - Psychological Review 115 (4):955-984.
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  8.  7
    On the Natural Selection of Reasoning Theories.Patricia W. Cheng & Keith J. Holyoak - 1989 - Cognition 33 (3):285-313.
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  9.  1
    Conceptual Structure and the Procedural Affordances of Rational Numbers: Relational Reasoning with Fractions and Decimals.Melissa DeWolf, Miriam Bassok & Keith J. Holyoak - 2015 - Journal of Experimental Psychology: General 144 (1):127-150.
  10.  1
    Modeling Discrete and Continuous Entities with Fractions and Decimals.Monica Rapp, Miriam Bassok, Melissa DeWolf & Keith J. Holyoak - 2015 - Journal of Experimental Psychology: Applied 21 (1):47-56.
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  11.  7
    Analogical and Category-Based Inference: A Theoretical Integration with Bayesian Causal Models.Keith J. Holyoak, Hee Seung Lee & Hongjing Lu - 2010 - Journal of Experimental Psychology: General 139 (4):702-727.
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  12.  37
    Moral Principles or Consumer Preferences? Alternative Framings of the Trolley Problem.Tage S. Rai & Keith J. Holyoak - 2010 - Cognitive Science 34 (2):311-321.
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  13.  51
    A Neurocomputational System for Relational Reasoning.Barbara J. Knowlton, Robert G. Morrison, John E. Hummel & Keith J. Holyoak - 2012 - Trends in Cognitive Sciences 16 (7):373-381.
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  14.  9
    Conceptual and Procedural Distinctions Between Fractions and Decimals: A Cross-National Comparison.Hee Seung Lee, Melissa DeWolf, Miriam Bassok & Keith J. Holyoak - 2016 - Cognition 147:57-69.
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  15.  2
    Predictive and Diagnostic Learning Within Causal Models: Asymmetries in Cue Competition.Michael R. Waldmann & Keith J. Holyoak - 1992 - Journal of Experimental Psychology: General 121 (2):222-236.
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  16.  17
    Conceptual Integration of Arithmetic Operations With Real‐World Knowledge: Evidence From Event‐Related Potentials.Amy M. Guthormsen, Kristie J. Fisher, Miriam Bassok, Lee Osterhout, Melissa DeWolf & Keith J. Holyoak - 2016 - Cognitive Science 40 (3):723-757.
    Research on language processing has shown that the disruption of conceptual integration gives rise to specific patterns of event-related brain potentials —N400 and P600 effects. Here, we report similar ERP effects when adults performed cross-domain conceptual integration of analogous semantic and mathematical relations. In a problem-solving task, when participants generated labeled answers to semantically aligned and misaligned arithmetic problems, the second object label in misaligned problems yielded an N400 effect for addition problems. In a verification task, when participants judged arithmetically (...)
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  17.  17
    Bidirectional Reasoning in Decision Making by Constraint Satisfaction.Keith J. Holyoak & Dan Simon - 1999 - Journal of Experimental Psychology: General 128 (1):3.
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  18.  8
    Causal Models and the Acquisition of Category Structure.Michael R. Waldmann, Keith J. Holyoak & Angela Fratianne - 1995 - Journal of Experimental Psychology: General 124 (2):181.
  19. The Proper Treatment of Symbols in a Connectionist Architecture.Keith J. Holyoak & John E. Hummel - 2000 - In Eric Dietrich Art Markman (ed.), Cognitive Dynamics: Conceptual Change in Humans and Machines. Lawrence Erlbaum. pp. 229--263.
     
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  20.  4
    Semantic Alignment Across Whole-Number Arithmetic and Rational Numbers: Evidence From a Russian Perspective.Yulia A. Tyumeneva, Galina Larina, Ekaterina Alexandrova, Melissa DeWolf, Miriam Bassok & Keith J. Holyoak - 2018 - Thinking and Reasoning 24 (2):198-220.
    Solutions to word problems are moderated by the semantic alignment of real-world relations with mathematical operations. Categorical relations between entities are aligned with addition, whereas certain functional relations between entities are aligned with division. Similarly, discreteness vs. continuity of quantities is aligned with different formats for rational numbers. These alignments have been found both in textbooks and in the performance of college students in the USA and in South Korea. The current study examined evidence for alignments in Russia. Textbook analyses (...)
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  21.  53
    Pragmatic Reasoning with a Point of View.Keith J. Holyoak & Patricia W. Cheng - 1995 - Thinking and Reasoning 1 (4):289 – 313.
  22.  3
    Dissociation Between Magnitude Comparison and Relation Identification Across Different Formats for Rational Numbers.Maureen E. Gray, Melissa DeWolf, Miriam Bassok & Keith J. Holyoak - 2018 - Thinking and Reasoning 24 (2):179-197.
    The present study examined whether a dissociation among formats for rational numbers can be obtained in tasks that require comparing a number to a non-symbolic quantity. In Experiment 1, college students saw a discrete or else continuous image followed by a rational number, and had to decide which was numerically larger. In Experiment 2, participants saw the same displays but had to make a judgment about the type of ratio represented by the number. The magnitude task was performed more quickly (...)
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  23. Induction: Processes of Inference, Learning, and Discovery.John H. Holland, Keith J. Holyoak, Richard E. Nisbett & Paul R. Thagard - 1988 - Behaviorism 16 (2):181-184.
     
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  24.  8
    Relational Priming Based on a Multiplicative Schema for Whole Numbers and Fractions.Melissa DeWolf, Ji Y. Son, Miriam Bassok & Keith J. Holyoak - 2017 - Cognitive Science 41 (8):2053-2088.
    Why might it be beneficial for adults to process fractions componentially? Recent research has shown that college-educated adults can capitalize on the bipartite structure of the fraction notation, performing more successfully with fractions than with decimals in relational tasks, notably analogical reasoning. This study examined patterns of relational priming for problems with fractions in a task that required arithmetic computations. College students were asked to judge whether or not multiplication equations involving fractions were correct. Some equations served as structurally inverse (...)
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  25.  29
    The Analogical Mind.Keith J. Holyoak & P. Thagard - 1997 - American Psychologist 52:35-44.
    We examine the use of analogy in human thinking from the perspective of a multiconstraint theory, which postulates three basic types of constraints: similarity, structure and purpose. The operation of these constraints is apparent in both laboratory experiments on analogy and in naturalistic settings, including politics, psychotherapy, and scientific research. We sketch how the multiconstraint theory can be implemented in detailed computational simulations of the analogical human mind.
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  26.  4
    Relational Priming Based on a Multiplicative Schema for Whole Numbers and Fractions.Melissa DeWolf, Ji Y. Son, Miriam Bassok & Keith J. Holyoak - 2017 - Cognitive Science 41 (8):2053-2088.
    Why might it be beneficial for adults to process fractions componentially? Recent research has shown that college-educated adults can capitalize on the bipartite structure of the fraction notation, performing more successfully with fractions than with decimals in relational tasks, notably analogical reasoning. This study examined patterns of relational priming for problems with fractions in a task that required arithmetic computations. College students were asked to judge whether or not multiplication equations involving fractions were correct. Some equations served as structurally inverse (...)
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  27.  20
    Rational Hypocrisy: A Bayesian Analysis Based on Informal Argumentation and Slippery Slopes.Tage S. Rai & Keith J. Holyoak - 2014 - Cognitive Science 38 (7):1456-1467.
    Moral hypocrisy is typically viewed as an ethical accusation: Someone is applying different moral standards to essentially identical cases, dishonestly claiming that one action is acceptable while otherwise equivalent actions are not. We suggest that in some instances the apparent logical inconsistency stems from different evaluations of a weak argument, rather than dishonesty per se. Extending Corner, Hahn, and Oaksford's (2006) analysis of slippery slope arguments, we develop a Bayesian framework in which accusations of hypocrisy depend on inferences of shared (...)
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  28.  7
    Bayesian Analogy with Relational Transformations.Hongjing Lu, Dawn Chen & Keith J. Holyoak - 2012 - Psychological Review 119 (3):617-648.
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  29. The Taming of Content: Some Thoughts About Domains and Modules.Keith J. Holyoak & Patricia W. Cheng - forthcoming - Thinking and Reasoning.
  30.  56
    Structural Constraints and Object Similarity in Analogical Mapping and Inference.Daniel C. Krawczyk, Keith J. Holyoak & John E. Hummel - 2004 - Thinking and Reasoning 10 (1):85 – 104.
    Theories of analogical reasoning have viewed relational structure as the dominant determinant of analogical mapping and inference, while assigning lesser importance to similarity between individual objects. An experiment is reported in which these two sources of constraints on analogy are placed in competition under conditions of high relational complexity. Results demonstrate equal importance for relational structure and object similarity, both in analogical mapping and in inference generation. The human data were successfully simulated using a computational analogy model (LISA) that treats (...)
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  31.  43
    The Impact of Anxiety on Analogical Reasoning.Jean M. Tohill & Keith J. Holyoak - 2000 - Thinking and Reasoning 6 (1):27 – 40.
    The effect of state anxiety on analogical reasoning was investigated by examining qualitative differences in mapping performance between anxious and non-anxious individuals reasoning about pictorial analogies. The working-memory restriction theory of anxiety, coupled with theories of analogy that link complexity of mapping with working-memory capacity, predicts that high anxiety will impair the ability to find correspondences based on relations between multiple objects relative to correspondences based on overlap of attributes between individual objects. Anxiety was induced in one condition by a (...)
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  32.  17
    The Impact of Goal Specificity on Strategy Use and the Acquisition of Problem Structure.Regina Vollmeyer, Bruce D. Burns & Keith J. Holyoak - 1996 - Cognitive Science 20 (1):75-100.
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  33. Induction: Processes of Inference, Learning, and Discovery.John H. Holland, Keith J. Holyoak, Richard E. Nisbett & Paul R. Thagard - 1991 - British Journal for the Philosophy of Science 42 (2):269-272.
     
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  34.  7
    The One‐to‐One Constraint in Analogical Mapping and Inference.Daniel C. Krawczyk, Keith J. Holyoak & John E. Hummel - 2005 - Cognitive Science 29 (5):797-806.
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  35. Thinking and Reasoning: A Reader's Guide.Keith J. Holyoak & Robert G. Morrison - 2005 - In K. Holyoak & B. Morrison (eds.), The Cambridge Handbook of Thinking and Reasoning. Cambridge University Press. pp. 1--9.
     
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  36.  40
    Pragmatic Reasoning From Multiple Points of View: A Response.Keith J. Holyoak & Patricia W. Cheng - 1995 - Thinking and Reasoning 1 (4):373 – 389.
  37.  18
    Relational Integration in Older Adults.Indre V. Viskontas, Keith J. Holyoak & Barbara J. Knowlton - 2005 - Thinking and Reasoning 11 (4):390 – 410.
    Reasoning requires making inferences based on information gleaned from a set of relations. The relational complexity of a problem increases with the number of relations that must be considered simultaneously to make a correct inference. Previous work (Viskontas, Morrison, Holyoak, Hummel, & Knowlton, 2004) has shown that older adults have difficulty integrating multiple relations during analogical reasoning, especially when required to inhibit irrelevant information. We report two experiments that examined the ability to integrate multiple relations in younger, middle-aged, and older (...)
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  38.  14
    Intuitive Physics: Current Research and Controversies.James R. Kubricht, Keith J. Holyoak & Hongjing Lu - 2017 - Trends in Cognitive Sciences 21 (10):749-759.
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  39.  18
    LISA: A Computational Model of Analogical Inference and Schema Induction.John E. Hummel & Keith J. Holyoak - 1996 - In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum. pp. 352--357.
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  40.  11
    Distributing Structure Over Time.John E. Hummel & Keith J. Holyoak - 1993 - Behavioral and Brain Sciences 16 (3):464.
  41.  6
    No Way to Start a Space Program: Associationism as a Launch Pad for Analogical Reasoning.Keith J. Holyoak & John E. Hummel - 2008 - Behavioral and Brain Sciences 31 (4):388-389.
    Humans, including preschool children, exhibit role-based relational reasoning, of which analogical reasoning is a canonical example. The connectionist model proposed in the target article is only capable of conditional paired-associate learning.
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  42.  18
    The Problem with Using Associations to Carry Binding Information.Leonidas A. A. Doumas, Keith J. Holyoak & John E. Hummel - 2006 - Behavioral and Brain Sciences 29 (1):74-75.
    van der Velde & de Kamps argue for the importance of considering the binding problem in accounts of human mental representation. However, their proposed solution fails as a complete account because it represents the bindings between roles and their fillers through associations (or connections). In addition, many criticisms leveled by the authors towards synchrony-based bindings models do not hold.
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  43.  14
    Transfer and Expertise.Daniel R. Kimball & Keith J. Holyoak - 2000 - In Endel Tulving (ed.), The Oxford Handbook of Memory. Oxford University Press. pp. 109--122.
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  44.  35
    Catherine Pelachaud, Norman I. Badler, and Mark Steedman.Risto Miikkulainen, Regina Vollmeyer, Bruce D. Burns, Keith J. Holyoak, Maartje E. J. Raijmakers, Sylvester van Koten, Peter C. M. Molenaar, Daniel Jurafsky, Gerhard Weber & Giuseppe Mantovani - 1996 - Cognitive Science 20:618-619.
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  45.  77
    There is More to Thinking Than Propositions.Derek C. Penn, Patricia W. Cheng, Keith J. Holyoak, John E. Hummel & Daniel J. Povinelli - 2009 - Behavioral and Brain Sciences 32 (2):221-223.
    We are big fans of propositions. But we are not big fans of the proposed by Mitchell et al. The authors ignore the critical role played by implicit, non-inferential processes in biological cognition, overestimate the work that propositions alone can do, and gloss over substantial differences in how different kinds of animals and different kinds of cognitive processes approximate propositional representations.
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  46.  18
    Deductive Reasoning.John H. Holland, Keith J. Holyoak, Richard E. Nisbett & Paul R. Thagard - 1993 - In Alvin Goldman (ed.), Readings in Philosophy and Cognitive Science. Cambridge: MIT Press.
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  47.  58
    Darwin's Triumph: Explaining the Uniqueness of the Human Mind Without a Deus Ex Machina.Derek C. Penn, Keith J. Holyoak & Daniel J. Povinelli - 2008 - Behavioral and Brain Sciences 31 (2):153-178.
    In our target article, we argued that there is a profound functional discontinuity between the cognitive abilities of modern humans and those of all other extant species. Unsurprisingly, our hypothesis elicited a wide range of responses from commentators. After responding to the commentaries, we conclude that our hypothesis lies closer to Darwin's views on the matter than to those of many of our contemporaries.
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  48.  20
    Absence Makes the Thought Grow Stronger: Reducing Structural Overlap Can Increase Inductive Strength.Hee Seung Lee & Keith J. Holyoak - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society.
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  49.  21
    A Solution to the Binding Problem for Compositional Connectionism.John E. Hummel, Keith J. Holyoak, Collin Green, Leonidas Aa Doumas, Derek Devnich, Aniket Kittur & Donald J. Kalar - 2004 - In Simon D. Levy & Ross Gayler (eds.), Compositional Connectionism in Cognitive Science. Aaai Press.
  50.  8
    Generative Inferences Based on Learned Relations.Dawn Chen, Hongjing Lu & Keith J. Holyoak - 2017 - Cognitive Science 41 (S5):1062-1092.
    A key property of relational representations is their generativity: From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from non-relational inputs. In the present paper, we show that a bottom-up model of relation learning, initially developed to discriminate between positive and negative examples of comparative relations, can be extended to make generative inferences. (...)
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