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  1.  72
    The role of covariation versus mechanism information in causal attribution.Woo-Kyoung Ahn,Charles W. Kalish,Douglas L. Medin &Susan A. Gelman -1995 -Cognition 54 (3):299-352.
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  2.  52
    Feature Centrality and Conceptual Coherence.Steven A. Sloman,Bradley C. Love &Woo-Kyoung Ahn -1998 -Cognitive Science 22 (2):189-228.
    Conceptual features differ in how mentally tranformable they are. A robin that does not eat is harder to imagine than a robin that does not chirp. We argue that features are immutable to the extent that they are central in a network of dependency relations. The immutability of a feature reflects how much the internal structure of a concept depends on that feature; i.e., how much the feature contributes to the concept's coherence. Complementarily, mutability reflects the aspects in which a (...) concept is flexible. We show that features can be reliably ordered according to their mutability using tasks that require people to conceive of objects missing a feature, and that mutability (conceptual centrality) can be distinguished from category centrality and from diagnosticity and salience. We test a model of mutability based on asymmetric, unlabeled, pairwise dependency relations. With no free parameters, the model provides reasonable fits to data. Qualitative tests of the model show that mutability judgments are unaffected by the type of dependency relation and that dependency structure influences judgments of variability. (shrink)
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  3.  111
    Why essences are essential in the psychology of concepts.Woo-Kyoung Ahn,Charles Kalish,Susan A. Gelman,Douglas L. Medin,Christian Luhmann,Scott Atran,John D. Coley &Patrick Shafto -2001 -Cognition 82 (1):59-69.
  4.  65
    Why are different features central for natural kinds and artifacts?: the role of causal status in determining feature centrality.Woo-Kyoung Ahn -1998 -Cognition 69 (2):135-178.
  5.  44
    A Two‐Stage Model of Category Construction.Woo-Kyoung Ahn &Douglas L. Medin -1992 -Cognitive Science 16 (1):81-121.
    The current consensus is that most natural categories are not organized around strict definitions (a list of singly necessary and jointly sufficient features) but rather according to a family resemblance (FR) principle: Objects belong to the same category because they are similar to each other and dissimilar to objects in contrast categories. A number of computational models of category construction have been developed to provide an account of how and why people create FR categories (Anderson, 1990; Fisher, 1987). Surprisingly, however, (...) only a few experiments on category construction or free sorting have been run and they suggest that people do not sort examples by the FR principle. We report several new experiments and a two‐stage model for category construction. This model is contrasted with a variety of other models with respect to their ability to account for when FR sorting will and will not occur. The experiments serve to identify one basis for FR sorting and to support the two‐stage model. The distinctive property of the two‐stage model is that it assumes that people impose more structure than the examples support in the first stage and that the second stage adjusts for this difference between preferred and perceived structure. We speculate that people do not simply assimilate probabilistic structures but rather organize them in terms of discrete structures plus noise. (shrink)
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  6.  79
    Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment.Samuel G. B. Johnson &Woo-Kyoung Ahn -2015 -Cognitive Science 39 (7):1468-1503.
    Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, (...) that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory led to transitive causal judgments. On the other hand, chains schematized as multiple chunks led to intransitive judgments despite strong intermediate links. Normative accounts of causal intransitivity could not explain these intransitive judgments. (shrink)
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  7.  46
    Causal status effect in children's categorization.Woo-Kyoung Ahn,Susan A. Gelman,Jennifer A. Amsterlaw,Jill Hohenstein &Charles W. Kalish -2000 -Cognition 76 (2):B35-B43.
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  8.  142
    Mental Health Clinicians' Beliefs About the Biological, Psychological, and Environmental Bases of Mental Disorders.Woo-Kyoung Ahn,Caroline C. Proctor &Elizabeth H. Flanagan -2009 -Cognitive Science 33 (2):147-182.
    The current experiments examine mental health clinicians’ beliefs about biological, psychological, and environmental bases of the DSM‐IV‐TR mental disorders and the consequences of those causal beliefs for judging treatment effectiveness. Study 1 found a large negative correlation between clinicians’ beliefs about biological bases and environmental/psychological bases, suggesting that clinicians conceptualize mental disorders along a single continuum spanning from highly biological disorders (e.g., autistic disorder) to highly nonbiological disorders (e.g., adjustment disorders). Study 2 replicated this finding by having clinicians list what (...) they thought were the specific causes of nine familiar mental disorders and rate their bio–psycho–environmental bases. Study 3 further found that clinicians believe medication to be more effective for biologically based mental disorders and psychotherapy to be more effective for psychosocially based mental disorders. These results demonstrate that even expert mental health clinicians make strong distinctions between psychological and biological phenomena. (shrink)
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  9.  43
    BUCKLE: A model of unobserved cause learning.Christian C. Luhmann &Woo-Kyoung Ahn -2007 -Psychological Review 114 (3):657-677.
  10.  59
    Schema Acquisition From a Single Example.Woo-Kyoung Ahn,William F. Brewer &Raymond J. Mooney -1988 -Bulletin of the Psychonomic Society 26 (6):509-509.
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  11.  37
    The meaning and computation of causal power: Comment on Cheng (1997) and Novick and Cheng (2004).Christian C. Luhmann &Woo-Kyoung Ahn -2005 -Psychological Review 112 (3):685-692.
  12.  20
    Neurodualism: People Assume that the Brain Affects the Mind more than the Mind Affects the Brain.Jussi Valtonen,Woo-Kyoung Ahn &Andrei Cimpian -2021 -Cognitive Science 45 (9):e13034.
    People commonly think of the mind and the brain as distinct entities that interact, a view known as dualism. At the same time, the public widely acknowledges that science attributes all mental phenomena to the workings of a material brain, a view at odds with dualism. How do people reconcile these conflicting perspectives? We propose that people distort claims about the brain from the wider culture to fit their dualist belief that minds and brains are distinct, interacting entities: Exposure to (...) cultural discourse about the brain as the physical basis for the mind prompts people to posit that mind–brain interactions are asymmetric, such that the brain is able to affect the mind more than vice versa. We term this hybrid intuitive theory neurodualism. Five studies involving both thought experiments and naturalistic scenarios provided evidence of neurodualism among laypeople and, to some extent, even practicing psychotherapists. For example, lay participants reported that “a change in a person's brain” is accompanied by “a change in the person's mind” more often than vice versa. Similarly, when asked to imagine that “future scientists were able to alter exactly 25% of a person's brain,” participants reported larger corresponding changes in the person's mind than in the opposite direction. Participants also showed a similarly asymmetric pattern favoring the brain over the mind in naturalistic scenarios. By uncovering people's intuitive theories of the mind–brain relation, the results provide insights into societal phenomena such as the allure of neuroscience and common misperceptions of mental health treatments. (shrink)
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  13.  863
    The effect of abstract versus concrete framing on judgments of biological and psychological bases of behavior.Kim Nancy,Samuel Johnson,Woo-Kyoung Ahn &Joshua Knobe -forthcoming -Cognitive Research: Principles and Implications.
    Human behavior is frequently described both in abstract, general terms and in concrete, specific terms. We asked whether these two ways of framing equivalent behaviors shift the inferences people make about the biological and psychological bases of those behaviors. In five experiments, we manipulated whether behaviors are presented concretely (i.e. with reference to a specific person, instantiated in the particular context of that person’s life) or abstractly (i.e. with reference to a category of people or behaviors across generalized contexts). People (...) judged concretely framed behaviors to be less biologically based and, on some dimensions, more psychologically based than the same behaviors framed in the abstract. These findings held true for both mental disorders (Experiments 1 and 2) and everyday behaviors (Experiments 4 and 5) and yielded downstream consequences for the perceived efficacy of disorder treatments (Experiment 3). Implications for science educators, students of science, and members of the lay public are discussed. (shrink)
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  14. Causal inference when observed and unobserved causes interact.Benjamin M. Rottman &Woo-Kyoung Ahn -2009 - In N. A. Taatgen & H. van Rijn,Proceedings of the 31st Annual Conference of the Cognitive Science Society. pp. 1477--1482.
    When a cause interacts with unobserved factors to produce an effect, the contingency between the observed cause and effect cannot be taken at face value to infer causality. Yet, it would be computationally intractable to consider all possible unobserved, interacting factors. Nonetheless, two experiments found that when an unobserved cause is assumed to be fairly stable over time, people can learn about such interactions and adjust their inferences about the causal efficacy of the observed cause. When they observed a period (...) in which a cause and effect were associated followed by a period of the opposite association, rather than concluding a complete lack of causality, subjects inferred an unobserved, interacting cause. The interaction explains why the overall contingency between the cause and effect is low and allows people to still conclude that the cause is efficacious. (shrink)
     
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  15. Acknowledgment: Guest Reviewers.Frederick Adams,Wilson Geisler,David Over,Woo-Kyoung Ahn,LouAnn Gerken,Thomas Palmeri,Kathleen Akins,Lisa Gershkoff-Stowe,David Papineau &Gerry Altmann -2002 -Cognitive Science 26:841-842.
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  16.  27
    Postscript: Abandonment of Causal Power.Christian C. Luhmann &Woo-Kyoung Ahn -2005 -Psychological Review 112 (3):692-693.
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  17. When and how do people reason about unobserved causes.Benjamin Rottman,Woo-Kyoung Ahn &Christian Luhmann -2011 - In Phyllis McKay Illari Federica Russo,Causality in the Sciences. Oxford University Press. pp. 150.
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