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Daniel Osherson [59]Daniel N. Osherson [33]
  1.  2
    Category-Based Induction.Daniel N. Osherson, Edward E. Smith, Ormond Wilkie & Alejandro López - 1990 - Psychological Review 97 (2):185-200.
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  2. On the Adequacy of Prototype Theory as a Theory of Concepts.Daniel N. Osherson & Edward E. Smith - 1981 - Cognition 9 (1):35-58.
  3.  20
    Comparison of Confirmation Measures.Katya Tentori, Vincenzo Crupi, Nicolao Bonini & Daniel Osherson - 2007 - Cognition 103 (1):107-119.
  4.  22
    Probabilistic Coherence and Proper Scoring Rules.Joel Predd, Robert Seiringer, Elliott Lieb, Daniel Osherson, H. Vincent Poor & Sanjeev Kulkarni - 2009 - IEEE Transactions on Information Theory 55 (10):4786-4792.
    We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The theorem recapitulates insights achieved by other investigators, and clarifi es the connection of coherence and proper scoring rules to Bregman divergence.
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  5.  51
    Updating Beliefs in Light of Uncertain Evidence: Descriptive Assessment of Jeffrey's Rule.Daniel Osherson & Jiaying Zhao - 2010 - Thinking and Reasoning 16 (4):288-307.
  6.  36
    Updating: Learning Versus Supposing.Jiaying Zhao, Vincenzo Crupi, Katya Tentori, Branden Fitelson & Daniel Osherson - 2012 - Cognition 124 (3):373-378.
  7.  15
    Combining Prototypes: A Selective Modification Model.Edward E. Smith, Daniel N. Osherson, Lance J. Rips & Margaret Keane - 1988 - Cognitive Science 12 (4):485-527.
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  8. Elements of Scientific Inquiry.Eric Martin & Daniel N. Osherson - 1998
     
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  9.  43
    On the Provenance of Judgments of Conditional Probability.Jiaying Zhao, Anuj Shah & Daniel Osherson - 2009 - Cognition 113 (1):26-36.
  10.  25
    The Conjunction Fallacy: A Misunderstanding About Conjunction?Daniel Osherson - 2004 - Cognitive Science 28 (3):467-477.
    It is easy to construct pairs of sentences X, Y that lead many people to ascribe higher probability to the conjunction X-and-Y than to the conjuncts X, Y. Whether an error is thereby committed depends on reasoners’ interpretation of the expressions “probability” and “and.” We report two experiments designed to clarify the normative status of typical responses to conjunction problems. © 2004 Cognitive Science Society, Inc. All rights reserved.
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  11.  3
    Task-Specificity and Species-Specificity in the Study of Language: A Methodological Note.Daniel N. Osherson & Thomas Wasow - 1976 - Cognition 4 (2):203-214.
  12.  55
    On the Psychology of Vague Predicates.Nicolao Bonini, Daniel Osherson, Riccardo Viale & Timothy Williamson - 1999 - Mind and Language 14 (4):377–393.
    Most speakers experience unclarity about the application of predicates like tall and red to liminal cases. We formulate alternative psychological hypotheses about the nature of this unclarity, and report experiments that provide a partial test of them. A psychologized version of the ‘vagueness-as-ignorance’ theory is then advanced and defended.
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  13.  24
    A Different Conjunction Fallacy.Nicolao Bonini, Katya Tentori & Daniel Osherson - 2004 - Mind and Language 19 (2):199–210.
    Because the conjunction pandq implies p, the value of a bet on pandq cannot exceed the value of a bet on p at the same stakes. We tested recognition of this principle in a betting paradigm that (a) discouraged misreading p as pandnotq, and (b) encouraged genuinely conjunctive reading of pandq. Frequent violations were nonetheless observed. The findings appear to discredit the idea that most people spontaneously integrate the logic of conjunction into their assessments of chance.
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  14.  7
    Gradedness and Conceptual Combination.Daniel N. Osherson & Edward E. Smith - 1982 - Cognition 12 (3):299-318.
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  15.  7
    Conceptual Combination with Prototype Concepts.Edward E. Smith & Daniel N. Osherson - 1984 - Cognitive Science 8 (4):337-361.
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  16. Preference Based on Reasons.Daniel Osherson & Scott Weinstein - 2012 - Review of Symbolic Logic 5 (1):122-147.
    We describe a logic of preference in which modal connectives reflect reasons to desire that a sentence be true. Various conditions on models are introduced and analyzed.
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  17.  39
    Similarity and Induction.Matthew Weber & Daniel Osherson - 2010 - Review of Philosophy and Psychology 1 (2):245-264.
    We advance a theory of inductive reasoning based on similarity, and test it on arguments involving mammal categories. To measure similarity, we quantified the overlap of neural activation in left Brodmann area 19 and the left ventral temporal cortex in response to pictures of different categories; the choice of of these regions is motivated by previous literature. The theory was tested against probability judgments for 40 arguments generated from 9 mammal categories and a common predicate. The results are interpreted in (...)
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  18.  14
    On Typicality and Vagueness.Daniel Osherson & Edward E. Smith - 1997 - Cognition 64 (2):189-206.
  19.  46
    Extrapolating Human Probability Judgment.Daniel Osherson, Edward E. Smith, Tracy S. Myers, Eldar Shafir & Michael Stob - 1994 - Theory and Decision 36 (2):103-129.
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  20. A Universal Inductive Inference Machine.Daniel N. Osherson, Michael Stob & Scott Weinstein - 1991 - Journal of Symbolic Logic 56 (2):661-672.
    A paradigm of scientific discovery is defined within a first-order logical framework. It is shown that within this paradigm there exists a formal scientist that is Turing computable and universal in the sense that it solves every problem that any scientist can solve. It is also shown that universal scientists exist for no regular logics that extend first-order logic and satisfy the Löwenheim-Skolem condition.
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  21.  48
    Aggregating Large Sets of Probabilistic Forecasts by Weighted Coherent Adjustment.Guanchun Wang, Sanjeev R. Kulkarni & Daniel N. Osherson - unknown
    Stochastic forecasts in complex environments can benefit from combining the estimates of large groups of forecasters (“judges”). But aggregating multiple opinions faces several challenges. First, human judges are notoriously incoherent when their forecasts involve logically complex events. Second, individual judges may have specialized knowledge, so different judges may produce forecasts for different events. Third, the credibility of individual judges might vary, and one would like to pay greater attention to more trustworthy forecasts. These considerations limit the value of simple aggregation (...)
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  22.  6
    Some Origins of Belief.Daniel N. Osherson, Edward E. Smith & Eldar B. Shafir - 1986 - Cognition 24 (3):197-224.
  23.  26
    Paradigms of Truth Detection.Daniel N. Osherson & Scott Weinstein - 1989 - Journal of Philosophical Logic 18 (1):1 - 42.
    Alternative models of idealized scientific inquiry are investigated and compared. Particular attention is devoted to paradigms in which a scientist is required to determine the truth of a given sentence in the structure giving rise to his data.
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  24.  9
    Default Probability.Daniel N. Osherson, Joshua Stern, Ormond Wilkie, Michael Stob & Edward E. Smith - 1991 - Cognitive Science 15 (2):251-269.
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  25.  5
    Evidential Diversity and Premise Probability in Young Children's Inductive Judgment.Yafen Lo, Ashley Sides, Joseph Rozelle & Daniel Osherson - 2002 - Cognitive Science 26 (2):181-206.
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  26. A Note on Superadditive Probability Judgment.Laura Macchi, Daniel Osherson & David H. Krantz - 1999 - Psychological Review 106 (1):210-214.
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  27.  16
    Identification in the Limit of First Order Structures.Daniel Osherson & Scott Weinstein - 1986 - Journal of Philosophical Logic 15 (1):55 - 81.
  28.  45
    A Reason for Theoretical Terms.Haim Gaifman, Daniel N. Osherson & Scott Weinstein - 1990 - Erkenntnis 32 (2):149 - 159.
    The presence of nonobservational vocabulary is shown to be necessary for wide application of a conservative principle of theory revision.
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  29.  4
    Language and the Ability to Evaluate Contradictions and Tautologies.Daniel N. Osherson & Ellen Markman - 1974 - Cognition 3 (3):213-226.
  30.  35
    Scientific Discovery on Positive Data Via Belief Revision.Eric Martin & Daniel Osherson - 2000 - Journal of Philosophical Logic 29 (5):483-506.
    A model of inductive inquiry is defined within a first-order context. Intuitively, the model pictures inquiry as a game between Nature and a scientist. To begin the game, a nonlogical vocabulary is agreed upon by the two players along with a partition of a class of structures for that vocabulary. Next, Nature secretly chooses one structure ("the real world") from some cell of the partition. She then presents the scientist with a sequence of atomic facts about the chosen structure. With (...)
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  31.  10
    A Source of Bayesian Priors.Daniel Osherson, Edward E. Smith, Eldar Shafir, Antoine Gualtierotti & Kevin Biolsi - 1995 - Cognitive Science 19 (3):377-405.
  32.  49
    A Note on Concave Utility Functions.Martin M. Monti, Simon Grant & Daniel N. Osherson - 2005 - Mind and Society 4 (1):85-96.
    The classical theory of preference among monetary bets represents people as expected utility maximizers with concave utility functions. Critics of this account often rely on assumptions about preferences over wide ranges of total wealth. We derive a prediction of the theory that bears on bets at any fixed level of wealth, and test the prediction behaviorally. Our results are discrepant with the classical account. Competing theories are also examined in light of our data.
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  33.  40
    Order Dependence and Jeffrey Conditionalization.Daniel Osherson - manuscript
    A glance at the sky raises my probability of rain to .7. As it happens, the conditional probabilities of each state given rain remain the same, and similarly for their conditional probabilities given no rain. As Jeffrey (1983, Ch. 11) points out, my new distribution P2 is therefore fixed by the law of total probability. For example, P2(RC) = P2(RC | R)P2(R)+P2(RC | ¯.
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  34.  36
    Scientific Discovery Based on Belief Revision.Eric Martin & Daniel Osherson - 1997 - Journal of Symbolic Logic 62 (4):1352-1370.
    Scientific inquiry is represented as a process of rational hypothesis revision in the face of data. For the concept of rationality, we rely on the theory of belief dynamics as developed in [5, 9]. Among other things, it is shown that if belief states are left unclosed under deductive logic then scientific theories can be expanded in a uniform, consistent fashion that allows inquiry to proceed by any method of hypothesis revision based on "kernel" contraction. In contrast, if belief states (...)
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  35.  20
    Mechanical Learners Pay a Price for Bayesianism.Daniel N. Osherson, Michael Stob & Scott Weinstein - 1988 - Journal of Symbolic Logic 53 (4):1245-1251.
  36.  25
    Aggregating Disparate Estimates of Chance.Daniel Osherson - manuscript
    We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistic incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group’s expertise.
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  37.  12
    The Diversity Principle and the Little Scientist Hypothesis.Riccardo Viale & Daniel Osherson - 2000 - Foundations of Science 5 (2):239-253.
    The remarkable transition from helpless infant to sophisticatedfive-year-old has long captured the attention of scholars interested inthe discovery of knowledge. To explain these achievements, developmentalpsychologists often compare children's discovery procedures to those ofprofessional scientists. For the child to be qualified as a ``littlescientist'', however, intellectual development must be shown to derivefrom rational hypothesis selection in the face of evidence. In thepresent paper we focus on one dimension of rational theory-choice,namely, the relation between hypothesis confirmation and evidencediversity. Psychological research suggests cultural (...)
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  38.  11
    Identifiable Collections of Countable Structures.Daniel N. Osherson & Scott Weinstein - 1989 - Philosophy of Science 56 (1):94-105.
    A model of idealized scientific inquiry is presented in which scientists are required to infer the nature of the structure that makes true the data they examine. A necessary and sufficient condition is presented for scientific success within this paradigm.
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  39.  77
    Learning to Coordinate; a Recursion Theoretic Perspective.Franco Montagna & Daniel Osherson - 1999 - Synthese 118 (3):363-382.
    We consider two players each of whom attempts to predict the behavior of the other, using no more than the history of earlier predictions. Behaviors are limited to a pair of options, conventionally denoted by 0, 1. Such players face the problem of learning to coordinate choices. The present paper formulates their situation recursion theoretically, and investigates the prospects for success. A pair of players build up a matrix with two rows and infinitely many columns, and are said to “learn” (...)
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  40.  22
    On Advancing Simple Hypotheses.Daniel N. Osherson & Scott Weinstein - 1990 - Philosophy of Science 57 (2):266-277.
    We consider drawbacks to scientific methods that prefer simple hypotheses to complex ones that cover the same data. The discussion proceeds in the context of a precise model of scientific inquiry.
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  41.  7
    Extracting the Coherent Core of Human Probability Judgement: A Research Program for Cognitive Psychology.Daniel Osherson, Eldar Shafir & Edward E. Smith - 1994 - Cognition 50 (1-3):299-313.
  42.  35
    Scientific Discovery From the Perspective of Hypothesis Acceptance.Eric Martin & Daniel Osherson - 2002 - Philosophy of Science 69 (S3):S331-S341.
    A model of inductive inquiry is defined within the context of first‐order logic. The model conceives of inquiry as a game between Nature and a scientist. To begin the game, a nonlogical vocabulary is agreed upon by the two players, along with a partition of a class of countable structures for that vocabulary. Next, Nature secretly chooses one structure from some cell of the partition. She then presents the scientist with a sequence of facts about the chosen structure. With each (...)
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  43. Compositionality and Typicality.E. E. Smith & Daniel Osherson - 1988 - In Stephen Schiffer & Susan Steele (eds.), Cognition and Representation. Westview Press. pp. 37--52.
     
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  44.  16
    A Note on Formal Learning Theory.Daniel N. Osherson & Scott Weinstein - 1982 - Cognition 11 (1):77-88.
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  45.  47
    On Charitable Translation.Daniel N. Osherson & Scott Weinstein - 1989 - Philosophical Studies 56 (2):127 - 134.
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  46.  58
    Inductive Inference Based on Probability and Similarity.Matthew Weber & Daniel Osherson - unknown
    We advance a theory of inductive inference designed to predict the conditional probability that certain natural categories satisfy a given predicate given that others do (or do not). A key component of the theory is the similarity of the categories to one another. We measure such similarities in terms of the overlap of metabolic activity in voxels of various posterior regions of the brain in response to viewing instances of the category. The theory and similarity measure are tested against averaged (...)
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  47.  23
    Response to Tzourio-Mazoyer and Zago: Yes, There is a Neural Dissociation Between Language and Reasoning.Martin M. Monti, Lawrence M. Parsons & Daniel N. Osherson - 2012 - Trends in Cognitive Sciences 16 (10):495-496.
  48. Cognitive Development, Culture, and Inductive Judgment.Riccardo Viale & Daniel Osherson - 2006 - In Riccardo Viale, D. Andler & Lawrence A. Hirschfeld (eds.), Biological and Cultural Bases of Human Inference. Lawerence Erlbaum.
     
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  49.  2
    Three Conditions on Conceptual Naturalness.Daniel N. Osherson - 1978 - Cognition 6 (4):263-289.
  50.  7
    Coherent Probability From Incoherent Judgment.Daniel Osherson, David Lane, Peter Hartley & Richard R. Batsell - 2001 - Journal of Experimental Psychology: Applied 7 (1):3.
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