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Daniel Osherson [60]Daniel N. Osherson [31]
  1. Daniel N. Osherson & Edward E. Smith (1981). On the Adequacy of Prototype Theory as a Theory of Concepts. Cognition 9 (1):35-58.
  2.  10
    Katya Tentori, Vincenzo Crupi, Nicolao Bonini & Daniel Osherson (2007). Comparison of Confirmation Measures. Cognition 103 (1):107-119.
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  3. Eric Martin & Daniel N. Osherson (1998). Elements of Scientific Inquiry. Monograph Collection (Matt - Pseudo).
     
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  4.  9
    Edward E. Smith, Daniel N. Osherson, Lance J. Rips & Margaret Keane (1988). Combining Prototypes: A Selective Modification Model. Cognitive Science 12 (4):485-527.
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  5.  1
    Daniel N. Osherson & Thomas Wasow (1976). Task-Specificity and Species-Specificity in the Study of Language: A Methodological Note. Cognition 4 (2):203-214.
  6.  23
    Jiaying Zhao, Anuj Shah & Daniel Osherson (2009). On the Provenance of Judgments of Conditional Probability. Cognition 113 (1):26-36.
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  7.  9
    Joel Predd, Robert Seiringer, Elliott Lieb, Daniel Osherson, H. Vincent Poor & Sanjeev Kulkarni (2009). Probabilistic Coherence and Proper Scoring Rules. 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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  8.  21
    Nicolao Bonini, Katya Tentori & Daniel Osherson (2004). A Different Conjunction Fallacy. 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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  9.  35
    Nicolao Bonini, Daniel Osherson, Riccardo Viale & Timothy Williamson (1999). On the Psychology of Vague Predicates. 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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  10.  8
    Jiaying Zhao, Vincenzo Crupi, Katya Tentori, Branden Fitelson & Daniel Osherson (2012). Updating: Learning Versus Supposing. Cognition 124 (3):373-378.
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  11. Daniel N. Osherson, Michael Stob & Scott Weinstein (1991). A Universal Inductive Inference Machine. 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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  12.  4
    Edward E. Smith & Daniel N. Osherson (1984). Conceptual Combination with Prototype Concepts. Cognitive Science 8 (4):337-361.
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  13.  4
    Daniel N. Osherson & Edward E. Smith (1982). Gradedness and Conceptual Combination. Cognition 12 (3):299-318.
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  14.  18
    Daniel Osherson & Jiaying Zhao (2011). Updating Beliefs in Light of Uncertain Evidence: Descriptive Assessment of Jeffrey's Rule. Thinking and Reasoning 16 (4):288-307.
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  15.  30
    Matthew Weber & Daniel Osherson (2010). Similarity and Induction. 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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  16.  10
    Daniel Osherson & Edward E. Smith (1997). On Typicality and Vagueness. Cognition 64 (2):189-206.
  17.  57
    Daniel Osherson & Scott Weinstein (2012). Preference Based on Reasons. 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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  18.  19
    Daniel Osherson (2004). The Conjunction Fallacy: A Misunderstanding About Conjunction? 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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  19.  15
    Daniel Osherson, Edward E. Smith, Tracy S. Myers, Eldar Shafir & Michael Stob (1994). Extrapolating Human Probability Judgment. Theory and Decision 36 (2):103-129.
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  20.  4
    Daniel N. Osherson, Edward E. Smith & Eldar B. Shafir (1986). Some Origins of Belief. Cognition 24 (3):197-224.
  21.  12
    Daniel Osherson & Scott Weinstein (1986). Identification in the Limit of First Order Structures. Journal of Philosophical Logic 15 (1):55 - 81.
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  22.  21
    Daniel N. Osherson & Scott Weinstein (1989). Paradigms of Truth Detection. 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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  23.  35
    Guanchun Wang, Sanjeev R. Kulkarni & Daniel N. Osherson, Aggregating Large Sets of Probabilistic Forecasts by Weighted Coherent Adjustment.
    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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  24.  3
    Yafen Lo, Ashley Sides, Joseph Rozelle & Daniel Osherson (2002). Evidential Diversity and Premise Probability in Young Children's Inductive Judgment. Cognitive Science 26 (2):181-206.
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  25.  5
    Daniel N. Osherson, Joshua Stern, Ormond Wilkie, Michael Stob & Edward E. Smith (1991). Default Probability. Cognitive Science 15 (2):251-269.
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  26.  33
    Haim Gaifman, Daniel N. Osherson & Scott Weinstein (1990). A Reason for Theoretical Terms. 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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  27.  27
    Eric Martin & Daniel Osherson (2000). Scientific Discovery on Positive Data Via Belief Revision. 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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  28.  4
    Daniel N. Osherson & Ellen Markman (1974). Language and the Ability to Evaluate Contradictions and Tautologies. Cognition 3 (3):213-226.
  29.  29
    Daniel Osherson (2005). A Note on Concave Utility Functions. 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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  30.  23
    Daniel Osherson, Order Dependence and Jeffrey Conditionalization.
    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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  31.  16
    Daniel Osherson, Aggregating Disparate Estimates of Chance.
    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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  32.  13
    Daniel N. Osherson, Michael Stob & Scott Weinstein (1988). Mechanical Learners Pay a Price for Bayesianism. Journal of Symbolic Logic 53 (4):1245-1251.
  33.  3
    Daniel Osherson, Edward E. Smith, Eldar Shafir, Antoine Gualtierotti & Kevin Biolsi (1995). A Source of Bayesian Priors. Cognitive Science 19 (3):377-405.
  34.  9
    Daniel N. Osherson & Scott Weinstein (1989). Identifiable Collections of Countable Structures. 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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  35.  19
    Daniel N. Osherson & Scott Weinstein (1990). On Advancing Simple Hypotheses. 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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  36.  28
    Eric Martin & Daniel Osherson (2002). Scientific Discovery From the Perspective of Hypothesis Acceptance. 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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  37.  10
    Martin M. Monti, Simon Grant & Daniel N. Osherson (2005). A Note on Concave Utility Functions. 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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  38.  41
    Daniel N. Osherson & Scott Weinstein (1989). On Charitable Translation. Philosophical Studies 56 (2):127 - 134.
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  39.  6
    Martin M. Monti, Lawrence M. Parsons & Daniel N. Osherson (2012). Response to Tzourio-Mazoyer and Zago: Yes, There is a Neural Dissociation Between Language and Reasoning. Trends in Cognitive Sciences 16 (10):495-496.
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  40.  5
    Daniel Osherson, Eldar Shafir & Edward E. Smith (1994). Extracting the Coherent Core of Human Probability Judgement: A Research Program for Cognitive Psychology. Cognition 50 (1-3):299-313.
  41. E. E. Smith & Daniel Osherson (1988). Compositionality and Typicality. In Stephen Schiffer & Susan Steele (eds.), Cognition and Representation. Westview Press 37--52.
     
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  42.  5
    Daniel Osherson, David Lane, Peter Hartley & Richard R. Batsell (2001). Coherent Probability From Incoherent Judgment. Journal of Experimental Psychology: Applied 7 (1):3.
  43.  14
    Daniel Osherson, Adding Dense, Weighted Connections to WORDNET.
    WORDNET, a ubiquitous tool for natural language processing, suffers from sparsity of connections between its component concepts (synsets). Through the use of human annotators, a subset of the connections between 1000 hand-chosen synsets was assigned a value of “evocation” representing how much the first concept brings to mind the second. These data, along with existing similarity measures, constitute the basis of a method for predicting evocation between previously unrated pairs.
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  44. Riccardo Viale & Daniel Osherson (2006). Cognitive Development, Culture, and Inductive Judgment. In Riccardo Viale, D. Andler & Lawrence A. Hirschfeld (eds.), Biological and Cultural Bases of Human Inference. Lawrence Erlbaum Associates
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  45.  22
    Eric Martin & Daniel Osherson (1997). Scientific Discovery Based on Belief Revision. 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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  46.  2
    Daniel N. Osherson (1978). Three Conditions on Conceptual Naturalness. Cognition 6 (4):263-289.
  47.  10
    Riccardo Viale & Daniel Osherson (2000). The Diversity Principle and the Little Scientist Hypothesis. 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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  48.  12
    Katya Tentori, Nicolao Bonini & Daniel Osherson (forthcoming). Conjunction and the Conjunction Fallacy. Cognitive Science.
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  49.  8
    Daniel N. Osherson & Scott Weinstein (1982). A Note on Formal Learning Theory. Cognition 11 (1):77-88.
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  50.  27
    Richard E. Nisbett & Daniel Osherson, Detecting Deception by Loading Working Memory.
    Compared to truthful answers, deceptive responses to queries are expected to take longer to initiate. Yet attempts to detect lies through reaction time (RT) have met with limited success. We describe a new procedure that seems to increase the RT difference between truth-telling and lies. It relies on a Stroop-like procedure in which responses to the labels true and false are sometimes reversed. The utility of this method is assessed in a laboratory study involving both statements of fact and attitude. (...)
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