31 found
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  1.  4
    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.  24
    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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  4. Elements of Scientific Inquiry.Eric Martin & Daniel N. Osherson - 1998
     
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  5.  4
    Task-Specificity and Species-Specificity in the Study of Language: A Methodological Note.Daniel N. Osherson & Thomas Wasow - 1976 - Cognition 4 (2):203-214.
  6.  8
    Gradedness and Conceptual Combination.Daniel N. Osherson & Edward E. Smith - 1982 - Cognition 12 (3):299-318.
  7.  17
    Conceptual Combination with Prototype Concepts.Edward E. Smith & Daniel N. Osherson - 1984 - Cognitive Science 8 (4):337-361.
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  8.  54
    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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  9.  10
    Some Origins of Belief.Daniel N. Osherson, Edward E. Smith & Eldar B. Shafir - 1986 - Cognition 24 (3):197-224.
  10.  14
    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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  11. 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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  12.  33
    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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  13.  10
    Language and the Ability to Evaluate Contradictions and Tautologies.Daniel N. Osherson & Ellen Markman - 1974 - Cognition 3 (3):213-226.
  14.  39
    Mechanical Learners Pay a Price for Bayesianism.Daniel N. Osherson, Michael Stob & Scott Weinstein - 1988 - Journal of Symbolic Logic 53 (4):1245-1251.
  15.  58
    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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  16.  30
    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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  17.  26
    A Note on Formal Learning Theory.Daniel N. Osherson & Scott Weinstein - 1982 - Cognition 11 (1):77-88.
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  18.  25
    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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  19.  9
    Three Conditions on Conceptual Naturalness.Daniel N. Osherson - 1978 - Cognition 6 (4):263-289.
  20.  61
    On Charitable Translation.Daniel N. Osherson & Scott Weinstein - 1989 - Philosophical Studies 56 (2):127 - 134.
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  21.  26
    Relevant Consequence and Empirical Inquiry.Daniel N. Osherson & Scott Weinstein - 1993 - Journal of Philosophical Logic 22 (4):437 - 448.
    A criterion of adequacy is proposed for theories of relevant consequence. According to the criterion, scientists whose deductive reasoning is limited to some proposed subset of the standard consequence relation must not thereby suffer a reduction in scientific competence. A simple theory of relevant consequence is introduced and shown to satisfy the criterion with respect to a formally defined paradigm of empirical inquiry.
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  22.  59
    Wishful Thinking and Social Influence in the 2008 U.S. Presidential Election.Michael K. Miller, Guanchun Wang, Sanjeev R. Kulkarni & Daniel N. Osherson - unknown
    This paper analyzes individual probabilistic predictions of state outcomes in the 2008 U.S. presidential election. Employing an original survey of more than 19,000 respondents, ours is the first study of electoral forecasting to involve multiple subnational predictions and to incorporate the influence of respondents’ home states. We relate a range of demographic, political, and cognitive variables to individual accuracy and predictions, as well as to how accuracy improved over time. We find strong support for wishful thinking bias in expectations, as (...)
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  23.  34
    An Invitation to Cognitive Science.Daniel N. Osherson & Edward E. Smith (eds.) - 1990 - MIT Press.
    The volumes are self contained and can be used individually in upper-level undergraduate and graduate courses ranging from introductory psychology, linguistics, ...
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  24.  25
    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.
  25.  53
    Improving Aggregated Forecasts of Probability.Guanchun Wang, Sanjeev Kulkarni & Daniel N. Osherson - unknown
    ��The Coherent Approximation Principle (CAP) is a method for aggregating forecasts of probability from a group of judges by enforcing coherence with minimal adjustment. This paper explores two methods to further improve the forecasting accuracy within the CAP framework and proposes practical algorithms that implement them. These methods allow flexibility to add fixed constraints to the coherentization process and compensate for the psychological bias present in probability estimates from human judges. The algorithms were tested on a data set of nearly (...)
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  26.  15
    Ideal Learning Machines.Daniel N. Osherson, Michael Stob & Scott Weinstein - 1982 - Cognitive Science 6 (3):277-290.
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  27.  4
    Computer Output.Daniel N. Osherson - 1985 - Cognition 20 (3):261-264.
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  28.  13
    Finite Axiomatizability and Scientific Discovery.Daniel N. Osherson & Scott Weinstein - 1988 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1988:409 - 412.
    This paper provides a mathematical model of scientific discovery. It is shown in the context of this model that any discovery problem that can be solved by a computable scientist can be solved by a computable scientist all of whose conjectures are finitely axiomatizable theories.
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  29.  9
    On the Adequacy of Prototype Theory as a Theory of Concepts Daniel N. Osherson and Edward E. Smith.Daniel N. Osherson - 1999 - In Eric Margolis & Stephen Laurence (eds.), Concepts: Core Readings. MIT Press. pp. 261.
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  30.  2
    Modularity as an Issue for Cognitive Science.Daniel N. Osherson - 1981 - Cognition 10 (1-3):241-242.
  31. Learning Theory and Neural Reduction: A Comment.Daniel N. Osherson - 1985 - In Jacques Mehler & R. Fox (eds.), Neonate Cognition: Beyond the Blooming Buzzing Confusion. Lawrence Erlbaum. pp. 399.
     
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