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Daniel Osherson [60]Daniel N. Osherson [30]
  1. Branden Fitelson & Daniel Osherson, Remarks on “Random Sequences”.
    We consider how to reach reasonable belief about whether a (possibly idealized) physical process is producing its output randomly. For definiteness, we’ll consider a coin-flipper C which reports a sequence of outcomes of tosses of a coin. C outputs “H” for a heads outcome and “T” for a tails outcome. By C producing its output “randomly,” we mean that the probability of C issuing an H on any given trial is the same as the probability of issuing a T, and (...)
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  2. Branden Fitelson & Daniel Osherson, The Problem: First Pass.
    Intuitively, it seems that S 1 is “more random” or “less regular” than S 2. In other words, it seems more plausible (in some sense) that S 1 (as opposed to S 2) was generated by a random process ( e.g. , by tossing a fair coin eight times, and recording an H for a heads outcome and a T for a tails outcome). We will use the notation x σ 1 ą σ 2y to express the claim that xstring (...)
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  3. Richard Grandy & Daniel Osherson, Sentential Logic for Psychologists.
    Students often study logic on the assumption that it provides a normative guide to reasoning in English. In particular, they are taught to associate connectives like “and” with counterparts in Sentential Logic. English conditionals go over to formulas with → as principal connective. The well-known difficulties that arise from such translation are not emphasized. The result is the conviction that ordinary reasoning is faulty when discordant with the usual representation in standard logic. Psychologists are particularly susceptible to this attitude.
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  4. David H. Krantz, Daniel Osherson & Nicolao Bonini, The Relation Between Probability and Evidence Judgment: An Extension of Support Theory*†.
    We propose a theory that relates perceived evidence to numerical probability judgment. The most successful prior account of this relation is Support Theory, advanced in Tversky and Koehler (1994). Support Theory, however, implies additive probability estimates for binary partitions. In contrast, superadditivity has been documented in Macchi, Osherson, and Krantz (1999), and both sub- and superadditivity appear in the experiments reported here. Nonadditivity suggests asymmetry in the processing of focal and nonfocal hypotheses, even within binary partitions. We extend Support (...)
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  5. Eric Martin & Daniel Osherson, Advanced Topics in Inductive Logic.
    The inductive logic developed in the second and third essays is limited in important ways. For example: (a) the logic makes no provision for missing or misleading data; (b) it gives the scientist no control over the evidence reaching him; (c) revision-based scientist must work with theories written in the cramped idiom of firstorder logic; (d) the idea of efficient induction is only weakly expressed (in terms of “dominance”).
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  6. Eric Martin & Daniel Osherson, Scientific Discovery From the Point of View of Acceptance.
    In the four papers available on our web site (of which this is the first), we propose to develop an inductive logic. By “inductive logic” we mean a set of principles that distinguish between successful and unsuccessful strategies for scientific inquiry. Our logic will have a technical character, since it is built from the concepts and terminology of (elementary) model theory. The reader may therefore wish to know something about the kind of results on offer before investing time in definitions (...)
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  7. Michael K. Miller, Guanchun Wang, Sanjeev R. Kulkarni & Daniel N. Osherson, Wishful Thinking and Social Influence in the 2008 U.S. Presidential Election.
    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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  8. 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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  9. Daniel Osherson, Inductive Inference Based on Probability and Similarity.
    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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  10. Daniel Osherson, Note on an Observation by Neil Tennant.
    Neil Tennant (Tennant, 2005) has offered an important observation about the AGM theory of belief revision (G¨ardenfors, 1988). We attempt to restate and demonstrate his result in a slightly different way. Fix a formal language L that embeds sentential logic. Given K ⊆ L and ϕ ∈ L, K ⊥ ϕ denotes the class of maximally consistent subsets of K that do not imply ϕ. That is, A ∈ K ⊥ ϕ iff A ⊆ K, A |= ϕ, and there (...)
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  11. 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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  12. Guanchun Wang, Sanjeev Kulkarni & Daniel N. Osherson, Improving Aggregated Forecasts of Probability.
    ��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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  13. Matthew Weber & Daniel Osherson, From Similarity to Inference.
    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 37 (LBA37) in response to pictures of different categories; the choice of LBA37 is motivated by previous literature. The theory was tested against probability judgments for 160 arguments generated from 16 mammal categories and a common predicate. The theory’s predictions (based on neural similarity) correlate strongly with these (...)
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  14. Matthew Weber & Daniel Osherson, Inductive Inference Based on Probability and Similarity.
    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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  15. Jiaying Zhao & Daniel Osherson, Detecting Deviations From Randomness.
    We explore the ability to distinguish random from non-random events. Randomness is defined in terms of radioactive decay whereas non-randomness is quantified by excess repetitions (“repeat”) or alternations (“switch”) between successive bits. In the first four experiments no mention was made of randomness, probability, or related concepts in task instructions. We found superior performance in distinguishing random stimuli from repeat stimuli compared to switch stimuli. The last three experiments explicitly evoked the concept of randomness, thus allowing comparison of perceptual and (...)
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  16. Daniel Osherson, A Different Conjunction Fallacy.
    Because the conjunction p-and-q implies p, the value of a bet on p-and-q 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 p-and-not-q, and (b) encouraged genuinely conjunctive reading of p-and-q. 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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  17. 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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  18. 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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  19. Daniel Osherson, Aggregating Forecasts of Chance From Incoherent and Abstaining Experts.
    Decision makers often rely on expert opinion when making forecasts under uncertainty. In doing so, they confront two methodological challenges: the elicitation problem, which requires them to extract meaningful information from experts; and the aggregation problem, which requires them to combine expert opinion by resolving disagreements. Linear averaging is a justifiably popular method for addressing aggregation, but its robust simplicity makes two requirements on elicitation. First, each expert must offer probabilistically coherent forecasts; second, each expert must respond to all our (...)
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  20. Daniel Osherson, Formal Learning Theory in Context.
    One version of the problem of induction is how to justify hypotheses in the face of data. Why advance hypothesis A rather than B — or in a probabilistic context, why attach greater probability to A than B? If the data arrive as a stream of observations (distributed through time) then the problem is to justify the associated stream of hypotheses. Several perspectives on this problem have been developed including Bayesianism (Howson and Urbach, 1993) and belief-updating (Hansson, 1999). These are (...)
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  21. Daniel Osherson, From Similarity to Inference.
    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 37 (lBA37) in response to pictures of different categories; the choice of lBA37 is motivated by previous literature. The theory was tested against estimated probability judgments for 160 arguments generated from 16 categories and a common predicate. The theory’s predictions (based on neural similarity) correlate strongly with these estimates. (...)
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  22. Daniel Osherson, From Similarity to Chance.
    “In reality, all arguments from experience are founded on the similarity which we discover among natural objects, and by which we are induced to expect effects similar to those which we have found to follow from such objects. ... From causes which appear similar we expect similar effects.”.
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  23. Daniel Osherson, Inductive Logic is the Study of Belief Allocation.
    links to current studies in the theory of scientific discovery. Our own theory is elaborated in four papers accessible in pdf format through the links on the left.
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  24. Daniel Osherson, No Method of Ampliative Inference Respects Conditionalization.
    Let two events A, B be given. We consider probability distributions over the partition P = {A ∩ B, A ∩ ¯ B, ¯ A ∩ B, ¯ A ∩ ¯ B}. By a “constraint” is meant a probabilistically coherent set of statements each of the form Prob(E) = x, where E is a subset of P . Let C be the class of constraints. By a “method of ampliative inference” is meant any total function M from C to the (...)
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  25. Daniel Osherson, Notes on Statistical Tests.
    Let an unbiased coin be used to form an ω-sequence S of independent tosses. Let N be the positive integers. The finite initial segment of length n ∈ N is denoted by Sn (thus, S1 holds exactly the first toss). For n ∈ N , let Hn be the proportion of heads that show up in Sn.
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  26. 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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  27. Daniel Osherson, Scalable Algorithms for Aggregating Disparate Forecasts of Probability.
    J. B. Predd S. R. Kulkarni H. V. Poor D. N. Osherson Department of Electrical Engineering Department of Psychology Princeton University Princeton University Princeton, NJ 08544 Princeton, NJ 08544 {jpredd,kulkarni,poor}@princeton.edu osherson@princeton.edu..
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  28. Jiaying Zhao & Daniel Osherson, Descriptive Assessment of Jeffrey's Rule.
    Jeffrey (1983) proposed a generalization of conditioning as a means of updating probability distributions when new evidence drives no event to certainty. His rule requires the stability of certain conditional probabilities through time. We tested this assumption (“invariance”) from the psychological point of view. In Experiment 1 participants offered probability estimates for events in Jeffrey’s candlelight example. Two further scenarios were investigated in Experiment 2, one in which invariance seems justified, the other in which it does not. Results were in (...)
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  29. Katya Tentori, Nicolao Bonini & Daniel Osherson (forthcoming). Conjunction and the Conjunction Fallacy. Cognitive Science.
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  30. Jiaying Zhao & Daniel Osherson (2013). Category-Based Updating. Thinking and Reasoning 20 (1):1-15.
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  31. 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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  32. 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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  33. Jiaying Zhao, Vincenzo Crupi, Katya Tentori, Branden Fitelson & Daniel Osherson (2012). Updating: Learning Versus Supposing. Cognition 124 (3):373-378.
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  34. 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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  35. 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 <span class='Hi'>test</span> 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 (...)
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  36. 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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  37. Jiaying Zhao, Anuj Shah & Daniel Osherson (2009). On the Provenance of Judgments of Conditional Probability. Cognition 113 (1):26-36.
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  38. Daniel Osherson (2008). Recognizing Strong Random Reals. Review of Symbolic Logic 1 (1):56-63.
    1. Characterizing randomness. Consider a physical process that, if suitably idealized, generates an indefinite sequence of independent random bits. One such process might be radioactive decay of a lump of uranium whose mass is kept at just the level needed to ensure that the probability is one-half that no alpha particle is emitted in the nth microsecond of the experiment. Let us think of the bits as drawn from {0, 1} and denote the resulting sequence by x with coordinates x0, (...)
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  39. Katya Tentori, Vincenzo Crupi, Nicolao Bonini & Daniel Osherson (2007). Comparison of Confirmation Measures. Cognition 103 (1):107-119.
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  40. 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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  41. 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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  42. 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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  43. 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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  44. 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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  45. John R. Anderson, Deb K. Roy, Alex P. Pentland, Vincent Awmm Aleven, Kenneth R. Koedinger, Yafen Lo, Ashley Sides, Joseph Rozelle, Daniel Osherson & Bruno Laeng (2002). Regular Articles Perceiving Temporal Regularity in Music* 1 Edward W. Large, Caroline Palmer Memory for Goals: An Activation-Based Model* 39 Erik M. Altmann, J. Gregory Trafton. [REVIEW] Cognitive Science 26 (837):839.
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  46. 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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  47. Eric Martin & Daniel Osherson (2002). Scientific Discovery From the Perspective of Hypothesis Acceptance. Proceedings of the Philosophy of Science Association 2002 (3):S331-S341.
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  48. Daniel Osherson, David Lane, Peter Hartley & Richard R. Batsell (2001). Coherent Probability From Incoherent Judgment. Journal of Experimental Psychology: Applied 7 (1):3.
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  49. Riccardo Viale & Daniel Osherson (2001). The Diversity Phenomenon. Foundations of Science 5 (2).
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