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Reasoning with quantifiers

Cognition 86 (3):223--251 (2003)

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  1. The influence of polarity items on inferential judgments.Milica Denić, Vincent Homer, Daniel Rothschild & Emmanuel Chemla - 2021 - Cognition 215 (C):104791.
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  • If so many are “few,” how few are “many”?Stefan Heim, Corey T. McMillan, Robin Clark, Stephanie Golob, Nam E. Min, Christopher Olm, John Powers & Murray Grossman - 2015 - Frontiers in Psychology 6.
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  • The effect of negative polarity items on inference verification.Anna Szabolcsi, Lewis Bott & Brian McElree - 2008 - Journal of Semantics 25 (4):411-450.
    The scalar approach to negative polarity item (NPI) licensing assumes that NPIs are allowable in contexts in which the introduction of the NPI leads to proposition strengthening (e.g., Kadmon & Landman 1993, Krifka 1995, Lahiri 1997, Chierchia 2006). A straightforward processing prediction from such a theory is that NPI’s facilitate inference verification from sets to subsets. Three experiments are reported that test this proposal. In each experiment, participants evaluated whether inferences from sets to subsets were valid. Crucially, we manipulated whether (...)
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  • Logic and reasoning: Do the facts matter?Johan van Benthem - 2008 - Studia Logica 88 (1):67-84.
    Modern logic is undergoing a cognitive turn, side-stepping Frege’s ‘antipsychologism’. Collaborations between logicians and colleagues in more empirical fields are growing, especially in research on reasoning and information update by intelligent agents. We place this border-crossing research in the context of long-standing contacts between logic and empirical facts, since pure normativity has never been a plausible stance. We also discuss what the fall of Frege’s Wall means for a new agenda of logic as a theory of rational agency, and what (...)
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  • Confidence judgments in syllogistic reasoning: the role of consistency and response cardinality.Igor Bajšanski, Valnea Žauhar & Pavle Valerjev - 2018 - Thinking and Reasoning 25 (1):14-47.
    ABSTRACTIn two experiments, we examined the resolution of confidence judgments in syllogistic reasoning and their heuristic bases. Based on the assumptions of Koriat's Self-Consistency Model of confidence, we expected the confidence judgments to be related to conclusion consensuality, reflecting the role of consistency as a heuristic cue to confidence. In Experiment 1, the participants evaluated 24 syllogisms with conclusions that varied with respect to validity and consensuality. In Experiment 2, the participants produced conclusions to 64 pairs of premises. The correlation (...)
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  • Why do humans reason? Arguments for an argumentative theory.Dan Sperber - 2011 - Behavioral and Brain Sciences 34 (2):57.
    Short abstract (98 words). Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given humans’ exceptional dependence on communication and vulnerability to misinformation. A wide range of (...)
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  • Natural logic for natural language.Jan van Eijck - manuscript
    We implement the extension of the logical consequence relation to a partial order ≤ on arbitary types built from e (entities) and t (Booleans) that was given in [1], and the definition of monotonicity preserving and monotonicity reversing functions in terms of ≤. Next, we present a new algorithm for polarity marking, and implement this for a particular fragment of syntax. Finally, we list the reseach agenda that these definitions and this algorithm suggest. The implementations use Haskell [8], and are (...)
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  • Generalized Update Semantics.Simon Goldstein - 2019 - Mind 128 (511):795-835.
    This paper explores the relationship between dynamic and truth conditional semantics for epistemic modals. It provides a generalization of a standard dynamic update semantics for modals. This new semantics derives a Kripke semantics for modals and a standard dynamic semantics for modals as special cases. The semantics allows for new characterizations of a variety of principles in modal logic, including the inconsistency of ‘p and might not p’. Finally, the semantics provides a construction procedure for transforming any truth conditional semantics (...)
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  • How Diagrams Can Support Syllogistic Reasoning: An Experimental Study.Yuri Sato & Koji Mineshima - 2015 - Journal of Logic, Language and Information 24 (4):409-455.
    This paper explores the question of what makes diagrammatic representations effective for human logical reasoning, focusing on how Euler diagrams support syllogistic reasoning. It is widely held that diagrammatic representations aid intuitive understanding of logical reasoning. In the psychological literature, however, it is still controversial whether and how Euler diagrams can aid untrained people to successfully conduct logical reasoning such as set-theoretic and syllogistic reasoning. To challenge the negative view, we build on the findings of modern diagrammatic logic and introduce (...)
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  • Processing of Numerical and Proportional Quantifiers.Sailee Shikhare, Stefan Heim, Elise Klein, Stefan Huber & Klaus Willmes - 2015 - Cognitive Science 39 (7):1504-1536.
    Quantifier expressions like “many” and “at least” are part of a rich repository of words in language representing magnitude information. The role of numerical processing in comprehending quantifiers was studied in a semantic truth value judgment task, asking adults to quickly verify sentences about visual displays using numerical or proportional quantifiers. The visual displays were composed of systematically varied proportions of yellow and blue circles. The results demonstrated that numerical estimation and numerical reference information are fundamental in encoding the meaning (...)
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  • To do or not to do? A cognitive consistency model for drawing conclusions from conditional instructions and advice.Christophe Schmeltzer & Denis J. Hilton - 2014 - Thinking and Reasoning 20 (1):16-50.
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  • Précis of bayesian rationality: The probabilistic approach to human reasoning.Mike Oaksford & Nick Chater - 2009 - Behavioral and Brain Sciences 32 (1):69-84.
    According to Aristotle, humans are the rational animal. The borderline between rationality and irrationality is fundamental to many aspects of human life including the law, mental health, and language interpretation. But what is it to be rational? One answer, deeply embedded in the Western intellectual tradition since ancient Greece, is that rationality concerns reasoning according to the rules of logic – the formal theory that specifies the inferential connections that hold with certainty between propositions. Piaget viewed logical reasoning as defining (...)
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  • Logic, Probability, and Pragmatics in Syllogistic Reasoning.Michael Henry Tessler, Joshua B. Tenenbaum & Noah D. Goodman - 2022 - Topics in Cognitive Science 14 (3):574-601.
    Topics in Cognitive Science, Volume 14, Issue 3, Page 574-601, July 2022.
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  • Solving categorical syllogisms with singular premises.Hugo Mercier & Guy Politzer - 2008 - Thinking and Reasoning 14 (4):434-454.
    We elaborate on the approach to syllogistic reasoning based on “case identification” (Stenning & Oberlander, 1995; Stenning & Yule, 1997). It is shown that this can be viewed as the formalisation of a method of proof that dates back to Aristotle, namely proof by exposition ( ecthesis ), and that there are traces of this method in the strategies described by a number of psychologists, from St rring (1908) to the present day. We hypothesised that by rendering individual cases explicit (...)
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  • Mental probability logic.Niki Pfeifer & Gernot D. Kleiter - 2009 - Behavioral and Brain Sciences 32 (1):98-99.
    We discuss O&C's probabilistic approach from a probability logical point of view. Specifically, we comment on subjective probability, the indispensability of logic, the Ramsey test, the consequence relation, human nonmonotonic reasoning, intervals, generalized quantifiers, and rational analysis.
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  • Generalized quantifiers.Dag Westerståhl - 2008 - Stanford Encyclopedia of Philosophy.
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  • How our brains reason logically.Markus Knauff - 2007 - Topoi 26 (1):19-36.
    The aim of this article is to strengthen links between cognitive brain research and formal logic. The work covers three fundamental sorts of logical inferences: reasoning in the propositional calculus, i.e. inferences with the conditional “if...then”, reasoning in the predicate calculus, i.e. inferences based on quantifiers such as “all”, “some”, “none”, and reasoning with n-place relations. Studies with brain-damaged patients and neuroimaging experiments indicate that such logical inferences are implemented in overlapping but different bilateral cortical networks, including parts of the (...)
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  • Reasoning, robots, and navigation: Dual roles for deductive and abductive reasoning.Janet Wiles - 2011 - Behavioral and Brain Sciences 34 (2):92-92.
    Mercier & Sperber (M&S) argue for their argumentative theory in terms of communicative abilities. Insights can be gained by extending the discussion beyond human reasoning to rodent and robot navigation. The selection of arguments and conclusions that are mutually reinforcing can be cast as a form of abductive reasoning that I argue underlies the construction of cognitive maps in navigation tasks.
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  • Logic, Language, and Computation: 6th International Tbilisi Symposium on Logic, Language, and Computation, Tbillc 2005, Batumi, Georgia, September 12-16, 2005. Revised Selected Papers.Balder D. ten Cate (ed.) - 2007 - Berlin and Heidelberg: Springer.
    Edited in collaboration with FoLLI, the Association of Logic, Language and Information, this book represents the thoroughly refereed post-proceedings of the 6th International Tbilisi Symposium on Logic, Language, and Computation, TbiLLC 2005, held in Batumi, Georgia. The 19 revised full papers presented were carefully reviewed and selected from numerous presentations at the symposium. The papers present current research in all aspects of linguistics, logic and computation.
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  • Can natural language semantics explain syllogistic reasoning?Stephen E. Newstead - 2003 - Cognition 90 (2):193-199.
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  • The uncertain reasoner: Bayes, logic, and rationality.Mike Oaksford & Nick Chater - 2009 - Behavioral and Brain Sciences 32 (1):105-120.
    Human cognition requires coping with a complex and uncertain world. This suggests that dealing with uncertainty may be the central challenge for human reasoning. In Bayesian Rationality we argue that probability theory, the calculus of uncertainty, is the right framework in which to understand everyday reasoning. We also argue that probability theory explains behavior, even on experimental tasks that have been designed to probe people's logical reasoning abilities. Most commentators agree on the centrality of uncertainty; some suggest that there is (...)
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  • Presuppositions of quantified sentences: experimental data. [REVIEW]Emmanuel Chemla - 2009 - Natural Language Semantics 17 (4):299-340.
    Some theories assume that sentences like (i) with a presupposition trigger in the scope of a quantifier carry an existential presupposition, as in (ii); others assume that they carry a universal presupposition, as in (iii). No student knows that he is lucky. Existential presupposition: At least one student is lucky.Universal presupposition: Every student is lucky. This work is an experimental investigation of this issue in French. Native speakers were recruited to evaluate the robustness of the inference from (i) to (iii). (...)
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  • The chronometrics of confirmation bias: Evidence for the inhibition of intuitive judgements.Edward Jn Stupple & Linden J. Ball - 2011 - Behavioral and Brain Sciences 34 (2):89-90.
    Mercier & Sperber (M&S) claim that the phenomenon of belief bias provides fundamental support for their argumentative theory and its basis in intuitive judgement. We propose that chronometric evidence necessitates a more nuanced account of belief bias that is not readily captured by argumentative theory.
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  • Syllogistics = monotonicity + symmetry + existential import.Jan van Eijck - unknown
    Syllogistics reduces to only two rules of inference: monotonicity and symmetry, plus a third if one wants to take existential import into account. We give an implementation that uses only the monotonicity and symmetry rules, with an addendum for the treatment of existential import. Soundness follows from the monotonicity properties and symmetry properties of the Aristotelean quantifiers, while completeness for syllogistic theory is proved by direct inspection of the valid syllogisms. Next, the valid syllogisms are decomposed in terms of the (...)
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  • Monotonicity and syllogistic inference: a reply to Newstead.Bart Geurts - 2003 - Cognition 90 (2):201-204.
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  • Illusory inferences with quantifiers.Salvador Mascarenhas & Philipp Koralus - 2017 - Thinking and Reasoning 23 (1):33-48.
    The psychological study of reasoning with quantifiers has predominantly focused on inference patterns studied by Aristotle about two millennia ago. Modern logic has shown a wealth of inference patterns involving quantifiers that are far beyond the expressive power of Aristotelian syllogisms, and whose psychology should be explored. We bring to light a novel class of fallacious inference patterns, some of which are so attractive that they are tantamount to cognitive illusions. In tandem with recent insights from linguistics that quantifiers like (...)
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  • The processes of inference.Sangeet Khemlani & P. N. Johnson-Laird - 2013 - Argument and Computation 4 (1):4 - 20.
    (2013). The processes of inference. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 4-20. doi: 10.1080/19462166.2012.674060.
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  • Popper's severity of test as an intuitive probabilistic model of hypothesis testing.Fenna H. Poletiek - 2009 - Behavioral and Brain Sciences 32 (1):99-100.
    Severity of Test (SoT) is an alternative to Popper's logical falsification that solves a number of problems of the logical view. It was presented by Popper himself in 1963. SoT is a less sophisticated probabilistic model of hypothesis testing than Oaksford & Chater's (O&C's) information gain model, but it has a number of striking similarities. Moreover, it captures the intuition of everyday hypothesis testing.
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