
Contents
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23 On the Psychology of the Recognition Heuristic: Retrieval Primacy as a Key Determinant of Its Use
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THE PRAGMATICS OF CONDITIONAL STATEMENTS THE PRAGMATICS OF CONDITIONAL STATEMENTS
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DRAWING INFERENCES FROM CONDITIONAL STATEMENTS DRAWING INFERENCES FROM CONDITIONAL STATEMENTS
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CONDITIONALS STATEMENTS AND DOMAIN SPECIFICITY CONDITIONALS STATEMENTS AND DOMAIN SPECIFICITY
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THE PRAGMATIC CUES APPROACH THE PRAGMATIC CUES APPROACH
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The Pragmatic Cues Algorithm The Pragmatic Cues Algorithm
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Overview of Experiment 1 Overview of Experiment 1
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EXPERIMENT 1 EXPERIMENT 1
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Method Method
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Participants and materials. Participants and materials.
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Design and procedure. Design and procedure.
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Results and Discussion Results and Discussion
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THE SYNTACTIC CUES APPROACH THE SYNTACTIC CUES APPROACH
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The Syntactic Cue Algorithm The Syntactic Cue Algorithm
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CONCLUSIONS CONCLUSIONS
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ACKNOWLEDGMENTS ACKNOWLEDGMENTS
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NOTES NOTES
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38 “If… ”: Satisficing Algorithms for Mapping Conditional Statements onto Social Domains
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Published:April 2011
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Abstract
People regularly use conditional statements to communicate promises and threats, advice and warnings, permissions and obligations to other people. Given that all conditionals are formally equivalent—“if P, then Q”— the question is: When confronted with a conditional statement, how do people know whether they are facing a promise, a threat, or something else? In other words, what is the cognitive algorithm for mapping a particular conditional statement onto its corresponding social domain? This paper introduces the pragmatic cues algorithm and the syntactic cue algorithm as partial answers to this question. Two experiments were carried out to test how well these simple satisficing algorithms approximate the performance of the actual cognitive algorithm people use to classify conditional statements into social domains. Conditional statements for promises, threats, advice, warnings, permissions, and obligations were collected from people, and given to both other people and the algorithms for their classification. Their corresponding performances were then compared. Results revealed that even though these algorithms utilized a minimum number of cues and drew only a restricted range of inferences from these cues, they performed well above chance in the task of classifying conditional statements as promises, threats, advice, warnings, permissions, and obligations. Moreover, these simple satisficing algorithms performed comparable to actual people given the same task.
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