Literature DB >> 8681511

A hypothesis-assessment model of categorical argument strength.

J McDonald1, M Samuels, J Rispoli.   

Abstract

According to the proposed hypothesis-assessment model, the strength of inductive categorical arguments, such as {All Robins Have Substance X therefore All Birds Have Substance X}, is determined by the same factors that affect hypothesis plausibility in the everyday social milieu. The premises of such arguments are viewed as evidence and the conclusion is viewed as a hypothesis. Specifically, the proposed model predicts that the perceived strength of general-conclusion categorical arguments will be a function of (a) the number of premises that instantiate the conclusion; (b) the scope of the conclusion; and (c) the number of accessed alternatives to the conclusion. In Experiment 1, one group rated the strength of individual arguments and another constructed superordinate hypotheses in response to the premise information alone. Most of the variance in perceived argument strength was accounted for by the proposed predictors, R = .94. Experiment 2 employed a new set of arguments and included an additional forced-choice condition in which subjects had to choose the stronger of two arguments. Again, the correlation between predictors and argument strength was high, R = .91, and, all significant forced-choice preferences except one were correctly predicted by the model. The one unpredicted preference suggests the need to include conclusion accessibility as a fourth factor. Also, on a subset of the forced-choice pairs in which no significant preference was observed, two distinct patterns of responding were detected-one predicted and the other unanticipated. Some strengths and limitations of the proposed hypothesis-assessment model are discussed in light of these results.

Mesh:

Year:  1996        PMID: 8681511     DOI: 10.1016/0010-0277(95)00702-4

Source DB:  PubMed          Journal:  Cognition        ISSN: 0010-0277


  9 in total

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3.  How many processes underlie category-based induction? Effects of conclusion specificity and cognitive ability.

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4.  Raising argument strength using negative evidence: a constraint on models of induction.

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Review 5.  The diversity principle and the evaluation of evidence.

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6.  Category-based induction from similarity of neural activation.

Authors:  Matthew J Weber; Daniel Osherson
Journal:  Cogn Affect Behav Neurosci       Date:  2014-03       Impact factor: 3.526

7.  How types of premises modulate the typicality effect in category-based induction: diverging evidence from the P2, P3, and LPC effects.

Authors:  Xiuling Liang; Qingfei Chen; Yi Lei; Hong Li
Journal:  Sci Rep       Date:  2016-12-16       Impact factor: 4.379

8.  Premise typicality as feature inference decision-making in perceptual categories.

Authors:  Emma L Morgan; Mark K Johansen
Journal:  Mem Cognit       Date:  2021-10-08

9.  Can you catch Ebola from a stork bite? Inductive reasoning influences generalization of perceived zoonosis risk.

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Journal:  PLoS One       Date:  2017-11-08       Impact factor: 3.240

  9 in total

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