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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
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The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
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Abductive conditionals as a test case for inferentialism.

Patricia Mirabile1, Igor Douven2

  • 1SND, France; Sorbonne University, France.

Cognition
|June 5, 2020
PubMed
Summary
This summary is machine-generated.

Inferentialism accurately predicts semantic judgments for realistic abductive conditionals. This theory, which posits a strong inferential link for truth, shows robust support across three experiments testing explanatory connections.

Keywords:
Abductive conditionalsExplanationInferenceInferentialismModus ponensSemantics

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Area of Science:

  • Cognitive Science
  • Philosophy of Language
  • Experimental Semantics

Background:

  • Inferentialism posits that a true indicative conditional requires a strong inferential link between its antecedent and consequent.
  • Prior research supported inferentialism in abstract contexts, but its predictive power for realistic materials remained untested.
  • Abductive conditionals, where the consequent explains the antecedent, provide a realistic context to evaluate inferentialism.

Purpose of the Study:

  • To test the predictive accuracy of inferentialism for semantic judgments using realistic abductive conditionals.
  • To compare inferentialism against alternative accounts, including the mental models account and the suppositional account.

Main Methods:

  • Conducted three experiments using abductive conditionals with varying degrees of explanatory goodness.
  • Experiment 1 & 2: Predicted truth ratings based on judgments of explanatory goodness, contrasting with the mental models account.
  • Experiment 3: Examined endorsement rates of modus ponens arguments with abductive conditionals, comparing inferentialism with suppositional and probabilistic accounts.

Main Results:

  • Found strong support for inferentialism across all three experiments.
  • Truth ratings and argument endorsement rates correlated positively with the strength of the explanatory-inferential connection.
  • Results provided evidence against the mental models account and the suppositional account in this context.

Conclusions:

  • Inferentialism provides a robust framework for understanding semantic judgments of conditionals in realistic, explanatory contexts.
  • The strength of the inferential connection, specifically explanatory goodness, is a key determinant of conditional truth and argument validity.
  • Experimental evidence supports inferentialism over competing theories for abductive conditionals.