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Related Concept Videos

Deductive Reasoning01:16

Deductive Reasoning

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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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Inductive Reasoning00:59

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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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Reasoning01:30

Reasoning

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Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
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Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Reason and Intuition01:37

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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Logic, Probability, and Pragmatics in Syllogistic Reasoning.

Michael Henry Tessler1,2, Joshua B Tenenbaum1, Noah D Goodman2,3

  • 1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.

Topics in Cognitive Science
|January 10, 2022
PubMed
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Human syllogistic reasoning, using natural language, deviates from formal logic. A new probabilistic pragmatic model explains these deviations as rational communication strategies, outperforming previous theories.

Keywords:
PragmaticsRational Speech ActReasoningSemanticsSyllogisms

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Area of Science:

  • Cognitive Science
  • Linguistics
  • Artificial Intelligence

Background:

  • Syllogistic reasoning bridges formal logic and natural language.
  • Conclusions drawn by humans often differ from purely logical deductions.
  • This discrepancy raises questions about the role of natural language understanding.

Purpose of the Study:

  • To introduce a probabilistic pragmatic perspective on syllogistic reasoning.
  • To model natural language argument comprehension and production.
  • To explain deviations from logical reasoning through pragmatic inference.

Main Methods:

  • Decomposition of reasoning into language comprehension and production.
  • Formalization within the Rational Speech Act framework.
  • Testing models on a large dataset of syllogistic reasoning responses.

Main Results:

  • The pragmatic speaker model, aiming to align beliefs, best predicts human conclusion selection.
  • This model quantitatively predicts response distributions effectively.
  • It outperforms previous models like Mental Models and Probability Heuristics with fewer parameters.

Conclusions:

  • Human syllogistic reasoning is better understood as rational probabilistic inference for communication.
  • Pragmatic reasoning, not flawed logic, drives natural language conclusions.
  • This perspective offers a more parsimonious explanation for observed reasoning patterns.