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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.
For example, a researcher can deduce specific predictions...
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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.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
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Inductive Reasoning00:59

Inductive Reasoning

59.0K
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.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Critical Thinking II01:25

Critical Thinking II

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Critical thinking is a cognitive process with several attributes. The attributes of critical thinking include the following:
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Theorems of Pappus and Guldinus: Problem Solving01:12

Theorems of Pappus and Guldinus: Problem Solving

1.2K
Pappus and Guldinus's theorems are powerful mathematical principles that are used for finding the surface area and volume of composite shapes. For example, consider a cylindrical storage tank with a conical top. Finding the surface area or volume can be challenging for such complex shapes. These theorems are particularly useful in calculating the volume and surface area of such systems. Here, the cylindrical storage tank with a conical top can be broken down into two simple shapes: a...
1.2K
Problem-Solving01:29

Problem-Solving

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Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
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Related Experiment Video

Updated: May 6, 2026

Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task
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Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task

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Error processes in syllogistic reasoning.

L S Dickstein1

  • 1Wellesley College, 02181, Wellesley, Massachusetts.

Memory & Cognition
|November 9, 2013
PubMed
Summary

This study explains errors in deductive reasoning from invalid syllogisms. It proposes three new error processes: backward conclusion conversion, faulty information integration, and ignoring possibilities.

Area of Science:

  • Cognitive Psychology
  • Human Reasoning
  • Deductive Inference

Background:

  • Previous theories on errors in deductive reasoning from invalid syllogisms include erroneous premise conversion, probabilistic inference, and feature selection.
  • These existing models do not fully explain errors in all 32 invalid categorical syllogisms.

Purpose of the Study:

  • To propose a novel explanation for errors in deductive inferences from invalid syllogisms.
  • To focus on syllogisms where premise conversion errors are insufficient explanations.
  • To introduce three specific error processes: backward conclusion conversion, erroneous information integration, and failure to consider hypothetical possibilities.

Main Methods:

  • Analysis of 32 invalid categorical syllogisms.
  • Development of a theoretical framework based on three proposed error processes.

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  • Derivation of empirical predictions for syllogism difficulty and correlational patterns.

    • Main Results:

    • The proposed three-process model accounts for errors in syllogisms not explained by premise conversion.
    • Empirical predictions regarding differential difficulty and correlations between premise combinations were derived.
    • Presented data support the derived empirical predictions, validating the proposed error processes.

    Conclusions:

    • The study offers a more comprehensive explanation for errors in deductive reasoning from invalid syllogisms.
    • Backward conclusion conversion, erroneous information integration, and failure to consider hypothetical possibilities are key error mechanisms.
    • The findings have implications for understanding cognitive processes in logical reasoning.