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Related Concept Videos

Problem-Solving01:29

Problem-Solving

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:
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light bulb,...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the problem,...
Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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Principle of Virtual Work: Problem Solving01:13

Principle of Virtual Work: Problem Solving

The principle of virtual work is an essential concept in the field of mechanics and engineering. This is used to solve problems related to the equilibrium of a structure or system. It is based on the assumption that if a system is in equilibrium, the work done by all the forces during a virtual displacement is zero. This principle is applied by considering virtual displacements of the system and the corresponding work done by internal and external forces.
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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
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Analogy as a strategy for supporting complex problem solving under uncertainty.

Joel Chan1, Susannah B F Paletz, Christian D Schunn

  • 1Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA, USA. joc59@pitt.edu

Memory & Cognition
|July 21, 2012
PubMed
Summary

Scientists use analogy to solve complex problems when facing uncertainty. Analogy helps reduce uncertainty during naturalistic problem-solving, particularly in scientific research.

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Area of Science:

  • Cognitive Psychology
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Complex problem solving in real-world settings involves significant uncertainty.
  • Existing theories of problem solving need to account for strategies used to manage uncertainty.
  • Analogy is proposed as a key cognitive strategy for navigating uncertainty.

Purpose of the Study:

  • To investigate the role of analogy as a strategy for managing uncertainty in complex problem solving.
  • To examine the temporal relationship between expressed uncertainty and the use of analogy.
  • To differentiate uncertainty related to miscommunication from substantive problem-solving uncertainty.

Main Methods:

  • Statistical analysis of temporal dynamics in problem-solving conversations.
  • Analysis of expressed uncertainty and analogy use in scientists' discussions.
  • Qualitative analysis to understand the nature of uncertainty.

Main Results:

  • Increased uncertainty reliably predicted subsequent use of analogy.
  • Analogy use led to a reduction in expressed uncertainty.
  • The observed relationship was primarily linked to substantive problem-solving issues, not miscommunication.

Conclusions:

  • Analogy serves as an effective uncertainty reduction strategy in naturalistic complex problem solving.
  • Understanding how scientists use analogy can inform theories of cognitive strategy and problem solving.
  • This research highlights the practical application of analogy in high-stakes scientific endeavors.