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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:
Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
Biot-Savart Law: Problem-Solving00:59

Biot-Savart Law: Problem-Solving

The magnitude and direction of a magnetic field created by a steady current can be calculated using the Biot-Savart law.
Consider a mobile phone battery bank as a source of steady current, which flows through the wire connected between the two. What is the magnitude of the magnetic field created by this current at a field point P?
To estimate the magnitude of the total magnetic field, we first consider a small current element of length dl, at a distance r from the field point. Now the following...
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,...
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...
Principle of Moments: Problem Solving01:30

Principle of Moments: Problem Solving

The principle of moments is a fundamental concept in physics and engineering. It refers to the balancing of forces and moments around a point or axis, also known as the pivot. This principle is used in many real-life scenarios, including construction, sports, and daily activities like opening doors and pushing objects.
One such scenario involves a pole placed in a three-dimensional system with a cable attached. When a tension is applied to the cable, the moment about the z-axis passing through...

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Related Experiment Video

Updated: May 14, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Brain dynamics of mathematical problem solving.

Chun-Ling Lin1, Melody Jung, Ying Choon Wu

  • 1National Chiao-Tung University, 1001 University Rd., Hsinchu 300, Taiwan. ginny.ece95g@g2.nctu.edu.tw

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary
This summary is machine-generated.

Brain activity during mental math problems shows decreased power in theta, alpha, and beta frequencies as difficulty increases. Performance differences reveal distinct hemispheric patterns in slow versus fast problem solvers.

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Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
06:57

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

Related Experiment Videos

Last Updated: May 14, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
06:57

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Educational Psychology

Background:

  • Mental math problem solving involves complex cognitive processes.
  • Understanding the neural correlates of mathematical cognition is crucial for educational advancements.

Purpose of the Study:

  • To investigate brain activity, specifically electroencephalogram (EEG) patterns, during mental math problem solving.
  • To examine how task difficulty and individual problem-solving speed influence neural responses.

Main Methods:

  • Participants solved mental math problems while their EEG was recorded.
  • Analysis focused on changes in theta, alpha, and beta power across different brain regions.
  • Solution latencies and performance (slow vs. fast solvers) were correlated with EEG data.

Main Results:

  • Solution latencies increased with problem difficulty.
  • Increased task difficulty led to decreased power in right-central beta, left-parietal theta, left-occipital theta and alpha, right-parietal alpha and beta, medial-frontal beta, and medial central theta.
  • Slow solvers showed greater right-hemisphere frontal theta activity compared to fast solvers.

Conclusions:

  • Cortical activity, including alpha- and beta-power suppression and theta-power augmentation, changes dynamically during problem solving.
  • Neural patterns differ based on task difficulty and individual differences in problem-solving efficiency.
  • Findings enhance understanding of the neural basis of math problem solving and knowledge acquisition, with implications for math education.