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Related Concept Videos

Arithmetic Sequences01:30

Arithmetic Sequences

An arithmetic sequence is a structured arrangement of numbers where each term is derived by adding a constant value, known as the common difference, to the previous term. This consistent pattern allows for the efficient computation of any term within the sequence as well as the cumulative sum of multiple terms. The formula for finding the nth term of an arithmetic sequence is:Here, aₙ represents the nth term of the sequence, a is the first term, d is the common difference, and n is the term...
Arithmetic Mean01:08

Arithmetic Mean

The arithmetic mean is the most commonly used measure of the central tendency of a data set. It is defined as the sum of all the elements constituting the data set, divided by the total number of elements. It is sometimes loosely referred to as the “average.”
When all the values in a data set are not unique, the sum in the numerator can be calculated by multiplying each distinct value by its frequency.
Sometimes, the arithmetic mean of a sample can be affected by a few data points that are...

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Related Experiment Video

Updated: Jun 21, 2026

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics (BM-PROMA)
10:58

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics (BM-PROMA)

Published on: August 28, 2021

Neural correlates of arithmetic calculation strategies.

Miriam Rosenberg-Lee1, Marsha C Lovett, John R Anderson

  • 1Stanford University, Palo Alto, California, USA. miriamrl@stanford.edu

Cognitive, Affective & Behavioral Neuroscience
|August 15, 2009
PubMed
Summary

Different math strategies activate distinct brain regions. The school strategy for multiplication engaged attention and mental representation areas more than the expert strategy, impacting working memory.

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

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Related Experiment Videos

Last Updated: Jun 21, 2026

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics (BM-PROMA)
10:58

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics (BM-PROMA)

Published on: August 28, 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:

  • Cognitive Neuroscience
  • Mathematical Cognition

Background:

  • Research in math cognition has identified brain areas for number processing and problem-solving.
  • Behavioral studies show individuals use diverse strategies for calculations, contrary to assumptions of single-strategy use.

Purpose of the Study:

  • To investigate cortical activation differences between two mental multiplication strategies: the school strategy (right-to-left) and the expert strategy (left-to-right).
  • To compare the working memory demands of these distinct calculation approaches.

Main Methods:

  • Examined brain activity using fMRI (BOLD responses) during mental multidigit multiplication.
  • Utilized an ACT-R cognitive architecture model to predict brain activity patterns.

Main Results:

  • The school strategy showed significantly greater early activation in the posterior superior parietal lobule (PSPL) and posterior parietal cortex (PPC).
  • These areas are associated with attentional aspects of number processing and mental representation, respectively.
  • No significant differences were found in the horizontal intraparietal sulcus (HIPS) or lateral inferior prefrontal cortex (LIPFC).
  • The ACT-R model accurately predicted BOLD responses across PSPL, PPC, HIPS, and LIPFC.

Conclusions:

  • Mental calculation strategy significantly influences brain activation patterns, particularly in attention and working memory networks.
  • The school strategy's higher working memory load correlates with increased early activation in parietal regions.
  • Computational modeling can effectively simulate and predict neural correlates of cognitive strategies in mathematical problem-solving.