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Related Concept Videos

Problem-Solving

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:

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Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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Related Experiment Video

Updated: Oct 8, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Analyzing Complex Problem Solving by Dynamic Brain Networks.

Abdullah Alchihabi¹, Omer Ekmekci¹, Baran B Kivilcim¹

¹Department of Computer Engineering, Middle East Technical University, Ankara, Turkey.

Frontiers in Neuroinformatics

|December 27, 2021

Summary

This summary is machine-generated.

This study introduces a new computational model to analyze brain networks during complex problem solving tasks. The model reveals distinct network patterns during planning versus execution phases, identifying more hubs during planning.

Keywords:

brain networks complex problem solving fMRI machine learning tower of London (TOL)

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Published on: March 21, 2019

Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Complex problem solving is a key cognitive function studied using tasks like the Tower of London (TOL).
Understanding the dynamic brain network structure underlying problem-solving subtasks (planning and execution) is crucial.

Purpose of the Study:

To explore the cognitive network structure of complex problem solving and its subtasks.
To propose and validate a novel computational model for dynamic brain network estimation using fMRI data.

Main Methods:

A new computational model using Artificial Neural Networks (ANNs) to estimate dynamic brain networks from fMRI data.
Preprocessing fMRI recordings to enhance temporal resolution and reduce spatial redundancy.
Analyzing estimated brain networks to identify hubs and densely connected regions.

Main Results:

The proposed model successfully decodes planning and execution subtasks of complex problem solving.
More brain network hubs were observed during the planning phase compared to the execution phase.
Brain network clusters showed stronger connectivity during planning than during execution.

Conclusions:

The findings align with existing experimental psychology results on complex problem solving.
Dynamic brain network analysis provides insights into cognitive processes like planning and execution.
The developed computational model offers a powerful tool for investigating brain network dynamics.