Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Parallel Processing

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Neural Representations of Death-Related Concepts Identify Conceptual Alteration of Self in Suicidal Youth.

Human brain mapping·2026

Same author

The neural and cognitive basis of expository text comprehension.

NPJ science of learning·2024

Same author

Retraction Note: Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth.

Nature human behaviour·2023

Same author

Similarities and differences in the neural representations of abstract concepts across English and Mandarin.

Human brain mapping·2022

Same author

Publisher Correction: The neuroscience of advanced scientific concepts.

NPJ science of learning·2021

Same author

The neuroscience of advanced scientific concepts.

NPJ science of learning·2021

Same journal

Neural Markers of Interocular Grouping During Binocular Rivalry With MEG.

Human brain mapping·2026

Same journal

Neural Correlates of Explicit Outcome Expectation Effects: An Activation Likelihood Estimation Meta-Analysis.

Human brain mapping·2026

Same journal

Benchmarking fMRI Denoising Pipelines.

Human brain mapping·2026

Same journal

Modeled Long-Term Effects of Psilocybin on Dynamic Activity and Effective Connectivity of Fronto-Striatal-Thalamic Circuits.

Human brain mapping·2026

Same journal

Intrinsic Functional Architecture Reflects Individual Differences in Passive Working Memory: An Exploratory Resting-State fMRI Study.

Human brain mapping·2026

Same journal

Symptom Overlap and Neurobiological Similarities Between Posttraumatic Stress Disorder and Tinnitus.

Human brain mapping·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 23, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Decoding the representation of numerical values from brain activation patterns.

Saudamini Roy Damarla¹, Marcel Adam Just

¹Center for Cognitive Brain Imaging, Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania.

Human Brain Mapping

|April 17, 2012

Summary

This summary is machine-generated.

Researchers decoded neural representations of object quantities using brain imaging. Quantities were decodable from parietal cortex activity, especially when depicted pictorially, revealing stable number representations.

Keywords:

fMRI multivoxel pattern analysis number representation parietal cortex

More Related Videos

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Published on: April 15, 2015

Related Experiment Videos

Last Updated: May 23, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Published on: April 15, 2015

Area of Science:

Neuroscience
Cognitive Science
Brain Imaging

Background:

Neuroimaging studies suggest number representations are decodable from brain activity, particularly in the parietal cortex.
Machine learning has decoded neural representations of concrete nouns from fMRI data, with some patterns being general across individuals.

Purpose of the Study:

To investigate if neural codes for object quantities can be decoded using machine learning techniques.
To compare the neural decoding of quantities presented in a pictorial (nonsymbolic) mode versus a digit-object (symbolic) mode.

Main Methods:

Utilized multivariate machine learning techniques on fMRI data.
Presented quantities using two modes: pictorial (e.g., image of three tomatoes) and digit-object (e.g., digit '3' with one object image).
Analyzed neural activation patterns in response to different quantities and modes.

Main Results:

Quantities of objects were successfully decoded from neural activation patterns in parietal regions.
Pictorial representations showed common activation patterns across objects and participants for specific quantities.
Pictorial mode yielded better number identification than the digit-object mode, with some cross-mode pattern commonality.
Hemispheric asymmetry observed: pictorial numbers were bilateral, while digit-object numbers were primarily left-lateralized.

Conclusions:

Neural patterns allow for the identification of individual object quantities, indicating stable neural representations of numbers.
Pictorially depicted quantities have a predominant neural representation compared to the digit-object mode.
Findings highlight the role of the parietal cortex in quantity representation and suggest differences based on symbolic versus nonsymbolic presentation.