Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

State Space Representation01:27

State Space Representation

460
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
460
The Representativeness Heuristic02:13

The Representativeness Heuristic

16.6K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
16.6K
Protein Networks02:26

Protein Networks

4.4K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.4K
Protein Networks02:26

Protein Networks

2.7K
2.7K
Neural Circuits01:25

Neural Circuits

2.5K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.5K
Neuron Structure01:30

Neuron Structure

17.3K
Neurons are the main type of cell in the nervous system that generate and transmit electrochemical signals. They primarily communicate with each other using neurotransmitters at specific junctions called synapses. Neurons come in many shapes that often relate to their function, but most share three main structures: an axon and dendrites that extend out from a cell body.
Structure and Function of Neurons
The neuronal cell body—the soma— houses the nucleus and organelles vital to...
17.3K

You might also read

Related Articles

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

Sort by
Same author

Girls just wanna have funds: a new Transparent Reporting Scale for evaluating grant data reporting from funding agencies.

Frontiers in computational neuroscience·2026
Same author

DeepCor: denoising fMRI data with contrastive autoencoders.

Nature methods·2025
Same author

Valence as principal dimension of the semantic space in primary progressive aphasia semantic variant.

Brain communications·2025
Same author

Understanding heterogeneity in psychiatric disorders: A method for identifying subtypes and parsing comorbidity.

Psychiatry and clinical neurosciences·2025
Same author

Semantic representations in inferior frontal and lateral temporal cortex during picture naming, reading, and repetition.

Human brain mapping·2024
Same author

Improved prediction of behavioral and neural similarity spaces using pruned DNNs.

Neural networks : the official journal of the International Neural Network Society·2023
Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026
Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026
Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026
Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026
Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026
Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Dec 19, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.4K

General and feature-based semantic representations in the semantic network.

Antonietta Gabriella Liuzzi1, Aidas Aglinskas2, Scott Laurence Fairhall3

  • 1Center for Mind/Brain Sciences, University of Trento, Trento, 38068, Italy. antonietta.liuzzi@unitn.it.

Scientific Reports
|June 4, 2020
PubMed
Summary
This summary is machine-generated.

The brain uses both general semantic representations and feature-based representations for understanding word meanings. Specific brain regions process general concepts, while others focus on detailed features like touch or size.

More Related Videos

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

463

Related Experiment Videos

Last Updated: Dec 19, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.4K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

463

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Psycholinguistics

Background:

  • The neural basis of semantic representation is debated, with theories suggesting either generalized semantic characteristics or specific feature-based information.
  • Understanding how the brain encodes abstract concepts versus concrete sensory details is crucial for cognitive neuroscience.

Purpose of the Study:

  • To investigate whether brain regions within the semantic system encode general semantic representations, feature-based representations, or both.
  • To identify specific brain areas associated with generalized semantic processing and feature-specific semantic processing.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) was used with 18 participants performing a word typicality judgment task.
  • Multivariate pattern analysis (MVPA) and representational similarity analysis (RSA) were applied to analyze brain activity patterns.

Main Results:

  • General semantic similarity was confirmed in the posterior middle/inferior temporal gyrus (pMTG/ITG) and precuneus (PC), with new evidence in ventromedial prefrontal cortex (PFC).
  • The left pMTG/ITG showed sensitivity to haptic perception features, and the left ventral temporal cortex (VTC) was sensitive to size features.

Conclusions:

  • The findings support a dual-coding model where the brain utilizes both generalized and feature-specific semantic representations.
  • Specific brain regions contribute to abstract semantic knowledge, while others are tuned to concrete sensory details like haptics and size.