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

Tactile and Chemical Senses01:27

Tactile and Chemical Senses

618
Tactile senses encompass touch, temperature, and pain, each mediated by specific receptors. Touch receptors detect mechanical energy or pressure against the skin. Sensory fibers from these receptors enter the spinal cord and relay information to the brain stem. Here, most fibers cross over to the opposite side of the brain. The touch information then moves to the thalamus, which projects a map of the body's surface onto the somatosensory areas of the parietal lobes in the cerebral cortex.
618
Sensory Perception: Organization of the Somatosensory System01:11

Sensory Perception: Organization of the Somatosensory System

10.9K
The somatosensory system is the central and peripheral nervous system component that senses and processes touch, pressure, pain, temperature, and body position or proprioception. The process of sensation takes place at three levels:
The receptor level:
The receptor level is the first stage of sensation. It involves the detection of a stimulus by specialized sensory receptors. The stimulus must arrive within the receptor's receptive field. Next, the receptor converts the energy of the...
10.9K
Sensory Modalities01:15

Sensory Modalities

3.6K
Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
General senses refer to the broad category of sensory information detected by receptors in the body and can be further grouped into somatic and visceral senses. Somatic sensations include touch, pressure, temperature, and pain and are essential for navigating our environment and...
3.6K
Perception01:28

Perception

894
Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
Bottom-up processing begins at the sensory level, where receptors detect external environmental stimuli. These could include the tactile sensation of...
894
Parallel Processing01:20

Parallel Processing

548
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...
548
Purposive Learning01:22

Purposive Learning

376
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
376

You might also read

Related Articles

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

Sort by
Same author

Functionalized carbon nanotube-assisted dual-mode CRISPR/Cas12a detection of hepatitis C virus via catalytic assembly circuit-driven Y-shaped dsDNA activators.

Biosensors & bioelectronics·2026
Same author

An Electrical Capacitance Tomography Dataset for Image Reconstruction Benchmarking.

Scientific data·2026
Same author

Dynamic Manipulation Skill Learning for Tactile Myoelectric Prosthetic Hands in Tool Handling.

Cyborg and bionic systems (Washington, D.C.)·2026
Same author

Proactive collaboration via autonomous interaction.

Nature communications·2026
Same author

DynamicTHOR: A Scalable Dataset of Human-Centric Dynamic Scenes for Embodied AI.

Scientific data·2026
Same author

Siamese foundation models for crystal structure prediction.

Nature communications·2026

Related Experiment Video

Updated: Dec 24, 2025

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another
05:12

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

Published on: September 18, 2017

548.2K

Lifelong Visual-Tactile Cross-Modal Learning for Robotic Material Perception.

Wendong Zheng, Huaping Liu, Fuchun Sun

    IEEE Transactions on Neural Networks and Learning Systems
    |April 11, 2020
    PubMed
    Summary

    Robots can now better perceive material properties using a new visual-tactile learning framework. This lifelong learning approach improves robotic material perception, especially with unfamiliar objects.

    More Related Videos

    Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback
    05:43

    Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback

    Published on: May 23, 2019

    5.8K
    Investigating Motor Skill Learning Processes with a Robotic Manipulandum
    07:52

    Investigating Motor Skill Learning Processes with a Robotic Manipulandum

    Published on: February 12, 2017

    9.1K

    Related Experiment Videos

    Last Updated: Dec 24, 2025

    Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another
    05:12

    Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

    Published on: September 18, 2017

    548.2K
    Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback
    05:43

    Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback

    Published on: May 23, 2019

    5.8K
    Investigating Motor Skill Learning Processes with a Robotic Manipulandum
    07:52

    Investigating Motor Skill Learning Processes with a Robotic Manipulandum

    Published on: February 12, 2017

    9.1K

    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Material Science

    Background:

    • Material properties are crucial for robotic interaction and manipulation.
    • Tactile sensing is effective for capturing surface material properties.
    • Conventional tactile classification methods struggle with unfamiliar objects and dynamic data.

    Purpose of the Study:

    • To propose a visual-tactile cross-modal learning framework for robotic material perception.
    • To enhance robotic material perception capabilities in a lifelong learning setting.
    • To develop a novel lifelong cross-modal learning model for incremental improvement.

    Main Methods:

    • A visual-tactile cross-modal learning framework was developed.
    • The framework was implemented within a lifelong learning paradigm.
    • A novel lifelong cross-modal learning model was proposed and utilized.

    Main Results:

    • The proposed method demonstrated effectiveness in robotic material perception.
    • Experimental results were validated on three publicly available datasets.
    • The lifelong learning approach showed benefits for incremental cross-modal perception.

    Conclusions:

    • The visual-tactile cross-modal learning framework significantly improves robotic material perception.
    • Lifelong learning enhances the adaptability and performance of robotic material perception systems.
    • The proposed model offers a robust solution for inferring material properties in unstructured environments.