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

Introducing Social Perception01:29

Introducing Social Perception

453
Perceiving others accurately is fundamental to effective communication and relationship-building. Social perception, a key concept in social psychology, refers to the cognitive processes through which individuals gather and interpret information about others to understand their actions, intentions, and motivations. This process extends beyond spoken words and overt behaviors, incorporating subtle nonverbal cues and contextual factors.Nonverbal Cues and Their SignificanceNonverbal cues play a...
453
Perception01:28

Perception

1.6K
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...
1.6K
Cognitive Learning01:21

Cognitive Learning

1.5K
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
1.5K
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

290
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
290
Purposive Learning01:22

Purposive Learning

556
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...
556
Impression Management Techniques III: Aligning Actions01:29

Impression Management Techniques III: Aligning Actions

174
Aligning actions are communicative strategies individuals employ to maintain social harmony and preserve personal identity in the face of potential disruptions to social norms. These actions are particularly important in managing social impressions when one's behavior might be seen as inappropriate, incompetent, or morally questionable.Types of Aligning ActionsThe three principal types of aligning actions are disclaimers, accounts, and apologies.DisclaimersDisclaimers are preventive; they are...
174

You might also read

Related Articles

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

Sort by
Same author

Author Correction: A T<sub>reg</sub>-specific long noncoding RNA maintains immune-metabolic homeostasis in aging liver.

Nature aging·2026
Same author

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same author

Decompensated liver cirrhosis as a rare consequence of long-term untreated panhypopituitarism after craniopharyngioma resection: a case report.

Frontiers in endocrinology·2026
Same author

Uncovering Hierarchical Asymmetries in Artificial Intelligence Transformation: Navigating the Bright and Dark Sides Across Organizational Levels.

Journal of visualized experiments : JoVE·2026
Same author

Effects of divalent cations on diffusion dynamics of biological water confined between lipid membranes.

The Journal of chemical physics·2026
Same author

Neural Network Copulas for Generating Synthetic Test Data Preserving Psychometric Properties.

Journal of Intelligence·2026

Related Experiment Video

Updated: Mar 6, 2026

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
05:21

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

8.4K

Understanding human intention by connecting perception and action learning in artificial agents.

Sangwook Kim1, Zhibin Yu2, Minho Lee1

  • 1School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, Republic of Korea.

Neural Networks : the Official Journal of the International Neural Network Society
|March 21, 2017
PubMed
Summary

This study introduces an Object Augmented-Supervised Multiple Timescale Recurrent Neural Network (OA-SMTRNN) for advanced human-robot interaction. The system demonstrates how connected perception and action learning enables robots to understand human intention.

Keywords:
AffordanceCognitive agentHuman–robot interactionIntention understandingObject-Augmented Multiple Timescale Recurrent Neural NetworkPerception-action connected learning

More Related Videos

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios
07:43

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios

Published on: August 4, 2023

2.8K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.6K

Related Experiment Videos

Last Updated: Mar 6, 2026

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
05:21

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

8.4K
Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios
07:43

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios

Published on: August 4, 2023

2.8K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.6K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Understanding human intention is crucial for advanced human-robot interaction.
  • Human perception and action are interconnected processes, influencing cognitive development.
  • Replicating these psychological and neurological interactions in machines is a key challenge.

Purpose of the Study:

  • To propose an intention understanding system for human-robot interaction.
  • To demonstrate the effects of perception-action connected learning in artificial agents.
  • To bridge the gap between human cognitive processes and machine learning.

Main Methods:

  • Development of an Object Augmented-Supervised Multiple Timescale Recurrent Neural Network (OA-SMTRNN).
  • Construction of perception and action modules using supervised multiple timescale recurrent neural networks and deep auto-encoders.
  • Integration of perception and action modules to facilitate intention understanding.

Main Results:

  • Experimental validation of perception-action connected learning in an artificial agent.
  • Demonstration that the OA-SMTRNN can effectively learn from connected perception and action.
  • Evidence that robots can understand human intention through this learning paradigm.

Conclusions:

  • Perception-action connected learning is effective for developing intention understanding in robots.
  • The OA-SMTRNN provides a viable framework for advanced human-robot interaction.
  • The proposed system is inspired by human psychological and neurological learning mechanisms.