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

Cognitive Learning01:21

Cognitive Learning

136
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...
136
Purposive Learning01:22

Purposive Learning

96
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...
96
Observational Learning01:12

Observational Learning

117
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
117
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

505
Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
505
Inductive Reasoning00:59

Inductive Reasoning

59.8K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
59.8K
Perception01:28

Perception

424
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...
424

You might also read

Related Articles

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

Sort by
Same author

Challenges and prospects for malaria elimination in the Greater Mekong Subregion.

Acta tropica·2011
Same author

[Optimization of extraction procedure of tongmai granules by orthogonal design with pharmacodynamic index].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2011
Same author

Determinants of postoperative corneal edema and impact on goldmann intraocular pressure.

Cornea·2011
Same author

Z-palatopharyngoplasty plus genioglossus advancement and hyoid suspension for obstructive sleep apnea hypopnea syndrome.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2011
Same author

Efficient and selective photodimerization of 2-naphthalenecarbonitrile mediated by cucurbit[8]uril in an aqueous solution.

Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology·2011
Same author

Quality assurance and quality improvement in U.S. clinical molecular genetic laboratories.

Current protocols in human genetics·2011

Related Experiment Video

Updated: May 23, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K

HCPI-HRL: Human Causal Perception and Inference-driven Hierarchical Reinforcement Learning.

Bin Chen1, Zehong Cao2, Wolfgang Mayer2

  • 1University of South Australia, Adelaide, SA, Australia; Xi'an Jiao Tong-Liverpool University, Jiangsu, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 9, 2025
PubMed
Summary

This study introduces Human Causal Perception and Inference-driven Hierarchical Reinforcement Learning (HCPI-HRL), a novel method that uses human causal insights to automatically discover subgoals. HCPI-HRL enhances agent training efficiency and adaptability in complex environments.

Keywords:
Causal inferenceDeep reinforcement learningHierarchical reinforcement learningSubgoal discovery

More Related Videos

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.3K
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

1.8K

Related Experiment Videos

Last Updated: May 23, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K
Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.3K
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

1.8K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Reinforcement Learning

Background:

  • Hierarchical Reinforcement Learning (HRL) often requires extensive expert knowledge for subgoal definition, limiting its efficiency and adaptability.
  • Complex and dynamic environments pose challenges for current HRL agents due to restricted training and adaptability.

Purpose of the Study:

  • To develop a novel method, Human Causal Perception and Inference-driven Hierarchical Reinforcement Learning (HCPI-HRL), that infers effective subgoal structures and critical objects using causal relationships.
  • To enhance HRL agents' exploration direction and promote the reuse of learned subgoal structures across tasks.
  • To overcome the dependency on expert knowledge in HRL for improved training efficiency and adaptability.

Main Methods:

  • Proposed HCPI-HRL with a two-level architecture: a meta-controller for assigning subgoals and a Proximal Policy Optimisation (PPO) based lower level for subgoal execution.
  • Utilized human-guided causal perception and inference to discover subgoal structures and identify critical objects from dynamic environmental states.
  • Incorporated stable causal relationships to guide intrinsic reward generation and agent exploration.

Main Results:

  • HCPI-HRL demonstrated superior performance compared to benchmark methods (hierarchical and adjacency PPO) in discrete and continuous control environments.
  • The method showed significant improvements in training efficiency, exploration capability, and transferability of learned policies.
  • Experiments validated the effectiveness of human-guided causal modeling in inferring subgoal relationships and enhancing agent learning.

Conclusions:

  • HCPI-HRL successfully addresses the limitations of traditional HRL by automating subgoal discovery through human causal insights.
  • The proposed approach enhances agent capabilities in dynamic environments with sparse rewards, paving the way for more adaptable and efficient HRL agents.
  • This research highlights the potential of integrating causal inference with HRL for more sophisticated and autonomous AI systems.