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

Implicit Memories01:24

Implicit Memories

208
Implicit memories, also known as non-declarative memories, are long-term memories that function outside of conscious awareness. These memories influence behavior and skills without explicit knowledge. This type of memory is evident in tasks like playing tennis, snowboarding, and texting. Implicit memory has three subsystems: procedural memory, conditioning, and priming. This type of memory is essential in various activities, from everyday tasks to specialized skills.
One key aspect of implicit...
208
Observational Learning01:12

Observational Learning

349
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...
349
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

988
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
988
Cognitive Learning01:21

Cognitive Learning

686
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...
686
Self-Evaluation Maintenance Model01:29

Self-Evaluation Maintenance Model

17
The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
17
Introspection01:29

Introspection

8
Introspection, long upheld as a reliable route to self-knowledge, involves examining one's thoughts, emotions, and mental processes. It underpins many psychological practices, from mindfulness meditation to psychotherapy and self-help strategies. However, empirical evidence challenges the accuracy of introspection as a means of understanding oneself.Limitations of Introspective InsightSeminal work by Nisbett and Wilson demonstrated that individuals are frequently unaware of the true causes...
8

You might also read

Related Articles

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

Sort by
Same author

Higher-order interactions in neuronal function: From genes to ionic currents in biophysical models.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

A general framework for interpretable neural learning based on local information-theoretic goal functions.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Bits and pieces: understanding information decomposition from part-whole relationships and formal logic.

Proceedings. Mathematical, physical, and engineering sciences·2022
Same author

BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition.

Entropy (Basel, Switzerland)·2020
Same author

A model for time interval learning in the Purkinje cell.

PLoS computational biology·2020
Same author

Efficient neural decoding of self-location with a deep recurrent network.

PLoS computational biology·2019

Related Experiment Video

Updated: Sep 29, 2025

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

8.0K

Quantifying Reinforcement-Learning Agent's Autonomy, Reliance on Memory and Internalisation of the Environment.

Anti Ingel1, Abdullah Makkeh2, Oriol Corcoll1

  • 1Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia.

Entropy (Basel, Switzerland)
|March 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to calculate agent autonomy using information theory. The approach monitors how reinforcement learning agents

Keywords:
autonomydeep learninginformation theorynon-trivial informational closurepartial information decompositionreinforcement learning

More Related Videos

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents
09:01

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

Published on: July 8, 2015

12.7K
Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

34.3K

Related Experiment Videos

Last Updated: Sep 29, 2025

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

8.0K
The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents
09:01

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

Published on: July 8, 2015

12.7K
Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

34.3K

Area of Science:

  • Artificial Intelligence
  • Information Theory
  • Computational Neuroscience

Background:

  • Agent autonomy is intuitively linked to goal and behavior decoupling from environmental control.
  • Quantifying autonomy, especially during agent training, remains a challenge in artificial intelligence.

Purpose of the Study:

  • To introduce an algorithm for calculating agent autonomy using an information-theoretic framework.
  • To investigate how autonomy levels evolve during the training of reinforcement learning agents.
  • To utilize Partial Information Decomposition (PID) to monitor autonomy and environment internalization.

Main Methods:

  • Developed an algorithm to compute autonomy in a time step limit approaching infinity.
  • Applied the Partial Information Decomposition (PID) framework to analyze reinforcement learning agents.
  • Conducted experiments in a grid world and a sequence imitation environment.

Main Results:

  • Demonstrated that specific PID terms correlate with acquired rewards in reinforcement learning agents.
  • Showed that PID quantifies agent reliance on internal memory versus direct observations.
  • Observed correlations between PID terms and agent behavior robustness against observational perturbations.

Conclusions:

  • The proposed information-theoretic approach effectively quantifies agent autonomy during learning.
  • Partial Information Decomposition provides insights into agent internal states and environmental interaction.
  • This framework aids in understanding and designing more autonomous and robust artificial agents.