Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

What is an Electrochemical Gradient?

What is an Electrochemical Gradient?

Adenosine triphosphate, or ATP, is considered the primary energy source in cells. However, energy can also be stored in the electrochemical gradient of an ion across the plasma membrane, which is determined by two factors: its chemical and electrical gradients.
The chemical gradient relies on differences in the abundance of a substance on the outside versus the inside of a cell and flows from areas of high to low ion concentration. In contrast, the electrical gradient revolves around an...

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

Energy Line and Hydraulic Gradient Line

Energy Line and Hydraulic Gradient Line

Based on Bernoulli's equation, the energy line (EL) and hydraulic grade line (HGL) provide graphical representations of energy distribution in a fluid flow system. For steady, incompressible, inviscid flows, Bernoulli's equation is expressed as:

Gradient and Del Operator

Gradient and Del Operator

In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...

ATP Driven Pumps I: An Overview

ATP Driven Pumps I: An Overview

ATP-driven pumps, also known as transport ATPases, are integral membrane proteins. They have binding sites for ATP located on the membrane's cytosolic side and the ion-conducting domain in the transmembrane region. These pumps use the free energy released from ATP hydrolysis to move the solutes across cell membranes against an electrochemical gradient.
There are four main types of ATP-driven pumps - P-type, V-type, F-type, and ABC transporter. All these pumps are of varying complexities and...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

You might also read

Related Articles

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

Sort by

Same author

A Japan-origin motivational framework for diversive and specific curiosity: development of the English version of the Japanese Epistemic Curiosity scale.

Frontiers in psychology·2026

Same author

Identifying indicators of consciousness in AI systems.

Trends in cognitive sciences·2025

Same author

Meta-representations as representations of processes.

Neuroscience of consciousness·2025

Same author

Optical Neuroimage Studio (OptiNiSt): Intuitive, scalable, extendable framework for optical neuroimage data analysis.

PLoS computational biology·2025

Same author

A novel virtual reality blowgun game for rehabilitation of older adults: A preliminary case series.

Journal of prosthodontic research·2025

Same author

Unsupervised method for representation transfer from one brain to another.

Frontiers in neuroinformatics·2024

Search research articles

Related Experiment Video

Updated: Jan 29, 2026

Light-driven Enzymatic Decarboxylation

Light-driven Enzymatic Decarboxylation

Published on: May 22, 2016

Boredom-Driven Curious Learning by Homeo-Heterostatic Value Gradients.

Yen Yu¹, Acer Y C Chang¹, Ryota Kanai¹

¹Araya, Inc., Tokyo, Japan.

Frontiers in Neurorobotics

|February 7, 2019

Summary

This summary is machine-generated.

The Homeo-Heterostatic Value Gradients (HHVG) algorithm formally links boredom and curiosity for better exploration and learning. This novel approach reconciles opposing motivations, enhancing an agent

Keywords:

boredom curiosity goal-directedness heterostatic motivation homeostatic motivation intrinsic motivation outcome devaluation satiety

More Related Videos

Gradient Echo Quantum Memory in Warm Atomic Vapor

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

Denaturing Gradient Gel Electrophoresis DGGE

Denaturing Gradient Gel Electrophoresis DGGE

Published on: February 25, 2007

Related Experiment Videos

Last Updated: Jan 29, 2026

Light-driven Enzymatic Decarboxylation

Light-driven Enzymatic Decarboxylation

Published on: May 22, 2016

Gradient Echo Quantum Memory in Warm Atomic Vapor

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

Denaturing Gradient Gel Electrophoresis DGGE

Denaturing Gradient Gel Electrophoresis DGGE

Published on: February 25, 2007

Area of Science:

Artificial Intelligence
Reinforcement Learning
Computational Neuroscience

Background:

Effective exploration and learning in artificial agents are crucial for advancing AI.
Intrinsic motivation, encompassing curiosity and boredom, plays a key role in agent cognition and behavior.
Previous models often treated curiosity and boredom as separate or opposing forces.

Purpose of the Study:

To introduce the Homeo-Heterostatic Value Gradients (HHVG) algorithm.
To formally model the constructive interplay between boredom and curiosity.
To demonstrate how reconciling these motivations leads to superior exploration and forward model learning.

Main Methods:

Developed the Homeo-Heterostatic Value Gradients (HHVG) algorithm.
Incorporated devaluation and devaluation progress as core algorithmic components.
Instantiated homeostatic (boredom) and heterostatic (curiosity) intrinsic motivation.
Empirically evaluated agent performance on model building benchmarks.

Main Results:

The HHVG algorithm successfully integrates homeostatic and heterostatic motivations.
Boredom-enabled agents demonstrated superior performance in model building tasks.
Agents utilizing the HHVG algorithm showed enhanced self-assisted experience accumulation.
The interplay between boredom and curiosity was shown to be critical for effective exploration.

Conclusions:

The HHVG algorithm provides a formal framework for understanding the synergy between boredom and curiosity.
Reconciling seemingly opposite intrinsic motivations is essential for effective exploration and learning in artificial agents.
This work offers a novel perspective on intrinsic motivation, advancing the field of reinforcement learning.