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

Incentive Theory: Pull Theory of Motivation

Incentive Theory: Pull Theory of Motivation

Incentive theory, or the "pull theory" of motivation, suggests that external rewards primarily drive behavior. Individuals are motivated to engage in activities when they anticipate a desirable outcome. This is why people often work hard for promotions or study intensively to achieve high grades. These incentives can be tangible, physical rewards such as money or promotions, or intangible, non-physical rewards like praise and social recognition.
The theory differentiates between...

Entropy and the Second Law of Thermodynamics

Entropy and the Second Law of Thermodynamics

The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...

Entropy

Entropy

The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...

The Second Law of Thermodynamics

The Second Law of Thermodynamics

In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be...

Drive-Reduction Theory: Push Theory of Motivation

Drive-Reduction Theory: Push Theory of Motivation

Clark Hull's drive-reduction theory, introduced in the 1940s and 1950s and often termed the "push theory" of motivation, provides a framework for understanding how biological and learned drives influence behavior. Hull suggested that motivation originates from the need to alleviate physiological tension caused by unmet biological necessities. The theory proposes that when a basic need, such as hunger or sleep, goes unfulfilled, it creates an internal imbalance. This imbalance, or...

Entropy Change in Reversible Processes

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.

You might also read

Related Articles

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

Sort by

Same author

Embeddings as Dirichlet counts: Attention is the tip of the iceberg.

Cognitive neuroscience·2025

Same author

Bayesian realism and structural representation.

The Behavioral and brain sciences·2022

See all related articles

Search research articles

Related Experiment Video

Updated: May 10, 2025

Studying Food Reward and Motivation in Humans

Studying Food Reward and Motivation in Humans

Published on: March 19, 2014

Intrinsic Motivation as Constrained Entropy Maximization.

Alex B Kiefer^1,2

¹VERSES, Los Angeles, CA 90016, USA.

Entropy (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

This study frames intrinsic motivation in intelligent systems as constrained maximum entropy inference. It connects active inference and empowerment, offering a unified perspective on endogenous motivation.

Keywords:

active inference empowerment entropy intrinsic motivation

More Related Videos

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Published on: January 16, 2016

Quantifying Social Motivation in Mice Using Operant Conditioning

Quantifying Social Motivation in Mice Using Operant Conditioning

Published on: August 8, 2015

Related Experiment Videos

Last Updated: May 10, 2025

Studying Food Reward and Motivation in Humans

Studying Food Reward and Motivation in Humans

Published on: March 19, 2014

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Published on: January 16, 2016

Quantifying Social Motivation in Mice Using Operant Conditioning

Quantifying Social Motivation in Mice Using Operant Conditioning

Published on: August 8, 2015

Area of Science:

Artificial Intelligence
Computational Neuroscience
Cognitive Science

Background:

Intrinsic motivation drives endogenous goal-seeking behavior in intelligent systems.
Existing frameworks often focus on learned reward associations.
A unified theoretical perspective is needed to encompass diverse intrinsic motivation accounts.

Purpose of the Study:

To present a unified framework for intrinsic motivation based on constrained maximum entropy inference.
To explore the relationship between active inference, empowerment, and intrinsic motivation.
To provide a complementary perspective to existing theories of endogenous motivation.

Main Methods:

Formal analysis of active inference and empowerment principles.
Connection of these principles to maximum entropy inference.
Exploration of the link between free energy and empowerment.

Main Results:

Active inference, empowerment, and related concepts are viewed as variations of constrained maximum entropy inference.
A formal link between free energy and empowerment is further elucidated.
The maximum-occupancy approach is shown to implicitly include a model-evidence constraint.

Conclusions:

Constrained maximum entropy inference offers a general perspective on intrinsic motivation.
This framework unifies diverse approaches to endogenous motivation in intelligent systems.
Understanding intrinsic motivation is crucial for developing more sophisticated artificial agents.