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Related Concept Videos

Entropy02:39

Entropy

Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
Entropy01:18

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 Entropy as a State Function01:14

The Entropy as a State Function

Consider an arbitrary process that moves between two specific states (A and B) in a cyclic manner. This process is reversible and broken down into smaller parts that each follow a Carnot cycle. A Carnot cycle has two isothermal (constant temperature) processes. During these processes, the ratio of the amount of heat transferred to their respective temperature remains constant. The other two processes in the Carnot cycle are also reversible but adiabatic, which means they occur without any heat...
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Entropy Change in Reversible Processes01:10

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.

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Related Experiment Video

Updated: Jun 1, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

An entropy model for artificial grammar learning.

Emmanuel M Pothos1

  • 1Department of Psychology, Swansea University Swansea, UK.

Frontiers in Psychology
|May 25, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a model using Shannon entropy to measure complexity in artificial grammar learning (AGL). Higher predictability from training data suggests items are compatible, aligning with cognitive processing

Keywords:
artificial grammar learningchunking modelsentropyinformation theory

Related Experiment Videos

Last Updated: Jun 1, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning
05:33

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Artificial grammar learning (AGL) investigates how humans acquire implicit knowledge of complex rules.
  • Existing AGL measures may not fully capture the complexity of learned representations.
  • Cognitive processing is theorized to favor entropy reduction.

Purpose of the Study:

  • To propose and validate a novel model for characterizing knowledge in AGL using information theory.
  • To quantify the complexity of test items relative to training data in AGL tasks.
  • To explore the relationship between predictability and rule compatibility in AGL.

Main Methods:

  • Utilized Shannon entropy to compute item complexity within AGL tasks.
  • Compared entropy-based predictions with existing AGL datasets and measures.
  • Analyzed the predictability of test items from training exemplars.

Main Results:

  • The proposed entropy model effectively characterizes knowledge acquisition in AGL.
  • Higher predictability of test items from training data correlated with perceived compatibility.
  • The model's predictions align with findings from multiple AGL datasets.

Conclusions:

  • Shannon entropy provides a robust measure for understanding AGL knowledge representation.
  • The model supports the hypothesis that cognitive systems aim to reduce uncertainty and complexity.
  • This approach offers a unified perspective linking AGL with categorization and reasoning models.