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

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

Instinctive Drift

Instinctive Drift

Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...

Population Growth

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

Observational Learning

Observational Learning

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 because...

Modeling with Differential Equations

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

You might also read

Related Articles

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

Sort by

Same author

Metastable dynamical computing with energy landscapes: A primer.

Chaos (Woodbury, N.Y.)·2026

Same author

Author Correction: Myocardial reprogramming by HMGN1 underlies heart defects in trisomy 21.

Nature·2026

Same author

Way More than the Sum of Their Parts: From Statistical to Structural Mixtures.

Entropy (Basel, Switzerland)·2026

Same author

Deep-learning prediction of gene expression from personal genomes.

Genome biology·2026

Same author

FOXP3 expression depends on cell-type-specific cis-regulatory elements and transcription factor circuitry.

Immunity·2025

Same author

Myocardial reprogramming by HMGN1 underlies heart defects in trisomy 21.

Nature·2025

Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026

Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026

Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026

Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026

Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026

Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 21, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Structural drift: the population dynamics of sequential learning.

James P Crutchfield¹, Sean Whalen

¹Complexity Sciences Center, Physics Department, University of California Davis, Davis, California, United States of America. chaos@ucdavis.edu

Plos Computational Biology

|June 12, 2012

Summary

This summary is machine-generated.

This study presents a new theory for sequential causal inference, modeling learning as a chain where information is passed from teacher to student. It extends genetic drift theory, offering insights into learning, inference, and evolution dynamics.

More Related Videos

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Related Experiment Videos

Last Updated: May 21, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

Causal Inference
Evolutionary Dynamics
Information Theory

Background:

Sequential learning involves passing information through a chain of learners.
Existing models often lack mechanisms to explain information fidelity and loss.
Population dynamics, like genetic drift, offer a framework for understanding information transmission.

Purpose of the Study:

To introduce a novel theory of sequential causal inference.
To extend population dynamics, specifically genetic drift, to model sequential learning.
To analyze how structured populations with memory influence information flow and model estimation.

Main Methods:

Developed a theoretical framework for sequential causal inference.
Extended genetic drift models to incorporate structured populations and memory.
Analyzed diffusion and fixation properties of generalized drift processes.

Main Results:

Recasted Kimura's neutral theory as a special case of generalized drift.
Demonstrated how population structure and memory affect learning fidelity and innovation.
Identified mechanisms for information loss in sequential learning systems.

Conclusions:

The proposed theory provides a unified framework for sequential causal inference and evolutionary dynamics.
Understanding drift processes in structured populations is key to optimizing sequential learning.
The model has potential applications in machine learning, evolutionary biology, and cognitive science.