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

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.

Conservation of Small Populations

Conservation of Small Populations

Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...

What is Population Genetics?

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.

What are Populations and Communities?

What are Populations and Communities?

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

System of Memory

System of Memory

Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...

You might also read

Related Articles

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

Sort by

Same author

A population vector model of visual working memory for real-world scenes.

Journal of experimental psychology. General·2026

Same author

Increased Reporting of Speech in Degraded Stimuli in Schizophrenia: A Case Control Study with Sine-Wave-Speech.

Schizophrenia bulletin·2026

Same author

Experience-dependent Changes in the Visual Processing of Letters: Evidence from Electroencephalography Decoding.

Journal of cognitive neuroscience·2025

Same author

Ignoring Salient Distractors Inside and Outside the Attentional Window.

Journal of cognitive neuroscience·2025

Same author

Signal suppression 2.0: An updated account of attentional capture and suppression.

Psychonomic bulletin & review·2025

Same author

Enhanced Working Memory Representations for Rare Events.

Psychophysiology·2025

Same journal

Genetic Impacts on Variability of Body Fat Distribution Uncover Gene-Environment and Gene-Gene Interactions.

bioRxiv : the preprint server for biology·2026

Same journal

16S ribosomal RNA modification drives transcript-specific translation efficiency.

bioRxiv : the preprint server for biology·2026

Same journal

FlcE latches onto the FliL-stator complex to turbocharge flagellar motility in <i>Borrelia burgdorferi</i>.

bioRxiv : the preprint server for biology·2026

Same journal

Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence.

bioRxiv : the preprint server for biology·2026

Same journal

Structural and functional insights into the Rcs phosphorelay.

bioRxiv : the preprint server for biology·2026

Same journal

The structural basis of RanGAP1 regulation and catalysis in nuclear transport.

bioRxiv : the preprint server for biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 10, 2026

A Real-world What-Where-When Memory Test

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

A Population Vector Model of Visual Working Memory for Real-World Scenes.

John E Kiat¹, Steven J Luck^1,2

¹Center for Mind & Brain, University of California, Davis.

Biorxiv : the Preprint Server for Biology

|February 9, 2026

Summary

This summary is machine-generated.

A new population vector model of visual working memory can represent complex, real-world scenes. This model accounts for behavioral performance and brain activity during naturalistic scene recall.

Keywords:

Deep Neural Networks EEG Natural Scenes Representational Similarity Analysis

More Related Videos

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Published on: August 22, 2019

Eye Movement Monitoring of Memory

Eye Movement Monitoring of Memory

Published on: August 15, 2010

Related Experiment Videos

Last Updated: Feb 10, 2026

A Real-world What-Where-When Memory Test

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Published on: August 22, 2019

Eye Movement Monitoring of Memory

Eye Movement Monitoring of Memory

Published on: August 15, 2010

Area of Science:

Cognitive Neuroscience
Computational Neuroscience

Background:

Visual working memory (VWM) is crucial for real-world interaction.
Existing VWM models struggle with complex, naturalistic scenes, often using simplified stimuli.
A gap exists in quantitatively modeling VWM for ecologically valid visual input.

Purpose of the Study:

To introduce a novel population vector model of VWM tailored for real-world scenes.
To bridge the gap between simplified VWM models and the complexity of natural visual input.
To develop a model that predicts both behavior and neural activity for scene storage.

Main Methods:

Developed a population vector model representing scenes as neural firing rate vectors.
Utilized deep neural networks to estimate neural representations from natural scenes.
Tested the model's ability to predict behavioral performance and brain activity.

Main Results:

The population vector model successfully accounts for behavioral variations in VWM tasks.
The model accurately predicts patterns of brain activity in ventral pathway areas.
Demonstrated the model's efficacy in representing complex, naturalistic visual information.

Conclusions:

The proposed population vector model offers a viable approach for understanding VWM of real-world scenes.
This modeling framework advances our ability to study complex visual memory.
Sets the foundation for future, more sophisticated models of natural scene memory.