Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Protein Complex Assembly02:41

Protein Complex Assembly

16.7K
Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
Many viruses self-assemble into a fully functional unit using the infected host cell to...
16.7K
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

2.9K
Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...
2.9K
Complex Numbers01:29

Complex Numbers

303
The real number system cannot represent the square root of a negative number, which restricts solutions for certain equations, such as quadratics with negative discriminants. To address this, the complex number system was developed, introducing the imaginary unit i, where i = √(-1). This extension allows for the representation of all roots, including those involving negative radicands.A complex number is written in the form x + yi, where x and y are real numbers. Here, x represents the...
303
Formation of Complex Ions03:45

Formation of Complex Ions

26.0K
A type of Lewis acid-base chemistry involves the formation of a complex ion (or a coordination complex) comprising a central atom, typically a transition metal cation, surrounded by ions or molecules called ligands. These ligands can be neutral molecules like H2O or NH3, or ions such as CN− or OH−. Often, the ligands act as Lewis bases, donating a pair of electrons to the central atom. These types of Lewis acid-base reactions are examples of a broad subdiscipline called coordination...
26.0K
Complex Power01:14

Complex Power

902
Power engineers have introduced the concept of complex power to determine the cumulative effect of parallel loads. This idea plays a crucial role in power analysis because it encompasses all the details related to the power consumed by a specific load.
Complex power is defined as the multiplication of the voltage and the complex conjugate of the current. The magnitude of this power, known as apparent power, is measured in volt-amperes (VA). Notably, the angle of the complex power equates to the...
902
Complexation Equilibria: Factors Influencing Stability of Complexes01:09

Complexation Equilibria: Factors Influencing Stability of Complexes

840
In complexation reactions, metal cations are the electron pair acceptors, and the ligands are the electron pair donors. The stability of the metal complexes depends primarily on the complexing ability of the central metal ion and the nature of the ligands. Generally, the complexing ability of the metal ion depends on the size and charge of the ion. As the metal ion size increases, the stability of the metal complexes decreases, provided that the valency of the metal ion and the ligands remain...
840

You might also read

Related Articles

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

Sort by
Same author

Single camera estimation of microswimmer depth with a convolutional network.

Journal of the Royal Society, Interface·2025
Same author

Theory of axo-axonic inhibition.

PLoS computational biology·2025
Same author

Visual activity enhances neuronal excitability in thalamic relay neurons.

Science advances·2025
Same author

Interaction of the mechanosensitive microswimmer <i>Paramecium</i> with obstacles.

Royal Society open science·2023
Same author

An electrophysiological and kinematic model of Paramecium, the "swimming neuron".

PLoS computational biology·2023
Same author

Does the present moment depend on the moments not lived?

The Behavioral and brain sciences·2022
Same journal

Are language models models?

The Behavioral and brain sciences·2026
Same journal

Large language models illuminate the mechanistic underpinnings of the creative aspect of language use (CALU), long regarded as a mystery.

The Behavioral and brain sciences·2026
Same journal

LLMs as a platform for studying constraint interaction: Motivation and challenges.

The Behavioral and brain sciences·2026
Same journal

Beyond the data gap: Children create languages, violate their input statistics, and exhibit critical periods.

The Behavioral and brain sciences·2026
Same journal

Not-so-strange love: Language models and generative linguistic theories are more compatible than they appear.

The Behavioral and brain sciences·2026
Same journal

Rich data drive generalization: Lessons from machine learning for linguistics and cognitive science.

The Behavioral and brain sciences·2026
See all related articles

Related Experiment Video

Updated: Jan 29, 2026

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments
10:59

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Published on: February 7, 2025

1.8K

The world is complex, not just noisy.

Romain Brette1

  • 1Sorbonne Universités,UPMC Univ Paris 06,INSERM,CNRS,Institut de la Vision,75012 Paris,France.romain.brette@inserm.frhttp://romainbrette.fr.

The Behavioral and Brain Sciences
|February 16, 2019
PubMed
Summary
This summary is machine-generated.

Human perception is not necessarily optimal, as optimality is often ill-defined. The study argues perception involves understanding the world

More Related Videos

Assembly and Characterization of Polyelectrolyte Complex Micelles
08:44

Assembly and Characterization of Polyelectrolyte Complex Micelles

Published on: March 2, 2020

11.5K
Identification of Post-translational Modifications of Plant Protein Complexes
10:07

Identification of Post-translational Modifications of Plant Protein Complexes

Published on: February 22, 2014

24.6K

Related Experiment Videos

Last Updated: Jan 29, 2026

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments
10:59

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Published on: February 7, 2025

1.8K
Assembly and Characterization of Polyelectrolyte Complex Micelles
08:44

Assembly and Characterization of Polyelectrolyte Complex Micelles

Published on: March 2, 2020

11.5K
Identification of Post-translational Modifications of Plant Protein Complexes
10:07

Identification of Post-translational Modifications of Plant Protein Complexes

Published on: February 22, 2014

24.6K

Area of Science:

  • Cognitive science
  • Psychology
  • Neuroscience

Background:

  • Human perception is often framed as a statistical inference problem.
  • The concept of perceptual optimality is frequently ill-defined in scientific literature.

Purpose of the Study:

  • To critically evaluate the notion of human perception being optimal.
  • To reframe the understanding of perceptual challenges beyond statistical inference.

Main Methods:

  • Conceptual analysis of perception research.
  • Critique of the statistical inference framework for perception.

Main Results:

  • Optimality in perception is not a well-defined concept.
  • The real-world perceptual challenge is discerning world structure, not fitting statistical models.

Conclusions:

  • Human perception's effectiveness should not be solely judged by optimality.
  • A more accurate framework for perception involves understanding the lawful structure of the environment.