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

Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
Types of Unit Cells
Imagine taking a large number of identical...
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
Second Uniqueness Theorem01:16

Second Uniqueness Theorem

Consider a region consisting of several individual conductors with a definite charge density in the region between these conductors. The second uniqueness theorem states that if the total charge on each conductor and the charge density in the in-between region are known, then the electric field can be uniquely determined.
In contrast, consider that the electric field is non-unique and apply Gauss's law in divergence form in the region between the conductors and the integral form to the surface...
Ladder Diagrams: Complexation Equilibria01:07

Ladder Diagrams: Complexation Equilibria

Ladder diagrams are useful for evaluating equilibria involving metal-ligand complexes. The vertical scale of the ladder diagram represents the concentration of unreacted or free ligand, pL. The horizontal lines on the scale depict the log of stepwise formation constants for metal-ligand complexes and indicate the dominant species in all the regions.
The formation constant, K1, for the formation of Cd(NH3)2+ complex from cadmium and ammonia is 3.55 × 102. Log K1 (i.e. pNH3) is 2.55, and...
Stability of Equilibrium Configuration01:23

Stability of Equilibrium Configuration

Understanding the stability of equilibrium configurations is a fundamental part of mechanical engineering. In any system, there are three distinct types of equilibrium: stable, neutral, and unstable.
A stable equilibrium occurs when a system tends to return to its original position when given a small displacement, and the potential energy is at its minimum. An example of a stable equilibrium is when a cantilever beam is fixed at one end and a weight is attached to the other end. If the weight...
Valence Bond Theory02:42

Valence Bond Theory

Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...

You might also read

Related Articles

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

Sort by
Same author

Tracing aesthetic experience from perception and conception to appraisal using deep convolutional neural networks.

iScience·2026
Same author

From pixels to perception: A benchmark for human-like symmetry detection.

Vision research·2026
Same author

Finding Closure: A Closer Look at the Gestalt Law of Closure in Convolutional Neural Networks.

Computational brain & behavior·2026
Same author

Variability and predictability as key factors in a new approach to choreographic complexity in dance.

Cognition·2026
Same author

Rethinking neuroaesthetics: Toward a multidimensional and integrative science of aesthetic experience.

Neuron·2026
Same author

The element of surprise distinguishes beauty from pleasure and interest in visuo-tactile perception of art.

Scientific reports·2026

Related Experiment Video

Updated: Jul 9, 2026

Nanofabrication of Gate-defined GaAs/AlGaAs Lateral Quantum Dots
15:47

Nanofabrication of Gate-defined GaAs/AlGaAs Lateral Quantum Dots

Published on: November 1, 2013

Eccentric grouping by proximity in multistable dot lattices.

Lizzy Bleumers1, Peter De Graef, Karl Verfaillie

  • 1Laboratory of Experimental Psychology, Department of Psychology, Faculty of Psychology and Educational Sciences, K.U. Leuven, Tiensestraat 102, B-3000 Leuven, Belgium. Lizzy.Bleumers@jubii.nl <Lizzy.Bleumers@jubii.nl>

Vision Research
|December 18, 2007
PubMed
Summary
This summary is machine-generated.

The Pure Distance Law accurately predicts visual grouping in central vision but struggles with peripheral displays. Random responses in peripheral vision suggest attention shifts may fail, impacting perceptual organization.

More Related Videos

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Related Experiment Videos

Last Updated: Jul 9, 2026

Nanofabrication of Gate-defined GaAs/AlGaAs Lateral Quantum Dots
15:47

Nanofabrication of Gate-defined GaAs/AlGaAs Lateral Quantum Dots

Published on: November 1, 2013

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Area of Science:

  • Visual perception
  • Cognitive psychology
  • Computational neuroscience

Background:

  • The Pure Distance Law posits that proximity influences perceptual grouping in dot lattices.
  • This law quantifies the relationship between perceived organization probability and dot distances.
  • Previous research primarily focused on central vision, leaving peripheral vision less explored.

Purpose of the Study:

  • To investigate the validity of the Pure Distance Law for both central and eccentric dot lattices.
  • To determine if scaling can account for differences in peripheral visual perception.
  • To explore the role of attention shifts in eccentric visual tasks.

Main Methods:

  • Dot lattices were presented in central vision and peripheral vision (3 and 15 degrees eccentricity).
  • The Pure Distance Law was applied to predict perceptual organization.
  • A modified model incorporating random responses was used to analyze eccentric data.
  • Interactions between scale, eccentricity, and interdot distance were examined.

Main Results:

  • The Pure Distance Law accurately predicted grouping for centrally displayed dot lattices.
  • The law's predictions were less accurate for eccentrically displayed dot lattices, even after scaling.
  • A model including random responses significantly improved the fit for eccentric data.
  • An interaction between scale and eccentricity was observed; relative distances had a stronger effect in peripheral vision at larger scales.

Conclusions:

  • The Pure Distance Law is a valid predictor for central visual organization but requires modification for peripheral vision.
  • Attention failures during required shifts to eccentric stimuli may explain random responses.
  • Perceptual organization in peripheral vision is influenced by eccentricity and scale, with interdot distance effects varying accordingly.