Jove
Visualize
Contact Us

Related Concept Videos

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
Vectors in Space: Problem Solving01:26

Vectors in Space: Problem Solving

A chandelier suspended by multiple cables can be analyzed using principles of three-dimensional static equilibrium. In this setup, a chandelier weighing 1000 N is positioned at the origin of a three-dimensional coordinate system, while three ceiling anchor points are fixed at known locations above it. Each cable connects the chandelier to one anchor point and transmits a tensile force along its length.To find out the forces in the cables, the spatial direction of each cable must first be...
Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a problem,...

You might also read

Related Articles

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

Sort by
Same author

Editorial: Reflecting on Thirty Years of ECJ.

Evolutionary computation·2023
Same author

Adaptive Stochastic Optimization to Improve Protein Conformation Sampling.

IEEE/ACM transactions on computational biology and bioinformatics·2021
Same author

Intraoperative fluoroscopy alone versus routine post-operative X-rays in identifying return to theatre after fracture fixation.

ANZ journal of surgery·2021
Same author

The Entanglement of Dialectal Variation and Speaker Normalization.

Language and speech·2020
Same author

Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types.

Frontiers in neuroinformatics·2018
Same author

Theoretical and Empirical Analysis of a Spatial EA Parallel Boosting Algorithm.

Evolutionary computation·2016
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 Experiment Video

Updated: Jul 11, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Searching for best exemplars in multidimensional stimulus spaces.

Eric Oglesbee1, Kenneth de Jong

  • 1Department of Linguistics, Indiana University, Bloomington, Indiana 47405, USA.

The Journal of the Acoustical Society of America
|October 2, 2007
PubMed
Summary

A new multidimensional search algorithm improves phonetic categorization by efficiently identifying best exemplars in complex stimulus spaces. This method offers a more general approach than previous vowel-specific algorithms.

More Related Videos

An Operant Intra-/Extra-dimensional Set-shift Task for Mice
08:35

An Operant Intra-/Extra-dimensional Set-shift Task for Mice

Published on: January 22, 2016

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Related Experiment Videos

Last Updated: Jul 11, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

An Operant Intra-/Extra-dimensional Set-shift Task for Mice
08:35

An Operant Intra-/Extra-dimensional Set-shift Task for Mice

Published on: January 22, 2016

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

  • Phonetics
  • Psychoacoustics
  • Speech Perception

Background:

  • Phonetic categorization in multidimensional stimulus spaces presents practical challenges.
  • Traditional forced identification methods are inefficient for large stimulus dimensions.
  • Existing adaptive algorithms are often specific to particular stimuli, like vowels.

Purpose of the Study:

  • To introduce a generalized multidimensional search algorithm.
  • To evaluate the algorithm's effectiveness through simulations and experiments.
  • To provide a more versatile tool for phonetic research.

Main Methods:

  • Development of a novel multidimensional search algorithm.
  • Simulation studies to test algorithm convergence and efficiency.
  • Experimental validation using human participants and auditory stimuli.

Main Results:

  • The proposed algorithm demonstrates efficient convergence on best exemplars.
  • Simulations confirm the algorithm's robustness across various stimulus dimensions.
  • Experimental results validate the algorithm's practical applicability.

Conclusions:

  • The generalized multidimensional search algorithm is effective for phonetic categorization.
  • This approach overcomes limitations of traditional methods and vowel-specific algorithms.
  • The algorithm offers a valuable tool for studying auditory perception in complex spaces.