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

Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.3K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.3K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.6K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.6K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

8.8K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
8.8K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.0K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.8K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.8K
Motion Of A Charged Particle In A Magnetic Field01:22

Motion Of A Charged Particle In A Magnetic Field

6.8K
A charged particle experiences a force when moving through a magnetic field. Consider the field to be uniform and the charged particle to move perpendicular to it. If the field is in a vacuum, the magnetic field is the dominant factor determining the motion. Since the magnetic force is perpendicular to the direction of motion, a charged particle follows a curved path. The particle continues to follow this curved path until it forms a complete circle. Another way to look at this is that the...
6.8K

You might also read

Related Articles

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

Sort by
Same author

Autism and Aphantasia.

Consciousness and cognition·2026
Same author

Modeling 2D spatio-tactile population receptive fields of the fingertip in human primary somatosensory cortex.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

FoMo: A unifying theory of visual foraging.

PLoS computational biology·2026
Same author

Recent breeding experience improves egg ejection behaviour.

Biology letters·2026
Same author

The development of visual acuity and crowding reveals the slow fine-tuning of foveal vision.

Scientific reports·2025
Same author

Predicting functional topography of the human visual cortex from cortical anatomy at scale.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Jan 24, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.7K

Population receptive field estimates for motion-defined stimuli.

Anna E Hughes1, John A Greenwood1, Nonie J Finlayson1

  • 1Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP, UK.

Neuroimage
|June 4, 2019
PubMed
Summary

Global motion processing emerges in higher visual areas, not early ones. Population receptive field mapping reveals retinotopic maps depend on neural signal-to-noise, not just stimulus type or visibility.

Keywords:
MotionPopulation receptive field analysisVision

More Related Videos

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

1.1K
Estimating Vestibular Perceptual Thresholds Using a Six-Degree-Of-Freedom Motion Platform
06:31

Estimating Vestibular Perceptual Thresholds Using a Six-Degree-Of-Freedom Motion Platform

Published on: August 4, 2022

3.6K

Related Experiment Videos

Last Updated: Jan 24, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.7K
Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

1.1K
Estimating Vestibular Perceptual Thresholds Using a Six-Degree-Of-Freedom Motion Platform
06:31

Estimating Vestibular Perceptual Thresholds Using a Six-Degree-Of-Freedom Motion Platform

Published on: August 4, 2022

3.6K

Area of Science:

  • Neuroscience
  • Visual Perception
  • Computational Neuroscience

Background:

  • Visual motion processing involves local and global motion across visual hierarchy.
  • Understanding retinotopic maps in response to motion stimuli is crucial.

Purpose of the Study:

  • To investigate spatially selective responses in visual cortex to motion-defined stimuli using fMRI.
  • To map retinotopic cortex using population receptive field (pRF) analyses with moving dot stimuli.
  • To determine how stimulus properties and background conditions influence these maps.

Main Methods:

  • Functional magnetic resonance imaging (fMRI) to measure brain activity.
  • Population receptive field (pRF) analyses to map retinotopic cortex.
  • Utilized random-dot stimuli with varying motion backgrounds (bar-only, kinetic boundary, global motion).

Main Results:

  • Clear retinotopic maps were found in early visual areas (V1-V3) for bar-only and kinetic boundary conditions.
  • Global motion stimuli yielded weaker maps in early areas, becoming clearer in higher dorsal areas.
  • Similar patterns observed for transparent motion and static stimuli, suggesting signal-to-noise ratio is key.

Conclusions:

  • Global motion processing emerges progressively through the visual hierarchy.
  • Retinotopic map clarity in dorsal extrastriate cortex is influenced by neural signal-to-noise ratio.
  • Caution is advised when interpreting stimulus selectivity from pRF experiments based solely on stimulus type.