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

3.1K
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...
3.1K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
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...
8.3K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.9K
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 +...
8.9K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

659
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
659
Cluster Sampling Method01:20

Cluster Sampling Method

12.3K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.3K
Probability in Statistics01:14

Probability in Statistics

14.3K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
14.3K

You might also read

Related Articles

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

Sort by
Same author

Silver bullets and sensory horizons.

The Behavioral and brain sciences·2026
Same author

Examining the relationship between ssVEP and psychophysical measures of contrast sensitivity, grating acuity, and orientation discrimination.

iScience·2026
Same author

Interrupting the perception-action cycle reshapes serial dependence and sensory processing.

Brain research·2026
Same author

What is next? Predictable visual sequences are encoded with anticipatory biases and reduced neural responses.

iScience·2026
Same author

How Are Grouping and Interference Related in Crowding?

Quarterly journal of experimental psychology (2006)·2026
Same author

Large-scale mega-analysis indicates that serial dependence deteriorates perceptual decision-making.

Nature human behaviour·2025
Same journal

Increased rates of hybridization in swordtails are associated with water pollution.

Current biology : CB·2026
Same journal

Visual uncertainty and task demands shape active sensing strategies in mice.

Current biology : CB·2026
Same journal

An adaptable, self-organizing, single-cell morphology circuit optimizes suctorian predatory trap structure.

Current biology : CB·2026
Same journal

Temporal tuning of switch-like virulence expression resolves environmental uncertainty through phenotypic heterogeneity.

Current biology : CB·2026
Same journal

An abstract relational map emerges in the human medial prefrontal cortex with consolidation.

Current biology : CB·2026
Same journal

Phloem evolved gradually and asynchronously to xylem in early vascular plants.

Current biology : CB·2026
See all related articles

Related Experiment Video

Updated: Aug 23, 2025

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.4K

Unlocking crowding by ensemble statistics.

Natalia A Tiurina1, Yuri A Markov1, Oh-Hyeon Choung1

  • 1Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.

Current Biology : CB
|October 29, 2022
PubMed
Summary
This summary is machine-generated.

Crowding perception depends on ensemble statistics, not specific object configurations. Understanding the average orientation of surrounding elements, rather than individual details, predicts visual crowding strength.

Keywords:
crowdingensemble codingensemble statisticsgroupingperipheral vision

More Related Videos

Improving 2D and 3D Skin In Vitro Models Using Macromolecular Crowding
09:14

Improving 2D and 3D Skin In Vitro Models Using Macromolecular Crowding

Published on: August 22, 2016

12.6K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Related Experiment Videos

Last Updated: Aug 23, 2025

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.4K
Improving 2D and 3D Skin In Vitro Models Using Macromolecular Crowding
09:14

Improving 2D and 3D Skin In Vitro Models Using Macromolecular Crowding

Published on: August 22, 2016

12.6K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Area of Science:

  • Visual perception
  • Cognitive psychology
  • Computational neuroscience

Background:

  • Crowding describes how object recognition fails in cluttered visual scenes.
  • Traditional explanations focused on local processing, but recent evidence highlights global stimulus configuration.
  • Understanding global configurations is complex due to the vast number of possibilities and sensitivity to small changes.

Purpose of the Study:

  • To investigate the role of ensemble statistics in visual crowding.
  • To determine if average properties of surrounding elements predict crowding strength.
  • To test if perceived ensemble statistics, rather than individual element orientations, drive crowding effects.

Main Methods:

  • Utilized a vernier offset discrimination task with flanked squares.
  • Manipulated the orientation statistics of flanking squares to vary ensemble properties.
  • Compared crowding strength when a central square's orientation differed from the mean versus when all squares shared similar orientations.

Main Results:

  • Crowding was stronger when a central square's orientation deviated from the mean, causing it to group with the vernier.
  • Crowding was weaker when all flanking squares shared similar orientations, allowing them to group together.
  • These effects were driven by the perceived ensemble statistics (mean orientation) rather than individual square orientations.

Conclusions:

  • Visual crowding strength can be predicted by ensemble statistics of surrounding elements.
  • The brain rapidly computes group properties, simplifying the processing of complex visual scenes.
  • This finding offers a new framework for understanding visual crowding and object perception.