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Related Concept Videos

Construction of Frequency Distribution01:15

Construction of Frequency Distribution

A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is best to...
Relative Frequency Distribution00:55

Relative Frequency Distribution

A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
Frequency-dependent Selection01:21

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
Relative Frequency Histogram01:14

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

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Related Experiment Video

Updated: Jul 9, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces
05:36

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

The Population Spatial Frequency Toolbox.

Luis D Ramirez1, Feiyi Wang2, Emily Wiecek3

  • 1Lead Contributor, Department of Psychology, University of California San Diego, US; Department of Psychological & Brain Sciences, Boston University, US; Center for Systems Neuroscience, Boston University, US.

Journal of Open Research Software
|July 8, 2026
PubMed
Summary
This summary is machine-generated.

Vision science uses computational models to understand visual cortex neuron responses. The Population Spatial Frequency Toolbox (pSF-Toolbox) analyzes spatial frequency tuning in fMRI data for detailed visual processing insights.

Keywords:
fMRIpopulation spatial frequency tuningvision sciencevisual cortex

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Topographical Estimation of Visual Population Receptive Fields by fMRI
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Last Updated: Jul 9, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces
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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

Area of Science:

  • Neuroscience
  • Computational Vision
  • Neuroimaging

Background:

  • Understanding neural response properties in the visual cortex is crucial for developing accurate computational models.
  • Spatial Frequency (SF) tuning describes how neural populations selectively respond to different levels of visual detail.
  • Characterizing SF tuning is essential for decoding visual information processing.

Purpose of the Study:

  • To introduce the Population Spatial Frequency Toolbox (pSF-Toolbox), an open-source MATLAB package.
  • To provide tools for analyzing spatial frequency tuning in neural populations using fMRI data.
  • To facilitate the development of computational models of visual cortex function.

Main Methods:

  • The pSF-Toolbox utilizes MATLAB for data analysis.
  • It incorporates stimulus presentation scripts for controlled visual input.
  • Voxel-wise parameter optimization algorithms are employed to characterize SF tuning.

Main Results:

  • The pSF-Toolbox enables the characterization of spatial frequency tuning in neural populations.
  • The toolbox has been validated across various vision studies.
  • It provides a standardized method for analyzing fMRI data related to SF processing.

Conclusions:

  • The pSF-Toolbox is a valuable resource for researchers in vision science.
  • It facilitates the quantitative analysis of spatial frequency representation in the brain.
  • The toolbox contributes to a deeper understanding of visual information processing and neural coding.