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

Types of Functions III01:28

Types of Functions III

302
Logarithmic and piecewise functions play central roles in mathematical modeling, particularly when capturing nonlinear or segmented behaviors in real-world phenomena. Although these functions differ fundamentally in structure and application, both serve to represent complex relationships in simplified mathematical terms.A logarithmic function is defined as the inverse of an exponential function, expressed as These functions grow quickly for small values of x but slow down as x increases,...
302
Modified Boxplots00:57

Modified Boxplots

11.6K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
11.6K
¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

7.0K
When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
7.0K
Interval Level of Measurement00:55

Interval Level of Measurement

19.7K
For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
19.7K
Histogram01:05

Histogram

18.5K
The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
18.5K
Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

2.8K
In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Variation between individuals and the consequences for diet selection by groups of animals.

Animal behaviour·2000
Same author

Erratum to "Can an increased intake of metabolizable protein affect the periparturient relaxation in immunity against teladorsagia circumcincta in sheep?".

Veterinary parasitology·2000
Same author

Towards a functional explanation for the occurrence of anorexia during parasitic infections.

Animal behaviour·1998
Same author

Satiety splits feeding behaviour into bouts

Journal of theoretical biology·1998

Related Experiment Video

Updated: Feb 28, 2026

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.4K

To split behaviour into bouts, log-transform the intervals.

Tolkamp1, Kyriazakis

  • 1Animal Biology Division, Scottish Agricultural College, Edinburgh

Animal Behaviour
|March 8, 1999
PubMed
Summary

New models for analyzing animal feeding behavior provide more biologically meaningful estimates of bout criteria. These log-normal models offer greater flexibility than previous methods for interval analysis.

Area of Science:

  • Animal behavior analysis
  • Ethology
  • Quantitative biology

Background:

  • Accurate estimation of bout criteria is crucial for analyzing bouted behaviors.
  • Current methods using log-transformed cumulative frequency distributions lack biological meaning for feeding behavior analysis.
  • A previous study proposed modeling the frequency distribution of log-transformed interval lengths.

Purpose of the Study:

  • To test a proposed method for estimating bout criteria in animal feeding behavior.
  • To evaluate the biological meaningfulness and flexibility of log-normal models for interval data.
  • To investigate the potential inclusion of drinking intervals in feeding bout analysis.

Main Methods:

  • Modeling the frequency distribution of log-transformed interval lengths between feeding events.

More Related Videos

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

8.3K
Behavioral Disturbances: An Innovative Approach to Monitor the Modulatory Effects of a Nutraceutical Diet
07:05

Behavioral Disturbances: An Innovative Approach to Monitor the Modulatory Effects of a Nutraceutical Diet

Published on: January 3, 2017

9.4K

Related Experiment Videos

Last Updated: Feb 28, 2026

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.4K
A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

8.3K
Behavioral Disturbances: An Innovative Approach to Monitor the Modulatory Effects of a Nutraceutical Diet
07:05

Behavioral Disturbances: An Innovative Approach to Monitor the Modulatory Effects of a Nutraceutical Diet

Published on: January 3, 2017

9.4K
  • Utilizing a large dataset of 35,171 feeding intervals from 38 cows across three dietary groups.
  • Fitting double and triple log-normal models to interval length distributions.

    • Main Results:

    • Initial application of a two-Gaussian model showed limitations for some individuals.
    • Incorporating a third log-normal component significantly improved model fit, suggesting drinking intervals.
    • Fitting double or triple log-normal models yielded meaningful meal criteria for all individuals.
    • Log-normal models demonstrated superior biological meaningfulness and flexibility compared to log (cumulative) frequency models.

    Conclusions:

    • Log-normal models provide a more biologically relevant and flexible approach to defining feeding bout criteria.
    • The inclusion of a third log-normal distribution effectively accounts for intervals that may include drinking behavior.
    • This refined methodology enhances the quantitative analysis of feeding patterns in animals.