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

Mate Choice01:20

Mate Choice

8.3K
Mate choice—the decision about whom to mate with—is a type of natural selection, since animals must reproduce to pass down their genes. Mate choice is also called intersexual selection because the behavior occurs between the sexes.
8.3K
Types of Selection01:46

Types of Selection

37.5K
Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
37.5K
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

5.2K
A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
5.2K

You might also read

Related Articles

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

Sort by
Same author

Connecting Patients with Clinical Trials Using Patient Navigation: A Scoping Review.

Current oncology (Toronto, Ont.)·2026
Same author

Understanding Patient Perceptions of Genetic Testing to Predict Type 2 Diabetes Risk After Gestational Diabetes.

Endocrinology, diabetes & metabolism·2026
Same author

Implementing a national clinical trial navigator program: a qualitative CFIR analysis of barriers and facilitators from the perspective of people with lived cancer experience.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026
Same author

Evaluation of the novel HEalthy Lifestyle Project (HELP) youth mental health e-intervention for lifestyle behaviour change and mental healthcare system impact: A randomized controlled trial protocol.

PloS one·2025
Same author

Anxiety and quality-of-life for parents of children with undiagnosed rare conditions: A multi-site quantitative survey study.

Journal of genetic counseling·2025
Same author

Gray hair influences perceived age and social perceptions.

Frontiers in psychology·2025

Related Experiment Video

Updated: May 6, 2026

A Method for Investigating Change Blindness in Pigeons Columba Livia
06:14

A Method for Investigating Change Blindness in Pigeons Columba Livia

Published on: September 7, 2018

6.0K

Numerical competence in peafowl: small quantity discrimination.

Ming-Ray Liao1, Ria Patel2, Elizabeth J Connor3

  • 1Department of Psychological & Brain Sciences, Texas A&M University, College Station, TX, USA.

Animal Cognition
|May 5, 2026
PubMed
Summary
This summary is machine-generated.

Peafowl can distinguish between different numbers of objects, a skill potentially used in mate selection. This numerical discrimination ability is based on quantity rather than other visual factors.

Keywords:
Avian cognitionCognitive ecologyNumerical cognitionNumerositySexual selectionWeber–Fechner law

More Related Videos

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

11.5K
Operant Conditioning Task to Measure Song Preference in Zebra Finches
06:40

Operant Conditioning Task to Measure Song Preference in Zebra Finches

Published on: December 26, 2019

5.5K

Related Experiment Videos

Last Updated: May 6, 2026

A Method for Investigating Change Blindness in Pigeons Columba Livia
06:14

A Method for Investigating Change Blindness in Pigeons Columba Livia

Published on: September 7, 2018

6.0K
Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

11.5K
Operant Conditioning Task to Measure Song Preference in Zebra Finches
06:40

Operant Conditioning Task to Measure Song Preference in Zebra Finches

Published on: December 26, 2019

5.5K

Area of Science:

  • Animal behavior
  • Comparative cognition
  • Avian biology

Background:

  • Numerical discrimination is observed in diverse animal species.
  • Enhanced numerical abilities may aid animals in social or ecological challenges.
  • Peafowl (Pavo cristatus) possess elaborate trains with numerous eyespot feathers, suggesting potential use of numerical assessment in mate choice.

Purpose of the Study:

  • To investigate the numerical discrimination abilities of peafowl.
  • To determine if peafowl's performance is influenced by numerosity or other quantity factors like size, density, or surface area.
  • To assess if peafowl's numerical abilities align with established psychophysical laws.

Main Methods:

  • A custom touchscreen apparatus was used to test captive peafowl.
  • Peafowl were presented with visual stimuli (squares with varying numbers of circles, 1-8).
  • Performance was evaluated by rewarding pecks on the square with more circles, while varying circle size, density, and surface area to control for confounding factors.

Main Results:

  • Peafowl's performance was primarily driven by numerosity, not by variations in circle size, density, or surface area.
  • Discrimination accuracy remained above chance levels irrespective of these other quantity factors.
  • The observed numerical discrimination followed the Weber-Fechner Law, indicating reliance on numerical ratios.

Conclusions:

  • Peafowl demonstrate a robust ability to discriminate between numerical quantities.
  • This numerical competence may play a role in their courtship behaviors, specifically in assessing potential mates.
  • The findings contribute to understanding the evolution and function of numerical cognition in non-human animals.