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

You might also read

Related Articles

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

Sort by
Same author

Continuous foraging behavior shapes patch-leaving decisions in pigeons: a 3D tracking study.

Animal cognition·2026
Same author

Neural correlates of appetitive extinction learning: an fMRI study with actively participating pigeons.

Scientific reports·2026
Same author

An individual participant data meta-analysis of how physical activity relates to affective well-being in daily life.

Nature human behaviour·2026
Same author

Achieving cell-type specific transduction with adeno-associated viral vectors in pigeons.

Current research in neurobiology·2026
Same author

Dopaminergic Innervation of the Nidopallium Caudolaterale in the Japanese Quail.

The Journal of comparative neurology·2026
Same author

Mapping functional connectivity in the pigeon brain with wide-field optical imaging.

Neurophotonics·2026

Related Experiment Video

Updated: Mar 29, 2026

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

12.5K

Categories in the pigeon brain: A reverse engineering approach.

Charlotte Koenen1,2, Roland Pusch1, Franziska Bröker1

  • 1Biopsychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, Germany.

Journal of the Experimental Analysis of Behavior
|November 30, 2015
PubMed
Summary

Pigeons

Keywords:
NFLavian braincategorizationkey peckpigeonsingle unit recording

More Related Videos

Extraction and Dissection of the Domesticated Pig Brain
09:16

Extraction and Dissection of the Domesticated Pig Brain

Published on: April 25, 2021

25.4K
Electroporation of the Hindbrain to Trace Axonal Trajectories and Synaptic Targets in the Chick Embryo
10:04

Electroporation of the Hindbrain to Trace Axonal Trajectories and Synaptic Targets in the Chick Embryo

Published on: May 29, 2013

11.4K

Related Experiment Videos

Last Updated: Mar 29, 2026

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

12.5K
Extraction and Dissection of the Domesticated Pig Brain
09:16

Extraction and Dissection of the Domesticated Pig Brain

Published on: April 25, 2021

25.4K
Electroporation of the Hindbrain to Trace Axonal Trajectories and Synaptic Targets in the Chick Embryo
10:04

Electroporation of the Hindbrain to Trace Axonal Trajectories and Synaptic Targets in the Chick Embryo

Published on: May 29, 2013

11.4K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Animal Behavior

Background:

  • Pigeons possess advanced visual categorization abilities.
  • Understanding the neural basis of categorization is crucial.

Purpose of the Study:

  • To investigate categorization learning using a reverse engineering approach.
  • To analyze neural representations of stimuli without predefined behavioral tasks.

Main Methods:

  • Recorded neural activity from the nidopallium frontolaterale (NFL) in pigeons.
  • Presented artificial pictorial and grating stimuli.
  • Computed representational dissimilarity matrices from neural data and behavior.

Main Results:

  • Neural activity in the NFL differentiated between pictorial and grating stimuli.
  • Pecking behavior showed similar, but less pronounced, differentiation.
  • No sub-clustering within pictorial (color/shape) or grating (frequency/amplitude) stimuli was observed.

Conclusions:

  • The reverse engineering approach successfully identified categorical information in neural data.
  • This method aids in understanding the neural underpinnings of categorization learning.