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Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning
Pigeons are classic model animals to study perceptual category learning. To achieve a deeper understanding of the cognitive mechanisms of categorization, a careful consideration of the employed stimulus material and a thorough analysis of the choice behavior is mandatory. In the present study, we co...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334434/ https://www.ncbi.nlm.nih.gov/pubmed/34989909 http://dx.doi.org/10.1007/s10071-021-01594-1 |
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author | Pusch, Roland Packheiser, Julian Koenen, Charlotte Iovine, Fabrizio Güntürkün, Onur |
author_facet | Pusch, Roland Packheiser, Julian Koenen, Charlotte Iovine, Fabrizio Güntürkün, Onur |
author_sort | Pusch, Roland |
collection | PubMed |
description | Pigeons are classic model animals to study perceptual category learning. To achieve a deeper understanding of the cognitive mechanisms of categorization, a careful consideration of the employed stimulus material and a thorough analysis of the choice behavior is mandatory. In the present study, we combined the use of “virtual phylogenesis”, an evolutionary algorithm to generate artificial yet naturalistic stimuli termed digital embryos and a machine learning approach on the pigeons’ pecking responses to gain insight into the underlying categorization strategies of the animals. In a forced-choice procedure, pigeons learned to categorize these stimuli and transferred their knowledge successfully to novel exemplars. We used peck tracking to identify where on the stimulus the animals pecked and further investigated whether this behavior was indicative of the pigeon’s choice. Going beyond the classical analysis of the binary choice, we were able to predict the presented stimulus class based on pecking location using a k-nearest neighbor classifier, indicating that pecks are related to features of interest. By analyzing error trials with this approach, we further identified potential strategies of the pigeons to discriminate between stimulus classes. These strategies remained stable during category transfer, but differed between individuals indicating that categorization learning is not limited to a single learning strategy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10071-021-01594-1. |
format | Online Article Text |
id | pubmed-9334434 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-93344342022-07-30 Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning Pusch, Roland Packheiser, Julian Koenen, Charlotte Iovine, Fabrizio Güntürkün, Onur Anim Cogn Original Article Pigeons are classic model animals to study perceptual category learning. To achieve a deeper understanding of the cognitive mechanisms of categorization, a careful consideration of the employed stimulus material and a thorough analysis of the choice behavior is mandatory. In the present study, we combined the use of “virtual phylogenesis”, an evolutionary algorithm to generate artificial yet naturalistic stimuli termed digital embryos and a machine learning approach on the pigeons’ pecking responses to gain insight into the underlying categorization strategies of the animals. In a forced-choice procedure, pigeons learned to categorize these stimuli and transferred their knowledge successfully to novel exemplars. We used peck tracking to identify where on the stimulus the animals pecked and further investigated whether this behavior was indicative of the pigeon’s choice. Going beyond the classical analysis of the binary choice, we were able to predict the presented stimulus class based on pecking location using a k-nearest neighbor classifier, indicating that pecks are related to features of interest. By analyzing error trials with this approach, we further identified potential strategies of the pigeons to discriminate between stimulus classes. These strategies remained stable during category transfer, but differed between individuals indicating that categorization learning is not limited to a single learning strategy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10071-021-01594-1. Springer Berlin Heidelberg 2022-01-06 2022 /pmc/articles/PMC9334434/ /pubmed/34989909 http://dx.doi.org/10.1007/s10071-021-01594-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Pusch, Roland Packheiser, Julian Koenen, Charlotte Iovine, Fabrizio Güntürkün, Onur Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning |
title | Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning |
title_full | Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning |
title_fullStr | Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning |
title_full_unstemmed | Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning |
title_short | Digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (Columba livia) using machine learning |
title_sort | digital embryos: a novel technical approach to investigate perceptual categorization in pigeons (columba livia) using machine learning |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334434/ https://www.ncbi.nlm.nih.gov/pubmed/34989909 http://dx.doi.org/10.1007/s10071-021-01594-1 |
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