Análisis experimental del comportamiento asistido por inteligencia artificial: Hacia un cambio de paradigma multidisciplinar

Autores:
León, Alejandro
Tipo de recurso:
Editorial
Fecha de publicación:
2022
Institución:
Universidad Católica de Colombia
Repositorio:
RIUCaC - Repositorio U. Católica
Idioma:
spa
OAI Identifier:
oai:repository.ucatolica.edu.co:10983/28576
Acceso en línea:
https://hdl.handle.net/10983/28576
https://doi.org/10.14718/ACP.2022.25.2.1
Palabra clave:
Rights
openAccess
License
Acta Colombiana de Psicología - 2022
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dc.title.spa.fl_str_mv Análisis experimental del comportamiento asistido por inteligencia artificial: Hacia un cambio de paradigma multidisciplinar
dc.title.translated.eng.fl_str_mv Análisis experimental del comportamiento asistido por inteligencia artificial: Hacia un cambio de paradigma multidisciplinar
title Análisis experimental del comportamiento asistido por inteligencia artificial: Hacia un cambio de paradigma multidisciplinar
spellingShingle Análisis experimental del comportamiento asistido por inteligencia artificial: Hacia un cambio de paradigma multidisciplinar
title_short Análisis experimental del comportamiento asistido por inteligencia artificial: Hacia un cambio de paradigma multidisciplinar
title_full Análisis experimental del comportamiento asistido por inteligencia artificial: Hacia un cambio de paradigma multidisciplinar
title_fullStr Análisis experimental del comportamiento asistido por inteligencia artificial: Hacia un cambio de paradigma multidisciplinar
title_full_unstemmed Análisis experimental del comportamiento asistido por inteligencia artificial: Hacia un cambio de paradigma multidisciplinar
title_sort Análisis experimental del comportamiento asistido por inteligencia artificial: Hacia un cambio de paradigma multidisciplinar
dc.creator.fl_str_mv León, Alejandro
dc.contributor.author.spa.fl_str_mv León, Alejandro
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-06-22 15:43:32
2023-01-23T15:44:55Z
dc.date.available.none.fl_str_mv 2022-06-22 15:43:32
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dc.date.issued.none.fl_str_mv 2022-06-22
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dc.relation.references.spa.fl_str_mv Datta, S. R., Anderson, D. J., Branson, K., Perona, P., & Leifer, A. (2019). Computational Neuroethology: A Call to Action. Neuron, 104(1), 11-24. https://doi.org/10.1016/j.neuron.2019.09.038 Escobar, R., & Pérez‐Herrera, C. A. (2015). Low‐cost USB interface for operant research using Arduino and Visual Basic. Journal of the Experimental Analysis of Behavior, 103(2), 427-435. https://doi.org/10.1002/jeab.135 Henton, W. W., & Iversen, I. H. (1978). Classical and Operant Conditioning: A Response Pattern Analysis. Springer-Verlag. https://doi.org/10.1007/978-1-4612-6310-4 Jarmolowicz, D. P., Greer, B. D., Killeen, P. R., & Huskinson, S. L. (2021). Applied Quantitative Analysis of Behavior: What It Is, and Why We Care—Introduction to the Special Section. Perspectives on Behavior Science, 44(4), 503–516. https://doi.org/10.1007/s40614-021-00323-w León, A., Tamayo, J. T., Eslava, V. H., Hernández, P. T., Garrido, M. L. A., Linares, C. A. H., & Navarro, E. E. (2020). MOTUS: software para el análisis conductual de patrones de desplazamiento. Revista Mexicana de Análisis de la Conducta, 46(1), 222-242. http://dx.doi.org/10.5514/rmac.v46.i1.76960 León, A., Hernandez, V., Lopez, J., Guzman, I., Quintero, V., Toledo, P., Avendaño-Garrido, M. L., Hernandez-Linares, C. A., & Escamilla, E. (2021). Beyond Single Discrete Responses: An Integrative and Multidimensional Analysis of Behavioral Dynamics Assisted by Machine Learning. Frontiers in behavioral neuroscience, 15, 681771. https://doi.org/10.3389/fnbeh.2021.681771 Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M. (2018). DeepLabCut: Markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience, 21(9), 1281-1289. https://doi.org/10.1038/s41593-018-0209-y Mathis, M. W., & Mathis, A. (2020). Deep learning tools for the measurement of animal behavior in neuroscience. Current opinion in neurobiology, 60, 1-11. https://doi.org/10.1016/j.conb.2019.10.008 Timberlake, W. (2004). Is the Operant Contingency Enough for a Science of Purposive Behavior? Behavior and Philosophy, 32(1), 197-229. http://www.jstor.org/stable/27759478 Walter, T., & Couzin, I. D. (2021). TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields. ELife, 10, e64000. https://doi.org/10.7554/eLife.64000 Watson, J. B. (1914). Behavior: An introduction to comparative psychology. Henry Holt and Co. https://doi.org/10.1037/10868-000
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spelling León, Alejandro94ac92e5-d4c6-4f9d-92fe-c9c3eb9a97633002022-06-22 15:43:322023-01-23T15:44:55Z2022-06-22 15:43:322023-01-23T15:44:55Z2022-06-22text/htmltext/htmlapplication/pdfapplication/pdftext/xml10.14718/ACP.2022.25.2.11909-97110123-9155https://hdl.handle.net/10983/28576https://doi.org/10.14718/ACP.2022.25.2.1spaUniversidad Católica de Colombiahttps://actacolombianapsicologia.ucatolica.edu.co/article/download/4601/4225https://actacolombianapsicologia.ucatolica.edu.co/article/download/4601/4226https://actacolombianapsicologia.ucatolica.edu.co/article/download/4601/4182https://actacolombianapsicologia.ucatolica.edu.co/article/download/4601/4186https://actacolombianapsicologia.ucatolica.edu.co/article/download/4601/4255Núm. 2 , Año 2022 : Acta Colombiana de Psicología72525Acta Colombiana de PsicologíaDatta, S. R., Anderson, D. J., Branson, K., Perona, P., & Leifer, A. (2019). Computational Neuroethology: A Call to Action. Neuron, 104(1), 11-24. https://doi.org/10.1016/j.neuron.2019.09.038 Escobar, R., & Pérez‐Herrera, C. A. (2015). Low‐cost USB interface for operant research using Arduino and Visual Basic. Journal of the Experimental Analysis of Behavior, 103(2), 427-435. https://doi.org/10.1002/jeab.135 Henton, W. W., & Iversen, I. H. (1978). Classical and Operant Conditioning: A Response Pattern Analysis. Springer-Verlag. https://doi.org/10.1007/978-1-4612-6310-4 Jarmolowicz, D. P., Greer, B. D., Killeen, P. R., & Huskinson, S. L. (2021). Applied Quantitative Analysis of Behavior: What It Is, and Why We Care—Introduction to the Special Section. Perspectives on Behavior Science, 44(4), 503–516. https://doi.org/10.1007/s40614-021-00323-w León, A., Tamayo, J. T., Eslava, V. H., Hernández, P. T., Garrido, M. L. A., Linares, C. A. H., & Navarro, E. E. (2020). MOTUS: software para el análisis conductual de patrones de desplazamiento. Revista Mexicana de Análisis de la Conducta, 46(1), 222-242. http://dx.doi.org/10.5514/rmac.v46.i1.76960 León, A., Hernandez, V., Lopez, J., Guzman, I., Quintero, V., Toledo, P., Avendaño-Garrido, M. L., Hernandez-Linares, C. A., & Escamilla, E. (2021). Beyond Single Discrete Responses: An Integrative and Multidimensional Analysis of Behavioral Dynamics Assisted by Machine Learning. Frontiers in behavioral neuroscience, 15, 681771. https://doi.org/10.3389/fnbeh.2021.681771 Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M. (2018). DeepLabCut: Markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience, 21(9), 1281-1289. https://doi.org/10.1038/s41593-018-0209-y Mathis, M. W., & Mathis, A. (2020). Deep learning tools for the measurement of animal behavior in neuroscience. Current opinion in neurobiology, 60, 1-11. https://doi.org/10.1016/j.conb.2019.10.008 Timberlake, W. (2004). Is the Operant Contingency Enough for a Science of Purposive Behavior? Behavior and Philosophy, 32(1), 197-229. http://www.jstor.org/stable/27759478 Walter, T., & Couzin, I. D. (2021). TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields. ELife, 10, e64000. https://doi.org/10.7554/eLife.64000 Watson, J. B. (1914). Behavior: An introduction to comparative psychology. Henry Holt and Co. https://doi.org/10.1037/10868-000Acta Colombiana de Psicología - 2022info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.http://creativecommons.org/licenses/by-nc-sa/4.0https://actacolombianapsicologia.ucatolica.edu.co/article/view/4601Análisis experimental del comportamiento asistido por inteligencia artificial: Hacia un cambio de paradigma multidisciplinarAnálisis experimental del comportamiento asistido por inteligencia artificial: Hacia un cambio de paradigma multidisciplinarArtículo de revistahttp://purl.org/coar/resource_type/c_b239http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTEDITinfo:eu-repo/semantics/publishedVersionPublicationOREORE.xmltext/xml3233https://repository.ucatolica.edu.co/bitstreams/882d98a0-2f01-4891-b1e7-7dee17785fde/download8825937026f0fe7c02f40f75a56f486cMD5110983/28576oai:repository.ucatolica.edu.co:10983/285762023-03-24 14:32:27.997http://creativecommons.org/licenses/by-nc-sa/4.0Acta Colombiana de Psicología - 2022https://repository.ucatolica.edu.coRepositorio Institucional Universidad Católica de Colombia - RIUCaCbdigital@metabiblioteca.com

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