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Too Good to be True? Ideomotor Theory from a Computational Perspective

In recent years, Ideomotor Theory has regained widespread attention and sparked the development of a number of theories on goal-directed behavior and learning. However, there are two issues with previous studies’ use of Ideomotor Theory. Although Ideomotor Theory is seen as very general, it is often...

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Autores principales: Herbort, Oliver, Butz, Martin V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3495337/
https://www.ncbi.nlm.nih.gov/pubmed/23162524
http://dx.doi.org/10.3389/fpsyg.2012.00494
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author Herbort, Oliver
Butz, Martin V.
author_facet Herbort, Oliver
Butz, Martin V.
author_sort Herbort, Oliver
collection PubMed
description In recent years, Ideomotor Theory has regained widespread attention and sparked the development of a number of theories on goal-directed behavior and learning. However, there are two issues with previous studies’ use of Ideomotor Theory. Although Ideomotor Theory is seen as very general, it is often studied in settings that are considerably more simplistic than most natural situations. Moreover, Ideomotor Theory’s claim that effect anticipations directly trigger actions and that action-effect learning is based on the formation of direct action-effect associations is hard to address empirically. We address these points from a computational perspective. A simple computational model of Ideomotor Theory was tested in tasks with different degrees of complexity. The model evaluation showed that Ideomotor Theory is a computationally feasible approach for understanding efficient action-effect learning for goal-directed behavior if the following preconditions are met: (1) The range of potential actions and effects has to be restricted. (2) Effects have to follow actions within a short time window. (3) Actions have to be simple and may not require sequencing. The first two preconditions also limit human performance and thus support Ideomotor Theory. The last precondition can be circumvented by extending the model with more complex, indirect action generation processes. In conclusion, we suggest that Ideomotor Theory offers a comprehensive framework to understand action-effect learning. However, we also suggest that additional processes may mediate the conversion of effect anticipations into actions in many situations.
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spelling pubmed-34953372012-11-16 Too Good to be True? Ideomotor Theory from a Computational Perspective Herbort, Oliver Butz, Martin V. Front Psychol Psychology In recent years, Ideomotor Theory has regained widespread attention and sparked the development of a number of theories on goal-directed behavior and learning. However, there are two issues with previous studies’ use of Ideomotor Theory. Although Ideomotor Theory is seen as very general, it is often studied in settings that are considerably more simplistic than most natural situations. Moreover, Ideomotor Theory’s claim that effect anticipations directly trigger actions and that action-effect learning is based on the formation of direct action-effect associations is hard to address empirically. We address these points from a computational perspective. A simple computational model of Ideomotor Theory was tested in tasks with different degrees of complexity. The model evaluation showed that Ideomotor Theory is a computationally feasible approach for understanding efficient action-effect learning for goal-directed behavior if the following preconditions are met: (1) The range of potential actions and effects has to be restricted. (2) Effects have to follow actions within a short time window. (3) Actions have to be simple and may not require sequencing. The first two preconditions also limit human performance and thus support Ideomotor Theory. The last precondition can be circumvented by extending the model with more complex, indirect action generation processes. In conclusion, we suggest that Ideomotor Theory offers a comprehensive framework to understand action-effect learning. However, we also suggest that additional processes may mediate the conversion of effect anticipations into actions in many situations. Frontiers Media S.A. 2012-11-12 /pmc/articles/PMC3495337/ /pubmed/23162524 http://dx.doi.org/10.3389/fpsyg.2012.00494 Text en Copyright © 2012 Herbort and Butz. http://www.frontiersin.org/licenseagreement This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
spellingShingle Psychology
Herbort, Oliver
Butz, Martin V.
Too Good to be True? Ideomotor Theory from a Computational Perspective
title Too Good to be True? Ideomotor Theory from a Computational Perspective
title_full Too Good to be True? Ideomotor Theory from a Computational Perspective
title_fullStr Too Good to be True? Ideomotor Theory from a Computational Perspective
title_full_unstemmed Too Good to be True? Ideomotor Theory from a Computational Perspective
title_short Too Good to be True? Ideomotor Theory from a Computational Perspective
title_sort too good to be true? ideomotor theory from a computational perspective
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3495337/
https://www.ncbi.nlm.nih.gov/pubmed/23162524
http://dx.doi.org/10.3389/fpsyg.2012.00494
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