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Dynamic simulation and static matching for action prediction: evidence from body part priming

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Accurately predicting other people's actions may involve two processes: internal real-time simulation (dynamic updating) and matching recently perceived action images (static matching). Using a priming of body parts, this study aimed to differentiate the two processes. Specifically, participant...

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Hauptverfasser: Springer, Anne (VerfasserIn) , Brandstädter, Simone (VerfasserIn) , Prinz, Wolfgang (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 2013
In: Cognitive science
Year: 2013, Jahrgang: 37, Heft: 5, Pages: 936-952
ISSN:1551-6709
DOI:10.1111/cogs.12044
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1111/cogs.12044
Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/cogs.12044
Volltext
Verfasserangaben:Anne Springer, Simone Brandstädter, Wolfgang Prinz
Beschreibung
Zusammenfassung:Accurately predicting other people's actions may involve two processes: internal real-time simulation (dynamic updating) and matching recently perceived action images (static matching). Using a priming of body parts, this study aimed to differentiate the two processes. Specifically, participants played a motion-controlled video game with either their arms or legs. They then observed arm movements of a point-light actor, which were briefly occluded from view, followed by a static test pose. Participants judged whether this test pose depicted a coherent continuation of the previously seen action (i.e., “action prediction task”). Evidence of dynamic updating was obtained after compatible effector priming (i.e., arms), whereas incompatible effector priming (i.e., legs) indicated static matching. Together, the results support action prediction as engaging two distinct processes, dynamic simulation and static matching, and indicate that their relative contributions depend on contextual factors like compatibility of body parts involved in performed and observed action.
Beschreibung:First published: 20 May 2013
Gesehen am 14.02.2022
Beschreibung:Online Resource
ISSN:1551-6709
DOI:10.1111/cogs.12044

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