Dynamic simulation and static matching for action prediction: evidence from body part priming
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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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2013
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| In: |
Cognitive science
Year: 2013, Volume: 37, Issue: 5, Pages: 936-952 |
| ISSN: | 1551-6709 |
| DOI: | 10.1111/cogs.12044 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1111/cogs.12044 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/cogs.12044 
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| Author Notes: | Anne Springer, Simone Brandstädter, Wolfgang Prinz |
| Summary: | 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. |
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| Item Description: | First published: 20 May 2013 Gesehen am 14.02.2022 |
| Physical Description: | Online Resource |
| ISSN: | 1551-6709 |
| DOI: | 10.1111/cogs.12044 |