Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/126909
Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Informatika / Informatics (N009)
Author(s): Ziaeetabar, Fatemeh;Pomp, Jennifer;Pfeiffer, Stefan;El-Sourani, Nadiya;Schubotz, Ricarda I;Tamošiūnaitė, Minija;Wörgötter, Florentin
Title: Using enriched semantic event chains to model human action prediction based on (minimal) spatial information
Is part of: PLoS ONE [electronic resource]. San Francisco : Public Library of Science, 2020, vol. 15, iss. 12
Extent: p. 1-25
Date: 2020
Abstract: Predicting other people’s upcoming action is key to successful social interactions. Previous studies have started to disentangle the various sources of information that action observers exploit, including objects, movements, contextual cues and features regarding the acting person’s identity. We here focus on the role of static and dynamic inter-object spatial relations that change during an action. We designed a virtual reality setup and tested recognition speed for ten different manipulation actions. Importantly, all objects had been abstracted by emulating them with cubes such that participants could not infer an action using object information. Instead, participants had to rely only on the limited information that comes from the changes in the spatial relations between the cubes. In spite of these constraints, participants were able to predict actions in, on average, less than 64% of the action’s duration. Furthermore, we employed a computational model, the so-called enriched Semantic Event Chain (eSEC), which incorporates the information of different types of spatial relations: (a) objects’ touching/untouching, (b) static spatial relations between objects and (c) dynamic spatial relations between objects during an action. Assuming the eSEC as an underlying model, we show, using information theoretical analysis, that humans mostly rely on a mixed-cue strategy when predicting actions. Machine-based action prediction is able to produce faster decisions based on individual cues. We argue that human strategy, though slower, may be particularly beneficial for prediction of natural and more complex actions with more variable or partial sources of information. Our findings contribute to the understanding of how individuals afford inferring observed actions’ goals even before full goal accomplishment, and may open new avenues for building robots for conflict-free human-robot cooperation
Internet: https://www.vdu.lt/cris/bitstream/20.500.12259/126909/2/ISSN1932-6203_2020_V_15_12.PG_1-25.pdf
https://hdl.handle.net/20.500.12259/126909
https://doi.org/10.1371/journal.pone.0243829
Affiliation(s): Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:1. Straipsniai / Articles
Universiteto mokslo publikacijos / University Research Publications

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