Reading Motor Intentions
| Autor: | Lewkowicz, Daniel, Delevoye, Yvonne |
|---|---|
| Přispěvatelé: | Université de Lille, CNRS, CHU Lille, Unité de Recherche en Sciences Cognitives et Affectives [URECA], Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193, Unité de Recherche en Sciences Cognitives et Affectives (URECA), PRES Université Lille Nord de France-Université de Lille, Sciences Humaines et Sociales, Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193 (SCALab), Université de Lille-Centre National de la Recherche Scientifique (CNRS), The Science Network, Université de Lille, Sciences Humaines et Sociales-PRES Université Lille Nord de France, Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 (SCALab) |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: | |
| Zdroj: | Lewkowwicz, Daniel; & Delevoye-Turrell, Yvonne. (2013). Reading Motor Intentions. Proceedings of the Cognitive Science Society, 35(35). Retrieved from: http://www.escholarship.org/uc/item/6h45q9d9 CogSci CogSci, The Science Network, Aug 2013, Berlin, Germany |
| Popis: | International audience; Some evidence in very recent psychological studies have demonstrated that motor simulation ability is crucial for the correct understanding of social intentions. The present study was conducted first to confirm that the nature of the motor intention leads to early modulations of movement kinematics. Then, we tested whether humans could read an agent’s intention when observing the very first element of a complex action sequence. Results revealed early variations in movement kinematics and further showed that human agents can use these deviants to distinguish above chance level between three different social actions. Similar performance levels were found using an artificial classifier (Neural Network) and this procedure demonstrated furthermore that decisions could be taken on the basis of information contained in the first 500ms of movement kinematics. Taken together these results confirm the importance of motor simulation for adapted social interaction, and suggest how robotic adaptive controllers may use as input low-level motor information (e.g., kinematics) to afford biologically inspired social behaviors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|