Touching events predict human action segmentation in brain and behavior
Jennifer Pomp,
Nina Heins,
Ima Trempler,
Tomas Kulvicius,
Minija Tamosiunaite,
Falko Mecklenbrauck,
Moritz F. Wurm,
Florentin Wörgötter,
Ricarda I. Schubotz
Affiliations
Jennifer Pomp
Department of Psychology, University of Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Germany; Corresponding author.
Nina Heins
Department of Psychology, University of Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Germany
Ima Trempler
Department of Psychology, University of Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Germany
Tomas Kulvicius
Institute for Physics 3 – Biophysics and Bernstein Center for Computational Neuroscience (BCCN), University of Göttingen, Germany; University Medical Center Göttingen, Child and Adolescent Psychiatry and Psychotherapy, Göttingen, Germany
Minija Tamosiunaite
Institute for Physics 3 – Biophysics and Bernstein Center for Computational Neuroscience (BCCN), University of Göttingen, Germany; Department of Informatics, Vytautas Magnus University, Kaunas, Lithuania
Falko Mecklenbrauck
Department of Psychology, University of Münster, Germany
Moritz F. Wurm
Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, Italy
Florentin Wörgötter
Institute for Physics 3 – Biophysics and Bernstein Center for Computational Neuroscience (BCCN), University of Göttingen, Germany
Ricarda I. Schubotz
Department of Psychology, University of Münster, Germany; Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Germany
Recognizing the actions of others depends on segmentation into meaningful events. After decades of research in this area, it remains still unclear how humans do this and which brain areas support underlying processes. Here we show that a computer vision-based model of touching and untouching events can predict human behavior in segmenting object manipulation actions with high accuracy. Using this computational model and functional Magnetic Resonance Imaging (fMRI), we pinpoint the neural networks underlying this segmentation behavior during an implicit action observation task. Segmentation was announced by a strong increase of visual activity at touching events followed by the engagement of frontal, hippocampal and insula regions, signaling updating expectation at subsequent untouching events. Brain activity and behavior show that touching-untouching motifs are critical features for identifying the key elements of actions including object manipulations.
