UMRAM, Psychology and Neuroscience faculty Dr. Burcu Ayşen Ürgen has published a paper titled “Distinct representations in occipito-temporal, parietal, and premotor cortex during action perception revealed by fMRI and computational modeling” in Neuropsychologia. Her collaborators in the project include Dr. Ayse P. Saygin from UC San Diego and Dr. Selen Pehlivan from TED University. The project investigates visual action representations in the human brain using fMRI and computational modeling.
Visual processing of actions is supported by a network consisting of occipito-temporal, parietal, and premotor regions in the human brain, known as the Action Observation Network (AON). In the present study, we investigate what aspects of visually perceived actions are represented in this network using fMRI and computational modeling. Human subjects performed an action perception task during scanning. We characterized the different aspects of the stimuli starting from purely visual properties such as form and motion to higher-aspects such as intention using computer vision and categorical modeling. We then linked the models of the stimuli to the three nodes of the AON with representational similarity analysis. Our results show that different nodes of the network represent different aspects of actions. While occipito-temporal cortex performs visual analysis of actions by means of integrating form and motion information, parietal cortex builds on these visual representations and transforms them into more abstract and semantic representations coding target of the action, action type and intention. Taken together, these results shed light on the neuro-computational mechanisms that support visual perception of actions and provide support that AON is a hierarchical system in which increasing levels of the cortex code increasingly complex features.
Urgen, B. A., Pehlivan, S., & Saygin, A. P. (2019). Distinct representations in occipito-temporal, parietal, and premotor cortex during action perception revealed by fMRI and computational modeling. Neuropsychologia, 127, 35–47. http://doi.org/10.1016/j.neuropsychologia.2019.02.006