Browsing by Subject "Naturalistic stimuli"
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Item Open Access A large video set of natural human actions for visual and cognitive neuroscience studies and its validation with fMRI(MDPI, 2022-12-29) Ürgen, Burcu Ayşen; Nizamoğlu, Hilal; Eroğlu, Aslı; Orban, G. A.The investigation of the perception of others’ actions and underlying neural mechanisms has been hampered by the lack of a comprehensive stimulus set covering the human behavioral repertoire. To fill this void, we present a video set showing 100 human actions recorded in natural settings, covering the human repertoire except for emotion-driven (e.g., sexual) actions and those involving implements (e.g., tools). We validated the set using fMRI and showed that observation of the 100 actions activated the well-established action observation network. We also quantified the videos’ low-level visual features (luminance, optic flow, and edges). Thus, this comprehensive video set is a valuable resource for perceptual and neuronal studies.Item Open Access Voxel-based state space modeling recovers task-related cognitive states in naturalistic fMRI experiments(Frontiers Media S.A., 2021-05-06) Zhang, T.; Gao, J. S.; Çukur, Tolga; Gallant, J. L.Complex natural tasks likely recruit many different functional brain networks, but it is difficult to predict how such tasks will be represented across cortical areas and networks. Previous electrophysiology studies suggest that task variables are represented in a low-dimensional subspace within the activity space of neural populations. Here we develop a voxel-based state space modeling method for recovering task-related state spaces from human fMRI data. We apply this method to data acquired in a controlled visual attention task and a video game task. We find that each task induces distinct brain states that can be embedded in a low-dimensional state space that reflects task parameters, and that attention increases state separation in the task-related subspace. Our results demonstrate that the state space framework offers a powerful approach for modeling human brain activity elicited by complex natural tasks.