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Simona Monaco, Giulia Malfatti, Laura Pizzato, Luigi Cattaneo, Luca Turella; Decoding action intention from the activity pattern in the Foveal Cortex. Journal of Vision 2018;18(10):72. doi: https://doi.org/10.1167/18.10.72.
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The human early visual cortex comprises feedback projections from higher-level cortical areas that profoundly affect perception. However, little is known about how the preparation of actions modulates the activity in the early visual cortex. To this explore this, we used a slow event-related functional magnetic resonance imaging (fMRI) paradigm in which participants (N=16) performed actions with the right dominant hand towards a centrally-located 3D-real-object. We manipulated the availability of visual information (Vision or No Vision) and the action type (Grasp or Open hand). In the Vision condition, participants fixated the object. Actions consisted of grasping the object or moving the open hand towards the object without interacting with it. At the beginning of each trial an auditory cue instructed participants about whether to close their eyes and the action to be performed at the end of the trial. A 10-s delay was followed by the go cue. We used retinotopic mapping standard procedures to localize the Foveal Cortex and identify the boundaries of the Primary Visual Cortex. With multivoxel pattern analyses we examined whether the activity pattern in the Foveal Cortex could be used to decode action intentions (Grasp or Open hand) during the planning phase preceding the action. Our results show successful decoding in the Foveal Cortex for the dissociation between action types (Grasp vs. Open hand) in Vision as well as in No Vision conditions. However, the activity pattern in Vision condition cannot be used to successfully decode the dissociation between action types in the No Vision condition, and vice versa. In addition, the decoding accuracy is higher in Vision than No Vision condition. These findings indicate that action planning modulates the activity patterns in Foveal Cortex even in absence of visual information, and suggest predictive coding related to action regardless of the availability of Visual information.
Meeting abstract presented at VSS 2018
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