Understanding how the human brain processes body movements is essential for clarifying the mechanisms underlying social cognition and interaction. This study investigates the encoding of biomechanically possible and impossible body movements in occipitotemporal cortex using ultra-high field 7T fMRI. By predicting the response of single voxels to impossible/possible movements using a computational modelling approach, our findings demonstrate that a combination of low-level, postural, biomechanical, and categorical features significantly predicts neural responses in the ventral visual cortex, particularly within the extrastriate body area (EBA), underscoring the brain's sensitivity to biomechanical plausibility.

High-level visual brain regions contain subareas in which neurons appear to respond more strongly to examples of a particular semantic category, like faces or bodies, rather than objects. However, recent work has shown that while this finding holds on average, some out-of-category stimuli also activate neurons in these regions. This may be due to visual features common among the preferred class also being present in other images. Here, we propose a deep-learning-based approach for visualizing these features. For each neuron, we identify relevant visual features driving its selectivity by modelling responses to images based on latent activations of a deep neural network. Given an out-of-category image which strongly activates the neuron, our method first identifies a reference image from the preferred category yielding a similar feature activation pattern. We then backpropagate latent activations of both images to the pixel level, while enhancing the identified shared dimensions and attenuating non-shared features. The procedure highlights image regions containing shared features driving responses of the model neuron. We apply the algorithm to novel recordings from body-selective regions in macaque IT cortex in order to understand why some images of objects excite these neurons. Visualizations reveal object parts which resemble parts of a macaque body, shedding light on neural preference of these objects.

Motor rehabilitation is in increasingly high demand to deal with minor functional motor impairments resulting from stroke, cerebellar ataxia, or Parkinson's disease. Juggling physiotherapy has shown to induce brain plasticity and to improve coordination and balance in this context. The physiotherapy, however, relies on large number of repetitions to be effective which prompts to deploy robots to release the burden on therapists both in terms of time as well as physical strain. This paper provides a framework to enable juggling games for patients in interacting with robots through Virtual Reality (VR). A set of throwing motions is recorded from the therapist and is retargeted to the humanoid robot COMAN's wrist. The respective whole-body motion is then solved in a stack of Quadratic Programs (QP) in a real-time architecture that integrates OROCOS and Gazebo. The resulting motion is finally streamed to VR for animation of the robot and the thrown ball, which the user can catch in VR using a controller device. We regard the VR setting as an essential step towards physiotherapeutic robotic juggling, because it ensures safety of the patients and effective testing of the methods and already has potential for actual therapeutic intervention. The control framework, however, is already validated in this paper for switching to full real-time operation on the physical robot.