Tactile feedback is essential for upper-limb prostheses functionality and embodiment, yet its practical implementation presents challenges. Users must adapt to non-physiological signals, increasing cognitive load. However, some prosthetic devices transmit tactile information through socket vibrations, even to untrained individuals. Our experiments validated this observation, demonstrating a user's surprising ability to identify contacted fingers with a purely passive, cosmetic hand. Further experiments with advanced soft articulated hands revealed decreased performance in tactile information relayed by socket vibrations as hand complexity increased. To understand the underlying mechanisms, we conducted numerical and mechanical vibration tests on four prostheses of varying complexity. Additionally, a machine-learning classifier identified the contacted finger based on measured socket signals. Quantitative results confirmed that rigid hands facilitated contact discrimination, achieving 83% accuracy in distinguishing index finger contacts from others. While human discrimination decreased with advanced hands, machine learning surpassed human performance. These findings suggest that rigid prostheses provide natural vibration transmission, potentially reducing the need for tactile feedback devices, which advanced hands may require. Nonetheless, the possibility of machine learning algorithms outperforming human discrimination indicates potential to enhance socket vibrations through active sensing and actuation, bridging the gap in vibration-transmitted tactile discrimination between rigid and advanced hands.