2025/04/23 by Wendy J. Adams, Sina Mehraeen, Marc O. Ernst · 1 voice
Engineering · Medicine · Neuroscience · #Motor Control and Adaptation #Muscle activation and electromyography studies #Stroke Rehabilitation and Recovery
paper · doi:10.1163/22134808-bja10146
openalex publication_date 2025/04/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/02
When picking up objects, we prefer stable grips with minimal torque by seeking grasp points that straddle the object's centre of mass (CoM). For homogeneous objects, the CoM is at the geometric centre (GC), computable from shape cues. However, everyday objects often include components of different materials and densities. In this case, the CoM depends on the object's geometry and the components' densities. We asked how participants estimate the CoM of novel, two-part objects. Across four experiments, participants used a precision grip to lift cylindrical objects comprised of steel and PVC in varying proportions (steel three times denser than PVC). In all experiments, initial grasps were close to objects' GCs; neither every-day experience (metals are denser than PVC) nor pre-exposure to the stimulus materials in isolation moved first grasps away from the GC. Within a few trials, however, grasps shifted towards the CoM, reducing but not eliminating torque. Learning transferred across the stimulus set, that is, observers learnt the materials' densities (or their ratio) rather than learning each object's CoM. In addition, there was a stable 'under-reaching' bias towards the grasping hand. An 'inverted density' stimulus set (PVC 3 × denser than steel) induced similarly fast learning, confirming that prior knowledge of materials has little effect on grasp point selection. When stimulus sets were covertly switched during an experiment, the unexpected force feedback caused even faster grasp adaptation. Torque minimisation is a strong driver of grasp point adaptation, but there is a surprising lack of transfer following pre-exposure to relevant materials.