Mechanical advantage is not invoked by additional cognitive load during multi-finger dexterous object manipulation

Abstract

Everyday interactions with objects depend on keeping the grasped item in static equilibrium by effectively distributing the contact forces from the fingertips. The mechanical advantage hypothesis (MAH) proposes that the central nervous system (CNS) optimises the distribution of grasping forces by favouring fingers with longer moment arms. While existing research has established that the applicability of MAH is influenced by biomechanical constraints, it remains unclear whether cognitive load, a distinct dimension of task difficulty, can similarly induce this force-sharing strategy. To investigate this, we examined whether increased cognitive demands during a multi-finger grasp- and-lift task would trigger force distribution patterns that exploit mechanical advantage. The results showed that while participants completed the task successfully, contrary to our hypothesis, the increased cognitive load did not result in a disproportionate rise in the normal force exerted by the little finger compared to the ring finger. This finding suggests that the employment of optimisation strategies like mechanical advantage may be predominantly sensitive to biomechanical rather than cognitive constraints. Significance StatementHuman grasping is a redundant control problem in that the hand has more ways to produce forces and moments than are strictly needed to achieve desired object interaction. The mechanical advantage hypothesis (MAH) is an optimisation principle proposing that the central nervous system (CNS) prefers fingers with greater leverage to address this redundancy during object manipulation. While MAH is known to emerge when mechanical difficulty increases, this study probes its boundaries and generality by asking whether nonmechanical demands also trigger it. The findings show that increased cognitive demand does not invoke MAH, highlighting that this optimisation arises primarily in response to physical and biomechanical task demands, with implications for dexterous control in rehabilitation, assistive technology, and robotics.