Abstract
We propose a computational model of Precision Grip (PG) performance in normal subjects and Parkinson's Disease (PD) patients. Prior studies on grip force generation in PD patients show an increase in grip force during ON medication and an increase in the variability of the grip force during OFF medication (Ingvarsson et al., 1997; Fellows et al., 1998). Changes in grip force generation in dopamine-deficient PD conditions strongly suggest contribution of the Basal Ganglia, a deep brain system having a crucial role in translating dopamine signals to decision making. The present approach is to treat the problem of modeling grip force generation as a problem of action selection, which is one of the key functions of the Basal Ganglia. The model consists of two components: (1) the sensory-motor loop component, and (2) the Basal Ganglia component. The sensory-motor loop component converts a reference position and a reference grip force, into lift force and grip force profiles, respectively. These two forces cooperate in grip-lifting a load. The sensory-motor loop component also includes a plant model that represents the interaction between two fingers involved in PG, and the object to be lifted. The Basal Ganglia component is modeled using Reinforcement Learning with the significant difference that the action selection is performed using utility distribution instead of using purely Value-based distribution, thereby incorporating risk-based decision making. The proposed model is able to account for the PG results from normal and PD patients accurately (Ingvarsson et al., 1997; Fellows et al., 1998). To our knowledge the model is the first model of PG in PD conditions.
Highlights
Precision grip (PG) is the ability to grip objects between the forefinger and thumb (Napier, 1956)
We present a computational model for human PG performance in controls and PD subjects in (ON/OFF) medicated states
Using risk-based decision making (DM) to model PG performance, we show the alteration of grip force (FG) in PD patients (Wolpert and Landy, 2012) in a modified Reinforcement Learning (RL) framework
Summary
Precision grip (PG) is the ability to grip objects between the forefinger and thumb (Napier, 1956). Successful performance of PG requires a delicate control of two forces (grip force, FG, and lift force, FL) exerted by two fingers on the object. In a grip-lift task FG is kept sufficiently high to couple FL with the object via the agency of friction between the object and the fingers. An optimal FL is required to overcome the object’s weight and lift it off the surface of the table on which it rests. These forces (FL and FG) are thought to be generated in parallel by different subsystems in the brain (Ehrsson et al, 2001, 2003). An excessive SM is undesirable as it would cause muscle fatigue and may even lead to crushing of the object
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
