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Updated: Jul 13, 2025
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Published on: August 8, 2019
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Distinct Neural Components of Visually Guided Grasping during Planning and Execution
Lina K Klein1, Guido Maiello2, Kevin Stubbs3
1Department of Experimental Psychology, Justus Liebig University Giessen, 35390 Giessen, Germany.
Summary
Brain networks involved in grasp planning and execution were investigated using fMRI. Findings reveal distinct neural representations for grasp axis, size, and object mass, highlighting sensorimotor transformations during grasping.
Area of Science:
- Neuroscience
- Cognitive Science
- Robotics
Background:
- Grasping is a complex visuomotor task requiring integration of object properties (shape, size, mass) and actor's body constraints (hand posture, aperture).
- Understanding the neural basis of grasp planning and execution is crucial for advancing human-robot interaction and understanding motor control.
Purpose of the Study:
- To investigate brain networks involved in processing distinct grasp-relevant factors during planning and execution using functional magnetic resonance imaging (fMRI).
- To disentangle the neural encoding of grasp axis, grasp size, and object mass during the visuomotor process of grasping.
Main Methods:
- Used fMRI to study human participants viewing and executing grasps on L-shaped objects.
- Employed a computational approach to select grasp points that isolated grasp axis, size, and object mass.
- Applied Representational Similarity Analysis (RSA) to decode neural representations.
Main Results:
- Grasp axis was encoded in dorsal-stream regions during planning.
- Grasp size was initially encoded in the ventral stream (planning) and later in premotor regions (execution).
- Object mass was encoded in ventral and premotor regions during execution, with premotor areas also encoding predicted grasp comfort.
Conclusions:
- Neural representations shift dynamically between planning and execution, reflecting sensorimotor transformations for successful grasping.
- Dorsal stream plays a key role in visuomotor planning, while the ventral stream becomes more involved during execution, potentially for haptic evaluation.
- Findings suggest a progression from encoding visual-motor parameters to integrating perceptual and motor feedback for refined grasp control.
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