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Related Experiment Video

Updated: Jul 13, 2025

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Published on: August 8, 2019

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.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience

|October 17, 2023

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.

Keywords:

functional magnetic resonance imaging grasp axis grasp size grasping object mass visual grasp selection

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Last Updated: Jul 13, 2025

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

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Published on: August 8, 2019

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

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Published on: August 1, 2018

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

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.