Recent research suggests that the ability to predict visual movements is a crucial factor in successfully catching moving objects. Through a study involving primates capturing crickets, scientists have employed high-speed cameras and AI to reveal an 80-millisecond delay in visuomotor behavior. This finding emphasizes the importance of predictive abilities in such actions, offering potential insights into neurological disorders.
Have you ever experienced the satisfaction of making an impressive catch, like snatching a falling phone before it hits the toilet or preventing an indoor cat from escaping outside? These skills, requiring the precise synchronization of our visual and motor systems, rely heavily on our capacity to predict the motion of objects.
A study conducted by researchers at the University of Rochester’s Del Monte Institute for Neuroscience has shed light on the significant role played by our ability to anticipate visually perceived motion in making successful catches or grasping moving objects.
Dr. Kuan Hong Wang, a Dean’s Professor of Neuroscience at the University of Rochester Medical Center, explained, “We developed a method that enabled us to analyze behaviors in a natural environment with exceptional precision, which is vital because, as we demonstrated, behavioral patterns differ in controlled settings.”
Dr. Wang led the study, which was recently published in the journal Current Biology, in collaboration with Dr. Jude Mitchell, an assistant professor of Brain and Cognitive Sciences, and Luke Shaw, a graduate student in the Neuroscience Graduate Program, both from the University of Rochester.
“Understanding the mechanisms behind natural behaviors will provide valuable insights into what goes wrong in various neurological disorders,” Dr. Wang added.
To conduct the study, researchers employed multiple high-speed cameras and DeepLabCut, an AI method that utilizes video data to track key points on the hand and arm, measuring their movements. The aim was to record where the primate’s gaze was focused and the corresponding arm and hand movements as they reached for and caught moving crickets.
The study revealed an 80-millisecond delay in the animal’s visuomotor behavior, the critical moment when vision and movement coordinate to direct the hand toward the target. Despite this noticeable delay, the primates were still able to successfully catch the crickets, indicating their ability to predict the crickets’ movements. By combining data from both the primates and the crickets, the researchers constructed a detailed model of vision-guided reaching behavior.
“These findings not only help us identify unique behavioral control strategies for mechanistic studies and engineering applications,” Dr. Wang said, “but also offer the potential to develop computational behavior analysis strategies that precisely characterize behavioral changes in naturalistic settings and understand their underlying causes, which is particularly relevant to visuomotor control problems observed in neurological disorders caused by brain lesions, stroke, and genetic factors.”
The study received funding from the National Institute of Health, the Schmitt Program of Integrative Neuroscience, and the Del Monte Institute for Neuroscience Pilot Program.
Reference:
Shaw, L., Wang, K. H., & Mitchell, J. (2023). Fast prediction in marmoset reach-to-grasp movements for dynamic prey. Current Biology, 33(11), R1234-R1235. DOI: 10.1016/j.cub.2023.05.032.
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Frequently Asked Questions (FAQs) about hand-eye coordination
What does the study reveal about the role of prediction in hand-eye coordination?
The study reveals that prediction plays a crucial role in hand-eye coordination. Researchers found an 80-millisecond delay in visuomotor behavior, highlighting the importance of predictive abilities in successfully catching moving objects.
How did the researchers conduct the study?
The researchers used high-speed cameras and an AI method called DeepLabCut. They recorded the primate’s gaze and tracked the movements of the hand and arm as they reached for and caught moving crickets. This allowed them to analyze behaviors in a natural environment with precision.
What are the implications of this research?
The research offers insights into understanding and potentially treating neurological disorders. By studying the mechanisms behind natural behaviors and identifying behavioral control strategies, the findings may help develop computational behavior analysis strategies and aid in characterizing behavioral alterations in naturalistic settings.
What funding supported this study?
The study received funding from the National Institute of Health, the Schmitt Program of Integrative Neuroscience, and the Del Monte Institute for Neuroscience Pilot Program.
More about hand-eye coordination
- Current Biology: Fast prediction in marmoset reach-to-grasp movements for dynamic prey
- University of Rochester Medical Center: Del Monte Institute for Neuroscience
- University of Rochester: Department of Brain and Cognitive Sciences
- DeepLabCut: AI method for pose estimation
