Mantis shrimp crustaceans perform ultra-fast, ballistic strikes for predation and defense. It is currently unknown what type of information is required to perform this completely anticipated, or predicted movement. The complex mantis shrimp visual system is hypothesized to provide key information for the initiation of the strike, however, the relationship between the position of a target and strike release has never been quantified. This investigation utilized a two-camera system to record mantis shrimp strikes in three dimensions. Sensory structures and the strike target in each 2D video recording were tracked and projected into three-dimensional space using the open-source machine learning software 3DeeplabCut. We predicted that mantis shrimp utilize a strike zone, like that of a baseball player, when deciding whether to strike or not strike at a target. The measured 3D coordinates were processed using R software to plot the path of the strike in relation to the target, providing a visualization of the predicted strike zone. This study investigates a new method for studying strikes and lays a foundation for future explorations of the relationship between sensory structures and the behaviors they inform.
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