SPiDbox: design and validation of an open-source "Skinner-box" system for the study of jumping spiders

Skinner-box systems are fundamental in behavioural research. They are objective, reliable and can be used to carry out procedures otherwise impossible with manual methodologies. Recently, jumping spiders have caught the interest of scientists for their remarkable cognitive abilities. However, inquiries on their learning abilities are still few, since we lacked a proper methodology capable of overcoming the inherent difficulties that this family poses when carrying out a conditioning protocol.

In this paper, a new, automated, open-source Skinner-box, intended for the study of jumping spiders is presented. The system is 3d printable, cheap, fully open-source; is controlled with a Raspberry Pi Zero by a Python script. Since spiders are too lightweight to activate large physical object, the SPiDbox employs photo-sensors.

To validate the methodology, 30 Phidippus regius underwent a training procedure for a simple discrimination task to validate the effectiveness of the system. The spiders managed to learn the task, establishing the effectiveness of the SPiDbox.

This automated training appears to be more reliable and effective than traditional methodologies. Moreover, its highly scalable, as many SPiDboxes could be used in parallel.

The SPiDbox appears to be an effective system to train jumping spiders, opening up the possibility to study learning in increasingly more complex tasks, possibly extending our understanding of jumping spiders' cognitive abilities.