Developing a Computer-Controlled Treat Dispenser for Canine Operant Conditioning

Abstract

When performing canine operant conditioning studies, the delivery of the reward can be a limiting factor of the study. While there are a few commercially available options for automatically delivering rewards, they generally require manual input, such as using a remote control, in accordance with the experiment script. This means that human reaction times and transmission distances can cause interruptions to the flow of the experiment. The potential for development of non-supervised conditioning studies is limited by this same factor. To remedy this, we retrofitted an off-the-shelf treat dispenser with new electronics that allow it to be remotely controllable as well as act as an experiment computation, data storage, and networking center. We present a fully integrated dispenser driver board with a complementary Raspberry Pi. With rather simple modifications, the commercial treat dispenser can be modified into a computercontrolled dispenser for canine cognition experiments or for other forms of canine training or games.

A Precise Dispenser Design for Canine Cognition Research

“…By using the common Raspberry Pi platform, Python-based experiment scripts can be used to control inputs and outputs and new modalities of canine interaction with the experiment can be easily added. This dispenser bridges the functionality and accuracy gap that was demonstrated in the previously developed retrofitted dispenser that we developed (Arce & Stevens 2020). The previous dispenser had an average error rate of 27%, so our presented dispenser achieves much more dependable precision with an error rate of 4%.…”

“…We built five dispensers and tested each one with a similar setup as the retrofitted dispenser (Arce & Stevens 2020). For each dispenser, we performed ten reliability trials for ten treat quantities: 1, 2, 3, …, 9, and 10 treats.…”

“…As shown in the General Testing section, this device is well-suited for canine research quite generally due to its low error rate. Given its higher precision, it can used in all of the scenarios developed for the retrofitted version (Arce & Stevens 2020) plus situations that require more precision dispensing.…”

“…While these designs may work in some situations, they can also lead to bias from human presence, introduce delays in reward timing, or limit where the studies can take place. Previously, we developed a system to automate treat dispensing by retrofitting an off-the-shelf dispenser with an integrated Raspberry Pi (Arce & Stevens 2020). Due to the precision of the off-the-shelf dispenser, this system has an error rate of 20-30%, resulting in dispensing more than one treat at a time.…”

“…Our previous work retrofitted the Treat & Train (PetSafe 2020) dispenser to provide a solid software framework for creating, delivering, and managing experimental setups (Arce & Stevens 2020). The new dispenser design needs to address the shortfalls of the retrofitted Treat & Train.…”