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.

Some forms of canine cognition research require a dispenser that can accurately dispense precise quantities of treats. When using off-the-shelf or retrofitted dispensers, there is no guarantee that a precise number of treats will be dispensed. Often, they will over-dispense treats, which may not be acceptable for some tasks. Here we describe a 3D-printed precise treat dispenser with a 59-treat capacity driven by a stepper motor drive and controlled by an integrated Raspberry Pi. The dispenser can be built for less than 200 USD and is fully 3D printable. While off-the-shelf dispensers can result in an error rate of 20-30%, the precision dispenser produces a 4% error rate. This lower error rate and the integrated Raspberry Pi allows for new possibilities for using treat dispensers across a range of canine research questions. METADATA OVERVIEWMain design files: https://github.com/unl-cchil/canine_precise_dispenser Target group: Scientists in canine cognition. Skills required: desktop 3d printing -easy; through-hole soldering -easy; hand tool use -easy Replication: No builds known to the authors so far.See section "Build Details" for more detail.

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Developed in the Virtual Reality Laboratory at the Munroe Meyer Institute, the Biosensor Framework library provides an interface for interacting with biosensors and performing affective computing tasks on their output data streams. Currently, it natively supports the Empatica E4 biosensor and communicates through a TCP connection with their provided server.This library provides the following capabilities: -Connection to the TCP client that Empatica provides for connecting their devices to a user's PC -Parsing and packing of commands to communicate bidirectionally with the TCP client -Collection and delivery of biometric readings to an end user's software application -Extraction of literature supported feature vectors to allow characterization of the biological signal features -Microsoft.ML support for inferencing on the feature vectors natively in C# in both multi-class and binary classification tasks -Support for simulating a data stream for debugging and testing applications without an Empatica E4 device -Support for parsing open source Empatica E4 datasets and training machine learning models on them (i.e., WESAD (Schmidt et al., 2018)) -Support for interfacing new sensors into the same pipeline -Contains models trained on WESAD dataset in the Microsoft.ML framework.