Master’s Thesis Presentation • Human-Computer Interaction • Designing a Unity Plugin to Predict Expected Affect in Games Using Biophilia

Please note: This master’s thesis presentation will be given online.

Licheng Zhang, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Mark Hancock

Video games can generate different emotional states and affective reactions, but it can sometimes be difficult for a game’s visual designer to predict the emotional response a player might experience when designing a game or game scene.

In this thesis, I conducted a study to collect emotional responses to video game images. I then used that data to both confirm past research that suggests images can be used to predict affect and to build a model for predicting emotion that is specific to games. I built both a linear regression model and three neural network models to predict affective response and found that the neural network that leveraged ResNet-50 was most effective. I then incorporated that model into a Unity plug-in so that designers can use it to predict affect of players in real-time.

To join this master’s thesis presentation on Zoom, please go to https://uwaterloo.zoom.us/j/98450061668?pwd=MzN0eVB1L0NpTUJyNGZiUGdhaVhOdz09.