Designing a Unity Plugin to Predict Expected Affect in Games Using Biophilia
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Date
2022-09-28
Authors
Zhang, Licheng
Advisor
Hancock, Mark
Vogel, Daniel
Vogel, Daniel
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
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 net 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.
Description
Keywords
affective computing, video games, machine learning