Modeling Player Personality Factors from In-Game Behavior and Affective Expression

TL;DR

This study investigates predicting player personality traits from in-game behavior and affective expressions using machine learning, aiming to enhance personalized gaming experiences and reduce reliance on lengthy questionnaires.

Contribution

It introduces a method to predict multiple personality metrics from gameplay and affective data, extending prior work by incorporating affective dialog decisions and using random forest regression.

Findings

Some personality traits can be predicted from in-game actions and affective expressions.

Not all traits are predictable with current data and methods.

Further research with larger datasets and advanced models is planned.

Abstract

Developing a thorough understanding of the target audience (and/or single individuals) is a key factor for success - which is exceptionally important and powerful for the domain of video games that can not only benefit from informed decision making during development, but ideally even tailor game content, difficulty and player experience while playing. The granular assessment of individual personality and differences across players is a particularly difficult endeavor, given the highly variant human nature, disagreement in psychological background models and because of the effortful data collection that most often builds upon long, time-consuming and deterrent questionnaires. In this work, we explore possibilities to predict a series of player personality questionnaire metrics from recorded in-game behavior and extend related work by explicitly adding affective dialog decisions to the game environment which could elevate the model's accuracy. Using random forest regression, we predicted a wide variety of personality metrics from seven established questionnaires across 62 players over 60 minute gameplay of a customized version of the role-playing game Fallout: New Vegas. While some personality variables could already be identified from reasonable underlying in-game actions and affective expressions, we did not find ways to predict others or encountered questionable correlations that could not be justified by theoretical background literature. Yet, building on the initial opportunities of this explorative study, we are striving to massively enlarge our data set to players from an ecologically valid industrial game environment and investigate the performance of more sophisticated machine learning approaches.