Modeling Player-character Engagement in Single-player Character-driven Games

Petri Lankoski

In ACE 2013 Netherlands, pp. 572-575.  Copyright Springer 2013. This is author’s version. The definitive version DOI: 10.1007/978-3-319-03161-3_56.

Abstract

This pilot study looks at how the formal features of character-driven games can be used to explain player-character engagement. Questionnaire data (N=206), formal game features (in 11 games), and ordinal regression were used in the analysis. The results show that interactive dialogue and cut-scenes showing the romances between the player-character and another character relates to higher character engagement scores, while romance modeling and friendship modeling relate to lower character engagement scores.

Keywords: ordinal regression, player-character, engagement, identification

Continue reading “Modeling Player-character Engagement in Single-player Character-driven Games”

Classical game now at Internet Achieve

Today, the Internet Archive announces the Historical Software Archive, a collection of prominent and historically notable pieces of software, able to be run immediately in your browser.  They range from pioneering applications to obscure forgotten utilities, and from peak-of-perfection designs to industry-crashing classics. (http://blog.archive.org/2013/10/25/microcomputer-software-lives-again-this-time-in-your-browser/)

And the direct link to the archive: https://archive.org/details/historicalsoftware

 

 

R^2 for Generalized Mixed Models

Note for myself. Check this:

Nakagawa, S. & Schielzeth, H., 2013, A general and simple method for obtaining R2 from generalized linear mixed-effects models.  Methods in Ecology and Evolution, 4: 2, pp 133–142, DOI: 10.1111/j.2041-210x.2012.00261.x

EDIT 20131027:

R implementation is available: http://jslefche.wordpress.com/2013/03/13/r2-for-linear-mixed-effects-models/ 

Also MuMIn package contains implementation (http://cran.r-project.org/web/packages/MuMIn/index.html)

Facial expressions of (some) emotions are inborn?

Couple of staring points for reading (beyond Paul Ekman’s publications):

  • Peleg et al, 2006, Hereditary family signature of facial expression. PNAS, 103: 43. DOI: 10.1073/pnas.0607551103 
  • Matsumoto &  Willingham, 2009, Spontaneous facial expressions of emotion of congenitally and noncongenitally blind individuals. Journal of Personality and Social Psychology, 96: 1. DOIi: 10.1037/a0014037

 

 

Models for Story Consistency and Interestingness in Single-Player RPGs

Petri Lankoski

Published in Academic MindTrek 2013

(c) Petri Lankoski 2013. This is the author’s version of the work. It is posted here for your own personal use. Not for redistribution. The definitive version was published in Academic MindTrek 2013. http://dx.doi.org/ [LINK TO BE ADDED]

ABSTRACT

What are the elements that aect story interestingness or consistency in single-player videogames? The question is approached by comparing player evaluations (N=206) of 11 videogames against a set of features derived by formal (qualitative) analysis. Ordinal regression was used to analyze the collected data. The study posits that dialogue system, romance, moral choice, appearance customization, and support for dierent play styles relate to story evaluation. Females tend to judge game stories more favorably and those with doctoral degree less favorably than players with other education.

Categories and Subject Descriptors K.8.4 [Personal Computing]: General|Games

General Terms: Experimentation

Keywords: ordinal regression, games, storytelling, story consistency, story interestingness

Continue reading “Models for Story Consistency and Interestingness in Single-Player RPGs”

My MindTrek2013 presentation on game story

My MindTrek 2013 presentation: http://www.slideshare.net/slideshow/embed_code/26820610

The presentation relates to my paper

Model–data comparison

I wrote some code to check my ordinal / clmm models against the data (and to learn to use ggplo2).

The function pred() is from clmm tutorial to calculate predictions based on the model. The function plot.probabilities3() is for plotting prediction and distribution form the data.

Update: changed extreme subject visualization.  Area seemed not appropriate when average player is not always inside the area.

Continue reading “Model–data comparison”