TY - GEN
T1 - Play style
T2 - 2013 IEEE Conference on Computational Intelligence in Games, CIG 2013
AU - Tekofsky, Shoshannah
AU - Spronck, Pieter
AU - Plaat, Aske
AU - Van Den Herik, Jaap
AU - Broersen, Jan
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Age has been shown to influence our preferences, choices, and cognitive performance. We expect this influence to be visible in the play style of an individual. Player models would then benefit from incorporating age, allowing developers to offer an increasingly personalized game experience to the player. To investigate the relationship between age and play style, we set out to determine how much of the variance in a player's age can be explained by his play style. For this purpose, we used the data from a survey ('PsyOps') among 13,376 'Battlefield 3' players. Starting out with 60 play style variables, we found that 45.7% of the variance in age can be explained by 46 play style variables. Furthermore, similar percentages of variance in age are explained when the sample is divided along gaming platform: 31 play style variables explain 43.1% on PC; 30 play style variables explain 53.9% on Xbox 360; 28 play style variables explain 51.7% on Playstation 3. Our findings have a high external validity due to the large and heterogeneous nature of the sample. The strength of the relationship between age and play style is considered 'large' according to Cohen's classification. Previous research indicates that the nature of the relationship between age and play style is likely to be based on life-time developments in cognitive performance, motivation, and personality. All in all, our findings merit a recommendation to incorporate age in future player models.
AB - Age has been shown to influence our preferences, choices, and cognitive performance. We expect this influence to be visible in the play style of an individual. Player models would then benefit from incorporating age, allowing developers to offer an increasingly personalized game experience to the player. To investigate the relationship between age and play style, we set out to determine how much of the variance in a player's age can be explained by his play style. For this purpose, we used the data from a survey ('PsyOps') among 13,376 'Battlefield 3' players. Starting out with 60 play style variables, we found that 45.7% of the variance in age can be explained by 46 play style variables. Furthermore, similar percentages of variance in age are explained when the sample is divided along gaming platform: 31 play style variables explain 43.1% on PC; 30 play style variables explain 53.9% on Xbox 360; 28 play style variables explain 51.7% on Playstation 3. Our findings have a high external validity due to the large and heterogeneous nature of the sample. The strength of the relationship between age and play style is considered 'large' according to Cohen's classification. Previous research indicates that the nature of the relationship between age and play style is likely to be based on life-time developments in cognitive performance, motivation, and personality. All in all, our findings merit a recommendation to incorporate age in future player models.
UR - http://www.scopus.com/inward/record.url?scp=84892427769&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84892427769&partnerID=8YFLogxK
U2 - 10.1109/CIG.2013.6633616
DO - 10.1109/CIG.2013.6633616
M3 - Conference contribution
AN - SCOPUS:84892427769
SN - 9781467353113
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - 2013 IEEE Conference on Computational Intelligence in Games, CIG 2013
Y2 - 11 August 2013 through 13 August 2013
ER -