Improve gaming experience by using psychology
A game, called 'Perfection', came to my attention while checking the latest videos from one of my favorite YouTubers and started a chain of thoughts in my head.
'Perfection' is gathering behavioral data from the players, like their choices, in-game interactions and answers to specific questions. Then, the game is building upon this data and influencing the game experience. Instead of a linear or a branched playing experience, the game itself accommodates by changing the environment, music, and characters to provide a more immersive and personalized experience. The aim is to tap into the players' personalities and fears to give them a captivatingly fearful experience. In the end, the players are presented with a report on how they were evaluated by the game, allowing them to comprehend their experience.
This idea might seem fresh as Perfection was published in 2018 Autumn. However, this isn't the first time that a game is using behavioral data for a personalized experience. In 2009, Konami released the game ‘Silent Hill: Shattered Memories’, starting with the following alert:
This is a seriously scary warning which is not just there for the sake of being scary. The game actually does as it says and more. It will not only personally change the gaming experience, but will also present the players with a psychological profile based on the clusters of actions and answers they have given. Apart from this, in 2015, Sony Computer Entertainment has also released a game, 'Until Dawn', which collects answers from some specific questions to be able to tap into personal fears and improve the gaming experience.
There seems to be a trend in video games, particularly horror games, which is about exploring and exploiting the knowledge about the player, in order to give them a better and more challenging experience. By nature, games are n-dimensional, meaning that each gaming experience is unique, each decision and action are different and have different meanings for different players (Díaz, Dorner, Hussmann, & Strijbos, 2016); and this new trend is exceptionally good at exploiting that.
The question is, how can designers and developers get a glimpse of our fears out some of a few behaviors and some answers to specific questions? There are two techniques that allow them to do it: Stealth Assessment and Player Profiling.
Stealth assessment is a technique that allows collecting data from certain meaningful actions (for example, if players look at naughty ads, or if they close the drawers after they open them in the game), and to create assessment without the users knowing they are being evaluated (Shute & Ventura, 2013). But making a game based only on stealth assessment would be infinite (remember, games are n-dimensional). This is where Player profiling comes in.
Player profiling means generating a profile for players based on how different player behaviors are grouped. For example, if certain choices are frequently made (Do you drink every beer bottle you find in the game?) or certain actions are preferred over others (Do you save the people you find in trouble or you ignore them and save yourself?). Different groups of actions generate a different player profile, allowing the developers to make scalable personalized experiences (Bartle, 1996; Drachen & Canossa, 2009).
Using these techniques for improving gaming experience is amazing. Imagine if every game could read us and give us options and challenges based on us liking stealth over combat, or action over the story! And there’s even more to it.
These techniques have been used before by scientists such as Shute (2015), embedding these on commercial games such as ‘Crayon Physics Deluxe’ or ‘Plants vs Zombies’ for learning about the way people solve problems, make plans, and even develop their creativity.
These are the type of techniques we are using to develop Skill Lab: Science Detective. While you play, we are collecting data on how you solve different problems. Each game is related to a different type of problem, and each type of problem to a different cognitive skill. Knowing how people solve different problems help us understand how cognitive skills are used and develop, and how they interact when they are being used. It also allows us to discover which groups of people are better at solving specific types of problems and which types of games people like to play in Citizen Science research.
The potential for both science and game design is huge! Scientifically speaking, it means that cognitive scientists, psychologists, and anthropologists can learn more about how people interact with their environment, make decisions, and learn from interactive experiences (Steinkuehler & Squire, 2009). For game design, it means better gaming experience tailored to specific player personalities and gaming styles.
We should probably expect more to see this technique more in the future, and embrace both as an opportunity to improve our gaming experiences and as a way to learn more about the human mind and our society. However, it also opens an ethical discussion on how much do we want gaming companies to know about us and to build and use psychological profiles based on our gaming behavior. But this is a discussion for another day!
- Bartle, R. (1996). Hearts, clubs, diamonds, spades: players who suit MUDs. Retrieved 23 September 2013, from http://www.mud.co.uk/richard/hcds.htm
- Díaz, C. M., Dorner, B., Hussmann, H., & Strijbos, J.-W. (2016). Eppur si Muove. Considerations in the Research of Commercial Video Games. In Proceedings of the 8th International Conference on Games and Virtual Worlds for Serious Applications (VS-Games) (pp. 1–4). Barcelona, Spain: IEEE. http://doi.ieeecomputersociety.org/10.1109/VS-GAMES.2016.7590346
- Drachen, A., & Canossa, A. (2009). Player modeling using self-organization in Tomb Raider: Underworld. In IEEE Symposium on Computational Intelligence and Games, 2009. Italy.
- Shute, V., & Ventura, M. (2013). Stealth Assessment. Measuring and Supporting Learning in Video Games. England: MIT Press.
- Shute, V., Moore, G., & Wang, L. (2015). Measuring Problem Solving Skills in Plants vs. Zombies 2. In Proceedings of the 8th International Conference on Educational Data Mining (pp. 428–431). Spain: ERIC.
- Steinkuehler, C., & Squire, K. (2009). Virtual worlds and learning. On the Horizon, 17(1), 8–11. https://doi.org/10.1108/10748120910936108