Research
ZootFly experimented extensively with the possibilities of reading and interpreting players' inputs and adapting the gameplay experience accordingly based on specific psychological profiles extrapolated via psychometrics. According to ZootFly, users' inputs reveal comprehensive information about them—reactions in tight situations, how they use resources, how they interact and communicate, how they deal with challenges, keypress sequences, mouse movement jerkiness, actions performed when entering a new space: i.e. whether they go in the middle of the room and look around or explore details first, the average speed of their movements, etc. Zoofly researched building psychological profiles of players using their inputs and adapting the game accordingly. For example, a game could have three distinctively different conclusions and the psychometrics engine would pick the right one for the player. The goal was to avoid unwanted endings, as the game would hopefully have enough feedback to deliver what the player wanted. The immediate reactions of the game would work on positive feedback. If the player were cerebral, they would receive more cerebral challenges; if violent, more violence. Immediate responses could improve the usability/learning curves as well. For example, if the player were to quick-save often, the game might be too difficult, and if they were to not discover many rooms in a time period, they might be lost and need additional stimuli to proceed. If they were too fast and the game was not fun any-more, they might require more interesting obstacles.
A psychometrics engine would change gameplay subtly to provide entertainment without frustration, automatically tailored to the specific player. Additionally, ZootFly experimented with mood changes induced by infra-sound. They employed barely audible sound (low frequency) effects to invoke deep and subconscious feelings. For example, when the engine interpreted a player as passive, the aural stimuli would make them more aggressive.
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