Several studies have indicated the need for personalizing gamified systems to users' personalities. However, mapping user personality onto design elements is difficult. Hexad is a gamification user types model that attempts this mapping but lacks a standard procedure to assess user preferences. Therefore, we created a 24-items survey response scale to score users' preferences towards the six different motivations in the Hexad framework. We used internal and testretest reliability analysis, as well as factor analysis, to validate this new scale. Further analysis revealed significant associations of the Hexad user types with the Big Five personality traits. In addition, a correlation analysis confirmed the framework's validity as a measure of user preference towards different game design elements. This scale instrument contributes to games user research because it enables accurate measures of user preference in gamification.

Player preferences for different gaming styles or game elements has been a topic of interest in human-computer interaction for over a decade. However, current models suggested by the extant literature are generally based on classifying abstract gaming motivations or player archetypes. These concepts do not directly map onto the building blocks of games, taking away from the utility of the findings. To address this issue, we propose a conceptual framework of player preferences based on two dimensions: game elements and game playing styles. To investigate these two concepts, we conducted an exploratory empirical investigation of player preferences, which allowed us to create a taxonomy of nine groups of game elements and five groups of game playing s tyles. These two concepts are foundational to games, which means that our model can be used by designers to create games that are tailored to their target audience. In addition, we demonstrate that there are significant effects of gender and age on participants' preferences and discuss the implications of these findings.

Typologies for understanding players' preferences towards different gameplay styles have gained popularity in research. However, attempts to model players' preferences are based on type models instead of trait models, contrary to the latest personality research. One such model, BrainHex, was designed as an interim model to enable investigations towards a definitive player trait model. However, it lacks empirical validation in support of its psychometric properties. The present work analysed a dataset with over 50,000 respondents to devise a player traits model based off the BrainHex scale. Results indicate three player traits: action, esthetic, and goal orientation. Furthermore, we analysed the games listed by participants as examples of what they enjoy, to understand which factors influence player preferences. Results illustrate that the emergent player traits and participants' genders and attitudes towards story can partially explain player preferences towards certain games. Finally, we present the implications towards a definitive player traits model.

People often learn game-related information in video games by taking turns playing and watching each other play. This type of in-game learning involves both observation and imitation of actions. However, games are also made to be learnt individually during gameplay. Our study seeks to assess which is more effective for learning: just playing a game yourself or watching somebody play it first. We compare two gameplay situations: playing a digital game before watching a gameplay video and playing a digital game after watching a gameplay video. Using a between-participants design, to measure learning effectiveness we recorded Mu rhythms, which are indirectly linked to mirror neuron activation during imitation learning. We also analyze hemispheric frontal alpha asymmetry. Our results indicate that presentation order of the video game matters and players are more aroused when watching a gameplay video before playing.

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