Research on financial decision-making shows that traders and investors with high emotion regulation capabilities perform better in trading. But how can the others learn to regulate their emotions? ‘Learning by doing’ sounds like a straightforward approach. But how can one perform ‘learning by doing’ when there is no feedback? This problem particularly applies to learning emotion regulation, because learners can get practically no feedback on their level of emotion regulation. Our research aims at providing a learning environment that can help decision-makers to improve their emotion regulation. The approach is based on a serious game with real-time biofeedback. The game is settled in a financial context and the decision scenario is directly linked to the individual biofeedback of the learner’s heart rate data. More specifically, depending on the learner’s ability to regulate emotions, the decision scenario of the game continuously adjusts and thereby becomes more (or less) difficult. The learner wears an electrocardiogram sensor that transfers the data via Bluetooth to the game. The game itself is evaluated at several levels.

This study investigates individuals’ cognitive load processing abilities while engaged on a decision-making task in serious games, to explore how a substantial cognitive load dominates over the physiological arousal effect on pupil diameter. A serious game was presented to the participants, which displayed the on–line biofeedback based on physiological measurements of arousal. In such dynamic decision-making environment, the pupil diameter was analyzed in relation to the heart rate, to evaluate if the former could be a useful measure of cognitive abilities of individuals. As pupil might reflect both cognitive activity and physiological arousal, the pupillary response will show an arousal effect only when the cognitive demands of the situation are minimal. Evidence shows that in a situation where a substantial level of cognitive activity is required, only that activity will be observable on the pupil diameter, dominating over the physiological arousal effect indicated by the pupillary response. It is suggested that it might be possible to design serious games tailored to the cognitive abilities of an individual player, using the proposed physiological measurements to observe the moment when such dominance occurs. © 2018, The Author(s).

The distinction between implicit and unselfconscious design cultures on one hand and explicit, self-conscious design cultures on the other provides a principle for interrelating a variety of game design approaches within a coherent game design meta-model. The design approaches in order of increasing design self-consciousness include implicit design, ‘cookbook’ design methods, taxonomy and ontology-based game design, theory-driven design and formalist reflexive design. Implicit design proceeds by copying existing examples of game designs, while ‘cookbook’ methods generalize from examples to create lists of design heuristics. Taxonomy and ontology-based game design is based upon more systematic models of the types, features, elements, structure and properties of games. The theory-driven level involves the design of game systems to facilitate game play motivated by cognitive, scientific and/or rhetorical theories of game affect and functionality, or incorporating technical innovations providing the basis for new game mechanics and experiences. The formalist level represents the application of reflexive contemporary artistic perspectives to games, resulting in games that reflect upon, question or reveal game form. In placing these different approaches within a hierarchy of increasing self-consciousness of design practices, the meta-model provides a clear account of the roles of research and artistic methods in game design and innovation, providing a foundation for more explicit design decision making and game education curriculum development integrated with higher-level research.

Schema theory provides a foundation for the analysis of game play patterns created by players during their interaction with a game. Schema models derived from the analysis of play provide a rich explanatory framework for the cognitive processes underlying game play, as well as detailed hypotheses for the hierarchical structure of pleasures and rewards motivating players. Game engagement is accounted for as a process of schema selection or development, while immersion is explained in terms of levels of attentional demand in schema execution. However, schemas may not only be used to describe play, but might be used actively as cognitive models within a game engine. Predesigned schema models are knowledge representations constituting anticipated or desired learned cognitive outcomes of play. Automated analysis of player schemas and comparison with predesigned target schemas can provide a foundation for a game engine adapting or tuning game mechanics to achieve specific effects of engagement, immersion, and cognitive skill acquisition by players. Hence, schema models may enhance the play experience as well as provide a foundation for achieving explicitly represented pedagogical or therapeutic functions of games. This paper has described an approach to the analysis of game play based upon schema theory and attention theory. An empirically basedmethod has been described as a basis for identifying and validating hypothetical game play schemas. Automated schema recognition and the potential uses of explicit schema representations within game systems have been explored. This approach provides for explicit modeling of the C. A. Lindley and C. C. Sennersten 7 cognitive systems and processes underlying game play, both for analytical studies of play and as a potential implementation mechanism for adaptive games. Work on the analysis of games using this approach is ongoing. It is hoped that the results of this work will provide the foundations for future implementation of schema-based adaptive game systems.