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Playing With Neuroscience: Past, Present and Future of Neuroimaging and Games
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1st Paolo BurelliDigital Design Department - brAIn labIT University of CopenhagenCopenhagen, Denmarkpabu@itu.dk2nd Laurits DixenDigital Design Department - brAIn labIT University of CopenhagenCopenhagen, Denmarkldix@itu.dkAbstractVideogames have been a catalyst for advances in many research fields, such as artificial intelligence, human-computer interaction, or virtual reality. Over the years, research in fields such as artificial intelligence has enabled the design of new types of games, while games have often served as a powerful tool for testing and simulation. Can this also happen with neuroscience? What is the current relationship betweenneuroscience and games research? what can we expect from the future? In this article, we’ll try to answer these questions, analysing the current state-of-the-art at the crossroads between neuroscience and games and envisioning future directions. Index Terms—EEG, fMRI, games, BCI, Player Experience, UX

This entails keeping track of as many game events, player inputs, visual, and auditory stimuli as possible, making the gameplay experience ideally fully reconstructable. Similarly, raw neuroimaging data should also be conserved and available. While feature extraction and preprocessing are common strategies for analysis, other researchers might be able to make different strategies work. Raw data is the format which is maximally flexible for repurposing datasets. Although, here it must be mentioned that explicit and informed consent must be gathered from subjects to publish and use their data for scientific purposes. We expand on this point in a later section. Some early attempts have been made to produce such datasets ; however, we believe that more is needed. We recommend following the Brain Imaging Data Structure (BIDS) . Lastly, annotating and documenting the dataset
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Events should be clearly labelled, and recommended preprocessing, feature extraction and analysis pipelines should be clearly described. This would allow other researchers to build on existing work more easily. Data is a valuable resource and should be shared openly, making non-experts able to expand on its use.
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C. Ecological Validity Games have the potential to greatly improve the protocols used in cognitive neuroscience, both to study cognitive phenomena within games and phenomena that potentially translate beyond games and are of interest to neuroscientists, psychologists, social scientists and other researchers using psychological experiments. To ensure the reliability, validity, and allow the researchers to reasonably look for patterns in the data, the standard experimental procedures in neuroscience require the participant to engage in mostly simple tasks and stimuli, repeated multiple times, often with minimal variations. This is done with the purpose of isolating the phenomenon of interest, making sure the differences between conditions can be isolated in the recorded data.
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However, there are three major drawbacks of this approach: the rigidity of the experiment makes the setting artificial reducing ecological validity and, therefore, the ability to generalise results to the real world, the static nature of the experiment makes an assumption of a constant stereotypical behaviour to each repeated stimulus, missing out on potential complexity in how contexts, environments and duration might affect the cognitive processes. the experiment is potentially very repetitive and the subject cannot sustain engagement in the task for longer periods of time, risking poor data quality and limiting the length of recording sessions. We argue that games have the potential to solve all three drawbacks and, if implemented correctly, while still keeping the desired properties of the original design.
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