Barreres i oportunitats de les cobertes mosaic

Annex 1: ResumAnnex 2: Manifest per superar les barreres i potenciar les oportunitats de les cobertes mosai

Video game interfaces and diegesis: The impact on experts and novices’ performance and experience in virtual reality

International audienceWhen playing action video games, an optimal experience implies the presence of a head-up display that informs players on their status in regard of their goal like health points or their localisation in the environment. However, how can this type of information can be integrated in new gaming contexts like virtual reality? Should this information be integrated into the game universe (diegetic design) or stay out of it (non-diegetic design)? For this purpose, the performance, presence and enjoyment of 41 players have been measured during a virtual reality first-person shooter game session with a diegetic and a non-diegetic interface. The results showed that diegetic integration has a positive effect on the player’s performance but not on the subjective experience (presence and enjoyment). The study also shed light on the moderator role of expertise in action games on this effect because the diegetic interface only benefited novice players

Generalized RAICAR: Discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks

Existing spatial independent component analysis (ICA) methods for multi-subject fMRI datasets have mainly focused on detecting common components across subjects, under the assumption that all the subjects in a group share the same (identical) components. However, as a data-driven approach, ICA could potentially serve as an exploratory tool at multi-subject level, and help us uncover inter-subject differences in patterns of connectivity (e.g., find subtypes in patient populations). In this work, we propose a methodology named gRAICAR that exploits the data-driven nature of ICA to allow discovery of sub-groupings of subjects based on reproducibility of their ICA components. This technique allows us not only to find highly reproducible common components across subjects but also to explore (without a priori subject groupings) components that could classify all subjects into sub-groups. gRAICAR generalizes the reproducibility framework previously developed for single subjects (Ranking and averaging independent component analysis by reproducibility-RAICAR-Yang et al., Hum Brain Mapp, 2008) to multiple-subject analysis. For each group-level component, gRAICAR generates its reproducibility matrix and further computes two metrics, inter-subject consistency and intra-subject reliability, to characterize inter-subject variability and reflect contributions from individual subjects. Nonparametric tests are employed to examine the significance of both the inter-subject consistency and the separation of subject groups reflected in the component. Our validations based on simulated and experimental resting-state fMRI datasets demonstrated the advantage of gRAICAR in extracting features reflecting potential subject groupings. It may facilitate discovery of the underlying brain functional networks with substantial potential to inform our understandings of development, neurodegenerative conditions, and psychiatric disorders. (C) 2012 Elsevier Inc. All rights reserved