Dimensionality reduction is defined as the search for a low-dimensional space that captures the “essence” of the original high-dimensional data. Principal Component Analysis (PCA) is one of the most used dimensionality reduction technique in psychology and behavioral sciences for data analysis and measure development. However, PCA can capture linear correlations between variables, but fails when this assumption is violated. In recent years, a variety of nonlinear dimensionality reduction techniques have been proposed in other research fields to overcome this limitation. In this paper, we focus on non-linear autoencoder, a multi-layer perceptron, with as many inputs as outputs and a smaller number of hidden nodes. We investigate the relation between the intrinsic dimensionality of data and the autoencoder’s internal nodes in a simulation study, comparing autoencoders and PCA performances in term of reconstruction error. The evidence from this study suggests that autoencoder’s ability in dimensionality reduction is very similar to PCA, and that there is a relation between internal nodes and data dimensionality.
The growing interest in Human-Robot Interaction (HRI) based early intervention therapies for autism spectrum disorder (ASD) children brings about the prospect of adapting this platform as assistive tool during therapy. Though this field has garnered myriad of attention, more research is needed in developing systematic design for human-robot interaction using standardized process models. One of the most well-established systematic process models in developing instructions is the Analysis, Design, Develop, Implement and Evaluate (ADDIE) model. While the ADDIE model was used to design the interaction during the first phase, Applied Behavioral Analysis (ABA) technique, an evidence-based practice for ASD children were also integrated into our modified model. This preliminary study discusses three subjects: 1) guideline in adapting ADDIE model for HRI-based ASD intervention, 2) a preliminary modified ADDIE model for human-robot interaction with ASD children and 3) robot’s behavior integrated with ABA technique. Preliminary evaluations on the developed framework were done by a therapist with experience working with ASD children on the severe end of the spectrum.
Nowadays, virtual technology with embedded virtual agents is increasingly present in everyday life. Therefore, understanding the characteristics of psychological experience in social interaction with virtual agents can be useful for theoretical and application purposes. Here, we aim to understand whether individual differences in empathy can influence social interaction with virtual agents. To this end, we designed a correlational study comparing individual propensity towards empathic traits and the ability to take the perspective of a virtual agent (VA) to understand whether and how they are associated. In an Immersive Virtual Reality (IVR) scenario, participants had to locate a glass according to the perspective of a virtual agent. They were seated behind a circular virtual table around which, in various positions closer and further away, VAs with a glass placed in front of them could appear. Participants had to decide whether the glass was to the right or left of the VA’s body midline. The results showed an association between some components of empathy and localization time: the higher the tendency to identify with a fictional character, the faster the participants were to locate the glass in all positions of the virtual agents around the table. Likewise, the higher the tendency to experience feelings of empathy, the faster they were in locating only when the VA was close to the observer. These preliminary results suggest that individual differences in empathy and the location of virtual agents help define how people experience virtual social interactions.
In the framework of the EULALIA project (Enhancing University Language courses with an App powered by game-based Learning and tangible user Inter-faces Digital Creativity Enhanced in Teacher education) the authors develop a blended embodied approach to cultural competence education by using scenario based and multisensorial methodologies applying Tangible User Interfaces (TUIs) and Mobile Learning. Combining virtual and multisensorial physical dimensions thanks to the application TUIs and VR elements, situational affordances are distributed across a network of Learning Objects (Smart) storing a variety of digital contents. Exploring a physical map the user experiment learning scenarios constructing personal meanings, self-directing and serf regulating their learning path. In this view, the students and teachers are able to creatively, and collaboratively design Open Educational Resource (OER), co-creating their Language Learning Strategy (LLS), in the form of a game scenario. The paper explores the potential of OERs co-design approach in the in the educational context presenting the EULALIA workshops experience.
Legal Design is inspired by the concepts of Design Thinking and User Experience: the ultimate goal is to make citizens more aware with well-designed rules and procedures in terms of ‘Proactive law’, providing policies that are truly user-centered, tailored to the cognitive needs both expressed and hidden through continuous and transparent communication. Talking about privacy, there is a clear need, starting from the analysis of the texts of some documents (e.g.‘information on the use of personal data’ and ‘consent’), to translate ‘bureaucratic’ requests into simpler questions, as to allow the more intuitive, usable and inclusive user experience. So, in the wake of the GDPR view too, we have been experimenting how the advantages of communicating and involving the citizens by design and by default can prove to be the right key so that the privacy documents they encounter in the everyday life can finally transmit transparency and a sense of control of the situation that are both much more substantial and immediately perceptible. The future developments of the project will be focused on enhancing access to safeguards for digital privacy, as well as citizens’ awareness of their digital rights, a fortiori for the weakest subjects, with a chatbot providing personalized educational/assistance services. In the final stage I will be concepting a prototype of such an educational chatbot ranging between rights to be deployed, user experience to be exalted and training effectiveness to be ensured.
