Abstract-Virtual agents have been investigated as an educational tool for use with children on the autistic spectrum with positive results being gained for language skills with the use of autonomous agents and social skills with humancontrolled agents. This project combines these ideas to investigate the utility of autonomous agents for teaching social skills. The virtual agent used in this project, known as the Thinking Head, has an ability to realistically portray facial expressions that lends it to this task. Two prototype modules were developed for this agent platform, one teaching basic conversation skills and the other dealing with bullying. In a pre-test-post-test evaluation, a group of children with autism who were exposed to the training modules obtained significantly higher post-test scores on their knowledge of these two topics. In addition, responses to a post-training survey indicated that participants found the virtual tutor enjoyable and useful.

No abstract

BackgroundTwo experiments investigated the effect of features of human behaviour on the quality of interaction with an Embodied Conversational Agent (ECA).MethodsIn Experiment 1, visual prominence cues (head nod, eyebrow raise) of the ECA were manipulated to explore the hypothesis that likeability of an ECA increases as a function of interpersonal mimicry. In the context of an error detection task, the ECA either mimicked or did not mimic a head nod or brow raise that humans produced to give emphasis to a word when correcting the ECA’s vocabulary. In Experiment 2, presence versus absence of facial expressions on comprehension accuracy of two computer-driven ECA monologues was investigated.ResultsIn Experiment 1, evidence for a positive relationship between ECA mimicry and lifelikeness was obtained. However, a mimicking agent did not elicit more human gestures. In Experiment 2, expressiveness was associated with greater comprehension and higher ratings of humour and engagement.ConclusionInfluences from mimicry can be explained by visual and motor simulation, and bidirectional links between similarity and liking. Cue redundancy and minimizing cognitive load are potential explanations for expressiveness aiding comprehension.Electronic supplementary materialThe online version of this article (doi:10.1186/s40469-016-0008-2) contains supplementary material, which is available to authorized users.

No abstract

EEG signals usually have a high dimensionality which makes it difficult for classifiers to learn the difference of various classes in the underlying pattern in the signal. This paper investigates several evolutionary algorithms used to reduce the dimensionality of the data. The study presents electrode and feature reduction methods based on Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Evolution-based methods are used to generate a set of indexes presenting either electrode seats or feature points that maximizes the output of a weak classifier. The results are interpreted based on the dimensionality reduction achieved, the significance of the lost accuracy, and the possibility of improving the accuracy by passing the chosen electrode/feature sets to alternative classifiers.