Possible applications of robots are growing in educational contexts, where they can support and enhance the traditional learning at any level, including kindergarten. However, the acceptance of such novel technology among the kids is not fully understood, especially for the youngest ones. In this abstract, we present an experiment that investigates the attitude of 52 pre-schooler children before and after the interaction with a humanoid robot in kindergarten setting. The main hypothesis is that ideas and prejudices can change after a controlled interaction with a physical robot. The study found that children exposed to the robot decrease their distress and positively change their attitude toward the technological device. The results suggest that an early, controlled exposure may facilitate future acceptance.

Attention has been shown to be a predictor of flight performance and, therefore, it is necessary to assess this cognitive ability to evaluate candidate aviation pilots and to verify if the pilot has the sufficient attention level required for the flight duties. In this paper, we present a study that uses a Neuro-fuzzy approach to identify a benchmark model of the aviation pilot attention level. The model is learned from the data examples collected using a computerized battery of seven tests, which was specifically built and validated to assess the main cognitive factors related with the aviation pilot attention in realistic scenarios. Data examples were collected in experimental session with a total of 180 participants, 96 military aviation pilots and 84 untrained people as controls. The aim is to build the model as a classifier that is able to discriminate between the two groups, allowing to identify the peculiar profile of the aviation pilot as opposed to control subjects. Classification performance analysis shows that a hierarchical fuzzy system has a better accuracy than single stage classification algorithms and gives more details about the different attention factors. Moreover, a fuzzy system for our model because it can be readable by human instructors and used as guidance for the training. The ultimate objective of our work is an expert system that will be able to assess the attention performance and compare it against the typical profile of the aviation pilot. This will be used as an instrument for more accurate selection and training of aviation pilots by identifying the areas of deficit that need to be improved, and to measure if a pilot has a sufficient level of attention before the flight.

Research in robotics has made available numerous possibilities and tools for further innovation in the psychological practice. For instance, recent research provided many examples of possible applications of robots in the education and rehabilitation of people with learning difficulties and/or intellectual disabilities. In this paper, we present a study on how cultural backgrounds can influence the perception and intention to use a robot as an instrument in the future practice. The study involved 37 Italian students and 37 UK students, as future professionals in the field of psychology, which experienced the actual capabilities of a humanoid robot through a live demo. In this work, we explored the main factors of the Unified Theory of Acceptance and Use of Technology (UTAUT) with the aim to reveal cultural differences. The instrument used was the UTAUT questionnaire, which was designed and validated to investigate the robot acceptance and use. A significant difference on the intention to use the robot is reported in our results. The discriminant analysis produced a very high degree of separation between the two groups, confirming that there is a different approach toward the use of robotics between the two cultures.

Autism Spectrum Disorder (ASD) is a condition in which deficits in social interaction and social communication can make everyday life difficult. The use of mechanical and electronic devices has proven to be effective in ASD therapy and recently Socially Assistive Robotics (SAR) research has suggested that robots are promising tools for the treatment of this disorder. Starting from these findings, our on-going research aims to identify effective modalities for treatment of ASD through interaction with a robot, and to integrate them into existing therapeutic protocols to improve their efficacy. In this paper we present some preliminary findings of our current work towards this objective. We detail the methodology and give the results of a pilot clinical trial, focused on imitation skills, with three children affected by ASD and Intellectual Disability (ID) under treatment in a research centre specialized in the care of children with disabilities. The success of the experiment suggests that the robot can be effectively integrated in the ASD therapies currently used in the centre. Analysis of these initial results encourages the development of effective protocols in which the robot becomes a mediator between the child with ASD and humans and suggests some research avenues for focus in the future.

Among the artificial intelligence techniques that successfully support computer assisted decision making, fuzzy logic has proved to be a powerful tool in various fields. In particular it is appreciated by clinical practitioners because of their approaches to take a decision require to deal with uncertainties and vagueness in the knowledge and information. One field in which fuzzy sets theory can be applied with great benefit is psychopathology due to the high prominence of sources of uncertainty, that should be taken into account when the diagnosis of intellectual disability must be formulated. Therefore clinical psychologists have often to deal with comorbidities that make the decision process harder because they must evaluate different assessment tools for a correct diagnosis. In our work we investigate the application of computational intelligence methods, and in particular of approaches based on fuzzy logic and its hybridizations, in the psychological assessment by means of theoretical studies and practical experiments with data collected from patients affected by different levels of intellectual disability. In this paper we present a detailed review of the experimental application, with patients under treatment in a clinical centre, of methodologies we propose to generate fuzzy expert systems for the assessment of intellectual disability. Specifically we highlight, with numerical results, how they can be beneficial for the diagnosis and improve efficacy of the administration of psycho-diagnostic instruments and the efficiency of the assessment.

Thanks to recent technological advances and the increasing interest towards the Cognitive Developmental Robotics (CDR) paradigm, many popular platforms for scientific research have been designed in order to resemble the shape of the human body. The motivation behind this strongly humanoid design is the embodied cognition hypothesis, which affirms that all aspects of cognition are shaped by aspects of the body. Thus CDR is based on a synthetic approach that aims to provide new understanding on how human beings develop their higher cognitive functions. Following this paradigm we have developed an artificial model, based on artificial neural networks, to explore finger counting and the association of number words (or tags) to the fingers, as bootstrapping for the representation of numbers in the humanoid robot iCub. In this paper, we detail experiments of our model with the iCub robotic platform. Results of the number learning with propri-oceptive data from the real platform are reported and compared with the ones obtained instead, with the simulated platform. These results support the thesis that learning the number words in sequence, along with finger configurations helps the building of the initial representation of number in the robot. Moreover, the comparison between the real and simulated iCub gives insights on the use of these platforms as a tool for CDR.

Understanding the tight relationship that exists between mental imagery and motor activities (i.e. how images in the mind can influence movements and motor skills) has become a topic of interest and is of particular importance in domains in which improving those skills is crucial for obtaining better performance, such as in sports and rehabilitation. In this paper, using an embodied cognition approach and a cognitive robotics platform, we introduce initial results of an ongoing study that explores the impact linguistic stimuli could have in processes of mental imagery practice and subsequent motor execution and performance. Results are presented to show that the robot used, is able to “imagine” or “mentally” recall and accurately execute movements learned in previous training phases, strictly on the basis of the verbal commands issued. Further tests show that data obtained with “imagination” could be used to simulate “mental training” processes such as those that have been employed with human subjects in sports training, in order to enhance precision in the performance of new tasks, through the association of different verbal commands.

Understanding the process behind the human ability of creating mental images of events and experiences is a still crucial issue for psychologists. Mental imagery may be considered a multimodal biological simulation that activates the same, or very similar, sensorial and motor modalities that are activated when we interact with the environment in real time. Neuro-psychological studies show that neural mechanisms underlying real-time visual perception and mental visualization are the same when a task is mentally recalled. Nevertheless, the neural mechanisms involved in the active elaboration of mental images might be different from those involved in passive elaborations. The enhancement of this active and creative imagery is the aim of most psychological and educational processes, although, more empirical effort is needed in order to understand the mechanisms and the role of active mental imagery in human cognition. In this work we present some results of on ongoing investigation about mental imagery using cognitive robotics. Here we focus on the capability to estimate, from proprioceptive and visual information, the position into a soccer field when the robot acquires the goal. Results of simulation with the iCub platform are given to show that the computational model is able to efficiently estimate the robot's position. The final objective of our work is to replicate with a cognitive robotics model the mental imagery when it is used during the training phase of athletes that are allowed to imaginary practice to score a goal.

The estimation of a person's intelligence quotient (IQ) by means of psychometric tests is indispensable in order to determine possible mental retardation or intellectual disability based on the most common classification systems. With some subjects, however, it is not possible to use more complex tools such as the Wechsler scales, which are universally recognized as being the most reliable, in that they require minimum capabilities which are not always possessed by people affected by serious cognitive defects. This means it is necessary to use other psychodiagnostic tools that are better suited to a subject's specific condition, but also that it is then necessary to reach a common metric so as to compare the reliability of the results obtained and thus ensure a homogeneous diagnosis. The concrete problem arising in the diagnosis of mental retardation using the IQ is thus the need to match the scores, obtained using different tests, with the Wechsler IQ, which is the most commonly used and universally recognized test for the diagnosis of degrees of retardation. In this paper we present the use of feedforward artificial neural networks (ANNs) to search for the best estimate of the Wechsler IQ provided by four different psychodiagnostic tools. To this end, a database was created, administering four different tests, besides the Wechsler scale, to the same group of mentally retarded subjects, in order to generate IQ estimation models on the basis of the scores obtained in the other tests via the use of ANNs. The results were then compared with other statistic modeling methods, in terms of accuracy and reliability.