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Cristina Gena

Publications and source records attributed to Cristina Gena.

At least 19 recordsLinked to original sources

Mind Modeling: A ToM-Based Framework for Personalization

User modeling has traditionally relied on inferring preferences, traits, or intents from observable behaviour. While effective in many adaptive systems, this paradigm treats behaviour as the primary object of modeling and leaves mental-state attribution implicit. This assumption becomes limiting in socially situated and longitudinal interaction, where behaviour must be interpreted in context and over time. We introduce mind modeling, a perspective in which user modeling is grounded in the explicit and revisable attribution of mental states, including beliefs, intentions, emotions, and knowledge. Drawing on Theory of Mind (ToM), this approach treats behaviour as evidence for hypotheses about internal states, supporting personalization that is more interpretable and coherent across interaction episodes. We present M3, a conceptual framework that integrates perception, mentalisation, and action within a unified structure, enabling the continuous update of mental-state hypotheses in embodied interaction. We further illustrate this perspective through an embodied interaction trace, providing an initial operationalization of mind modeling in practice.

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Using the Pepper Robot to Support Sign Language Communication

Social robots are increasingly experimented in public and assistive settings, but their accessibility for Deaf users remains quite underexplored. Italian Sign Language (LIS) is a fully-fledged natural language that relies on complex manual and non-manual components. Enabling robots to communicate using LIS could foster more inclusive human robot interaction, especially in social environments such as hospitals, airports, or educational settings. This study investigates whether a commercial social robot, Pepper, can produce intelligible LIS signs and short signed LIS sentences. With the help of a Deaf student and his interpreter, an expert in LIS, we co-designed and implemented 52 LIS signs on Pepper using either manual animation techniques or a MATLAB based inverse kinematics solver. We conducted a exploratory user study involving 12 participants proficient in LIS, both Deaf and hearing. Participants completed a questionnaire featuring 15 single-choice video-based sign recognition tasks and 2 open-ended questions on short signed sentences. Results shows that the majority of isolated signs were recognized correctly, although full sentence recognition was significantly lower due to Pepper's limited articulation and temporal constraints. Our findings demonstrate that even commercially available social robots like Pepper can perform a subset of LIS signs intelligibly, offering some opportunities for a more inclusive interaction design. Future developments should address multi-modal enhancements (e.g., screen-based support or expressive avatars) and involve Deaf users in participatory design to refine robot expressivity and usability.

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The Impact of Adaptive Emotional Alignment on Mental State Attribution and User Empathy in HRI

The paper presents an experiment on the effects of adaptive emotional alignment between agents, considered a prerequisite for empathic communication, in Human-Robot Interaction (HRI). Using the NAO robot, we investigate the impact of an emotionally aligned, empathic, dialogue on these aspects: (i) the robot's persuasive effectiveness, (ii) the user's communication style, and (iii) the attribution of mental states and empathy to the robot. In an experiment with 42 participants, two conditions were compared: one with neutral communication and another where the robot provided responses adapted to the emotions expressed by the users. The results show that emotional alignment does not influence users' communication styles or have a persuasive effect. However, it significantly influences attribution of mental states to the robot and its perceived empathy

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Social and Telepresence Robots for Accessibility and Inclusion in Small Museums

There are still many museums that present accessibility barriers, particularly regarding perceptual, cultural, and cognitive aspects. This is especially evident in low-density population areas. The aim of the ROBSO-PM project is to improve the accessibility of small museums through the use of social robots and social telepresence robots, focusing on three museums as case studies: the Museum of the Holy Shroud in Turin, a small but globally known institution, and two lesser known mountain museums: the Museum of the Champlas du Col Carnival and the Pragelato Museum of Alpine Peoples' Costumes and Traditions. The project explores two main applications for robots: as guides supporting inclusive visits for foreign or disabled visitors, and as telepresence tools allowing people with limited mobility to access museums remotely. From a research perspective, key topics include storytelling, robot personality, empathy, personalization, and, in the case of telepresence, collaboration between the robot and the person, with clearly defined roles and autonomy.

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How Age Influences the Interpretation of Emotional Body Language in Humanoid Robots -- long paper version

This paper presents an empirical study investigating how individuals across different age groups, children, young and older adults, interpret emotional body language expressed by the humanoid robot NAO. The aim is to offer insights into how users perceive and respond to emotional cues from robotic agents, through an empirical evaluation of the robot's effectiveness in conveying emotions to different groups of users. By analyzing data collected from elderly participants and comparing these findings with previously gathered data from young adults and children, the study highlights similarities and differences between the groups, with younger and older users more similar but different from young adults.

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On the usability of generative AI: Human generative AI

Generative AI systems are transforming content creation, but their usability remains a key challenge. This paper examines usability factors such as user experience, transparency, control, and cognitive load. Common challenges include unpredictability and difficulties in fine-tuning outputs. We review evaluation metrics like efficiency, learnability, and satisfaction, highlighting best practices from various domains. Improving interpretability, intuitive interfaces, and user feedback can enhance usability, making generative AI more accessible and effective.

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How personality and memory of a robot can influence user modeling in Human-Robot Interaction

In recent years, robotics has evolved, placing robots in social contexts, and giving rise to Human-Robot Interaction (HRI). HRI aims to improve user satisfaction by designing autonomous social robots with user modeling functionalities and user-adapted interactions, storing data on people to achieve personalized interactions. Personality, a vital factor in human interactions, influences temperament, social preferences, and cognitive abilities. Despite much research on personality traits influencing human-robot interactions, little attention has been paid to the influence of the robot's personality on the user model. Personality can influence not only temperament and how people interact with each other but also what they remember about an interaction or the person they interact with. A robot's personality traits could therefore influence what it remembers about the user and thus modify the user model and the consequent interactions. However, no studies investigating such conditioning have been found. This paper addresses this gap by proposing distinct user models that reflect unique robotic personalities, exploring the interplay between individual traits, memory, and social interactions to replicate human-like processes, providing users with more engaging and natural experiences

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Investigating the relationship between empathy and attribution of mental states to robots

This paper describes an experimental evaluation aimed at detecting the users' perception of the robot's empathic abilities during a conversation. The results have been then analyzed to search for a possible relationship between the perceived empathy and the attribution of mental states to the robot, namely the user's perception of the robot's mental qualities as compared to humans. The involved sample consisted of 68 subjects, including 34 adults and 34 between teenagers and children. By conducting the experiment with both adult and child participants, make possible to compare the results obtained from each group and identify any differences in perception between the various age groups.

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An empirical evaluation for defining a mid-air gesture dictionary for web-based interaction

This paper presents an empirical evaluation of mid-air gestures in a web setting. Fifty-six (56) subjects, all of them HCI students, were divided into 16 groups and involved as designers. Each group worked separately with the same requirements. Firstly, designers identified the main actions required for a web-based interaction with a university classroom search service. Secondly, they proposed a set of mid-air gestures to carry out the identified actions: 99 different mid-air gestures for 16 different web actions were produced in total. Then, designers validated their proposals involving external subjects, namely 248 users in total. Finally, we analyzed their results and identified the most recurring or intuitive gestures as well as the potential criticalities associated with their proposals. Hence, we defined a mid-air gesture dictionary that contains, according to our analysis, the most suitable gestures for each identified web action. Our results suggest that most people tend to replicate gestures used in touch-based and mouse-based interfaces also in touchless interactions, ignoring the fact that they can be problematic due to the different distance between the user and the device in each interaction context.

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A Robot Expressing Emotions Through Gestures: Everyone Outside of Italy Would Understand this?

In the context of our research activities on affective computing and human-robot interaction we are working on both the recognition of human's emotions and the expression of emotions by robots. In our vision, robots will be increasingly present in schools, factories, and homes, and their empathetic behavior may foster their acceptance. In particular, in one of our research, we sought to replicate gestures associated with specific emotions on a social robot, NAO. Our focus was on Ekman's six primary emotions, along with five emotions selected from Plutchik's wheel of emotions. In our opinion the cultural component linked to the expression of emotions through gestures certainly influenced both us and the participants. Thus, we would like to investigate the influence of our culture in the gestural expression of emotion.

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Combining Multiple View Components for Exploratory Visualization

The analysis of structured complex data, such as clustered graph based datasets, usually applies a variety of visual representation techniques and formats. The majority of currently available tools and approaches to exploratory visualization are built on integrated schemes for simultaneous displaying of multiple aspects of studying objects and processes. Usually, such schemes partition screen space that is composed of multiple views and adopt interaction patterns to focus on data-driven items. Widely known concepts as overview plus-detail and focus-plus-context are ambiguous in interpretation by means of technical terms. Therefore, their implementation by UI design practitioners need reviews and a classification of the basic approaches to visual composition of graphical representation modules. We propose a description of basic components of the view and focus and an overview of their multiple combinations.

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Can empathy affect the attribution of mental states to robots?

This paper presents an experimental study showing that the humanoid robot NAO, in a condition already validated with regards to its capacity to trigger situational empathy in humans, is able to stimulate the attribution of mental states towards itself. Indeed, results show that participants not only experienced empathy towards NAO, when the robot was afraid of losing its memory due to a malfunction, but they also attributed higher scores to the robot emotional intelligence in the Attribution of Mental State Questionnaire, in comparison with the users in the control condition. This result suggests a possible correlation between empathy toward the robot and humans' attribution of mental states to it.

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Behavioural analysis of interaction between individuals and a robot in the window (cAESAR2023 workshop)

The aim of the current research is to analyse and discover, in a real context, behaviours, reactions and modes of interaction of social actors (people) with the humanoid robot Pepper. Indeed, we wanted to observe in a real, highly frequented context, the reactions and interactions of people with Pepper, placed in a shop window, through a systematic observation approach. The most interesting aspects of this research will be illustrated, bearing in mind that this is a preliminary analysis, therefore, not yet definitively concluded.

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Using the power of memes: The Pepper Robot as a communicative facilitator for autistic children (cAESAR2023 workshop)

This article describes the preliminary qualitative results of a therapeutic laboratory involving the Pepper robot, as a facilitator, to promote autonomy and functional acquisition in autistic children with low support needs (level 1 support). The lab, designed and led by a multidisciplinary team, involved 4 children, aged 11 to 13 years, and was organized in weekly meetings for the duration of four months. The following is the result of an in-depth qualitative evaluation of the interactions that took place between the children and the Pepper robot, with the aim of analyzing their effectiveness for the purpose of promoting the development of social and communication skills in the participants. The observations and analyses conducted during the interactions provided valuable insights into the dialogue and communication style employed and paved the way for possible strategies to make the robot more empathetic and engaging for autistic children.

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Novel BCI paradigm for ALS patients based on EEG and Pupillary Accommodative Response

Brain-computer interfaces (BCIs) are one of the few alternatives to enable locked-in syndrome (LIS) patients to communicate with the external world, while they are the only solution for complete locked-in syndrome (CLIS) patients, who lost the ability to control eye movements. However, successful usage of endogenous electroencephalogram(EEG)-based BCI applications is often not trivial, due to EEG variations between and within sessions and long user training required. In this work we suggest an approach to deal with this two main limitations of EEG-BCIs by inserting a progressive and expandable neurofeedback training program, able to continuously tailor the classifier to the specific user, into a multimodal BCI paradigm. We propose indeed the integration of EEG with a non-brain signal: the pupillary accommodative response (PAR). The PAR is a change in pupil size associated with gaze shifts from far to close targets; it is not governed by the somatic nervous system and is thus potentially preserved after the evolution from LIS to CLIS, which often occurs in neurodegenerative diseases, such as amyotrophic lateral sclerosis. Multimodal BCIs have been broadly investigated in literature, due to their ability to yield better overall control performances, but this would be the first attempt combining EEG and PAR. In the context of the BciPar4Sla, we are exploiting these two signals, with the aim of developing a more reliable BCI, adaptive to the extent of evolving together with the user's ability to elicit the brain phenomena needed for optimal control, and providing support even in the transition from LIS to CLIS.

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The influence of user personality and rating scale features on rating behaviour: an empirical study

User ratings are widely used in web systems and applications to provide personalized interaction and to help other users make better choices. Previous research has shown that rating scale features and user personality can both influence users' rating behaviour, but relatively little work has been devoted to understanding if the effects of rating scale features may vary depending on users' personality. In this paper, we study the impact of scale granularity and colour on the ratings of individuals with different personalities, represented according to the Big Five model. To this aim, we carried out a user study with 203 participants, in the context of a web-based survey where users were assigned an image rating task. Our results confirm that both colour and granularity can affect user ratings, but their specific effects also depend on user scores for certain personality traits, in particular agreeableness, openness to experience and conscientiousness.

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From psychological traits to safety warnings: three studies on recommendations in a smart home environment

In this paper, we report on three experiments we have carried out in the context of the EMPATHY project, with the aim of helping users make better configuration choices in a smart home environment, and discuss our results. We found that there are psychological traits, such as Need for Cognition, which influence the way individuals tend to use recommendations, that there are non obvious relationships between the perceived usefulness of recommendations in different domains and individuals' ability to exploit suggestions on configuration choices, and that detailed, easy-to-understand security explanations are more persuasive than simple security warnings, when it comes to make decisions on the applicability of rules which might cause privacy and security risks.

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Preliminary results of a therapeutic lab for promoting autonomies in autistic children

This extended abtract describes the preliminary qualitative results coming from a therapeutic laboratory focused on the use of the Pepper robot to promote autonomies and functional acquisitions in highly functioning (Asperger) children with autism. The field lab, ideated and led by a multidisciplinary team, involved 4 children, aged 11-13, who attended the laboratory sessions once a week for four months.

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