People do not always know best: Preschoolers’ trust in social robots versus humans

The main goal of my thesis was to investigate how 3- and 5-year-old children learn from robots versus humans using a selective trust paradigm. Children’s conceptualization of robots was also investigated. By using robots, which lack many of the social characteristics human informants possess by default, these studies sought to test young children’s reliance on epistemic characteristics conservatively. In Study 1, a competent humanoid robot, Nao, and an incompetent human, Ina, were presented to children. Both informants labelled familiar objects, like a ball, with Nao labelling them correctly and Ina labelling them incorrectly. Next, both informants labelled novel items with nonsense labels. Children were then asked what the novel item was called. Children were also asked what should go inside robots, something biological or something mechanical. Study 2 followed the same paradigm as Study 1, with the only change being the robot used, now the non-humanoid Cozmo. Eliminating the human-like appearance of the robot made for an even more conservative test than in Study 1. Both studies 1 and 2 found that 3-year-old children learned novel words equally from the robot and the human, regardless of the robot’s morphology. The 3-year-old children were also confused about both robot’s internal properties, attributing mechanical and biological insides to the robots equally. In contrast, the 5-year-olds in both studies preferred to learn from the accurate robot over the inaccurate human. The 5-year-olds also learned from both robots despite understanding that the robot is different from themselves; they attributed mechanical insides to both Nao and Cozmo over biological insides. Study 3 further investigated 3-year-olds ambivalence regarding their trust judgements, that is, who they choose to learn from. Instead of word learning, the robot demonstrated competence through pointing. The robot would accurately point at a toy inside a transparent box, and the human would point at an empty box. Next, both informants pointed at opaque boxes and the child was asked where the toy was located. Neither informant demonstrated the ability to speak, as speech is a salient social characteristic. 3-year-olds were still at chance, equally endorsing the robot and the human’s pointing. This suggests that goal-directedness and autonomous movement may be the most important characteristics used to signal agency for young children. The 3-year-olds were also still unsure about the robot’s biology, whereas they correctly identified the human as biological. This suggests that robots are confusing for children due to their dual nature as animate and yet not alive. This thesis shows that by the age of 5, children are willing and able to learn from a robot. These studies further add to the selective trust literature and have implications for educational settings

An investigation of fast and slow mapping

Children learn words astonishingly skilfully. Even infants can reliably “fast map” novel category labels to their referents without feedback or supervision (Carey & Bartlett, 1978; Houston-Price, Plunkett, & Harris, 2005). Using both empirical and neural network modelling methods this thesis presents an examination of both the fast and slow mapping phases of children's early word learning in the context of object and action categorisation. A series of empirical experiments investigates the relationship between within-category perceptual variability on two-year-old children’s ability to learn labels for novel categories of objects and actions. Results demonstrate that variability profoundly affects both noun and verb learning. A review paper situates empirical word learning research in the context of recent advances in the application of computational models to developmental research. Data from the noun experiments are then simulated using a Dynamic Neural Field (DNF) model (see Spencer & Schöner, 2009), suggesting that children’s early object categories can emerge dynamically from simple label-referent associations strengthened over time. Novel predictions generated by the model are replicated empirically, providing proofof- concept for the use of DNF models in simulations of word learning, as well emphasising the strong featural basis of early categorisation. The noun data are further explored using a connectionist architecture (Morse, de Greef, Belpaeme & Cangelosi, 2010) in a robotic system, providing the groundwork for future research in cognitive robotics. The implications of these different approaches to cognitive modelling are discussed, situating the current work firmly in the dynamic systems tradition whilst emphasising the value of interdisciplinary research in motivating novel research paradigms

WHEN A CHILD MEETS A ROBOT: THE PSYCHOLOGICAL FACTORS THAT MAKE INTERACTION POSSIBLE

La presente tesi di dottorato, dal titolo When a child meets a robot: the psychological factors that make interaction possible, vuole introdurre ed esplorare un campo abbastanza nuovo della ricerca psicologica che si occupa specificatamente dell’interazione bambino-robot, presentando le molteplici sfaccettature che la caratterizzano e i principali costrutti psicologici come la Teoria della Mente (ToM), la fiducia, la relazione di attaccamento, non tralasciando, inoltre, la valenza del design dei robot utilizzati nelle ricerche che hanno reso possibile questa tesi. I lavori di ricerca, qui presentati, vanno ad esplorare i costrutti sopra citati, indagandoli in profondità e con curiosità scientifica. Nel dettaglio, sono presentate le seguenti ricerche: 1) Shall I trust you? From Child-Robot Interaction To Trusting Relationship, 2) Can a robot lie? The role of false belief and intentionality understanding in children aged 5 and 6 years, 3) A robot is not worth another: exploring Children’s Mental State Attribution to Different Humanoid Robots, 4) Coding with me: exploring the effect of coding intervention on preschoolers’cognitive skills. Infine, nell’ultimo capitolo, relativo alle conclusioni, viene sviluppata una riflessione teorica riguardante il ruolo rilevante assunto dallo sviluppo cognitivo dei bambini nei processi interazionali con gli agenti robotici.This PhD thesis, title When a child meets a robot: the psychological factors that make interaction possible, wants to introduce and explore a fairly new field of psychological research that specifically deals with the child-robot interaction, presenting the many facets that characterize it and the main psychological constructs such as the Theory of Mind (ToM), trust, attachment relationship, without neglecting, moreover, the value of the design of the robots used in the studies that have made this thesis possible. The research works, presented here, exploring the constructs mentioned above, investigating them in depth and with scientific curiosity. In detail, the following studies are presented: 1) Shall I trust you? From Child-Robot Interaction To Trusting Relationship, 2) Can a robot lie? The role of false belief and intentionality understanding in children aged 5 and 6 years, 3) A robot is not worth another: exploring Children’s Mental State Attribution to Different Humanoid Robots, 4) Coding with me: exploring the effect of a coding intervention on preschoolers’cognitive skills. Finally, in the last chapter, concerning the conclusions, a theoretical reflection is developed regarding the relevant role assumed by children's cognitive development in interactional processes with robotic agents

Personal care robots for children: state of the art

In the last decades, the application of robots in variety of industries has remarkably been increased. Self-governing robots have been employed in various fields of human life, particularly in that of old and very young people. It is assumed that in the succeeding years, personal care robots will be in an intimate relation with human beings. But, what caring responsibilities will the future robots have before children at home? This is directly related to the kinds of robots as well as those aspects of getting old that are the matter of question. Among the topics that are germane to robot carers, Child-care is the least discussed one. In this article the duties of care robots in the life of youth is investigated. It studies the benefits of the application of robots in daily life along with the negative points, perils, and anxieties that rise from the application. In this article, we examine the benefits of the usage of robots in the child life. Besides, we will discuss the disadvantages, dangers and fears that are related to child-robot connections. In the end, we will recommend some useful points

Perceiving Sociable Technology: Exploring the Role of Anthropomorphism and Agency Perception on Human-Computer Interaction (HCI)

With the arrival of personal assistants and other AI-enabled autonomous technologies, social interactions with smart devices have become a part of our daily lives. Therefore, it becomes increasingly important to understand how these social interactions emerge, and why users appear to be influenced by them. For this reason, I explore questions on what the antecedents and consequences of this phenomenon, known as anthropomorphism, are as described in the extant literature from fields ranging from information systems to social neuroscience. I critically analyze those empirical studies directly measuring anthropomorphism and those referring to it without a corresponding measurement. Through a grounded theory approach, I identify common themes and use them to develop models for the antecedents and consequences of anthropomorphism. The results suggest anthropomorphism possesses both conscious and non-conscious components with varying implications. While conscious attributions are shown to vary based on individual differences, non-conscious attributions emerge whenever a technology exhibits apparent reasoning such as through non-verbal behavior like peer-to-peer mirroring or verbal paralinguistic and backchanneling cues. Anthropomorphism has been shown to affect users’ self-perceptions, perceptions of the technology, how users interact with the technology, and the users’ performance. Examples include changes in a users’ trust on the technology, conformity effects, bonding, and displays of empathy. I argue these effects emerge from changes in users’ perceived agency, and their self- and social- identity similarly to interactions between humans. Afterwards, I critically examine current theories on anthropomorphism and present propositions about its nature based on the results of the empirical literature. Subsequently, I introduce a two-factor model of anthropomorphism that proposes how an individual anthropomorphizes a technology is dependent on how the technology was initially perceived (top-down and rational or bottom-up and automatic), and whether it exhibits a capacity for agency or experience. I propose that where a technology lays along this spectrum determines how individuals relates to it, creating shared agency effects, or changing the users’ social identity. For this reason, anthropomorphism is a powerful tool that can be leveraged to support future interactions with smart technologies