The ITALK project : A developmental robotics approach to the study of individual, social, and linguistic learning

This is the peer reviewed version of the following article: Frank Broz et al, “The ITALK Project: A Developmental Robotics Approach to the Study of Individual, Social, and Linguistic Learning”, Topics in Cognitive Science, Vol 6(3): 534-544, June 2014, which has been published in final form at doi: http://dx.doi.org/10.1111/tops.12099 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." Copyright © 2014 Cognitive Science Society, Inc.This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.Peer reviewe

Embracing AI-Based Education: Perceived Social Presence of Human Teachers and Expectations About Machine Teachers in Online Education

Technological advancements in education have turned the idea of machines as teachers into a reality. To better understand this phenomenon, the present study explores how college students develop expectations (or anticipations) about a machine teacher, particularly an AI teaching assistant. Specifically, the study examines whether students’ previous experiences with online courses taught by a human teacher would influence their expectations about AI teaching assistants in future online courses. An online survey was conducted to collect data from college students in the United States. Findings indicate that positively experienced social presence of a human teacher helps develop positive expectations about an AI teaching assistant. The study provides meaningful implications and contributions to our understanding of a machine agent in education

Attribution of Autonomy and its Role in Robotic Language Acquisition

© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.The false attribution of autonomy and related concepts to artificial agents that lack the attributed levels of the respective characteristic is problematic in many ways. In this article we contrast this view with a positive viewpoint that emphasizes the potential role of such false attributions in the context of robotic language acquisition. By adding emotional displays and congruent body behaviors to a child-like humanoid robot’s behavioral repertoire we were able to bring naïve human tutors to engage in so called intent interpretations. In developmental psychology, intent interpretations can be hypothesized to play a central role in the acquisition of emotion, volition, and similar autonomy-related words. The aforementioned experiments originally targeted the acquisition of linguistic negation. However, participants produced other affect- and motivation-related words with high frequencies too and, as a consequence, these entered the robot’s active vocabulary. We will analyze participants’ non-negative emotional and volitional speech and contrast it with participants’ speech in a non-affective baseline scenario. Implications of these findings for robotic language acquisition in particular and artificial intelligence and robotics more generally will also be discussed.Peer reviewedFinal Published versio

From model of trust dynamics for short-term human-robot interaction

Rapid advance of robotic technologies in the last years have opened numerous venues and great challenges in the field of robotics technology. Interacting with those advanced technologies has carried huge debates on how such interactions can be instigated to create fluent interaction between humans and robots that can last for long time (human-robot interaction). One of the crucial factors that majorly influence the level of interaction between human and robot is the level of trust. Trust is the feeling of confidence that the reliance on other partner will not yield negative or dangerous consequences. In computational psychology domains, formal models (computational models) were used to acquire deep insights of human cognitive functions and behavior patterns. Therefore, this study implements formal model of trust in human robot interaction to answer how trust can be a reason to initiate interaction between human and robot. From related literature, eighteen basic factors have been established that include; personality, physical appearances, believable behavior, behavior cues, level of automation, positive experiences, transparency, perception, long term perceive risk, short term perceive risk, reliable behavior, perceive competency, positive deception, long term positive experiences, short term trust, short term distrust, long term trust, long term distrust. Those factors provide the fundamental knowledge of developing trust in robot. A formal model was developed based on a set of differential equations. Next, Five different cases were implemented to simulate various scenarios that explain the development of trust in HRI; namely, 1) high level of trust, 2) moderate high level of trust, 3) moderate level of trust, 4) moderate low level of trust, and 5) low level of trust. The developed model was verified by using mathematical analysis (stability analysis) and automated verification (temporal trace language

Social Responses to Media Technologies in the 21st Century: The Media are Social Actors Paradigm

Clifford Nass and his colleagues proposed the Computers Are Social Actors (CASA) paradigm in the 1990s and demonstrated that we treat computers in some of the ways we treat humans. To account for technological advances and to refine explanations for CASA results, this paper proposes the Media Are Social Actors (MASA) paradigm. We begin by distinguishing the roles of primary and secondary cues in evoking medium-as-social-actor presence and social responses. We then discuss the roles of individual differences and contextual factors in these responses and identify mindless and mindful anthropomorphism as two major complementary mechanisms for understanding MASA phenomena. Based on evolutionary psychology explanations for socialness, we conclude with nine formal propositions and suggestions for future research to test and apply MASA

The Communicative Activity of “Making Suggestions” as an Interactional Process: Towards a Dialog Model for HAI

Opfermann C, Pitsch K, Yaghoubzadeh R, Kopp S. The Communicative Activity of “Making Suggestions” as an Interactional Process: Towards a Dialog Model for HAI. In: Proceedings of the 5th International Conference on Human Agent Interaction. ACM Press; 2017: 161–170.Dialog modeling of making suggestions in human-agent interaction is a challenge due to the socially delicate nature of a suggestion and ensuing interactional negotiations. A basic first dialog model for making suggestions was tested in the context of schedule management assistance by an embodied conversational agent with elderly and mildly cognitively impaired persons. Analysis showed that users responded according to human social structures with most response types bearing potential challenges concerning the system's language understanding and the users' intention interpretation:next to explicit answers, users produced implicit versions for acceptance or resistance and further requests for information or modifications. Thus, an enhanced dialog model with a newly added clarification sequence and a new multi-conditional entry sequence was tested in a second study with the autonomous system. Initial observations show a promising performance of the dialog model

The Cognitive-Affective-Social Theory of Learning in digital Environments (CASTLE)

For a long time, research on individuals learning in digital environments was primarily based on cognitive-oriented theories. This paper aims at providing evidence that social processes affect individual learning with digital materials. Based on these theories and empirical results, a social-processes-augmented theory is suggested: the Cognitive-Affective-Social Theory of Learning in digital Environments (CASTLE). This CASTLE postulates that social cues in digital materials activate social schemata in learners leading to enhanced (para-)social, motivational, emotional, and metacognitive processes. To substantiate this theory, socio-cognitive theories are used, which predict social influences on learning with digital materials. Besides, previous empirical findings are presented assuming that with a rising number of social cues in digital materials, the influence of social processes increases. Finally, consequences regarding the design of digital learning media are discussed

Human-Machine Communication: Complete Volume. Volume 4

This is the complete volume of HMC Volume 4