Human-human Communication Research Articles

Chatgpt Revolutionizes Education: Improves Learning Through Conversational AI

The instructive atmosphere is assumed novel gears by artificial intelligence (AI), which takes the control to transform out-of-date education and knowledge approaches. This article delivers a full examination of artificial intelligence (AI) skills, their likely habits in teaching, and the encounters related with them. We dialog around chatbots and connected procedures that can impersonator human-human communications and generate script that seems humanoid when assumed natural language effort. Current chatbots, such as ChatGPT, have many aids, but their tender in the schoolroom grants important ethical and applied subjects as well. In adding to inspiring accountable and moral usage, the writers expect to proposal valued info on how AI may be positively combined into the instructive situation to advantage both teachers and scholars.

Can Facial Expressions Induce Haptic Perception?

A key challenge in haptics is designing human-human communications involving touch to facilitate positive effects on social interactions. An important consideration in designing social touch is understanding the effect of social stimuli on perception, in addition to that of a physical stimulus, because social touch always involves a partner. This study presents an experiment to demonstrate that facial expressions induce haptic perception. We developed a human-agent interaction system on a display in which participants moved the mouse cursor to click the target icon while the agent behaved as if it pulled the cursor back in the opposite direction, showing either a negative or neutral face. The perceived force during the interaction was quantified by the control display ratio using a psychophysical approach. The results show that the negative face induced a significantly greater perceived force than the neutral face. In addition, the perceived force correlated with the individual's evaluation of the facial expression; that is, the more unpleasant or aroused they perceived the facial expression to be, the more force they perceived. This study sheds light on the design of social touch performed by people who have physical or mediated contact with each other in physical space or cyberspace.

Co-Designing with Users the Explanations for a Proactive Auto-Response Messaging Agent

Explanations of AI Agents' actions are considered to be an important factor in improving users' trust in the decisions made by autonomous AI systems. However, as these autonomous systems evolve from reactive, i.e., acting on user input, to proactive, i.e., acting without requiring user intervention, there is a need to explore how the explanation for the actions of these agents should evolve. In this work, we explore the design of explanations through participatory design methods for a proactive auto-response messaging agent that can reduce perceived obligations and social pressure to respond quickly to incoming messages by providing unavailability-related context. We recruited 14 participants who worked in pairs during collaborative design sessions where they reasoned about the agent's design and actions. We qualitatively analyzed the data collected through these sessions and found that participants' reasoning about agent actions led them to speculate heavily on its design. These speculations significantly influenced participants' desire for explanations and the controls they sought to inform the agents' behavior. Our findings indicate a need to transform users' speculations into accurate mental models of agent design. Further, since the agent acts as a mediator in human-human communication, it is also necessary to account for social norms in its explanation design. Finally, user expertise in understanding their habits and behaviors allows the agent to learn from the user their preferences when justifying its actions.

Gender identity and influence in human-machine communication:A mixed-methods exploration

The advancement of conversational technologies stimulates new research agenda on the patterns, norms, and social impacts of human-machine communication (HMC) as a novel process. Conversational agents (CAs), a prevalent example of machines that communicate with users directly, are usually depicted as females in assisting roles. This study intends to explore empirical evidence of how “gendered” technologies might influence HMC and potentially reinforce gender stereotyping in human-human communication. We applied a mixed-methods approach to explore users' gender-related responses and evaluations in the interaction with CAs. First, we observed unrestricted interactions between 36 human participants and Amazon Alexa in a laboratory and qualitatively analyzed the transcripts to detect gendered communication cues. We then conducted a 2 × 3 (participant gender: female vs. male; CA gender: female vs. male vs. neutral) online experiment where 250 participants interacted with a customized chatbot created by the researcher. Results showed participants’ different emotions/tones, engagement, (non)accommodation, as well as credibility, attraction, and likeability evaluations between human-CA gender pairs.

Thoughts on the usage of audible smiling in speech synthesis applications

In this perspective paper we explore the question how audible smiling can be integrated in speech synthesis applications. In human-human communication, smiling can serve various functions, such as signaling politeness or as a marker of trustworthiness and other aspects that raise and maintain the social likeability of a speaker. However, in human-machine communication, audible smiling is nearly unexplored, but could be an advantage in different applications such as dialog systems. The rather limited knowledge of the details of audible smiling and their exploitation for speech synthesis applications is a great challenge. This is also true for modeling smiling in spoken dialogs and testing it with users. Thus, this paper argues to fill the research gaps in identifying factors that constitute and affect audible smiling in order to incorporate it in speech synthesis applications. The major claim is to focus on the dynamics of audible smiling on various levels.

Joint modelling of audio-visual cues using attention mechanisms for emotion recognition

Emotions play a crucial role in human-human communications with complex socio-psychological nature. In order to enhance emotion communication in human-computer interaction, this paper studies emotion recognition from audio and visual signals in video clips, utilizing facial expressions and vocal utterances. Thereby, the study aims to exploit temporal information of audio-visual cues and detect their informative time segments. Attention mechanisms are used to exploit the importance of each modality over time. We propose a novel framework that consists of bi-modal time windows spanning short video clips labeled with discrete emotions. The framework employs two networks, with each one being dedicated to one modality. As input to a modality-specific network, we consider a time-dependent signal deriving from the embeddings of the video and audio modalities. We employ the encoder part of the Transformer on the visual embeddings and another one on the audio embeddings. The research in this paper introduces detailed studies and meta-analysis findings, linking the outputs of our proposition to research from psychology. Specifically, it presents a framework to understand underlying principles of emotion recognition as functions of three separate setups in terms of modalities: audio only, video only, and the fusion of audio and video. Experimental results on two datasets show that the proposed framework achieves improved accuracy in emotion recognition, compared to state-of-the-art techniques and baseline methods not using attention mechanisms. The proposed method improves the results over baseline methods by at least 5.4%. Our experiments show that attention mechanisms reduce the gap between the entropies of unimodal predictions, which increases the bimodal predictions’ certainty and, therefore, improves the bimodal recognition rates. Furthermore, evaluations with noisy data in different scenarios are presented during the training and testing processes to check the framework’s consistency and the attention mechanism’s behavior. The results demonstrate that attention mechanisms increase the framework’s robustness when exposed to similar conditions during the training and the testing phases. Finally, we present comprehensive evaluations of emotion recognition as a function of time. The study shows that the middle time segments of a video clip are essential in the case of using audio modality. However, in the case of video modality, the importance of time windows is distributed equally.

Social Scripts and Expectancy Violations: Evaluating Communication with Human or AI Chatbot Interactants

ABSTRACT As artificial intelligence (AI) agents like chatbots play larger roles in daily life, questions arise regarding how people evaluate their communication. Perspectives applying communication scripts to human-AI interactions propose that outcomes are determined by messages and the embedded cues therein. The expectancy violations perspective posits that message characteristics are less important than whether they are expected or unexpected. A pilot study established baseline expectancies about humans’ and chatbots’ conversational contingency and response latencies. A 2 (contingency: more/less contingent responses) × 2 (latency: fast/slow responses) × 2 (communicator identity: human/chatbot) experiment then tested predictions derived from human-human communication scripts and expectancy violations using textual variations in an e-commerce chat. Communicators showing greater conversational contingency and faster responses were most credible, whether they were human or chatbots, but chatbots were consistently less socially attractive than humans. Results show that humans and chatbots are evaluated similarly regarding the functional, but not the relational aspects of communication. There was greater support for the communication script perspective than the expectancy violations perspective regarding interactions with chatbots.

Group Chat Ecology in Enterprise Instant Messaging: How Employees Collaborate Through Multi-User Chat Channels on Slack

Despite the long history of studying instant messaging usage, we know very little about how today's people participate in group chat channels and interact with others inside a real-world organization. In this short paper, we aim to update the existing knowledge on how group chat is used in the context of today's organizations. The knowledge is particularly important for the new norm of remote works under the COVID-19 pandemic. We have the privilege of collecting two valuable datasets: a total of 4,300 group chat channels in Slack from an R&D department in a multinational IT company; and a total of 117 groups' performance data. Through qualitative coding of 100 randomly sampled group channels from the 4,300 channels dataset, we identified and reported 9 categories such as Project channels, IT-Support channels, and Event channels. We further defined a feature metric with 21 meta-features (and their derived features) without looking at the message content to depict the group communication style for these group chat channels, with which we successfully trained a machine learning model that can automatically classify a given group channel into one of the 9 categories. In addition to the descriptive data analysis, we illustrated how these communication metrics can be used to analyze team performance. We cross-referenced 117 project teams and their team-based Slack channels and identified 57 teams that appeared in both datasets, then we built a regression model to reveal the relationship between these group communication styles and the project team performance. This work contributes an updated empirical understanding of human-human communication practices within the enterprise setting, and suggests design opportunities for the future of human-AI communication experience.

Exoskeletons and the Future of Work: Envisioning Power and Control in a Workforce Without Limits

Exoskeletons are an emerging form of technology that combines the skills of both machines and humans to give wearers the ability to complete physically demanding tasks that would be too strenuous for most humans. Exoskeleton adoption has the potential to both enhance and disrupt many aspects of work, including power dynamics in the workplace and the human-machine interactions that take place. Dyadic Power Theory (DPT) is a useful theory for exploring the impacts of exoskeleton adoption. In this conceptual paper, we extend DPT to relationships between humans and machines in organizations, as well as human-human communication where use of an exoskeleton has resulted in shifts of power.

A Robotic Cognitive Control Framework for Collaborative Task Execution and Learning

In social and service robotics, complex collaborative tasks are expected to be executed while interacting with humans in a natural and fluent manner. In this scenario, the robotic system is typically provided with structured tasks to be accomplished, but must also continuously adapt to human activities, commands, and interventions. We propose to tackle these issues by exploiting the concept of cognitive control, introduced in cognitive psychology and neuroscience to describe the executive mechanisms needed to support adaptive responses and complex goal-directed behaviors. Specifically, we rely on a supervisory attentional system to orchestrate the execution of hierarchically organized robotic behaviors. This paradigm seems particularly effective not only for flexible plan execution but also for human-robot interaction, because it directly provides attention mechanisms considered as pivotal for implicit, non-verbal human-human communication. Following this approach, we are currently developing a robotic cognitive control framework enabling collaborative task execution and incremental task learning. In this paper, we provide a uniform overview of the framework illustrating its main features and discussing the potential of the supervisory attentional system paradigm in different scenarios where humans and robots have to collaborate for learning and executing everyday activities.

Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions.

Listening to one another is essential to human-human interaction. In fact, we humans spend a substantial part of our day listening to other people, in private as well as in work settings. Attentive listening serves the function to gather information for oneself, but at the same time, it also signals to the speaker that he/she is being heard. To deduce whether our interlocutor is listening to us, we are relying on reading his/her nonverbal cues, very much like how we also use non-verbal cues to signal our attention. Such signaling becomes more complex when we move from dyadic to multi-party interactions. Understanding how humans use nonverbal cues in a multi-party listening context not only increases our understanding of human-human communication but also aids the development of successful human-robot interactions. This paper aims to bring together previous analyses of listener behavior analyses in human-human multi-party interaction and provide novel insights into gaze patterns between the listeners in particular. We are investigating whether the gaze patterns and feedback behavior, as observed in the human-human dialogue, are also beneficial for the perception of a robot in multi-party human-robot interaction. To answer this question, we are implementing an attentive listening system that generates multi-modal listening behavior based on our human-human analysis. We are comparing our system to a baseline system that does not differentiate between different listener types in its behavior generation. We are evaluating it in terms of the participant’s perception of the robot, his behavior as well as the perception of third-party observers.

Revisiting Human-Agent Communication: The Importance of Joint Co-construction and Understanding Mental States.

The study of human-human communication and the development of computational models for human-agent communication have diverged significantly throughout the last decade. Yet, despite frequently made claims of “super-human performance” in, e.g., speech recognition or image processing, so far, no system is able to lead a half-decent coherent conversation with a human. In this paper, we argue that we must start to re-consider the hallmarks of cooperative communication and the core capabilities that we have developed for it, and which conversational agents need to be equipped with: incremental joint co-construction and mentalizing. We base our argument on a vast body of work on human-human communication and its psychological processes that we reason to be relevant and necessary to take into account when modeling human-agent communication. We contrast those with current conceptualizations of human-agent interaction and formulate suggestions for the development of future systems.

I-It, I-Thou, I-Robot: The Perceived Humanness of AI in Human-Machine Communication

ABSTRACT As artificial intelligence (AI) technologies become more common and capable interaction partners (human-machine communication; HMC), understanding how people perceive and interact with them becomes increasingly important to study. This essay argues that one important avenue for this study is the application of relevant interpersonal and computer-mediated communication (CMC) theories. The paper suggests that these theories are relevant because the Computers as Social Actors (CASA) approach has shown that people tend to respond to technologies as they do to other people. It summarizes some theories that may be especially useful for future study in this field. Finally, a case is made that the study of AI and HMC may also be important for greater understanding of the human-human communication process as well.

Is human-human spoken interaction manageable? The emergence of the concept: ‘Conversation Intelligence’

Currently, via the mediation of audio mining technology and conversational user interfaces, and after years of constant improvements of Automatic Speech Recognition technology, conversation intelligence is an emerging concept, significant to the understanding of human-human communication in its most natural and primitive channel – our voice. This paper introduces the concept of Conversation Intelligence (CI), which is becoming crucial to the study of humanhuman speech interaction and communication management and is part of the field of speech analytics. CI is demonstrated on two established discourse terms – power relations and convergence. Finally, this paper highlights the importance of visualization for large-scale speech analytics.

Guest Editorial: Towards Machines Able to Deal with Laughter

The papers in this special section focus on the concept of laughter computing. Laughter is considered a significant feature of human-human communication. Laughter is characterized by a complex behavior that includes major modules: auditory, facial expressions, body movements, and postural attitudes, and physiological signals. The goal of this special section is to gather recent achievements in laughter computing in order to trigger new research directions in this area.

Mapping 3D Gestural Inputs to Traditional Touchscreen Interface Designs within the Context of Anesthesiology

Gestures are a natural means of every day human-human communication, and with the advances in gestural input technology, there is an opportunity to investigate gestures as a means of communicating with computers and other devices. The primary benefit of gestural input technology is that it facilitates a touchless interaction, so the ideal market demand for this technology is an environment where touch needs to be minimized. The perfect example of an environment that discourages touch are sterile or clean environments, such as operating rooms (ORs). Healthcare-associated infections are a great burden to the healthcare system, and gestural input technology can decrease the number of surfaces, computers, and other devices that a healthcare provider comes in contact with, thus reducing the likelihood of bacterial contamination. The objective of this research was to map 3D gestural inputs to traditional touchscreen interface designs within the context of anesthesiology. An experimental study was conducted to elicit intuitive gestures from users and assess the cognitive complexity of ten typical functions of anesthesia providers. Intuitive gestures were observed in six out of the ten functions without any cognitive complexity concerns. Two functions, of the remaining four, demonstrated a higher-level gesture mapping with no cognitive complexity concerns. Overall, gestural input technology demonstrated promise for the ten functions of anesthesia providers in the operating room, and future research will continue investigating the application of gestural input technology for anesthesiology in the OR.

Gestural Interaction with Three-Dimensional Interfaces; Current Research and Recommendations

New methods of interacting with latest three-dimensional (3D) display technologies are rapidly being tested, however, it is not yet certain, which kinds of interaction design would be most suitable for users to manipulate and interact with 3D content. Physical gestures have been a natural and intuitive mode of interaction for human-human communication and ways to replicate this for human-machine communication is increasingly being explored. This proceeding attempts to review the current state of technology and the existing research on the use of gestural interaction with three-dimensional interfaces. The focus is on the use of hand-based gestures, the scope is to identify challenges and limitations of using gestures with 3D interfaces, and the goal is to find guidelines to select appropriate gestures to interact with 3D displays. The paper also highlights existing gaps and potential future research areas, in the technological and usability domains, that must be addressed before gestures can be used seamlessly with 3D interfaces.

Recognition of the semantics and kinematics of gestures: Neural responses to “what” and “how”?

The extensive use of gestures for human-human communication, independently of culture and language, suggests an underlying universal neural mechanism for gesture recognition. The mirror neuron system (MNS) is known to respond to observed human actions, and overlaps with self-action. The minimal cues needed for activation of the MNS for gesture recognition, facial expressions and bodily dynamics, is not yet defined. Using LED-point representations of gestures, we compared two types of brain activations: 1) in response to human recognizable vs non-recognizable motion and 2) in response to human vs non-human motion. Our preliminary results show that parts of the MNS respond only to human kinematics, and not to nonhuman kinematics, suggesting that the brain has a mechanism of discriminating human from nonhuman motion, even if the pattern of motion is meaningless, but still follows biological motion patterns. This implies that mechanisms of learning-mimicking, empathy and emotional communication, are possibly constrained by biological motion patterns. We then suggest a two-tier-model of human-bodily-communication: (1) recognition of human biological kinematics; (2) recognition of meaning. Implications are both theoretical (understanding the underlying mechanism for action recognition) and applicative (in digital graphical social representations, motion should be reasonably biological to generate the same emotional and mimicking automatic mechanisms as in face-to-face social interactions).

Ontology Modeling for Intelligent Computer-Aided Design of Apparel Products

This paper presents a study developing a model of the ontology of an apparel product and the design thereof. Here, “ontology” means the concepts and relationships between them for apparel and apparel design; ontology models are also called conceptual models. To the best of our knowledge, such a model has not yet been made in the literature of apparel and apparel design. A conceptual model is a foundation for (1) effective human-human communication and collaboration, (2) facilitating the communication among different computer programs for computer-aided design (CAD) of apparel products, and (3) facilitating human-computer interactions necessary to design an apparel product. The conceptual model developed completely captures the semantics of apparel and design, including the 2D pattern and 3D apparel product. An example is presented, providing an impression of the usefulness of this model.

Experiments on Detection of Voiced Hesitations in Russian Spontaneous Speech

The development and popularity of voice-user interfaces made spontaneous speech processing an important research field. One of the main focus areas in this field is automatic speech recognition (ASR) that enables the recognition and translation of spoken language into text by computers. However, ASR systems often work less efficiently for spontaneous than for read speech, since the former differs from any other type of speech in many ways. And the presence of speech disfluencies is its prominent characteristic. These phenomena are an important feature in human-human communication and at the same time they are a challenging obstacle for the speech processing tasks. In this paper we address an issue of voiced hesitations (filled pauses and sound lengthenings) detection in Russian spontaneous speech by utilizing different machine learning techniques, from grid search and gradient descent in rule-based approaches to such data-driven ones as ELM and SVM based on the automatically extracted acoustic features. Experimental results on the mixed and quality diverse corpus of spontaneous Russian speech indicate the efficiency of the techniques for the task in question, with SVM outperforming other methods.