Complexity Science in Human Change

This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

The Mechanical Psychologist: How Computational Techniques Can Aid Social Researchers in the Analysis of High-Stakes Conversation

Qualitative coding is an essential observational tool for describing behaviour in the social sciences. However, it traditionally relies on manual, time-consuming, and error-prone methods performed by humans. To overcome these issues, cross-disciplinary researchers are increasingly exploring computational methods such as Natural Language Processing (NLP) and Machine Learning (ML) to annotate behaviour automatically. Automated methods offer scalability, error reduction, and the discovery of increasingly subtle patterns in data compared to human effort alone (N. C. Chen et al., 2018). Despite promising advancements, concerns regarding generalisability, mistrust of automation, and value alignment between humans and machines persist (Friedberg et al., 2012; Grimmer et al., 2021; Jiang et al., 2021; R. Levitan & Hirschberg, 2011; Mills, 2019; Nenkova et al., 2008; Rahimi et al., 2017; Yarkoni et al., 2021). This thesis investigates the potential of computational techniques, such as social signal processing, text mining, and machine learning, to streamline qualitative coding in the social sciences, focusing on two high-stakes conversational case studies. The first case study analyses political interviewing using a corpus of 691 interview transcripts from US news networks. Psychological behaviours associated with effective interviewing are measured and used to predict conversational quality through supervised machine learning. Feature engineering employs a Social Signal Processing (SSP) approach to extract latent behaviours from low-level social signals (Vinciarelli, Salamin, et al., 2009). Conversational quality, calculated from desired characteristics of interviewee speech, is validated by a human-rater study. The findings support the potential of computational approaches in qualitative coding while acknowledging challenges in interpreting low-level social signals. The second case study investigates the ability of machines to learn expert-defined behaviours from human annotation, specifically in detecting predatory behaviour in known cases of online child grooming. In this section, the author utilises 623 chat logs obtained from a US-based online watchdog, with expert annotators labelling a subset of these chat logs to train a large language model. The goal was to investigate the machine’s ability to detect eleven predatory behaviours based on expert annotations. The results show that the machine could detect several behaviours with as few as fifty labelled instances, but rare behaviours were frequently over-predicted. The author next implemented a collaborative human-AI approach to investigate the trade-off between human accuracy and machine efficiency. The results suggested that a human-in-the-loop approach could improve human efficiency and machine accuracy, achieving near-human performance on several behaviours approximately fifteen times faster than human effort alone. The conclusion emphasises the value of increased automation in social sciences while recognising the importance of social scientific expertise in cross-disciplinary re- search, especially when addressing real-world problems. It advocates for technology that augments and enhances human effort and expertise without replacing it entirely. This thesis acknowledges the challenges in interpreting computational signals and the importance of preserving human insight in qualitative coding. The thesis also highlights potential avenues for future research, such as refining computational methods for qualitative coding and exploring collaborative human-AI approaches to address the limitations of automated methods

Social robots as communication partners to support emotional well-being

Interpersonal communication behaviors play a significant role in maintaining emotional well being. Self-disclosure is one such behavior that can have a meaningful impact on our emotional state. When we engage in self-disclosure, we can receive and provide support, improve our mood, and regulate our emotions. It also creates a comfortable space to share our feelings and emotions, which can have a positive impact on our overall mental and physical health. Social robots are gradually being introduced in a range of social and health settings. These autonomous machines can take on various forms and shapes and interact with humans using social behaviors and rules. They are being studied and introduced in psychosocial health interventions, including mental health and rehabilitation settings, to provide much- needed physical and social support to individuals. In my doctoral thesis, I aimed to explore how humans self-disclose and express their emotions to social robots and how this behavior can affect our perception of these agents. By studying speech-based communication interactions between humans and social robots, I wanted to investigate how social robots can support human emotional well-being. While social robots show great promise in offering social support, there are still many questions to consider before deploying them in actual care contexts. It is important to carefully evaluate their utility and scope in interpersonal communication settings, especially since social robots do not yet offer the same opportunities as humans for social interactions. My dissertation consists of three empirical chapters that investigate the underlying psychological mechanisms of perception and behaviour within human–robot communication and their potential deployment as interventions for emotional wellbeing. Chapter 1 offers a comprehensive introduction to the topic of emotional well-being and self-disclosure from a psychological perspective. I begin by providing an overview of the existing literature and theory in this field. Next, I delve into the social perception of social robots, presenting a theoretical framework to help readers understand how people view these machines. To illustrate this, I review some of the latest studies on social robots in care settings, as well as those exploring how robots can encourage people to self-disclose more about themselves. Finally, I explore the key concepts of self disclosure, including how it is defined, operationalized, and measured in experimental psychology and human–robot interaction research. In my first empirical chapter, Chapter 2, I explore how a social robot’s embodiment influences people’s disclosures in measurable terms, and how these disclosures differ from disclosures made to humans and disembodied agents. Chapter 3 studies how prolonged and intensive long-term interactions with a social robot affect people’s self-disclosure behavior towards the robot, perceptions of the robot, and how it affected factors related to well-being. Additionally, I examine the role of the interaction’s discussion theme. In Chapter 4, the final empirical chapter, I test a long-term and intensive social robot intervention with informal caregivers, people living with considerably difficult life situations. I investigate the potential of employing a social robot for eliciting self-disclosure among informal caregivers over time, supporting their emotional well-being, and implicitly encouraging them to adapt emotion regulation skills. In the final discussion chapter, Chapter 5, I summarise the current findings and discuss the contributions, implications and limitations of my work. I reflect on the contribution and challenges of this research approach and provide some future directions for researchers in the relevant fields. The results of these studies provide meaningful evidence for user experience, acceptance, and trust of social robots in different settings, including care, and demonstrate the unique psychological nature of these dynamic social interactions with social robots. Overall, this thesis contributes to the development of social robots that can support emotional well-being through self-disclosure interactions and provide insights into how social robots can be used as mental health interventions for individuals coping with emotional distress

Steve, A Framework For Augmenting The Visual Identity Design Process With ML

This research is positioned in the field of graphic design and seeks to investigate the working processes in visual identity projects and their augmentation through Machine Learning (ML). It defines identity as the visual elements that, together, create an atmosphere around a client, involving its values and views of the world and society. Through a deep focus on the creative process, this thesis proposes functional approaches to integrate the designer's perspective on the development of new digital tools. My study reveals fruitful ways to augment identity design through ML rather than replace designers through automation. Since its blooming during the Industrial Revolution, visual identity remains the highest-order project in the discipline of graphic design. The parallel evolution of graphic and information technology has undergone numerous phases in which visual identity structures have become more dynamic, and its impact on society has grown along with the designer’s responsibilities. Increasing integration of automation into graphic design in the twenty-first century, as well as potential future developments in ML, represent new challenges for professionals and researchers. Investigation into the intersection of ML and graphic design has been led mainly by computer scientists, leading to misplaced assumptions of creativity. At the same time, research into graphic creative processes is limited. My research addresses these deficiencies, and the gap in the existing literature on the conjunction between graphic design theory and practice, by involving practitioners in the evaluation and proposal of novel design tools. Moreover, it creates a direct link between software development and the actual needs of graphic designers. The novelty of this research lies in the intersection of design methodology, visual identity and ML. Research on design processes is well established in other areas like architecture, industrial design and software development. An understanding of tools and concepts from these fields helps to investigate the possibilities of integrating ML into the design process. Three main questions are addressed in the research: – Is it possible to find coherent working methods in visual identity projects? – What are the most critical phases for the designers in visual identity projects? – How can these be augmented through ML? To answer these questions, I utilize grounded theory methodology, complemented by literature review, to construct a conceptual framework rooted in the expertise of practitioners. By conducting semi-structured interviews with a sample of twenty graphic design studios, I confirmed that they employ consistent and coherent working methods and that ML has the potential to help augment critical phases in the visual identity process. My findings are further explored via non-participant observation that, in conjunction with the interviews, has led to a primary hypothesis subsequently tested through a within-subject design survey. My findings collectively provide a series of propositions that constitute the basis for a concrete ML implementation proposal. The definition of a replicable conceptual framework that incorporates the shared semantic cognition of design teams into an ML recommendation system constitutes the main contribution to the knowledge offered by my thesis.This research is positioned in the field of graphic design and seeks to investigate the working processes in visual identity projects and their augmentation through Machine Learning (ML). It defines identity as the visual elements that, together, create an atmosphere around a client, involving its values and views of the world and society. Through a deep focus on the creative process, this thesis proposes functional approaches to integrate the designer's perspective on the development of new digital tools. My study reveals fruitful ways to augment identity design through ML rather than replace designers through automation. Since its blooming during the Industrial Revolution, visual identity remains the highest-order project in the discipline of graphic design. The parallel evolution of graphic and information technology has undergone numerous phases in which visual identity structures have become more dynamic, and its impact on society has grown along with the designer’s responsibilities. Increasing integration of automation into graphic design in the twenty-first century, as well as potential future developments in ML, represent new challenges for professionals and researchers. Investigation into the intersection of ML and graphic design has been led mainly by computer scientists, leading to misplaced assumptions of creativity. At the same time, research into graphic creative processes is limited. My research addresses these deficiencies, and the gap in the existing literature on the conjunction between graphic design theory and practice, by involving practitioners in the evaluation and proposal of novel design tools. Moreover, it creates a direct link between software development and the actual needs of graphic designers. The novelty of this research lies in the intersection of design methodology, visual identity and ML. Research on design processes is well established in other areas like architecture, industrial design and software development. An understanding of tools and concepts from these fields helps to investigate the possibilities of integrating ML into the design process. Three main questions are addressed in the research: – Is it possible to find coherent working methods in visual identity projects? – What are the most critical phases for the designers in visual identity projects? – How can these be augmented through ML? To answer these questions, I utilize grounded theory methodology, complemented by literature review, to construct a conceptual framework rooted in the expertise of practitioners. By conducting semi-structured interviews with a sample of twenty graphic design studios, I confirmed that they employ consistent and coherent working methods and that ML has the potential to help augment critical phases in the visual identity process. My findings are further explored via non-participant observation that, in conjunction with the interviews, has led to a primary hypothesis subsequently tested through a within-subject design survey. My findings collectively provide a series of propositions that constitute the basis for a concrete ML implementation proposal. The definition of a replicable conceptual framework that incorporates the shared semantic cognition of design teams into an ML recommendation system constitutes the main contribution to the knowledge offered by my thesis