Whereas sudden gains and losses (large shifts in symptom severity) in patients receiving psychotherapy appear abrupt and hence may seem unexpected, hypotheses from complex-systems theory suggest that sudden gains and losses are actually preceded by certain early-warning signals (EWSs). We tested whether EWSs in patients’ daily self-ratings of the psychotherapeutic process predicted future sudden gains and losses. Data were collected from 328 patients receiving psychotherapy for mood disorders who completed daily self-ratings about their therapeutic process using the Therapy Process Questionnaire (TPQ). Sudden gains and losses were classified from the Problem Intensity scale of the TPQ. The other items of the TPQ were used to compute the EWSs. EWSs predicted an increased probability for sudden gains and losses in a 4-day predictive window. These results show that EWSs can be used for real-time prediction of sudden gains and losses in clinical practice.

We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in realtime for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.

Anxiety disorders are among the most frequently diagnosed mental health problems in children, leading to potentially devastating outcomes on a personal level and high costs for society. Although evidence-based interventions are readily available, their outcomes are often disappointing and variable. In particular, existing interventions are not effective long-term nor tailored to differences in individual responsiveness. We therefore need a new approach to the prevention and treatment of anxiety in children and a commensurate scientific methodology to uncover individual profiles of change. We argue that applied games have a great deal of potential for both. The current paper presents results from a recent pilot study using a biofeedback virtual reality game (DEEP). DEEP integrates established therapeutic principles with an embodied and intuitive learning process towards improved anxiety regulation skills.

Both academic and public interest in social media and their effects have increased dramatically over the last decade. In particular, a plethora of studies have been conducted that aimed to uncover the relationship between social media use and youth well-being, fueled by recent concerns that declines in youth well-being may well be caused by a rise in digital technology use. However, reviews of the field strongly suggest that the picture may not be as clear-cut as previously thought, with some studies suggesting positive effects, and some studies suggesting negative effects on youth well-being. To shed light on this ambiguity, we have conducted a narrative review of 94 social media use and well-being studies. A number of patterns in methodological practices in the field have now become apparent: Self-report measures of general statistics around social media use dominate the field, which furthermore often falls short in terms of ecological validity and sufficient use of experimental designs that would enable causal inference. We go on to discuss why such practices are problematic in some cases, and more importantly, which concrete improvements can be made for future studies that aim to investigate the relationship between social media use and well-being.

The process of connected text reading has received very little attention in contemporary cognitive psychology. This lack of attention is in parts due to a research tradition that emphasizes the role of basic lexical constituents, which can be studied in isolated words or sentences. However, this lack of attention is in parts also due to the lack of statistical analysis techniques, which accommodate interdependent time series. In this study, we investigate text reading performance with traditional and nonlinear analysis techniques and show how outcomes from multiple analyses can used to create a more detailed picture of the process of text reading. Specifically, we investigate reading performance of groups of literate adult readers that differ in reading fluency during a self-paced text reading task. Our results indicate that classical metrics of reading (such as word frequency) do not capture text reading very well, and that classical measures of reading fluency (such as average reading time) distinguish relatively poorly between participant groups. Nonlinear analyses of distribution tails and reading time fluctuations provide more fine-grained information about the reading process and reading fluency.