Integration von physiologischem Feedback in Lernanwendungen unter Alltagsbedingungen

Diese Arbeit untersucht, wie herkömmliche Lernanwendungen um Informationen über den emotionalen Erregungszustand eines Nutzers erweitert werden können. Den Benutzer zu jedem Zeitpunkt des Lernens auf einem optimalen Erregungsniveau zu halten, wirkt sich positiv auf den Lernerfolg und im Zuge dessen auch auf die Motivation des Lernenden aus. Da während des Lernens sowohl auf Nutzer- als auch auf Systemseite eine Anpassung erfolgen kann, werden beide Aspekte in dieser Arbeit beleuchtet

Classification of Human- and AI-Generated Texts: Investigating Features for ChatGPT

Recently, generative AIs like ChatGPT have become available to the wide public. These tools can for instance be used by students to generate essays or whole theses. But how does a teacher know whether a text is written by a student or an AI? In our work, we explore traditional and new features to (1) detect text generated by AI from scratch and (2) text rephrased by AI. Since we found that classification is more difficult when the AI has been instructed to create the text in a way that a human would not recognize that it was generated by an AI, we also investigate this more advanced case. For our experiments, we produced a new text corpus covering 10 school topics. Our best systems to classify basic and advanced human-generated/AI-generated texts have F1-scores of over 96%. Our best systems for classifying basic and advanced human-generated/AI-rephrased texts have F1-scores of more than 78%. The systems use a combination of perplexity, semantic, list lookup, error-based, readability, AI feedback, and text vector features. Our results show that the new features substantially help to improve the performance of many classifiers. Our best basic text rephrasing detection system even outperforms GPTZero by 183.8% relative in F1-score

Exploring ChatGPT's Empathic Abilities

Empathy is often understood as the ability to share and understand another individual's state of mind or emotion. With the increasing use of chatbots in various domains, e.g., children seeking help with homework, individuals looking for medical advice, and people using the chatbot as a daily source of everyday companionship, the importance of empathy in human-computer interaction has become more apparent. Therefore, our study investigates the extent to which ChatGPT based on GPT-3.5 can exhibit empathetic responses and emotional expressions. We analyzed the following three aspects: (1) understanding and expressing emotions, (2) parallel emotional response, and (3) empathic personality. Thus, we not only evaluate ChatGPT on various empathy aspects and compare it with human behavior but also show a possible way to analyze the empathy of chatbots in general. Our results show, that in 91.7% of the cases, ChatGPT was able to correctly identify emotions and produces appropriate answers. In conversations, ChatGPT reacted with a parallel emotion in 70.7% of cases. The empathic capabilities of ChatGPT were evaluated using a set of five questionnaires covering different aspects of empathy. Even though the results show, that the scores of ChatGPT are still worse than the average of healthy humans, it scores better than people who have been diagnosed with Asperger syndrome / high-functioning autism

Emotion regulation and trader expertise: heart rate variability on the trading floor

We describe a psychophysiological study of the emotion regulation of investment bank traders. Building on work on the role of emotions in financial decision-making, we examine the relationship between market conditions, trader experience and emotion regulation whilst trading, as indexed by high frequency heart rate variability (HF HRV). We find a significant inverse relationship between HF HRV and market volatility and a positive relationship between HF HRV and trader experience. We argue that this suggests that emotion regulation may be an important facet of trader expertise and that learning effects demonstrated in financial markets may include improved emotion regulation as an important component of that learning. Our results also suggest the value of investigating the role of effective emotion regulation in a broader range of financial decision-making contexts. Keywords: Emotion Regulation, Financial Decision-Making, Market Volatility, Trading, Heart Rate Variabilit

xDelia final report: emotion-centred financial decision making and learning

xDelia is a 3-year pan-European project building on the knowledge, skills, and competences of seven partner organisations from a variety of research disciplines and from business. The principal objective of xDelia is to develop technology-enhanced learning approaches that help improve the financial decision making of investors who trade frequently using an electronic trading platform. We focus on emotions, and how they affect maladaptive decision biases and trading performance. Our earlier field work with traders has shown that the development of emotion regulation skills is a key facet of trader expertise. For that reason we consider expert traders our benchmark for adaptive behaviour rather than normative rationality. Our goal is to provide investors with the tools and techniques to develop greater self-awareness of internal states, increase their ability to reflect critically on emotion-informed choices, develop emotion management skills, and support the transfer of these skills to the real-world practice setting of financial trading. This report provides a comprehensive overview of what xDelia is about and what we have achieved over the life of the project. In the sections that follow, we explain the decision problems investors are faced with in a fast paced environment and the limitations of traditional approaches to reduce cognitive errors; introduce an alternative, technology-enhanced learning approach of diagnosis and feedback, skill development, and transfer; describe the learning intervention comprising twelve autonomous learning elements that we have developed; and present evidence from thirty-five studies we have conducted on learning effects and stakeholder acceptance

Towards Emotion Recognition from Electroencephalographic Signals

During the last decades, information about the emotional state of users has become more and more important in human-computer interaction. Automatic emotion recognition enables the computer to recognize a user’s emotional state and thus allows for appropriate reaction, which may pave the way for computers to act emotionally in the future. In the current study, we investigate different feature sets to build an emotion recognition system from electroencephalographic signals. We used pictures from the International Affective Picture System to induce three emotional states: pleasant, neutral, and unpleasant. We designed a headband with four build-in electrodes at the forehead, which was used to record data from five subjects. Compared to standard EEG-caps, the headband is comfortable to wear and easy to attach, which makes it more suitable for everyday life conditions. To solve the recognition task we developed a system based on support vector machines. With this system we were able to achieve an average recognition rate up to 66.7 % on subject dependent recognition, solely based on EEG signals

xDelia: D18-2.4.2 Learning Intervention Package - Development and Evaluation (Year 3)

The core purpose of xDelia is to develop learning approaches to improve the financial decision making of private investors who trade frequently using a trading platform. This group has significant economic importance in the EU, and is sufficiently well understood to be a viable target of learning interventions. Much financial training has, to date, focused primarily on imparting propositional knowledge and increasing people’s understanding. However, investors may have appropriate knowledge, but despite this go on to be ruled by their attitudes, habits, or emotional states. Emotions mediate both rapid expert situation recognition and the application of expert intuition but also important persistent biases in decision-making such as framing effects and the disposition effect in particular. There is an increasing body of evidence that effective emotion regulation can reduce maladaptive biases mediated via emotions whilst still allowing the application of expert intuition. Investigating this, the project has developed new, technologically supported approaches to training; and the project has developed support for non-formal and informal learning in real-world trading settings to tackle the challenges faced by investors when they make financial decisions. This document sets out the nature and scope of the final xDelia learning pathway, its pedagogical underpinnings and constituent elements. A summary of major functionalities are described and learning applications. This document focuses on the evolution of the learning pathway in Year 3 of the xDelia Project and presents the final form of the learning pathway we have designed and its constituent elements. To be maximally useful to those wishing either to deploy the approaches and tools we have developed or to carry out further research and development, we also include a summary account of our evaluation of our learning approach1 and (in the appendices) documentation for each of the learning elements