Neural foundations of cooperative social interactions

The embodied-embedded-enactive-extended (4E) approach to study cognition suggests that interaction with the world is a crucial component of our cognitive processes. Most of our time, we interact with other people. Therefore, studying cognition without interaction is incomplete. Until recently, social neuroscience has only focused on studying isolated human and animal brains, leaving interaction unexplored. To fill this gap, we studied interacting participants, focusing on both intra- and inter-brain (hyperscanning) neural activity. In the first study, we invited dyads to perform a visual task in both a cooperative and a competitive context while we measured EEG. We found that mid-frontal activity around 200-300 ms after receiving monetary rewards was sensitive to social context and differed between cooperative and competitive situations. In the second study, we asked participants to coordinate their movements with each other and with a robotic partner. We found significantly stronger EEG amplitudes at frontocentral electrodes when people interacted with a robotic partner. Lastly, we performed a comprehensive literature review and the first meta-analysis in the emerging field of hyperscanning that validated it as a method to study social interaction. Taken together, our results showed that adding a second participant (human or AI/robotic) fostered our understanding of human cognition. We learned that the activity at frontocentral electrodes is sensitive to social context and type of partner (human or robotic). In both studies, the participants’ interaction was required to show these novel neural processes involved in action monitoring. Similarly, studying inter-brain neural activity allows for the exploration of new aspects of cognition. Many cognitive functions involved in successful social interactions are accompanied by neural synchrony between brains, suggesting the extended form of our cognition

Icons of grace: covenant and gestalt in the theology of Karl Barth and Paul Tillich

This dissertation focuses on the aspects of grace in the theologies of Karl Barth and Paul Tillich. Following the phenomenology of Jean-Luc Marion, the threads of both the iconic and the idolic will be investigated as to their influence in Barth's exposition of the doctrine of the Covenant and Tillich's development of the Gestalt of grace. A chronological approach will be taken, showing both the similarities and differences between Barth and Tillich and the theological developments in their lives. The phenomenological analysis of the given, will show that Barth and Tillich were nearer in concepts of grace then is often accounted, but it will also be demonstrated that they were not compatible on that which manifests itself as the iconic. The covenant, as espoused by Barth, will be seen not to have a sufficient iconic presence in comparison with Tillich's Gestalt of grace symbolized by the cross. However, it will also be shown that Marion's eucharistic symbology is not completely adequate as a corrective to either Barth or Tillich.The introductory chapter will state Marion's thought on the "giveness" of the phenomenon of grace, both as concept and icon. Chapter 2 will focus on the theological and philosophical backgrounds ofBarth and Tillich. In Chapter 3 and 4, the early careers of Barth and Tillich will be narrated and their early meetings recounted. In Chapter 5, Barth's developing theology will be discussed, especially his shift from dialectical thought to an analogy of faith following his study of Anselm. Tillich's career as a philosopher will be analyzed with special emphasis on his theological essays. Chapter 7 will focus on Barth's early dogmatic thought leading up to his doctrine of election and the covenant. Tillich's mature theology in his systematic writings of the fifties will be the topic of Chapter 8. The final notions ofBarth's doctrine of the covenant in the last two volumes of his dogmatics will reveal his theology of the cross, and his understandings ofthe secular parables of grace. The conclusion will summarize and critique Barth and Tillich's ideas on the divine initiative of grace and Marion's icon of grace in the sacrament of the Lord's Supper.This study uses an approach not done before which will illuminate various understandings ofBarth and Tillich's theology and seeks to provide a fresh reading of their respective doctrines of grace

Active Observers in a 3D World: Human Visual Behaviours for Active Vision

Human-like performance in computational vision systems is yet to be achieved. In fact, human-like visuospatial behaviours are not well understood a crucial capability for any robotic system whose role is to be a real assistant. This dissertation examines human visual behaviours involved in solving a well-known visual task; The Same-Different Task. It is used as a probe to explore the space of active human observation during visual problem-solving. It asks a simple question: are two objects the same?. To study this question, we created a set of novel objects with known complexity to push the boundaries of the human visual system. We wanted to examine these behaviours as opposed to the static, 2D, display-driven experiments done to date. We thus needed to develop a complete infrastructure for an experimental investigation using 3D objects and active, free, human observers. We have built a novel, psychophysical experimental setup that allows for precise and synchronized gaze and head-pose tracking to analyze subjects performing the task. To the best of our knowledge, no other system provides the same characteristics. We have collected detailed, first-of-its-kind data of humans performing a visuospatial task in hundreds of experiments. We present an in-depth analysis of different metrics of humans solving this task, who demonstrated up to 100% accuracy for specific settings and that no trial used less than six fixations. We provide a complexity analysis that reveals human performance in solving this task is about O(n), where n is the size of the object. Furthermore, we discovered that our subjects used many different visuospatial strategies and showed that they are deployed dynamically. Strikingly, no learning effect was observed that affected the accuracy. With this extensive and unique data set, we addressed its computational counterpart. We used reinforcement learning to learn the three-dimensional same-different task and discovered crucial limitations which only were overcome if the task was simplified to the point of trivialization. Lastly, we formalized a set of suggestions to inform the enhancement of existing machine learning methods based on our findings from the human experiments and multiple tests we performed with modern machine learning methods

Active Observers in a 3D World: Human Visual Behaviours for Active Vision

Human-like performance in computational vision systems is yet to be achieved. In fact, human-like visuospatial behaviours are not well understood – a crucial capability for any robotic system whose role is to be a real assistant. This dissertation examines human visual behaviours involved in solving a well-known visual task; The Same-Different Task. It is used as a probe to explore the space of active human observation during visual problem-solving. It asks a simple question: “are two objects the same?”. To study this question, we created a set of novel objects with known complexity to push the boundaries of the human visual system. We wanted to examine these behaviours as opposed to the static, 2D, display-driven experiments done to date. We thus needed to develop a complete infrastructure for an experimental investigation using 3D objects and active, free, human observers. We have built a novel, psychophysical experimental setup that allows for precise and synchronized gaze and head-pose tracking to analyze subjects performing the task. To the best of our knowledge, no other system provides the same characteristics. We have collected detailed, first-of-its-kind data of humans performing a visuospatial task in hundreds of experiments. We present an in-depth analysis of different metrics of humans solving this task, who demonstrated up to 100% accuracy for specific settings and that no trial used less than six fixations. We provide a complexity analysis that reveals human performance in solving this task is about O(n), where n is the size of the object. Furthermore, we discovered that our subjects used many different visuospatial strategies and showed that they are deployed dynamically. Strikingly, no learning effect was observed that affected the accuracy. With this extensive and unique data set, we addressed its computational counterpart. We used reinforcement learning to learn the three-dimensional same-different task and discovered crucial limitations which only were overcome if the task was simplified to the point of trivialization. Lastly, we formalized a set of suggestions to inform the enhancement of existing machine learning methods based on our findings from the human experiments and multiple tests we performed with modern machine learning methods

The use of Eye Tracking Technology in Maritime High-Speed Craft Navigation

publishedVersio

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

WHERE DO YOU LOOK? RELATING VISUAL ATTENTION TO LEARNING OUTCOMES AND URL PARSING

Visual behavior provides a dynamic trail of where attention is directed. It is considered the behavioral interface between engagement and gaining information, and researchers have used it for several decades to study user\u27s behavior. This thesis focuses on employing visual attention to understand user\u27s behavior in two contexts: 3D learning and gauging URL safety. Such understanding is valuable for improving interactive tools and interface designs. In the first chapter, we present results from studying learners\u27 visual behavior while engaging with tangible and virtual 3D representations of objects. This is a replication of a recent study, and we extended it using eye tracking. By analyzing the visual behavior, we confirmed the original study results and added more quantitative explanations for the corresponding learning outcomes. Among other things, our results indicated that the users allocate similar visual attention while analyzing virtual and tangible learning material. In the next chapter, we present a user study\u27s outcomes wherein participants are instructed to classify a set of URLs wearing an eye tracker. Much effort is spent on teaching users how to detect malicious URLs. There has been significantly less focus on understanding exactly how and why users routinely fail to vet URLs properly. This user study aims to fill the void by shedding light on the underlying processes that users employ to gauge the UR L\u27s trustworthiness at the time of scanning. Our findings suggest that users have a cap on the amount of cognitive resources they are willing to expend on vetting a URL. Also, they tend to believe that the presence of www in the domain name indicates that the URL is safe

Analyse et détection des trajectoires d'approches atypiques des aéronefs à l'aide de l'analyse de données fonctionnelles et de l'apprentissage automatique

L'amélioration de la sécurité aérienne implique généralement l'identification, la détection et la gestion des événements indésirables qui peuvent conduire à des événements finaux mortels. De précédentes études menées par la DSAC, l'autorité de surveillance française, ont permis d'identifier les approches non-conformes présentant des déviations par rapport aux procédures standards comme des événements indésirables. Cette thèse vise à explorer les techniques de l'analyse de données fonctionnelles et d'apprentissage automatique afin de fournir des algorithmes permettant la détection et l'analyse de trajectoires atypiques en approche à partir de données sol. Quatre axes de recherche sont abordés. Le premier axe vise à développer un algorithme d'analyse post-opérationnel basé sur des techniques d'analyse de données fonctionnelles et d'apprentissage non-supervisé pour la détection de comportements atypiques en approche. Le modèle sera confronté à l'analyse des bureaux de sécurité des vols des compagnies aériennes, et sera appliqué dans le contexte particulier de la période COVID-19 pour illustrer son utilisation potentielle alors que le système global ATM est confronté à une crise. Le deuxième axe de recherche s'intéresse plus particulièrement à la génération et à l'extraction d'informations à partir de données radar à l'aide de nouvelles techniques telles que l'apprentissage automatique. Ces méthodologies permettent d'améliorer la compréhension et l'analyse des trajectoires, par exemple dans le cas de l'estimation des paramètres embarqués à partir des paramètres radar. Le troisième axe, propose de nouvelles techniques de manipulation et de génération de données en utilisant le cadre de l'analyse de données fonctionnelles. Enfin, le quatrième axe se concentre sur l'extension en temps réel de l'algorithme post-opérationnel grâce à l'utilisation de techniques de contrôle optimal, donnant des pistes vers de nouveaux systèmes d'alerte permettant une meilleure conscience de la situation.Improving aviation safety generally involves identifying, detecting and managing undesirable events that can lead to final events with fatalities. Previous studies conducted by the French National Supervisory Authority have led to the identification of non-compliant approaches presenting deviation from standard procedures as undesirable events. This thesis aims to explore functional data analysis and machine learning techniques in order to provide algorithms for the detection and analysis of atypical trajectories in approach from ground side. Four research directions are being investigated. The first axis aims to develop a post-op analysis algorithm based on functional data analysis techniques and unsupervised learning for the detection of atypical behaviours in approach. The model is confronted with the analysis of airline flight safety offices, and is applied in the particular context of the COVID-19 crisis to illustrate its potential use while the global ATM system is facing a standstill. The second axis of research addresses the generation and extraction of information from radar data using new techniques such as Machine Learning. These methodologies allow to \mbox{improve} the understanding and the analysis of trajectories, for example in the case of the estimation of on-board parameters from radar parameters. The third axis proposes novel data manipulation and generation techniques using the functional data analysis framework. Finally, the fourth axis focuses on extending the post-operational algorithm into real time with the use of optimal control techniques, giving directions to new situation awareness alerting systems