To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution.C.L. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007); L.M.D. was supported by ERC 647910 (KINSHIP); D.I.B. and N.I. received funding from CONICET, Argentina; L.K., F.K. and Á. Putz were supported by the European Social Fund (EFOP-3.6.1.-16-2016-00004; ‘Comprehensive Development for Implementing Smart Specialization Strategies at the University of Pécs’). K.U. and E. Vergauwe were supported by a grant from the Swiss National Science Foundation (PZ00P1_154911 to E. Vergauwe). T.G. is supported by the Social Sciences and Humanities Research Council of Canada (SSHRC). M.A.V. was supported by grants 2016-T1/SOC-1395 (Comunidad de Madrid) and PSI2017-85159-P (AEI/FEDER UE). K.B. was supported by a grant from the National Science Centre, Poland (number 2015/19/D/HS6/00641). J. Bonick and J.W.L. were supported by the Joep Lange Institute. G.B. was supported by the Slovak Research and Development Agency (APVV-17-0418). H.I.J. and E.S. were supported by a French National Research Agency ‘Investissements d’Avenir’ programme grant (ANR-15-IDEX-02). T.D.G. was supported by an Australian Government Research Training Program Scholarship. The Raipur Group is thankful to: (1) the University Grants Commission, New Delhi, India for the research grants received through its SAP-DRS (Phase-III) scheme sanctioned to the School of Studies in Life Science; and (2) the Center for Translational Chronobiology at the School of Studies in Life Science, PRSU, Raipur, India for providing logistical support. K. Ask was supported by a small grant from the Department of Psychology, University of Gothenburg. Y.Q. was supported by grants from the Beijing Natural Science Foundation (5184035) and CAS Key Laboratory of Behavioral Science, Institute of Psychology. N.A.C. was supported by the National Science Foundation Graduate Research Fellowship (R010138018). We acknowledge the following research assistants: J. Muriithi and J. Ngugi (United States International University Africa); E. Adamo, D. Cafaro, V. Ciambrone, F. Dolce and E. Tolomeo (Magna Græcia University of Catanzaro); E. De Stefano (University of Padova); S. A. Escobar Abadia (University of Lincoln); L. E. Grimstad (Norwegian School of Economics (NHH)); L. C. Zamora (Franklin and Marshall College); R. E. Liang and R. C. Lo (Universiti Tunku Abdul Rahman); A. Short and L. Allen (Massey University, New Zealand), A. Ateş, E. Güneş and S. Can Özdemir (Boğaziçi University); I. Pedersen and T. Roos (Åbo Akademi University); N. Paetz (Escuela de Comunicación Mónica Herrera); J. Green (University of Gothenburg); M. Krainz (University of Vienna, Austria); and B. Todorova (University of Vienna, Austria). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.https://www.nature.com/nathumbehav/am2023BiochemistryGeneticsMicrobiology and Plant Patholog

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

Verfolgung von mehreren, aufeinander bezogenen Zielobjekten in Videos

Abweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in dt. SpracheDiese Dissertation präsentiert Forschung auf dem Gebiet des Trackings (Verfolgung). Tracking ist eines der am gründlichsten erforschten Themen im computerunterstützten Sehen (Computer Vision). Das Ziel beim Tracking ist es ein gewähltes Objekt (Ziel) in einem Video zu verfolgen. Diese Dissertation konzentriert sich auf ein spezielles Problem bei dem mehrere Ziele verfolgt werden sollen die in Beziehung zueinander stehen. Zwei wichtige Fragen beim Tracking sind: Was ist das Ziel? und Wo ist das Ziel? Die zwei wichtigsten wissenschaftlichen Beiträge dieser Dissertation beantworten diese Fragen mit Hilfe von Graphen. Der erste Beitrag der Dissertation ist eine vollautomatische Initialisierung für Zielmodelle (Was?) basierend auf dem Prinzip: Dinge die sich gemeinsam bewegen gehören zusammen. Als Eingabe dient ein Video der sich bewegenden Ziele. In diesem Video werden interessante Punkte verfolgt und die Bewegungsinformation in Form von Trajektorien gespeichert. Basierend auf den Positionen der verfolgten Punkte im ersten Bild des Videos wird ein triangulierter Graph erstellt. Auf Grund der Bewegungsinformationen in den Trajektorien wird der Graph verformt. Die Verformung des Graphen wird zur Eingabe der folgenden, hierarchischen Gruppierung verwendet. Die Gruppierung wird durch eine unregelmäßige, duale Graphenpyramide umgesetzt. An der Spitze der Pyramide findet man die starren Komponenten des Videos (z.B. die Körperteile eines Menschen). Im letzten Schritt kann man durch Analyse der Bewegung feststellen, ob sich Komponenten durch Artikulationspunkte verknüpfen lassen (z.B. Ober- und Unterarm eines Menschen). Der zweite Beitrag ist ein innovativer Ansatz, um zeitliche Übereinstimmungen für mehrere voneinander abhängige Ziele zu finden (Wo?). In dieser Dissertation wird vorgeschlagen das Ziel als Graph zu repräsentieren, wobei jedes Ziel als Knoten und ihre räumlichen Zusammenhänge als Kanten im Graphen gespeichert werden. Um eine zeitliche Übereinstimmung für einen Graphen zwischen zwei Bildern eines Videos herzustellen, wird üblicherweise nach dem ähnlichsten Graphen im zweiten Bild gesucht. Im Gegensatz dazu wird in dieser Dissertation ein innovativer Ansatz vorgestellt, der die Übereinstimmung für jeden Knoten (jedes Ziel) einzeln sucht. Dabei werden Informationen eines einfachen Trackingverfahrens, die vom Aussehen des Ziels abhängen, mit strukturellen Informationen aus dem Graphen kombiniert. In einem iterativen Prozess, der dem bekannten Mean Shift Algorithmus ähnlich ist, werden diese zwei Arten von Information kombiniert. Das Ergebnis sind Übereinstimmungen für alle Knoten und Kanten im Graphen die lokal optimal bezüglich ihres Aussehens und ihrer Struktur sind. Das Ziel dieser Arbeit war das Potential von Graphen im Tracking aufzuzeigen. Durch die zwei Beiträge dieser Dissertation konnte dieses Ziel erreicht werden.This cumulative thesis presents research in the field of tracking. Tracking is one of the most thoroughly researched problems in computer vision. The aim of tracking is to follow an object of interest (target) in a video. In this thesis, I focus on a special problem: tracking related multiple targets. Two important questions in tracking are: What is the target? and Where is the target? The core contributions of this thesis answer these two questions with the help of graph-based representations and methods. The first core contribution is a fully automatic initialization for target models (What?), based on the principal that things which move together belong together. The input of the approach is a video showing the targets in motion. In this video a set of salient points is tracked to extract the necessary motion information in the form of trajectories. A triangulated graph is built based on the initial positions of the tracked points (i.e. 2D positions in the first frame). Then, the triangulated graph is deformed based on the motion encoded in the trajectories. This deformation of the triangulation over time is the input of a hierarchical grouping process, which is realized by an irregular dual graph pyramid. In the top level of the resulting pyramid the rigid entities (e.g. body parts of a human body) are identified. Finally, the motion of these rigid entities is analyzed to find possible points of articulation connecting them (e.g. upper and lower arm of a human). The second core contribution is a novel approach for finding temporal correspondences of multiple related targets (Where?). This thesis proposes to represent the targets by a graph model, where each target is represented by a vertex and their relationships are encoded by edges. The traditional solution to find the temporal correspondences of a graph model is graph matching. In contrast to that, this thesis proposes a novel approach, which finds the correspondence of each vertex (target) by combining the appearance cue of a simple tracker with the structural cue deduced from a graph model. These two cues are combined in an iterative process inspired by the well-known Mean Shift algorithm. The outcome are correspondences for all vertices and edges in the graph, which locally maximize the similarity in appearance and locally minimize the deviation from the structure encoded in the model. Finally, the main goal of this thesis is to show the potential of graph-based representations and methods in tracking. This goal has been achieved through these two core contributions.12

Special Issue on Discrete Geometry for Computer Imagery

International audienc

Motion detection as interaction technique for games & applications on mobile devices

Mobile devices become smaller and more powerful with each generation distributed. Because of the tiny enclosures the interaction with such devices offers limited input capabilities. In contrast there are hardly any mobile phones purchasable that do not have a built-in camera. We developed a concept of an intuitive interaction technique using optical inertial tracking on mobile phones. The key of this concept is the user moving the mobile device which results in a moving video stream of the camera. The direction of the movement can be calculated with a suitable algorithm. This paper outlines the algorithm Projection Shift Analysis developed to run on mobile phones. 1

Multi-scale 2D tracking of articulated objects using hierarchical spring systems

This paper presents a flexible framework to build a target-specific, part-based representation for arbitrary articulated or rigid objects. The aim is to successfully track the target object in 2D, through multiple scales and occlusions. This is realized by employing a hierarchical, iterative optimization process on the proposed representation of structure and appearance. Therefore, each rigid part of an object is described by a hierarchical spring system represented by an attributed graph pyramid. Hierarchical spring systems encode the spatial relationships of the features (attributes of the graph pyramid) describing the parts and enforce them by spring-like behavior during tracking. Articulation points connecting the parts of the object allow to transfer position information from reliable to ambiguous parts. Tracking is done in an iterative process by combining the hypotheses of simple trackers with the hypotheses extracted from the hierarchical spring systems