19 research outputs found

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

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    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

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

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    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

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

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    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

    What causes the strength-is-weakness effect in coalition formation: Passive adoption or active selection of self-serving allocation rules?

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    In coalition formation, bargainers with many resources are often excluded from coalitions (the Strength‐is‐Weakness effect). Literature suggests this effect is driven by high‐resource bargainers using self‐serving allocation rules that backfire, as they prefer equity over equality (while low‐resource bargainers prefer the opposite). Four studies test 1) whether this is actually the case and 2) whether high‐resource bargainers solely consider equitable allocations or whether they consider both equity and equality but actively choose equity as an allocation rule. We find the Strength‐is‐Weakness effect even when equality rules are made salient, strengthening the idea that the high‐resource bargainers actively select equity as their framework for fairness to attempt to maximize their outcomes. The studies, also suggest an additional reason for the exclusion of high‐resource bargainers. We find that high‐resource bargainers are likely avoided because they are expected to bargain self‐servingly, making the low‐resource bargainers seek out each other

    Strength is still a weakness in coalition formation:Replicating and understanding the Strength-is-Weakness effect

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    A key observation in coalition formation is that bargainers with most resources are often excluded from coalitions: the Strength-is-Weakness effect. Previous studies have suffered from low sample sizes and lack of (appropriate) incentives and have rarely focused on underlying processes. To address these issues, we conducted a cross-platform replication using the Online Coalition Game. We replicated the Strength-is-Weakness effect in a psychology laboratory, on Amazon Mechanical Turk, and on Prolific. Moreover, our results showed that the equity norm shapes the Strength-is-Weakness effect in two ways. First, strong bargainers claim a higher larger of the payoffs than weak bargainers do, making them less attractive coalition partners. Second, weak bargainers expect strong bargainers to make these larger claims, directing weak bargainers to each other from the outset. Finally, the studies suggest that the Online Coalition Game is a viable tool for conducting high-powered coalition formation research

    Strength is still a weakness in coalition formation: Replicating and understanding the Strength-is-Weakness effect

    No full text
    A key observation in coalition formation is that bargainers with most resources are often excluded from coalitions: the Strength-is-Weakness effect. Previous studies have suffered from low sample sizes and lack of (appropriate) incentives and have rarely focused on underlying processes. To address these issues, we conducted a cross-platform replication using the Online Coalition Game. We replicated the Strength-is-Weakness effect in a psychology laboratory, on Amazon Mechanical Turk, and on Prolific. Moreover, our results showed that the equity norm shapes the Strength-is-Weakness effect in two ways. First, strong bargainers claim a higher larger of the payoffs than weak bargainers do, making them less attractive coalition partners. Second, weak bargainers expect strong bargainers to make these larger claims, directing weak bargainers to each other from the outset. Finally, the studies suggest that the Online Coalition Game is a viable tool for conducting high-powered coalition formation research

    The Online Coalition Game: A tool for online interactive coalition formation research

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    In this paper, we present the Online Coalition Game (OCG): an open-source tool written for the open-access research platform oTree that enables high-powered interactive coalition formation experiments. Besides containing a tutorial on conducting and configuring studies using the OCG, we discuss two previous implementations. With these examples, we demonstrate that online use of the OCG provides the benefits of large sample sizes and fast data collection, while leading to convergent and robust findings. Moreover, we show that small changes in the experimental setup offer interesting opportunities to expand coalition formation theory by including insights from, amongst others, literature on bargaining, ostracism, and communication, and vice versa

    Experimental data for: Replicating the Strength-is-Weakness Effect Using the Online Coalition Game

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    This data package contains (meta) data, analysis scripts, and relevant documents for the project: Replicating the Strength-is-Weakness Effect Using the Online Coalition Game The research replicates the Strength-is-Weakness effect in coalition formatting, using the new Online Coalition Game in a social psychology lab setting and online (Amazon Mechanical Turk)

    Experimental data for: Why Bargainers with Many Resources in Coalition Formation Ask Too Much

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    This data package contains (meta) data, analysis scripts, and relevant documents for the project: Why Bargainers with Many Resources in Coalition Formation Ask Too Much The research pits two explanations for the use of proportional allocation rules by strong bargainers in coalition formation against each other: 1) a myopic focus on proportional allocation rules 2) strategic application of proportional allocation rules
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