The power of pictures: Vertical picture angles in power pictures

Abstract: Conventional wisdom suggests that variations in vertical picture angle cause the subject to appear more powerful when depicted from below and less powerful when depicted from above. However, do the media actually use such associations to represent individual differences in power? We argue that the diverse perspectives of evolutionary, social learning, and embodiment theories all suggest that the association between verticality and power is relatively automatic and should, therefore, be visible in the portrayal of powerful and powerless individuals in the media. Four archival studies (with six samples) provide empirical evidence for this hypothesis and indicate that a salience power context reinforces this effect. In addition, two experimental studies confirm these effects for individuals producing media content. We discuss potential implications of this effect

Low fingertip temperature rebound measured by digital thermal monitoring strongly correlates with the presence and extent of coronary artery disease diagnosed by 64-slice multi-detector computed tomography

Previous studies showed strong correlations between low fingertip temperature rebound measured by digital thermal monitoring (DTM) during a 5 min arm-cuff induced reactive hyperemia and both the Framingham Risk Score (FRS), and coronary artery calcification (CAC) in asymptomatic populations. This study evaluates the correlation between DTM and coronary artery disease (CAD) measured by CT angiography (CTA) in symptomatic patients. It also investigates the correlation between CTA and a new index of neurovascular reactivity measured by DTM. 129 patients, age 63 ± 9 years, 68% male, underwent DTM, CAC and CTA. Adjusted DTM indices in the occluded arm were calculated: temperature rebound: aTR and area under the temperature curve aTMP-AUC. DTM neurovascular reactivity (NVR) index was measured based on increased fingertip temperature in the non-occluded arm. Obstructive CAD was defined as ≥50% luminal stenosis, and normal as no stenosis and CAC = 0. Baseline fingertip temperature was not different across the groups. However, all DTM indices of vascular and neurovascular reactivity significantly decreased from normal to non-obstructive to obstructive CAD [(aTR 1.77 ± 1.18 to 1.24 ± 1.14 to 0.94 ± 0.92) (P = 0.009), (aTMP-AUC: 355.6 ± 242.4 to 277.4 ± 182.4 to 184.4 ± 171.2) (P = 0.001), (NVR: 161.5 ± 147.4 to 77.6 ± 88.2 to 48.8 ± 63.8) (P = 0.015)]. After adjusting for risk factors, the odds ratio for obstructive CAD compared to normal in the lowest versus two upper tertiles of FRS, aTR, aTMP-AUC, and NVR were 2.41 (1.02–5.93), P = 0.05, 8.67 (2.6–9.4), P = 0.001, 11.62 (5.1–28.7), P = 0.001, and 3.58 (1.09–11.69), P = 0.01, respectively. DTM indices and FRS combined resulted in a ROC curve area of 0.88 for the prediction of obstructive CAD. In patients suspected of CAD, low fingertip temperature rebound measured by DTM significantly predicted CTA-diagnosed obstructive disease

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

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. Protocol registration: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1

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

Abstract: 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. Protocol registration: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1. © 2021, The Author(s), under exclusive licence to Springer Nature Limited

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