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Abstract and Figures

Over the last ten 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 judgments 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 11,481 participants. When we used Oosterhof’s and Todorov’s original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative strategy that allowed for a more optimal number of correlated latent factors, we observed much less generalization. These results underscore how each analysis strategy embeds substantive assumptions that can strongly influence theoretical conclusions.
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To Which World Regions Does the Valence-Dominance Model of Social
Perception Apply?
(PSA001; Registered Report Stage 2)
This manuscript is under review at Nature Human Behaviour.
2
(BCJ, LMD, JKF joint first authors)
Benedict C. Jones1, Lisa M. DeBruine1, Jessica K. Flake2, Marco Tullio Liuzza3, Jan
Antfolk4, Nwadiogo C. Arinze5, Izuchukwu L. G. Ndukaihe5, Nicholas G. Bloxsom6,
Savannah C. Lewis6, Francesco Foroni7, Megan L. Willis7, Carmelo P. Cubillas8, Miguel
A. Vadillo8, Michael Gilead9, Almog Simchon9, S. Adil Saribay10, Nicholas C. Owsley11,
Dustin P. Calvillo12, Anna Wlodarczyk13, Yue Qi14, Kris Ariyabuddhiphongs15, Somboon
Jarukasemthawee15, Harry Manley15, Panita Suavansri15, Nattasuda Taephant15, Ryan
M. Stolier16, Thomas R. Evans17, Judson Bonick18, Jan W. Lindemans18, , Logan F.
Ashworth19, Coralie Chevallier20, Aycan Kapucu21, Aslan Karaaslan21, Juan David
Leongómez22, Oscar R. Sánchez22, Eugenio Valderrama22, Milena Vásquez-
Amézquita22, Balazs Aczel24, Nandor Hajdu23, 24, Peter Szecsi24, Michael Andreychik25,
Erica D. Musser26, Carlota Batres27, Chuan-Peng Hu28, Qing-Lan Liu29, Nicole Legate30,
Leigh Ann Vaughn31, Krystian Barzykowski32, Karolina Golik32, Irina Schmid33, Stefan
Stieger33, Richard Artner34, Chiel Mues34, Wolf Vanpaemel35, Zhongqing Jiang36, Qi
Wu36, Gabriela M. Marcu37, Ian D. Stephen38, Jackson G. Lu39, Michael C. Philipp40, Jack
D. Arnal41, Eric Hehman2, Sally Y. Xie2, William J. Chopik42, Martin Seehuus43, Soufian
Azouaghe44, 79, Abdelkarim Belhaj44, Jamal Elouafa44, John P. Wilson45, Elliott Kruse46,
Marietta Papadatou-Pastou47, Alan E. Barba-Sãnchez48, Anabel De La Rosa-Gómez48,
Isaac González-Santoyo49, Tsuyueh Hsu50, Chun-Chia Kung50, Hsiao-Hsin Wang50,
Jonathan B. Freeman51, DongWon Oh52, Vidar Schei53, Therese E. Sverdrup53, Carmel
A. Levitan54, Corey L. Cook 55, Priyanka Chandel56, Pratibha Kujur56, Arti Parganiha56,
Noorshama Parveen56, Atanu Kumar Pati56, Sraddha Pradhan56, Margaret M. Singh56,
Babita Pande57, Jozef Bavolar58, Pavol Kačmár58, Ilya Zakharov59, Sara Álvarez-Solas60,
Ernest Baskin61, Martin Thirkettle62, Kathleen Schmidt63, Cody D. Christopherson64,
Jordan W. Suchow65, Jonas K. Olofsson66, Ai-Suan Lee67, Jennifer L. Beaudry68, Taylor
D. Gogan68, Julian A. Oldmeadow68, Barnaby J. W. Dixson120, Laura M. Stevens70,
Gianni Ribeiro120, Mark J. Brandt72, Karlijn Hoyer72, Bastian Jaeger72, Dongning Ren72,
Willem W. A. Sleegers72, Joeri Wissink72, Gwenaël Kaminski73, Victoria A. Floerke74,
Heather L. Urry74, Sau-Chin Chen75, Gerit Pfuhl76, Zahir Vally77, Dana M. Basnight-
Brown78, Hans IJzerman79, Elisa Sarda79, Touhami Badidi81, Nicolas Van der Linden82,
Chrystalle B. Y. Tan83, Vanja Kovic84, Melissa F. Colloff70, Heather D. Flowe70Debora I.
Burin87, Gwendolyn Gardiner88, John Protzko89, Christoph Schild90, Karolina A. Scigala90,
Ingo Zettler90, Erin M. O'Mara Kunz91, Daniel Storage92, Fieke M. A. Wagemans93, Blair
Saunders94, Miroslav Sirota95, Guyan V. Sloane95, Tiago J. S. Lima96, Kim Uittenhove97,
Evie Vergauwe97, Katarzyna Jaworska1, Lilian Carvalho, Karl Ask99, Casper J. J. van
Zyl100, Anita Körner101, Sophia C. Weissgerber102, Jordane Boudesseul103, Fernando
Ruiz-Dodobara103, Kay L. Ritchie104, Nicholas M. Michalak105, Khandis R. Blake106, 142,
David White106, Alasdair R. Gordon-Finlayson107, Michele Anne108, Steve M. J.
Janssen108, Kean Mun Lee108, Tonje K. Nielsen109, Christian K. Tamnes109, Janis H.
Zickfeld109, Anna Dalla Rosa110, Ferenc Kocsor111, Luca Kozma111, Ádám Putz111,
Patrizio Tressoldi112, Michelangelo Vianello110, Natalia Irrazabal113, Armand Chatard114,
Samuel Lins115, Isabel R. Pinto115, Johannes Lutz116, Matus Adamkovic117, Peter
Babincak117, Gabriel Baník117, Ivan Ropovik118,143, Vinet Coetzee119, Kim Peters120, Niklas
K. Steffens120, Kok Wei Tan121, Christopher A. Thorstenson122, Ana Maria Fernandez123,
Rafael M. C. S. Hsu124, Jaroslava V. Valentova124, Marco A. C. Varella124, Nadia S.
Corral-Frías125, Martha Frias-Armenta125, Javad Hatami126, Arash Monajem126,
MohammadHasan Sharifian126, Brooke Frohlich127, Hause Lin128, Michael Inzlicht128,
Claus Lamm129, Ekaterina Pronizius129, Martin Voracek129, Jerome Olsen130, Erik Mac
Giolla131, Aysegul Akgoz132, Asil A. Özdoğru132, Matthew T. Crawford133, Brooke Bennett-
Day134, Monica A. Koehn135, Ceylan Okan136, Daniel Ansari137, Tripat Gill138, Jeremy K.
Miller139, Yarrow Dunham140, Xin Yang140, Sinan Alper141, Ravin Alaei128, Martha Lucia
Borras-Guevara144, Sun Jun Cai145, Alexander F. Danvers146, Vilius Dranseika147, Eva
Gilboa-Schechtman148, Amanda C. Hahn19, Chaning Jang11, Tara C. Marshall149, Randy
J. McCarthy150, Jose Antonio Muñoz-reyes151, Lison Neyroud80, Pablo Polo151, Nicholas
Rule128, Victor K.M. Shiramazu152, Dong Tiantian145, Enrique Turiegano8, Wen-Jing
Yan153, Benjamin Balas69, Paulo Ferreira154, Julia Jünger98, Georgina Mburu11, Walido
3
Sampaio155, Diana Santos154, Sami Gülgöz71, Julia Stern98, Patrick S. Forscher80,
Christopher R. Chartier6, Nicholas A. Coles127
1University of Glasgow, Institute of Neuroscience and Psychology, UK. 2McGill
University, Department of Psychology, Canada. 3"Magna Graecia" University of
Catanzaro, Department of Medical and Surgical Sciences, Italy. 4Åbo Akademi
University, Faculty of Arts, Psychology and Theology, Finland. 5Alex Ekwueme Federal
University Ndufu Alike, Department of Psychology, Nigeria. 6Ashland University,
Department of Psychology, USA. 7Australian Catholic University, School of Behavioural
and Health Sciences, Australia. 8Autonomous University of Madrid, Department of Basic
Psychology, Spain. 9Ben-Gurion University of the Negev, Department of Psychology,
Israel. 10Boğaziçi University, Department of Psychology, Turkey. 11Busara Center for
Behavioral Economics, Kenya. 12California State University San Marcos, Psychology
Department, USA. 13Catholic University of the North, School of Psychology, Chile. 14CAS
Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of
Sciences, Beijing, China. 15Chulalongkorn University, Faculty of Psychology, Thailand.
16Columbia University, Department of Psychology, USA. 17Coventry University, School of
Psychological, Social and Behavioural Sciences, UK. 18Duke University, Center for
Advanced Hindsight, USA. 19Humboldt State University, Department of Psychology,
USA. 20Laboratoire de Neurosciences Cognitives et Computationnelles, Département
d'Études Cognitives, INSERM U960, École Normale Supérieure, France. 21Ege
University, Psychology Department, Turkey. 22Universidad El Bosque, Faculty of
Psychology, Colombia. 23ELTE Eötvös Loránd University, Doctoral School of Psychology,
Hungary. 24ELTE Eötvös Loránd University, Institute of Psychology, Hungary. 25Fairfield
University, Department of Psychology, USA. 26Florida International University,
Department of Psychology, USA. 27Franklin and Marshall College, Department of
Psychology, USA. 28German Resilience Center, Germany. 29Hubei University,
Department of Psychology, China. 30Illinois Institute of Technology, Department of
Psychology, USA. 31Ithaca College, Department of Psychology, USA. 32Jagiellonian
University, Institute of Psychology, Poland. 33Karl Landsteiner University of Health
Sciences, Department of Psychology and Psychodynamics, Austria.
34Katholieke Universiteit Leuven, Research Group of Quantitative Psychology and
Individual Differences, Belgium. 35Katholieke Universiteit Leuven, Faculty of Psychology
and Educational Sciences, Belgium. 36Liaoning Normal University, Department of
Psychology, China. 37Lucian Blaga University of Sibiu, Department of Psychology,
Romania. 38Macquarie University, Department of Psychology, Australia. 39Massachusetts
Institute of Technology, Sloan School of Management, USA. 40Massey University, School
of Psychology, New Zealand. 41McDaniel College, Psychology Department, USA.
42Michigan State University, Department of Psychology, USA. 43Middlebury College,
Department of Psychology, USA. 44Mohammed V University, Department of Psychology,
Morocco. 45Montclair State University, Psychology Department, USA. 46Monterrey
Institute of Technology and Higher Education, EGADE Business School, Mexico.
47National and Kapodistrian University of Athens, School of Education, Greece.
48National Autonomous University of Mexico, School of Higher Studies Iztacala, Mexico.
49National Autonomous University of Mexico, Department of Psychology, 50National
Cheng Kung University, Department of Psychology, Taiwan. 51New York University,
Department of Psychology and Center for Neural Science, USA. 52New York University,
Department of Psychology, USA. 53NHH Norwegian School of Economics, Department of
Strategy and Management, Norway. 54Occidental College, Department of Cognitive
Science, USA. 55Pacific Lutheran University, Department of Psychology, USA. 56Pandit
Ravishankar Shukla University, School of Studies in Life Science, India. 57Pandit
Ravishankar Shukla University, Center for Basic Sciences, India. 58Pavol Jozef Šafárik
University in Košice, Department of Psychology, Slovakia. 59Psychological Institute of
Russian Academy of Education, Developmental Behavioral Genetics Lab, Russia.
60Universidad Regional Amazónica Ikiam, Department of Biosciences, Ecuador. 61Saint
Joseph's University, Department of Food Marketing, USA. 62Sheffield Hallam University,
Department of Psychology, Sociology and Politics, UK. 63Southern Illinois University,
4
Department of Psychology, USA. 64Southern Oregon University, Psychology Department,
USA. 65Stevens Institute of Technology, School of Business, USA. 66Stockholm
University, Department of Psychology, Sweden. 67Sunway University, Department of
Psychology, Malaysia. 68Swinburne University of Technology, Department of
Psychological Sciences, Australia.69North Dakota State University, Department of
Psychology, USA. 70University of Birmingham , School of Psychology, UK. 71Koç
University, Turkey. 72Tilburg University, Department of Social Psychology, Netherlands.
73Toulouse University, CLLE, France. 74Tufts University, Department of Psychology,
USA. 75Tzu-Chi University, Department of Human Development and Psychology, Taiwan.
76UiT The Arctic University of Norway, Department of Psychology, Norway. 77United Arab
Emirates University, Department of Psychology & Counseling, United Arab Emirates.
78United States International University - Africa, Kenya. 79Université Grenoble Alpes,
LIPPC2S, France. 80Université Grenoble Alpes, Department of Psychology, France.
81Université Ibn Tofail Kénitra, Department of Psychology, Morocco. 82Université Libre de
Bruxelles, Center for Social and Cultural Psychology, Belgium. 83Universiti Malaysia
Sabah, Department of Community and Family Medicine, Malaysia. 84University of
Belgrade, Department of Psychology, Faculty of Philosophy, Serbia. 85Department of
Psychology, University of Chinese Academy of Sciences, Beijing, China. 87Universidad
de Buenos Aires, Instituto de Investigaciones, Facultad de Psicologia, Argentina.
88University of California, Riverside, Department of Psychology, USA. 89University of
California, Santa Barbara, Department of Psychological & Brain Sciences, USA.
90University of Copenhagen, Department of Psychology, Denmark. 91University of
Dayton, Department of Psychology, USA. 92University of Denver, Department of
Psychology, USA. 93University of Duisburg-Essen, Institute for Socio-Economics,
Germany. 94University of Dundee, School of Social Sciences, UK. 95University of Essex,
Department of Psychology, UK. 96University of Fortaleza, Department of Psychology,
Brazil. 97University of Geneva, Faculty of Psychology and Educational Sciences,
Switzerland. 98University of Goettingen, Department of Psychology, Germany.
99University of Gothenburg, Department of Psychology, Sweden. 100University of
Johannesburg, Department of Psychology, South Africa. 101University of Kassel,
Department of Psychology, Germany. 102University of Kassel, Department of Psychology,
Germany. 103University of Lima, Institute of Scientific Research, Peru. 104University of
Lincoln, School of Psychology, UK 105University of Michigan, Department of Psychology,
USA. 106University of New South Wales Sydney, Evolution and Ecology Research
Centre, Australia. 107University of Northampton, Faculty of Health, Education & Society,
UK. 108University of Nottingham Malaysia, School of Psychology, Malaysia. 109University
of Oslo, Department of Psychology, Norway. 110University of Padova, Department of
Philosophy, Sociology, Education and Applied Psychology, Italy. 111University of Pécs,
Institute of Psychology, Hungary. 112University of Padova, Department of General
Psychology, Italy. 113University of Palermo, Faculty of Social Sciences, Argentina.
114University of Poitiers, Psychology Department, France. 115University of Porto,
Department of Psychology, Portugal. 116University of Potsdam, Department of
Psychology, Germany. 117University of Presov, Institute of Psychology, Faculty of Arts,
Slovakia. 118University of Presov, Faculty of Education, Slovakia. 119University of Pretoria,
Department of Biochemistry, Genetics and Microbiology, South Africa. 120University of
Queensland, School of Psychology, Australia. 121University of Reading Malaysia, School
of Psychology and Clinical Language Sciences, Malaysia. 122University of Rochester,
Department of Clinical and Social Sciences in Psychology, USA. 123University of
Santiago, Chile, School of Psychology, Chile. 124University of São Paulo, Department of
Experimental Psychology, Institute of Psychology, Brazil. 125University of Sonora,
Department of Psychology, Mexico. 126University of Tehran, Department of Psychology,
Iran. 127University of Tennessee, Knoxville, Department of Psychology, USA.
128University of Toronto, Department of Psychology, Canada. 129University of Vienna,
Department of Basic Psychological Research and Research Methods, Austria.
130University of Vienna, Department of Applied Psychology: Work, Education, and
Economy, Austria. 131University West, Department of Behavioral Sciences, Sweden.
132Üsküdar University, Department of Psychology, Turkey. 133Victoria University of
5
Wellington, School of Psychology, New Zealand. 134Wesleyan College, Department of
Psychology, USA. 135Western Sydney University, School of Psychology, Australia.
136Western Sydney University, School of Social Science and Psychology, Australia.
137Western University, Department of Psychology, Canada. 138Wilfrid Laurier University,
Lazaridis School of Business & Economics, Canada. 139Willamette University,
Department of Psychology, USA. 140Yale University, Department of Psychology, USA,
141Yasar University, Department of Psychology, Turkey. 142University of Melbourne,
Melbourne School of Psychological Sciences, Australia. 143Charles University, Faculty of
Education, Institute for Research and Development of Education, Czechia, 144University
of St. Andrews, United Kingdom, 145Qufu Normal University, China, 146University of
Oklahoma, USA, 147Vilnius University, Lithuania, 148Bar-Ilan University, Israel, 149Brunel
University London, UK, 150Northern Illinois University, 150Playa Ancha University of
Educational Sciences, Chile, 152Federal University of Rio Grande do Norte, Brazil,
153Wenzhou University, China, 154Universidade Federal da Grande Dourados, Brazil,
155Universidade Federal de São Carlos, Brazil.
Funding: Claus Lamm was supported by the Viennese Science and Technology Fund
(WWTF VRG13-007, to Claus Lamm); Lisa DeBruine was supported by ERC #647910
(KINSHIP); Luca Kozma, Ferenc Kocsor and Ádám 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”). Kim Uittenhove
and Evie Vergauwe were supported by a grant from the Swiss National Science
Foundation (PZ00P1_154911 to Evie Vergauwe). Tripat Gill is supported by the Social
Sciences and Humanities Research Council of Canada (SSHRC). Miguel Vadillo was
supported by grants 2016-T1/SOC-1395 (Comunidad de Madrid) and PSI2017-85159-P
(AEI/FEDER UE). Rystian Barzykowski was supported by a grant from the National
Science Centre, Poland [No.: 2015/19/D/HS6/00641]. Judson Bonick and Jan W.
Lindemans were supported by the Joep Lange Institute. Gabriel Baník was supported by
Slovak Research and Development Agency [APVV-17-0418]. Hans IJzerman and Elisa
Sarda were supported by a French National Research Agency “Investissements d’avenir”
program grant (ANR-15-IDEX-02). The Raipur Group is thankful to (1) the University
Grants Commission, New Delhi, India for the research grants received through its DRS-
SAP (Phase-III) scheme sanctioned to the School of Studies in Life Science and (2)
logistic support received from the Center for Translational Chronobiology at the School of
Studies in Life Science, PRSU, Raipur, India. Karl Ask was supported by a small grant
from the Department of Psychology, University of Gothenburg. Yue Qi was supported by
grants from the Beijing Natural Science Foundation (5184035) and CAS Key Laboratory
of Behavioral Science, Institute of Psychology. Nicholas Coles was supported by the
National Science Foundation Graduate Research Fellowship R010138018.
Acknowledgments: We would like to acknowledge the following research assistants:
Alan E. Barba-Sãnchez (National Autonomous University of Mexico); Jordan Muriithi and
Joyce Ngugi (United States International University - Africa); Elisa Adamo, Viviana
Ciambrone, Francesca Dolce, and Domenico Cafaro (“Magna Graecia” University of
Catanzaro); Eugenia De Stefano (University of Padova); Samile A. Escobar Abadia
(University of Lincoln); Lene Elisabeth Grimstad (NHH Norwegian School of Economics);
Teodor Jernsäther (Stockholm University); Luciana Chavarria Zamora (Franklin and
Marshall College); Ryan E Liang and Ruth Christy Lo (Universiti Tunku Abdul Rahman);
Ashley Short and Liam Allen (Massey University, New Zealand)’ Arda Ateş, Ezgi Güneş,
and Salih Can Özdemir (Boğaziçi University); Ida Pedersen and Tove Roos (Åbo
Akademi University); Nicole Paetz (Escuela de Comunicación Mónica Herrera); Johan
Green (University of Gothenburg); Morris Krainz, BSc (University of Vienna, Austria);
Boryana Todorova (University of Vienna, Austria).
6
The project contributions of each author according to author self-nominations using the CRediT taxonomy
(https://www.casrai.org/credit.html). Light blue indicates a supporting role, dark blue a leading role; bold names
indicate the leadership team, italics the administrative team. Figure format is adapted from Steinmetz
(https://twitter.com/SteinmetzNeuro/status/1147241128858570752). At the time of submission we had not received
CRediT responses from 36 co-authors, and they are omitted from the current version of the table. Figure detail and
code at https://osf.io/6dbxg/
7
Abstract
Over the last ten 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 judgments 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
11,481 participants. When we used Oosterhof’s and Todorov’s original
analysis strategy, the valence-dominance model generalized across regions.
When we used an alternative strategy that allowed for a more optimal number
of correlated latent factors, we observed much less generalization. These
results underscore how each analysis strategy embeds substantive
assumptions that can strongly influence theoretical conclusions.
8
To Which World Regions Does the Valence-Dominance Model of Social
Perception Apply?
People quickly and involuntarily form impressions of others based on
their facial appearance1-3. These impressions then influence important social
outcomes4,5. For example, people are more likely to cooperate in
socioeconomic interactions with individuals whose faces are evaluated as
more trustworthy6, vote for individuals whose faces are evaluated as more
competent7, and seek romantic relationships with individuals whose faces are
evaluated as more attractive8. Facial appearance can even influence life-or-
death outcomes. For example, untrustworthy-looking defendants are more
likely to receive death sentences9. Given that such evaluations influence
profound outcomes, understanding how people evaluate others’ faces can
provide insight into a potentially important route through which social
stereotypes impact behavior10,11.
Over the last decade, Oosterhof and Todorov’s valence-dominance
model12 has emerged as the most prominent account of how we evaluate
faces on social dimensions5. Oosterhof and Todorov identified 13 different
traits (aggressiveness, attractiveness, caringness, confidence, dominance,
emotional stability, unhappiness, intelligence, meanness, responsibility,
sociability, trustworthiness, and weirdness) that perceivers spontaneously use
to evaluate faces when forming trait impressions12. From these traits, they
derived a two-dimensional model of perception: valence and dominance.
Valence, best characterized by rated trustworthiness, was defined as the
extent to which the target was perceived as having the intention to harm the
viewer12. Dominance, best characterized by rated dominance, was defined as
9
the extent to which the target was perceived as having the ability to inflict
harm on the viewer12. Crucially, the model proposes that these two
dimensions are sufficient to drive social evaluations of faces. As a
consequence, the majority of research on the effects of social evaluations of
faces has focused on one or both of these dimensions4,5.
Successful replications of the valence-dominance model have only
been conducted in Western samples13,14. This focus on the West is consistent
with research on human behavior more broadly, which typically draws general
assumptions from analyses of Western participants’ responses15. Kline et al.
recently termed this problematic practice the Western centrality assumption
and argued that regional variation, rather than universality, is likely the default
for human behavior16.
Consistent with Kline et al’s notion that human behavior is best
characterized by regional variation, two recent studies of social evaluation of
faces by Chinese participants indicate different factors underlie their
impressions17,18. Both studies reported that Chinese participants’ social
evaluations of faces were underpinned by a valence dimension similar to that
reported by Oosterhof and Todorov for Western participants, but not by a
corresponding dominance dimension. Instead, both studies reported a second
dimension, referred to as capability, which was best characterized by rated
intelligence. Furthermore, the ethnicity of the faces rated only subtly affected
perceptions17. Research into potential cultural differences in the effects of
experimentally manipulated facial characteristics on social perceptions has
also found little evidence that cultural differences in social perceptions of
faces depend on the ethnicity of the faces presented19-21. Collectively, these
10
results suggest that the Western centrality assumption may be an important
barrier to understanding how people evaluate faces on social dimensions.
Crucially, these studies also suggest that the valence-dominance model is not
necessarily a universal account of social evaluations of faces and warrants
further investigation in the broadest set of samples possible.
Although the studies described above demonstrate that the valence-
dominance model is not perfectly universal, to which specific world regions it
does and does not apply are open and important questions. Demonstrating
differences between British and Chinese raters is evidence against the
universality of the valence-dominance model, but it does not adequately
address these questions. Social perception in China may be unique in not
fitting the valence-dominance model because of the atypically high general
importance placed on status-related traits, such as capability, during social
interactions in China22,23. Indeed, Tan et al. demonstrated face-processing
differences between Chinese participants living in mainland China and
Chinese participants living in nearby countries, such as Malaysia24. Insights
regarding the unique formation of social perceptions in other cultures and
world regions are lacking. Only a large-scale study investigating social
perceptions in many different world regions can provide such insights.
To establish the world regions to which the valence-dominance model
applies, we will replicate Oosterhof and Todorov’s methodology12 in a wide
range of world regions (Africa, Asia, Australia and New Zealand, Central
America and Mexico, Eastern Europe, the Middle East, the USA and Canada,
Scandinavia, South America, the UK, and Western Europe; see Table 1). Our
study will be the most comprehensive test of social evaluations of faces to
11
date, including more than 9,000 participants. Participating research groups
were recruited via the Psychological Science Accelerator project25-27. Previous
studies compared two cultures to demonstrate regional differences17,18. By
contrast, the scale and scope of our study will allow us to generate the most
comprehensive picture of the world regions to which the valence-dominance
model does and does not apply.
We will test two specific competing predictions.
Prediction 1. The valence-dominance model will apply to all world regions.
Prediction 2. The valence-dominance model will apply in Western-world
regions, but not other world regions.
Table 1
World Regions, Countries, and Localities of Planned Data Collection
World region
Countries and Localities
Africa
Kenya, Nigeria, South Africa
Asia
China, India, Malaysia, Taiwan,
Thailand
Australia and New Zealand
Australia, New Zealand
Central America and Mexico
Ecuador, El Salvador, Mexico
Eastern Europe
Hungary, Lithuania, Poland, Russia,
Serbia, Slovakia
The Middle East
Iran, Israel, Turkey
The USA and Canada
Canada, the USA
Scandinavia
Denmark, Finland, Norway, Sweden
South America
Argentina, Brazil, Chile, Colombia
The UK
England, Scotland, Wales
Western Europe
Austria, Belgium, France, Germany,
Greece, Italy, the Netherlands,
Portugal, Spain, Switzerland
12
Note. We collected data from a minimum of 350 raters per world region based
on the simulations described in the Methods section below.
Methods
Ethics
Each research group had approval from their local Ethics Committee or
IRB to conduct the study, had explicitly indicated that their institution did not
require approval for the researchers to conduct this type of face-rating task, or
had explicitly indicated that the current study was covered by a preexisting
approval. Although the specifics of the consent procedure differed across
research groups, all participants provided informed consent. All data was
stored centrally on University of Glasgow servers.
Procedure
Oosterhof and Todorov derived their valence-dominance model from a
principal components analysis of ratings (by US raters) of 66 faces for 13
different traits (aggressiveness, attractiveness, caringness, confidence,
dominance, emotional stability, intelligence, meanness, responsibility,
sociability, trustworthiness, unhappiness, and weirdness)12. Using the criteria
of the number of components with eigenvalues greater than 1.0, this analysis
produced two principal components. The first component explained 63% of
the variance in trait ratings, strongly correlated with rated trustworthiness (r =
.94), and weakly correlated with rated dominance (r = -.24). The second
component explained 18% of the variance in trait ratings, strongly correlated
with rated dominance (r = .93), and weakly correlated with rated
13
trustworthiness (r = -.06). We replicated Oosterhof and Todorov’s method12
and primary analysis in each world region we examined.
Stimuli in our study came from an open-access, full-color, face image
set28 consisting of 60 men and 60 women taken under standardized
photographic conditions (Mage = 26.4 years, SD = 3.6 years, Range = 18 to 35
years). These 120 images consisted of 30 Black (15 male, 15 female), 30
White (15 male, 15 female), 30 Asian (15 male, 15 female), and 30 Latin
faces (15 male, 15 female). As in Oosterhof and Todorov’s study12, the
individuals photographed posed looking directly at the camera with a neutral
expression, and all of background, lighting, and clothing (here, a grey t-shirt)
were constant across images.
In our study, adult raters were randomly assigned to rate the 13
adjectives tested by Oosterhof and Todorov using scales ranging from 1 (Not
at all) to 9 (Very) for all 120 faces in a fully randomized order at their own
pace. Because all researchers collected data through an identical interface
(except for differences in instruction language), data collection protocols were
highly standardized across labs. Each participant completed the block of 120
face-rating trials twice so that we could report test-retest reliabilities of ratings;
ratings from the first and second blocks were averaged for all analyses (see
CODE 1.5.5 in the Supplemental Materials).
Raters also completed a short questionnaire requesting demographic
information (sex, age, ethnicity). These variables were not considered in
Oosterhof and Todorov’s analyses but were collected in our study so that
other researchers could use them in secondary analyses of the published
data. The data from this study are the largest and most comprehensive open
14
access set of face ratings from around the world with open stimuli by far,
providing an invaluable resource for further research addressing the Western
centrality assumption in person perception research.
Raters completed the task in a language appropriate for their country
(see below). To mitigate potential problems with translating single-word
labels, dictionary definitions for each of the 13 traits were provided. Twelve of
these dictionary definitions had previously been used to test for effects of
social impressions on the memorability of face photographs19. Dominance
(not included in that study) was defined as “strong, important.
Participants
Simulations determined that we should obtain at least 25 different
raters for each of the 13 traits in every region (see https://osf.io/x7fus/ for
code and data). We focused on ratings of attractiveness and intelligence for
the simulations because they showed the highest and lowest agreement
among the traits analyzed by Oosterhof and Todorov, respectively. First, we
sampled from a population of 2,513 raters, each of whom had rated the
attractiveness of 102 faces; these simulations showed that more than 99% of
1,000 random samples of 25 raters produced good or excellent interrater
reliability coefficients (Cronbach’s αs >.80). We then repeated these
simulations sampling from a population of 37 raters, each of whom rated the
intelligence of 100 faces, showing that 93% of 1,000 random samples of 25
raters produced good or excellent interrater reliability coefficients (Cronbach’s
αs >.80). Thus, averages of ratings from 25 or more raters will produce
reliable dependent variables in our analyses; we plan to test at least 9,000
raters in total.
15
In addition to rating the faces for the 13 traits examined by Oosterhof
and Todorov, 25 participants in each region were randomly assigned to rate
the targets’ age in light of Sutherland et al.’s results showing that a
youth/attractiveness dimension emerged from analyses of a sample of faces
with a very diverse age range30. Age ratings were not included in analyses
relating to replications of Oosterhof and Todorov’s valence-dominance model.
Analysis Plan
The code used for our analyses is included in the Supplemental
Materials and publicly available from the Open Science Framework
(https://osf.io/87rbg/).
Ratings from each world region were analyzed separately and
anonymous raw data is published on the Open Science Framework. Our main
analyses directly replicated the principal component analysis reported by
Oosterhof and Todorov to test their theoretical model in each region sampled
(CODE 2.1). First, we calculated the average rating for each face separately
for each of the 13 traits (CODE 2.1.2). We then subjected these mean ratings
to principal component analysis with orthogonal components and no rotation,
as Oosterhof and Todorov did (CODE 2.1.3). Using the criteria they reported,
we retained and interpreted components with eigenvalues greater than 1.0
(CODE 2.1.3.1).
Criteria for replicating Oosterhof and Todorov’s valence-
dominance model. We used multiple sources of evidence to judge whether
Oosterhof and Todorov’s valence-dominance model replicated in a given
world region. First, we examined the solution from the principal components
analysis conducted in each region and determined if Oosterhof and Todorov’s
16
primary pattern replicated according to three criteria: (i) the first two
components had eigenvalues greater than 1.0, (ii) the first component (i.e.,
the one explaining more of the variance in ratings) correlated strongly with
trustworthiness ( > .7) and weakly with dominance ( < .5), and (iii) the
second component (i.e., the one explaining less of the variance in ratings)
correlated strongly with dominance ( > .7) and weakly with trustworthiness (
< .5). If the solution in a world region met all three of these criteria, we
concluded that the primary pattern of the model replicated in that region
(CODE 2.1.3.3).
In addition to reporting whether the primary pattern was replicated in
each region, we also reported Tucker’s coefficient of congruence31,32. The
congruence coefficient, ϕ, ranges from -1 to 1 and quantifies the similarity
between two vectors of loadings33. It is:
where xi and yi are the loadings of variable i (i = 1, …, n number of indicators
in the analysis) onto factors x and y. For the purposes of the current research,
we compared the vector of loadings from the first component from Oosterhof
and Todorov to the vector of loadings from the first component estimated from
each world region. We repeated this analysis for the second component. This
produced a standardized measure of component similarity for each
component in each world region that was not sensitive to the mean size of the
loadings34. Further, this coefficient was fitting for the current study because it
does not require an a priori specification of a factor structure for each group,
as would be needed if we were to compare the factor structures in a multiple-
17
group confirmatory factor analysis. Following previous guidelines34, we
concluded that the components in Oosterhof and Todorov were not similar to
those estimated in a given world region if the coefficient was less than .85,
were fairly similar if it was between .85 - .94, and were equal if it was greater
than .95. (CODE 2.1.4).
Thus, we reported whether the solution had the same primary pattern
that Oosterhof and Todorov found and quantified the degree of similarity
between each component and the corresponding component from Oosterhof
and Todorov’s work. This connects to our competing predictions:
Prediction 1 (The valence-dominance model applies to all world regions)
was supported if the solution from the principal components analysis
conducted in each region satisfied all of the criteria described above.
Specifically, the primary pattern was replicated and the components had at
least a fair degree of similarity as quantified by a ϕ of .85 or greater.
Prediction 2 (The valence-dominance model will applies in Western-
world regions, but not other world regions) was supported if the solutions from
the principal components analysis conducted in Australia and New Zealand,
The USA and Canada, Scandinavia, The UK, and Western Europe, but not
Africa, Asia, Central America and Mexico, Eastern Europe, The Middle East,
or South America, satisfied the criteria described above.
Exclusions. Data from raters who failed to complete all 120 ratings in
the first block of trials or who provided the same rating for 75% or more of the
faces was excluded from analyses (CODES 1.5.1,1.5.3, and 1.5.5).
Data-quality checks. Following previous research testing the valence-
dominance model12-14, data quality was checked by separately calculating the
18
interrater agreement (indicated by Cronbach’s α and test-retest reliability) for
each trait in every world region (CODE 2.1.1). A trait was only included in the
analysis for that region if the coefficient exceeded .70. Test-retest reliability of
traits was not used to exclude traits from analysis.
Power analysis. Simulations showed we had more than 95% power to
detect the key effect of interest (i.e., two components meeting the criteria for
replicating Oosterhof and Todorov’s work, as described above). We used the
open data from Morrison et al.’s replication13 of Oosterhof and Todorov’s
research to generate a variance-covariance matrix representative of typical
interrelationships among the 13 traits tested in our study. We then generated
1,000 samples of 120 faces from these distributions and ran our planned
principal components analysis (which is identical to that reported by Oosterhof
& Todorov) on each sample (see https://osf.io/87rbg/ for code and data).
Results of >99% of these analyses matched our criteria for replicating
Oosterhof and Todorov’s findings. Thus, 120 faces gave us more than 95%
power to replicate Oosterhof and Todorov’s results.
Robustness analyses. Oosterhof and Todorov extracted and
interpreted components with an eigenvalue greater than 1.0 using an
unrotated principal components analysis. As described above, we directly
replicated their method in our main analyses but acknowledge that this type of
analysis has been criticized.
First, it has been argued that exploratory factor analysis with rotation,
rather than an unrotated principal components analysis, is more appropriate
when one intends to measure correlated latent factors, as was the case in the
current study35,36. Second, the extraction rule of eigenvalues greater than 1.0
19
has been criticized for not indicating the optimal number of components, as
well as for producing unreliable components37,38.
To address these limitations, we repeated our main analyses using
exploratory factor analysis with an oblimin rotation as the model and a parallel
analysis to determine the number of factors to extract. We also recalculated
the congruence coefficient described above for these exploratory factor
analysis results (CODE 2.2.1).
We used parallel analysis to determine the number of factors to extract
because it has been described as yielding the optimal number of components
(or factors) across the largest array of scenarios35,39,40 (CODE 2.2.1). In a
parallel analysis, random data matrices are generated such that they have the
same number of cases and variables as the real data. The mean eigenvalue
from the components of the random data is compared to the eigenvalue for
each component from the real data. Components are then retained if their
eigenvalues exceed those from the randomly generated data41.
The purpose of these additional analyses was twofold. First, to address
potential methodological limitations in the original study and, second, to
ensure that the results of our replication of Oosterhof and Todorov’s study are
robust to the implementation of those more rigorous analytic techniques. The
same criteria for replicating Oosterhof and Todorov’s model described above
was applied to this analysis (CODE 2.2.1.3).
Results and Discussion
Analyzed data set. Following the planned data exclusions (see
supplemental materials for a break down of these exclusions, CODE 1.5), the
20
analyzed data set is summarized in Table 2.
Table 2
Number of participants per region and Cronbach’s alphas following data
quality checks and exclusions
Main analysis (principal components analysis, PCA, CODE 2.1).
Oosterhof and Todorov reported the results of a PCA with orthogonal
components, no rotation, and retaining components with eigenvalues > 1.
Using an identical analysis, we extracted the same number of components in
two world regions: Africa and South America. In the other world regions we
extracted three components, following the eigenvalues > 1 rule. In the world
regions where a third component was extracted the trait ratings of “unhappy”
and “weird’ tended to have the highest loadings on that component. We are
cautious to interpret or describe this component with any authority because it
varied across world regions and explained only a small proportion of
21
additional variance.
Figure 1. Principal component analysis (PCA) loading matrices for each
region. Positive loadings are shaded red and negative loadings shaded blue;
darker colors correspond to stronger loadings. The proportion of variance
explained by each component is included at the top of each table.
The primary pattern Oosterhof and Todorov reported (a first component
that was highly correlated with rated trustworthiness, but not rated dominance,
and a second component that was highly correlated with rated dominance, but
not rated trustworthiness) was present in all world regions except for Eastern
Europe and the Middle East. In those latter two regions, both dominance and
trustworthiness ratings were too highly correlated with the first factor. Figure 1
shows the full loading matrices for each region and Table 3 shows how these
relate to our replication criteria.
22
Table 3
Replication criteria for the principal component analysis (PCA) for each region
Component 1
Component 2
Region
Dominant
Trustworthy
Dominant
Trustworthy
Replicated
(Oosterhof &
Todorov, 2008)
-0.244
0.941
0.929
-0.060
Yes
Africa
0.271
0.924
0.843
-0.065
Yes
Asia
0.370
0.922
0.863
-0.006
Yes
Australia & New
Zealand
0.257
0.943
0.907
-0.076
Yes
Central America
& Mexico
-0.030
0.913
0.923
-0.066
Yes
Eastern Europe
0.599
0.938
0.755
-0.113
No
Middle East
0.528
0.816
0.778
-0.394
No
Scandinavia
0.392
0.953
0.881
-0.121
Yes
South America
0.343
0.899
0.894
-0.155
Yes
UK
0.331
0.944
0.851
-0.121
Yes
USA & Canada
0.406
0.966
0.841
-0.073
Yes
Western Europe
0.357
0.957
0.875
-0.166
Yes
Note: Oosterhof and Todorov’s valence-dominance model was judged to have
been replicated in a given world region if the first component had a loading <
.5 with dominance and > .7 with trustworthiness, and the second component
had a loading > .7 with dominance and < .5 with trustworthiness.
Tucker’s coefficient of congruence, ϕ, indicated that the first component
was congruent with the first component in Oosterhof and Todorov’s original
study for all world regions (i.e., ϕ > .95). The second component was also
congruent with the second component reported by Oosterhof and Todorov in
all of the world regions (i.e., all ϕ > .85), except Asia (ϕ = .848). Table 4
summarizes these results.
Table 4
23
Factor congruence for each region’s principal component analysis (PCA)
Component 1
Component 2
Region
Loading
Congruence
Loading
Congruence
Africa
0.980
equal
0.947
fairly similar
Asia
0.974
equal
0.843
not similar
Australia & New Zealand
0.982
equal
0.959
equal
Central America & Mexico
0.993
equal
0.953
equal
Eastern Europe
0.953
equal
0.948
fairly similar
Middle East
0.944
fairly similar
0.853
fairly similar
Scandinavia
0.973
equal
0.960
equal
South America
0.973
equal
0.948
fairly similar
UK
0.976
equal
0.938
fairly similar
USA & Canada
0.972
equal
0.952
equal
Western Europe
0.975
equal
0.936
fairly similar
Together, these results suggest the valence-dominance model
generalizes across world regions when using an identical analysis to
Oosterhof and Todorov’s original study. Thus, the results of our PCA support
prediction one (that the valence-dominance model will apply to all world
regions), but not prediction two (that the valence-dominance model will apply
in Western world regions, but not other world regions). However, we note here
that generalization to Eastern Europe and Middle East was poorer than for the
other regions.
Robustness analyses
*
(Exploratory Factor Analysis, CODE 2.2).
Following our analysis plan, we conducted additional robustness analyses
that directly addressed criticisms of the type of statistical analyses used by
*
Note for reviewers: During the robustness analysis, using our registered code, an error ("The estimated weights for
the factor scores are probably incorrect. Try a different factor extraction method.") appeared in the output for one
region (USA and Canada). Inspection of the weights did not show any aberrant or out of range values. By using a
parallel analysis extraction method, we did not even use an extraction technique as executed by the factor analysis
features of the psych package in R. It is possible this error is not even relevant. To fully understand if this error is
meaningful and impactful to the trustworthiness of the results, we will need to conduct some exploratory analyses
and consider if our results are sensitive across shifting around the extraction and estimation options and compare
them to results from other software packages. Given we did not register such analyses, we would like guidance from
the reviewers and editor on how to report on these and strategies for moving forward.
24
Oosterhof and Todorov (see42 for a discussion of these criticisms). These
analyses employed EFA with an oblimin rotation as the model and used
parallel analysis to identify the number of factors to extract. We conducted this
analysis on Oosterhof and Todorov’s original data and found a similar result to
their PCA solution. With the EFA, all other regions showed more than two
factors. Full EFA loading matrices for each region and Oosterhof and
Todorov’s original data are shown in Figure 2.
Figure 2
Exploratory factor analysis (EFA) loading matrices for each region. Positive
loadings are shaded red and negative loadings shaded blue; darker colours
correspond to stronger loadings. The proportion of variance explained by
each factor is included at the top of each table.
In contrast to our PCA, the results of our robustness analyses showed
25
little evidence that the valence-dominance model generalizes across world
regions. A summary of the results for our replication criteria is given in Table
5. These results showed that our replication criteria were met for Australia and
New Zealand, Africa, and Scandinavia, but for none of the other world
regions.
Table 5
Replication criteria for the exploratory factor analysis (EFA) for each region
Factor 1
Factor 2
Region
Dominant
Trustworthy
Dominant
Trustworthy
Replicated
(Oosterhof &
Todorov, 2008)
0.228
0.826
0.970
-0.288
Yes
Africa
0.200
0.786
0.796
-0.133
Yes
Asia
0.110
0.236
0.487
0.761
No
Australia & New
Zealand
0.157
0.730
0.873
-0.078
Yes
Central America
& Mexico
0.142
0.662
0.831
-0.311
No
Eastern Europe
0.750
0.843
0.609
-0.322
No
Middle East
0.427
0.112
0.566
-0.699
No
Scandinavia
0.428
0.744
0.806
-0.304
Yes
South America
0.278
0.255
0.757
-0.472
No
UK
0.265
0.510
0.766
-0.299
No
USA & Canada
0.320
0.426
0.711
-0.335
No
Western Europe
0.111
0.398
0.869
-0.172
No
Note: Oosterhof and Todorov’s valence-dominance model was judged to have
been replicated in a given world region if the first factor had a loading < .5 with
dominance and > .7 with trustworthiness, and the second factor had a loading
> .7 with dominance and < .5 with trustworthiness.
Tucker’s coefficient of congruence, ϕ, indicated that the first factor was
congruent with the first factor in Oosterhof and Todorov’s original study (i.e., ϕ
26
> .85) for Africa, Eastern Europe, and Scandinavia. The second factor was
congruent with the second factor reported by Oosterhof and Todorov in all of
world regions (i.e., all ϕ > .85), except Asia (ϕ = -.090). Table 6 summarizes
these results.
Table 6
Factor congruence for each region’s exploratory factor analysis (EFA)
Factor 1
Factor 2
Region
Loading
Congruence
Loading
Congruence
Africa
0.894
fairly similar
0.900
fairly similar
Asia
0.765
not similar
-0.090
not similar
Australia & New Zealand
0.810
not similar
0.933
fairly similar
Central America & Mexico
0.777
not similar
0.972
equal
Eastern Europe
0.891
fairly similar
0.957
equal
Middle East
0.736
not similar
0.855
fairly similar
Scandinavia
0.884
fairly similar
0.980
equal
South America
0.682
not similar
0.956
equal
UK
0.774
not similar
0.977
equal
USA & Canada
0.772
not similar
0.975
equal
Western Europe
0.774
not similar
0.949
fairly similar
Thus, the results of our EFA support neither Prediction one (that the
valence-dominance model will apply to all world regions) nor Prediction two
(that the valence-dominance model will apply to Western-world regions, but
not other world regions). There was, however, some evidence that a second
factor that was highly correlated with dominance was present in all world
regions except Asia.
Conclusions
Our primary analyses, PCAs identical to those reported by Oosterhof
and Todorov, suggested that the valence-dominance model of social
27
perception of faces generalizes relatively well across world regions. However,
most world regions showed a third component not discussed in the original
work. The presence of this third component suggests that a simple valence-
dominance model does not fully capture the richness of social perception in
many world regions. Further work is needed to interpret this component.
In contrast to the results of our PCAs, an alternative analysis that
addressed common criticisms of the type of analysis Oosterhof and Todorov
employed showed little evidence that the valence-dominance model is useful
for summarizing social perception of faces in different world regions. For
example, according to our primary replication criteria, the valence-dominance
model replicated in only four world regions. Although some previous research
on the generalizability of social perception of faces has focused on
investigating generalization across different world regions, our study extends
this work by emphasizing the additional importance of generalization across
analysis models. We show that conclusions about the extent to which models
of social perception generalize across world regions can depend, at least to
some extent, on the specific model employed to analyze the data.
A necessary next step for moving forward in person perception
research is addressing which analysis model (PCA or EFA) best aligns with
theory, so that those models and theories can be revised and expanded
appropriately in future research. Crucially, the two models make different
assumptions about trait ratings of faces. The PCA model does not assume
that a latent factor causes the trait ratings of the faces. The component simply
captures an aggregate, maximized to explain variance. Furthermore, in the
original valence-dominance model, those components were assumed to be
28
unrelated. By contrast, the theory underlying the EFA model is that a latent
factor causes the trait ratings, and any unexplained variance in that rating is
measurement error. Additionally, our EFA models allowed for the factors to be
correlated.
Theory can guide which model we use to analyze person perception
data. A person perception theory that aligns with a PCA model would state
that there are no underlying, latent factors that cause a person to rate a face
in a particular way. There are, instead, perceptual processes that vary across
contexts, those doing the rating, and those being rated, and the differential
processes give rise to components that can be used to reduce the data.
Because the ratings come from context specific processes (and not a causal,
latent factor) the estimated components can vary across contexts, raters, and
those being rated. This theory of person perception would move forward with
identifying the shared processes across contexts, those rating, and those
being rated, to see if there are predictable patterns in how the data are
reduced. However, a person perception theory that aligns with an EFA model
would state that latent factors (e.g., valence or dominance) cause the trait
ratings and, once we account for the correct latent factors, any variability left
in the ratings is measurement error. This theory would move forward with
identifying and defining those latent factors, confirming their existence,
reliability, and generalizability.
Our study is one of several recent studies that have begun to address
these key questions21,43,44 by exploring how the structure of trait ratings vary
systematically. This growing body of work catalogues variations in trait ratings
by target demographic21,43, 45, target status46, target age47, perceiver
29
knowledge48, and cultural factors17,18. Further, from this growing body of work
dynamic theories of person perception and more flexible statistical models for
capturing them have been proposed21,43,44,49.
Our results are consistent with this recent work in that they do not
provide strong evidence that there are a few generalizable latent factors that
cause the trait ratings across world regions. They do however, suggest a
dynamic process of person perception and elucidate the differential patterns
of ratings across world regions. We can use these data, representing
impressions formed on a global scale, to expand or refine our theories and
guide the selection of statistical models to represent those theories.
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