Multi-region investigation of ‘man’ as default in attitudes

Abstract

Though people usually imagine the typical person as a man rather than a woman, the effect is mixed for racial groups and understudied among traditionally male social groups (e.g., police and criminals) and non-U.S. populations. Results from a survey (N > 5000) collected via a globally distributed laboratory network in over 40 regions demonstrated that attitudes toward Black people and politicians had a stronger relationship with attitudes toward the men rather than the women of the group. However, attitudes toward White people had a stronger relationship with attitudes toward White women than White men, whereas attitudes toward East Asian people, police officers, and criminals did not have a stronger relationship with attitudes toward either the men or women of each respective group. Regional endorsement of liberal values was explored as a potential moderator. These findings have implications for understanding the unique forms of prejudice women face around the world.

Full text

Running head: MAN AS DEFAULT HUMAN? 1
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Multi-region investigation of ‘man’ as default in attitudes
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MAN AS DEFAULT HUMAN? 2
Curtis E. Phills5, Jeremy K. Miller2, Erin M. Buchanan3, Amanda Williams4, Chanel Meyers5,
6
Elizabeth R. Brown1, Janis Zickfeld6, Selina Volsa7, Stefan Stieger7, Elisabeth Oberzaucher8,
7
Vinka Mlakic7, Martin Vasilev9, İlker Dalgar10, Sami Çoksan11,71, Sinem Söylemez12, Çağlar
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Solak12, Asil Ali Özdoğru13,14, Belemir Çoktok14, Chun-Chia Kung15, Panita Suavansri16, Harry
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Manley16,72, Sara Álvarez-Solas17, Danilo Zambrano Ricaurte18, Ivan Ropovik19,73, Gabriel
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Baník20, Peter Babinčák21, Matúš Adamkovič22,74, Pavol Kačmár23, Monika Hricová23, Jozef
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Bavoľár23, Lisa Li24, Fei Gao24, Zhong Chen24, Vanja Ković25, Vasilije Gvozdenović25, Patrícia
12
Arriaga26, Katarzyna Filip27, Krystian Barzykowski27, Sylwia Adamus27, Gerit Pfuhl28,75, Sarah E.
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Martiny28, Kristoffer Klevjer28, Frederike S. Woelfert29, Christian K. Tamnes29, Jonas R. Kunst29,
14
Max Korbmacher30, Margaret Messiah Singh31, Sraddha Pradhan31, Noorshama Parveen31, Arti
15
Parganiha31, Babita Pande31, Pratibha Kujur31, Priyanka Chandel31, Niv Reggev32, Aviv
16
Mokady32, Marietta Papadatou-Pastou33, Roxane Schnepper34, Jan Philipp Röer34, Tilli Ripp34,
17
Ekaterina Pronizius35, Claus Lamm35, Martin Voracek36, Jerome Olsen36, Janina Enachescu36,
18
Carlota Batres37, Daniel Storage38, Carmel A. Levitan39, Manyu Li40, Leigh Ann Vaughn41,
19
William J. Chopik42, Kathleen Schmidt43, Peter R. Mallik44, Savannah Lewis45, Brynna Leach43,
20
Brianna Jurosic43, David Moreau46, Izuchukwu Lawrence Gabriel Ndukaihe47, Nwadiogo Chisom
21
Arinze47, Steve M. J. Janssen48, Alicia Foo48, Chrystalle B. Y. Tan49, Glenn P. Williams50, Danny
22
Riis51, Bethany M. Lane51, Dermot Lynott52, Thomas Rhys Evans53, Miroslav Sirota54, Dawn L.
23
Holford54, Kaitlyn M. Werner55, Kelly Wang55, Marina Milyavskaya55, Ian D. Stephen56,76,
24
Robert M. Ross57, Andrew Roberts56, Omid Ghasemi58, Niklas K. Steffens59, Kim Peters59,
25
Barnaby Dixson60, Marco Antonio Correa Varella61, Jaroslava V. Valentova61, Anthonieta
26
Looman Mafra62, Rafael Ming Chi Santos Hsu61, Yago Luksevicius de Moraes61, Luana Oliveira
27
da Silva61, Caio Santos Alves da Silva61, Mai Helmy63,77, Mariah Balderrama2, Ali H. Al-
28
Hoorie64, Tyler McGee37, Zahir Vally65, Attila Szuts66, Patrick Forscher67,78, Pablo Bernabeu68,79,
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MAN AS DEFAULT HUMAN? 3
Balazs Aczel66, Anna Szabelska69, Sau-Chin Chen70, Christopher R. Chartier43, & Zoltan
30
Kekecs66
31
1 University of North Florida
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2 Department of Psychology, Willamette University, Salem OR, USA
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3 Analytics, Harrisburg University of Science and Technology, Harrisburg, PA, USA
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4 Waterloo Regional Police Service, Cambridge, ON, Canada
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5 Department of Psychology, University of Oregon, Eugene, OR, USA
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6 Department of Management Aarhus University, Aarhus, Denmark
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7 Department of Psychology and Psychodynamics, Karl Landsteiner University of Health
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Sciences, Krems an der Donau, Austria
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8 Department of Evolutionary Anthropology, University of Vienna, Wien, Austria
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9 Bournemouth University,Talbot Campus, Poole, UK
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10 Department of Psychology, Ankara Medipol University, Ankara, Turkey
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11 Department of Psychology, Erzurum Technical University, Erzurum, Turkey
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12 Department of Psychology, Manisa Celal Bayar University, Manisa,Turkey
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13 Department of Psychology, Marmara University, İstanbul, Turkey
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14 Department of Psychology, Üsküdar University, İstanbul, Turkey
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MAN AS DEFAULT HUMAN? 4
15 Department of Psychology, National Cheng Kung University, Tainan, Taiwan
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16 Faculty of Psychology, Chulalongkorn University, Thailand
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17 Ecosystem Engineer, Universidad Regional Amazónica Ikiam, Tena, Ecuador
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18 Faculty of Psychology, Fundación Universitaria Konrad Lorenz, Bogotá, Colombia
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19 Institute for Research and Development of Education, Faculty of Education, Charles university,
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Czechia
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20 Pavol Jozef Safarik University, Kosice, Slovakia
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21 Institute of Psychology, University of Presov, Slovakia
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22 Institute of Social Sciences, CSPS, Slovak Academy of Sciences
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23 Department of Psychology, Faculty of Arts, Pavol Jozef Šafarik University in Košice, Slovakia
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24 Faculty of Arts and Humanities, University of Macau, Macau, China
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25 Laboratory for Neurocognition and Applied Cognition, Faculty of Philosophy, University of
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Belgrade
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26 Iscte-University Institute of Lisbon, CIS-IUL, Portugal
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27 Institute of Psychology, Jagiellonian University, Krakow, Poland
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28 Department of Psychology, UiT - The Arctic University of Norway, Tromsø, Norway
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29 Department of Psychology, University of Oslo, Oslo, Norway
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MAN AS DEFAULT HUMAN? 5
30 Department of Health and Functioning, Western Norway University of Applied Sciences,
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Bergen, Norway
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31 School of Studies in Life Science, Pt. Ravishankar Shukla University, Raipur, India
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32 Department of Psychology and School of Brain Sciences and Cognition, Ben Gurion
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University of the Negev, Beer Sheba, Israel
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33 School of Education, National and Kapodistrian University of Athens, Athens, Greece
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34 Department of Psychology and Psychotherapy, Witten/Herdecke University, Germany
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35 Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology,
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University of Vienna, Vienna, Austria
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36 Faculty of Psychology, University of Vienna, Wien, Austria
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37 Department of Psychology, Franklin and Marshall College, Lancaster, PA, USA
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38 Department of Psychology, University of Denver, Denver, CO, USA
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39 Department of Cognitive Science, Occidental College, Los Angeles, USA
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40 Department of Psychology, University of Louisiana at Lafayette, Lafayette, LA, USA
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41 Department of Psychology, Ithaca College, Ithaca, NY, USA
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42 Department of Psychology, Michigan State University, East Lansing, MI, USA
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43 Department of Psychology, Ashland University, Ashland, OH, USA
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44 Hubbard Decision Research, Glen Ellyn, IL, USA
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45 Department of Psychology, University of Alabama, Tuscaloosa, AL, USA
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46 School of Psychology, University of Auckland, Auckland, NZ
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47 Department of Psychology, Alex Ekwueme Federal University, Ndufu-Alike, Nigeria
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48 School of Psychology, University of Nottingham Malaysia, Selangor, Malaysia
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49 School of Psychology and Vision Sciences, University of Leicester, Leicester, UK
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50 School of Psychology, Faculty of Health Sciences and Wellbeing, University of Sunderland,
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Sunderland, UK.
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51 Division of Psychology, School of Social and Health Sciences, Abertay University, Dundee,
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UK
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52 Department of Psychology, Maynooth University, Maynooth, Ireland
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53 School of Social, Psychological and Behavioural Sciences, Coventry University, Coventry, UK
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54 Department of Psychology, University of Essex, Colchester, UK
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55 Department of Psychology, Carleton University, Ottawa, Canada
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56 Department of Psychology, Macquarie University, Sydney, Australia
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57 Department of Philosophy, Macquarie University, Australia
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58 School of Psychology, University of New South Wales
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59 School of Psychology, University of Queensland, Brisbane, Australia
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MAN AS DEFAULT HUMAN? 7
60 School of Health Psychology, University of the Sunshine Coast Queensland, Brisbane,
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Australia
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61 Department of Experimental Psychology, Institute of Psychology, University of Sao Paulo, Sao
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Paulo, Brazil
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62 Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
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63 Psychology Department, College of Education, Sultan Qaboos University, Muscat, Oman
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64 Royal Commission for Jubail and Yanbu, Jubail, Saudi Arabia
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65 Department of Clinical Psychology, United Arab Emirates University, Al Ain, UAE
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66 Institute of Psychology, ELTE, Eotvos Lorand University, Budapest, Hungary
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67 LIP/PC2S, Université Grenoble Alpes, Grenoble, France
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68 Department of Psychology, Lancaster University, Lancaster, United Kingdom
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69 Institute of Cognition and Culture, Queen’s University Belfast, UK
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70 Department of Human Development and Psychology, Tzu-Chi University, Hualien, Taiwan
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71 Network for Economic and Social Trends, Western University, London, ON, Canada
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72 Faculty of Behavioral Sciences, Education, & Languages, HELP University Subang 2,
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Malaysia
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73 Faculty of Education, University of Presov, Slovakia
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74 University of Jyväskylä, Finland
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MAN AS DEFAULT HUMAN? 8
75 Department of Psychology, Norwegian University of Science and Technology, Trondheim,
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Norway
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76 Department of Psychology, Nottingham Trent University, Nottingham, UK
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77 Psychology Department, Faculty of Arts, Menoufia University, Shebin El-Kom, Egypt
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78 Busara Center for Behavioral Economics, Nairobi, Kenya
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79 Department of Language and Culture, UiT The Arctic University of Norway, Tromsø, Norway
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Author note
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Funding.
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The funders did not play any role in the study design, data collection and analysis, decision to
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publish, or preparation of the manuscript.
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M.A. was supported by Slovak Research and Development Agency (APVV-20-0319)
135
(https://www.apvv.sk/?lang=en). R.M.R. was supported by Australian Research Council
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MAN AS DEFAULT HUMAN? 9
(DP180102384) (https://www.arc.gov.au/) and the John Templeton Foundation (62631) (
137
https://www.templeton.org/). A.L.M. was supported by FAPESP (n 2018/16370-5). Z.K. was
138
supported by János Bolyai Research Scholarship of the Hungarian Academy of Science
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(BO/00746/20) (https://mta.hu/bolyai-osztondij/bolyai-janos-kutatasi-osztondij-105319) G.P.W.
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was supported by Leverhulme Trust Research Project Grant (RPG-2016-093)
141
(https://www.leverhulme.ac.uk/research-project-grants). I.R. was supported by NPO Systemic
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Risk Institute (LX22NPO5101) (https://www.syri.cz)). K.B. was supported by National Science
143
Centre, Poland (2019/35/B/HS6/00528) (https://www.ncn.gov.pl/en). G.B. was supported by the
144
Slovak Research and Development Agency (APVV 22-0458) (https://www.apvv.sk/?lang=en)..
145
P.A. was supported by Portuguese National Foundation for Science and Technology (FCT
146
UID/PSI/03125/2019) (https://www.fct.pt/en/). M.H. was supported by VEGA 1/0145/23.
147
The authors made the following contributions. Curtis E. Phills: Conceptualization, Data curation,
148
Formal analysis, Investigation, Methodology, Visualization, Writing - original draft, Writing -
149
review & editing; Jeremy K. Miller: Project administration, Resources, Supervision, Writing -
150
review & editing; Erin Buchanan: Formal analysis, Project administration, Software, Writing -
151
review & editing; Amanda Williams: Writing - original draft, Writing - review & editing; Chanel
152
Meyers: Writing - original draft, Writing - review & editing; Elizabeth R. Brown:
153
Conceptualization, Writing - original draft, Writing - review & editing; Janis Zickfeld:
154
Resources, Writing - review & editing; Selina Volsa: Resources, Writing - review & editing;
155
Stefan Stieger: Resources, Writing - review & editing; Elisabeth Oberzaucher: Resources,
156
Writing - review & editing; Vinka Mlakic: Resources, Writing - review & editing; Martin
157
Vasilev: Writing - review & editing;; İlker Dalgar: Resources, Writing - review & editing; Sami
158
Çoksan: Investigation, Resources, Writing - review & editing; Sinem Söylemez: Investigation,
159

MAN AS DEFAULT HUMAN? 10
Writing - review & editing; Çağlar Solak: Investigation, Writing - review & editing; Asil Ali
160
Özdoğru: Investigation, Resources, Writing - review & editing; Belemir Çoktok: Investigation,
161
Resources, Writing - review & editing; Chun-Chia Kung: Investigation, Resources, Writing -
162
review & editing; Panita Suavansri: Investigation, Resources, Writing - review & editing; Harry
163
Manley: Investigation, Resources, Writing - review & editing; Sara Álvarez-Solas: Investigation,
164
Resources, Writing - review & editing; Danilo Zambrano Ricaurte: Investigation, Resources,
165
Writing - review & editing; Ivan Ropovik: Investigation, Writing - review & editing; Gabriel
166
Baník: Investigation, Writing - review & editing; Peter Babinčák: Investigation, Writing - review
167
& editing; Matúš Adamkovič: Investigation, Writing - review & editing; Pavol Kačmár:
168
Investigation, Resources, Writing - review & editing; Monika Hricová: Investigation, Resources,
169
Writing - review & editing; Jozef Bavoľár: Investigation, Resources, Writing - review & editing;
170
Lisa Li: Investigation, Resources, Writing - review & editing; Fei Gao: Investigation, Resources,
171
Writing - review & editing; Zhong Chen: Investigation, Writing - review & editing; Qing-Lan
172
Liu: Investigation, Writing - review & editing; Hu Chuan-Peng: Investigation, Writing - review
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& editing; Tao-tao Gan: Investigation, Writing - review & editing; Vanja Ković: Investigation,
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Resources, Writing - review & editing; Vasilije Gvozdenović: Investigation, Resources, Writing -
175
review & editing; Patrícia Arriaga: Funding acquisition, Investigation, Resources, Writing -
176
review & editing; Katarzyna Filip: Investigation, Resources, Writing - review & editing; Krystian
177
Barzykowski: Investigation, Resources, Supervision, Writing - review & editing; Sylwia
178
Adamus: Investigation, Resources, Writing - review & editing; Gerit Pfuhl: Investigation,
179
Resources, Supervision, Writing - review & editing; Sarah E. Martiny: Investigation, Writing -
180
review & editing; Kristoffer Klevjer: Investigation, Writing - review & editing; Frederike S.
181
Woelfert: Investigation, Writing - review & editing; Christian K. Tamnes: Investigation,
182
Resources, Writing - review & editing; Jonas R. Kunst: Investigation, Resources, Writing -
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MAN AS DEFAULT HUMAN? 11
review & editing; Max Korbmacher: Investigation, Writing - review & editing; Arti Parganiha:
184
Investigation, Resources, Supervision, Writing - review & editing; Babita Pande: Investigation,
185
Resources, Supervision, Writing - review & editing; Sraddha Pradhan: Investigation, Resources,
186
Writing - review & editing; Noorshama Parveen: Investigation, Resources, Writing - review &
187
editing; Pratibha Kujur: Investigation, Resources, Writing - review & editing; Priyanka Chandel:
188
Investigation, Resources, Writing - review & editing; Margaret Messiah Singh: Investigation,
189
Writing - review & editing; Niv Reggev: Investigation, Resources, Writing - review & editing;
190
Aviv Mokady: Investigation, Resources, Writing - review & editing; Marietta Papadatou-Pastou:
191
Investigation, Resources, Writing - review & editing; Roxane Schnepper: Investigation, Writing -
192
review & editing; Jan Philipp Röer: Investigation, Writing - review & editing; Tilli Ripp:
193
Investigation, Writing - review & editing; Ekaterina Pronizius: Investigation, Writing - review &
194
editing; Claus Lamm: Investigation, Resources, Writing - review & editing; Martin Voracek:
195
Investigation, Resources, Writing - review & editing; Jerome Olsen: Investigation, Resources,
196
Writing - review & editing; Janina Enachescu: Investigation, Resources, Writing - review &
197
editing; Carlota Batres: Investigation, Writing - review & editing; Daniel Storage: Investigation,
198
Writing - review & editing; Carmel A. Levitan: Investigation, Writing - review & editing; Manyu
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Li: Investigation, Writing - review & editing; Leigh Ann Vaughn: Investigation, Writing - review
200
& editing; William J. Chopik: Investigation, Writing - review & editing; Kathleen Schmidt:
201
Investigation, Resources, Writing - review & editing; Peter R. Mallik: Investigation, Writing -
202
review & editing; Savannah Lewis: Investigation, Writing - review & editing; Brynna Leach:
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Investigation, Writing - review & editing; Brianna Jurosic: Investigation, Writing - review &
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editing; David Moreau: Investigation, Writing - review & editing; Izuchukwu Lawrence Gabriel
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Ndukaihe: Investigation, Writing - review & editing; Nwadiogo Chisom Arinze: Investigation,
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Writing - review & editing; Steve M. J. Janssen: Investigation, Writing - review & editing; Alicia
207

MAN AS DEFAULT HUMAN? 12
Foo: Investigation, Writing - review & editing; Chrystalle B. Y. Tan: Investigation, Writing -
208
review & editing; Glenn P. Williams: Investigation, Writing - review & editing; Danny Riis:
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Investigation, Writing - review & editing; Bethany M. Lane: Investigation, Writing - review &
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editing; Dermot Lynott: Investigation, Writing - review & editing; Thomas Rhys Evans:
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Investigation, Writing - review & editing; Miroslav Sirota: Investigation, Writing - review &
212
editing; Dawn L. Holford: Investigation, Writing - review & editing; Kaitlyn M. Werner:
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Investigation, Writing - review & editing; Kelly Wang: Investigation, Writing - review & editing;
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Marina Milyavskaya: Investigation, Writing - review & editing; Ian D. Stephen: Investigation,
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Writing - review & editing; Robert M. Ross: Investigation, Writing - review & editing; Andrew
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Roberts: Investigation, Writing - review & editing; Omid Ghasemi: Investigation, Writing -
217
review & editing; Niklas K. Steffens: Investigation, Writing - review & editing; Kim Peters:
218
Investigation, Writing - review & editing; Barnaby Dixson: Investigation, Writing - review &
219
editing; Marco Antonio Correa Varella: Investigation, Resources, Writing - review & editing;
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Jaroslava V. Valentova: Investigation, Resources, Writing - review & editing; Anthonieta
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Looman Mafra: Investigation, Resources, Writing - review & editing; Rafael Ming Chi Santos
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Hsu: Investigation, Resources, Writing - review & editing; Yago Luksevicius de Moraes:
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Investigation, Resources, Writing - review & editing; Luana Oliveira da Silva: Investigation,
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Resources, Writing - review & editing; Caio Santos Alves da Silva: Investigation, Resources,
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Writing - review & editing; Mai Helmy: Investigation, Resources, Writing - review & editing;
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Mariah Balderrama: Investigation, Writing - review & editing; Ali H. Al-Hoorie: Investigation,
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Resources, Writing - review & editing; Zahir Vally: Investigation, Resources, Writing - review &
228
editing; Attila Szuts: Investigation, Methodology, Resources, Writing - review & editing; Patrick
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Forscher: Methodology, Writing - review & editing; Pablo Bernabeu: Investigation, Writing -
230
review & editing; Balazs Aczel: Investigation, Methodology, Resources, Writing - review &
231

MAN AS DEFAULT HUMAN? 13
editing; Anna Szabelska: Project administration, Writing - original draft, Writing - review &
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editing; Sau-Chin Chen: Investigation, Resources, Software, Writing - review & editing;
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Christopher R. Chartier: Investigation, Project administration, Writing - review & editing; Zoltan
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Kekecs: Data curation, Formal analysis, Methodology, Project administration, Software, Writing
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- original draft, Writing - review & editing.
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Correspondence concerning this article should be addressed to Curtis E. Phills. E-mail:
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phills@uoregon.edu
238
239

MAN AS DEFAULT HUMAN? 14
Abstract
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Though people usually imagine the typical person as a man rather than a woman, the
241
effect is mixed for racial groups and understudied among traditionally male social groups (e.g.,
242
police and criminals) and non-U.S. populations. Results from a survey (N > 5000) collected via a
243
globally distributed laboratory network in over 40 regions demonstrated that attitudes toward
244
Black people and politicians had a stronger relationship with attitudes toward the men rather than
245
the women of the group. However, attitudes toward White people had a stronger relationship with
246
attitudes toward White women than White men, whereas attitudes toward East Asian people,
247
police officers, and criminals did not have a stronger relationship with attitudes toward either the
248
men or women of each respective group. Regional endorsement of liberal values was explored as
249
a potential moderator. These findings have implications for understanding the unique forms of
250
prejudice women face around the world.
251
252
Keywords: attitudes, prejudice, intersectionality, androcentrism, liberal values
253
254
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MAN AS DEFAULT HUMAN? 15
Introduction
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When asked to imagine a person, people tend to think of a man (1). Similarly, many social
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categories like ‘politician’ are gendered in that group stereotypes are ascribed more strongly to
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the men, rather than the women of the group (2). Many racial groups (e.g., Black people) are also
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gendered in the sense that the group’s men are seen as the default (3). These effects are consistent
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with androcentrism, the belief that men are the default and women the exception or ‘other’ (4–6).
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Previous research has studied the extent to which men are the default members of social groups
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in memory (7), categorization (8), and stereotyping (9). The present research builds upon this
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literature by studying ‘man’ as default in attitudes which is important because of attitudes’
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relationship to behavior (10). We recruited a large, racially diverse sample of participants through
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a global distributed laboratory network, the Psychological Science Accelerator (PSA)(11), to
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examine whether men were default in attitudes toward occupation- (e.g., politician, police) and
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race-based (e.g., Black people, White people, East Asian people) groups.
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‘Man’ as default in attitudes
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In English, the word ‘people’ is ostensibly gender-neutral in that it could refer to persons
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of any gender. However, in practice, people write about ‘people’ as though they were men (12).
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The same is true for some racial groups (e.g., White) in that people write about them as though
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the group primarily consists of men (13).
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Assuming men are the ‘default’ has negative consequences because it prioritizes their
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experiences and ideals over those of women (4). For example, women are less motivated to apply
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for jobs that use ‘he’ pronouns to describe the ideal candidate (14). The assumption that men are
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the primary sufferers of heart disease, when it is actually more common in women (15), leads to
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women not receiving treatment until they are older and sicker (16). A policewoman was stabbed
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MAN AS DEFAULT HUMAN? 16
to death while operating a hydraulic ram because her protective armor was designed for the
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‘average man’ and, therefore, did not fit properly (17).
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Important to the present research, assuming men are ‘default’ in occupations related to
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politics and policing has negative consequences for attitudes toward women who pursue careers
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in those fields. Specifically, role congruity theory (18,19) argues that prejudice is partially rooted
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in the perceived misalignment between the characteristics typically ascribed to women (i.e.,
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communal traits) and the qualities desired of an occupant of a traditionally male role (i.e., agentic
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traits) (20,21). For example, women in leadership positions face backlash partly because they
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threaten the gender status quo (22). Thus, it is not surprising that in the United States only 29% of
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state legislative seats are held by women (23) and 14% of police officers are women (24).
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Though we do not encourage anyone, including women, to commit crimes, it is important to note
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that the gender gap is even starker among the U.S. federal prison inmate population–93% of
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people in U.S. federal prisons are men (25).
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Thus, we predict that attitudes toward traditionally male occupations like politician and
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police officer should be more similar to attitudes toward men rather than women in those roles
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because attitudes toward women in those roles are, partially, rooted in misogynistic beliefs about
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the traits typically ascribed to women (i.e., communal traits) and the traits desired in people
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performing traditionally male roles (i.e., agentic traits). Notably, this pattern should be moderated
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by the extent to which a region enforces traditional gender roles because strict enforcement of
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traditional gender roles strengthens the incongruity of a woman in a traditionally male role.
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‘Man’ as default in attitudes toward racial groups
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The intersection of race and gender often means that the men of a racial group are
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considered the default (26). For example, people tend to imagine a man when asked to think
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about a Black person or a White person (3). Similarly, Black men are categorized as Black faster
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MAN AS DEFAULT HUMAN? 17
than Black women (8). In regard to racial stereotypes, one study found that 11 of the 15 most
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frequent stereotypes participants listed about Black men overlapped with the most frequent
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stereotypes listed about Black people, but only five of the 15 most frequent stereotypes listed
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about Black women overlapped with the most frequent stereotypes listed about Black people (9).
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Moreover, in certain contexts negative attitudes (or prejudice) is often directed at the men rather
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than the women of outgroups (27).
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However, ‘man’ may not be the default for some racial groups. For example, in the United
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States, people are just as likely to imagine a woman as a man when thinking about an East Asian
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person (3). One possible explanation for this exception is that mental representations of racial
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groups may reflect the goals of a region’s dominant majority group (28). In the case of East
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Asian people in the United States, the dominant majority group (i.e., White men) often views
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East Asian people in terms of sex-based goals. Thus, for many people in the United States, East
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Asian women come to mind quicker than East Asian men.
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Notably, not only is it important to consider the gender of the target but also the gender of
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the participant (27), when studying the default gender in attitudes toward racial groups. For
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example, Black women are unlikely to exclude themselves from their representation of Black
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people. Thus, though we predicted that attitudes toward racial groups would be more similar to
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attitudes toward the men rather than the women of each racial group, we also predicted
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exceptions based on region (i.e., attitudes toward East Asians in the United States) and group
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membership (i.e., the women of each racial group).
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Overview
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The present research investigated ‘man’ as default in attitudes by testing whether attitudes
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toward a social group were more strongly related to attitudes toward the men or women of the
326
group. We predicted that for both traditionally male groups (i.e., politicians, police officers,
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MAN AS DEFAULT HUMAN? 18
criminals) as well as racial groups (i.e., Black people, White people, East Asian people), attitudes
328
toward the group would be more strongly related to attitudes toward the men of the group than
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attitudes toward the women of the group. Predictions related to racial groups were tested on sub-
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samples that did not include members of that group. However, because research in the U.S.
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suggests that White participants do not imagine a man when thinking about East Asians (3), we
332
predicted that among U.S. participants who were not East Asian, attitudes toward the group
333
would not be more strongly related to attitudes toward the group’s men.
334
Notably, we predicted Black women, White women, and East Asian women would not
335
view ‘man’ as the default member of their respective racial groups in attitudes.
336
Method
337
Our predictions and research methods were pre-registered (https://osf.io/w4q6t/) and we
338
note that we deviated from the pre-registration by not excluding participants who were police
339
officers or politicians from the sample when testing predictions about those groups. This was
340
done because the final version of the survey did not ask participants to identify whether they were
341
police officers or politicians. In addition to our pre-registered hypotheses, we also explored the
342
possibility that ‘man’ as default in attitudes would be moderated by the extent to which a region
343
enforces traditional gender roles. Specifically, we predicted that regions high in liberal values
344
would not associate attitudes toward traditionally male groups more strongly with attitudes
345
toward the men rather than the women of that group.
346
Participants
347
Participants (N = 5803; 5551 in lab vs 252 online) were recruited through the
348
Psychological Science Accelerator (PSA) from over 40 regions around the world. Full details
349
about running the study, including data preparation and pre-registration, are stored on the open
350
science framework (https://osf.io/w4q6t/). Of those that started the experiment, 5,177 participants
351

MAN AS DEFAULT HUMAN? 19
provided partial or complete data to be analyzed (72% female, 25% male, >1% non-binary trans,
352
3% other or unknown; 56% White, 5% Black, 14% East Asian, 24% other or unknown race). See
353
Table 1 for a breakdown of participant characteristics by region, race, and gender. Each
354
laboratory either obtained approval from their local/institutional Ethics Committee or IRB,
355
indicated that their institution did not require approval to conduct this type of study according to
356
the rules of their area, or explicitly indicated that the current study was covered by a pre-existing
357
approval. Related documents are stored on the open science framework (https://osf.io/bdycf/).
358
Data collection began on April 15, 2019 and ended June 15, 2021.
359
Table 1: Sample size by region, gender, and race
360
Region
N
Mal
e
Femal
e
Non
Binary
Trans
Other or
Unknown
Gender
Whit
e
Blac
k
East
Asian
Other or
Unknown
Race
Australia
316
81
211
2
22
90
3
44
179
Austria
203
43
157
1
2
185
0
3
15
Brazil
74
23
48
0
3
49
3
0
22
Canada
105
43
57
0
5
39
19
14
33
China
53
21
29
0
3
0
0
51
2
Colombia
70
21
49
0
0
28
1
0
41
Ecuador
73
28
42
0
3
1
1
0
71
Germany
65
7
54
2
2
59
0
0
6
Greece
264
61
181
0
22
233
0
0
31
Hong
Kong
20
3
16
0
1
0
0
19
1
Hungary
170
22
144
0
4
160
0
1
9
India
88
27
60
0
1
0
0
1
87
Israel
166
29
129
0
8
104
1
0
61
Macao
37
21
14
0
2
0
0
34
3
Malaysia
160
43
105
0
12
0
0
95
65
New
Zealand
331
64
254
3
10
139
0
74
118
Nigeria
74
33
41
0
0
0
70
2
2
Norway
163
52
110
1
0
135
3
4
21
Poland
132
34
94
0
4
110
0
0
22
Portugal
65
29
35
0
1
59
0
0
6

MAN AS DEFAULT HUMAN? 20
Region
N
Mal
e
Femal
e
Non
Binary
Trans
Other or
Unknown
Gender
Whit
e
Blac
k
East
Asian
Other or
Unknown
Race
Slovakia
316
29
283
0
4
307
0
0
9
Taiwan
219
88
128
0
3
1
1
192
25
Thailand
50
11
39
0
0
0
0
15
35
Turkey
286
48
236
0
2
33
2
2
249
United
Kingdom
221
51
167
1
2
153
24
18
26
United
States
1,42
2
374
1,012
12
24
923
127
160
212
Note. Regions with fewer than 10 participants are omitted. Many participants from Turkey
361
identified as an ethnicity that could be considered ‘White’ but we categorized them as ‘Other’
362
because our predictions involving group membership are related to identity.
363
364
Procedure
365
To make better use of the PSA’s globally distributed laboratory network, the current study
366
was presented to participants in combination with an experiment related to the object orientation
367
effect (https://osf.io/e428p/), and the stopping rules for data collection were related to that
368
experiment. Specifically, the first part of the object orientation study was always presented to
369
participants first and participants provided electronic consent to both studies during this phase by
370
reading and agreeing to an informed consent document presented via computer screen. The
371
second part of the object orientation study and the current study were presented to participants in
372
a random order such that the current study was either the second or third task participants
373
completed. Notably, during the pandemic, 252 participants completed this study as a standalone
374
experiment.
375
In the current study, participants evaluated seven social groups (people, Black people, East
376
Asian people, White people, police officers, politicians, and criminals) plus the women and men
377
of each group in random order. Demographics (including age, race, gender), and debriefing were
378
administered at the conclusion of the study.
379

MAN AS DEFAULT HUMAN? 21
Group Evaluation
380
Participants were instructed to ‘answer questions about social groups’ and to ‘respond
381
openly and honestly.’ Participants used onscreen sliders to indicate their responses to four
382
questions: how warm, positive, and favorable they felt towards each social group, as well as how
383
much they liked each social group. The sliders could be moved to any value between 0 (not at
384
all) and 100 (completely). Anchor points were displayed every 10 places (i.e., 10, 20, 30, etc.);
385
however, participants were free to place the slider at any whole number between 0 and 100. The
386
exact value participants chose was displayed on screen to the right of the slider. The social
387
groups, as well as the questions related to each group, were shown to participants in a random
388
order. Notably, participants could skip any question they did not want to answer. A sample of the
389
survey with the different translations can be found among the materials on OSF
390
(https://osf.io/bdycf/).
391
As pre-registered, because items were highly correlated (rs > .81), a mean attitude score
392
for each group was calculated by averaging responses on the four questions directed at the group.
393
Mean scores for each group are presented in Table 2.
394
Table 2: Mean attitudes by social group
395
Category
Group
Mean
Group
SD
Male
Mean
Male
SD
Female
Mean
Female
SD
Black People
73.51
22.28
68.91
23.41
75.66
21.67
Criminals
16.42
19.87
15.00
19.39
20.84
22.82
East Asians
71.40
23.24
67.28
24.12
72.98
22.88
People
71.03
22.31
66.15
23.46
80.84
18.62
Police
52.72
29.58
61.17
28.23
53.66
29.40
Politicians
35.19
24.36
35.17
25.73
52.43
27.96
White
People
73.07
21.97
63.80
25.38
69.29
22.98
Note. N > 5,000 for all groups. Scales ranged from 0 to 100 with higher scores indicating more
396
positive attitudes toward the group.
397
398

MAN AS DEFAULT HUMAN? 22
Creating relative attitude scores for each group
399
A relative attitude score for each target group was calculated by subtracting the mean
400
attitude score for each group from the mean attitude score for people, men, or women,
401
respectively. For example, relative attitudes toward Black people were calculated by subtracting
402
attitudes toward Black people from attitudes toward people. Thus, relative attitude scores reflect
403
how much more or less positive attitudes toward the target group are compared to attitudes
404
toward the comparison group (i.e., people, men, or women) with higher scores representing
405
higher prejudice against a group. See Table 3 for relative attitudes scores toward each group.
406
Table 3: Relative attitudes by social group
407
Group
Attitudes toward Group
Attitudes toward Group
men
Attitudes toward Group
women
MD
SDD
MD
SDD
MD
SDD
Black
people
-2.48
23.93
-2.75
25.32
5.20
17.31
White
people
1.75
21.40
2.36
17.22
7.76
18.54
East Asian
people
-0.37
24.91
-1.15
26.23
7.90
19.57
Politicians
35.88
27.18
30.97
27.28
28.42
28.21
Criminals
54.63
28.87
51.15
29.43
60.00
28.26
Police
officers
17.39
30.67
13.45
27.74
19.68
29.07
Note. Higher scores represent increased prejudice against a group. N > 5,000 for all groups.
408
409
Regional endorsement of liberal values
410

MAN AS DEFAULT HUMAN? 23
Data related to the extent to which a region endorsed liberal values were obtained from a
411
dataset created by Eriksson and colleagues (29). Based on questions from the World Values
412
Survey, participants around the world answered four questions about how justified they believed
413
homosexuality, divorce, abortion, and suicide are from 0 (never justified) to 10 (always justified).
414
Notably, three of the four items directly tap into beliefs related to gender roles (i.e.,
415
homosexuality, divorce, and abortion). The Eriksson and colleagues (29) dataset provided
416
information about the extent to which participants in twenty-one regions in our dataset endorsed
417
liberal values.
418
Treating missing data
419
If a participant answered at least one of the four questions related to a group then we
420
calculated the attitude score based on the available responses. Specifically, if only a single one of
421
the four questions are answered, we used its score as the attitude score. However, if more than
422
one question was answered, we calculated the average.
423
Statistical analysis
424
A simulation-based power analysis demonstrated that 2,300 participants provided 90%
425
statistical power in our design. Therefore, we conducted the planned confirmatory tests of our
426
hypotheses when the relevant sub-sample for each hypothesis reached 2,300 participants. If we
427
did not reach 2,300 participants for a particular sub-sample then we reported descriptive statistics
428
and reported differences in correlation sizes (Pearson correlation coefficient) with 99.5%
429
confidence interval, but we did not perform statistical tests nor draw any statistical inferences.
430
We tested for differences in the Group-Male and Group-Female correlations using the
431
cocor package in R (30). If the Group-Male correlation is .8 and the Group-Female correlation is
432
.7, then the 99.5% confidence interval will be calculated around .1. In our analyses, we used the
433
confidence intervals for statistical inference, always using a 99.5% interval, calculated using
434

MAN AS DEFAULT HUMAN? 24
Zou’s (31) formula as implemented in the cocor package. If the confidence interval fell within r =
435
-.1 and r =.1, we concluded that the difference in correlation size was not large enough to be of
436
interest. Otherwise, if the confidence interval did not include 0, we concluded either that the
437
prejudice against the group in general is more strongly correlated with prejudice against the
438
males of that group than females of that group if the lower bound of the confidence interval was
439
positive, or vice versa if the upper bound of the confidence interval was negative. If the
440
confidence interval included both 0 and either -.1 or .1, we concluded that the test yielded an
441
inconclusive result regarding the hypothesis, not supporting either the presence, or the absence of
442
an effect.
443
Results
444
As per our pre-registered analysis plan (https://osf.io/3gux4/), we conducted confirmatory
445
analyses on the first 2,300 participants to complete the study in each sub-sample (i.e., full sample,
446
each racial group, the women of each racial group). Exploratory analyses were conducted on data
447
from all participants who completed the study. In the descriptions below, we indicate whether we
448
are conducting confirmatory hypothesis testing or exploratory analyses. The final analysis code is
449
stored on the open science framework (https://osf.io/cj4tx). Figure 1 summarizes the findings
450
presented in the next two sections.
451
452
Figure 1. Plot of correlation differences by group. Positive scores indicate attitudes toward the
453
group had a stronger correlation with attitudes toward the men rather than the women of the
454
group. 99.5% confidence intervals calculated using the method outlined by Zou (31).
455

MAN AS DEFAULT HUMAN? 25
456
‘Man’ as default in traditionally male groups
457
As predicted, a confirmatory hypothesis test demonstrated that attitudes toward politicians
458
had a stronger relationship to attitudes toward male politicians than attitudes toward female
459
politicians (rdiff = 0.09, 99.5% CI [.05, .14]). See Figure 2. Notably, the analyses using the full
460
sample produced similar statistics, rdiff = .07, 99.5% CI [.04, .10].
461
462
Figure 2. Side by side comparison of how relative attitudes toward politicians correlates with
463
attitudes toward male politicians and female politicians. The sample is the first 2,300 participants
464
to complete the study.
465
466
Contrary to our predictions, confirmatory hypothesis tests found that attitudes toward
467
police officers (rdiff = 0.03, 99.5% CI [-0.01, 0.06]) and criminals (rdiff = -0.01, 99.5% CI [-0.04,
468
0.03]) did not have a stronger relationship with attitudes toward the men rather than the women
469
of the group (see Figures 3 and 4). Using the full sample, attitudes toward police officers (rdiff = -
470
0.01, 99.5% CI [-0.03, 0.02] and criminals (rdiff = 0.02, 99.5% CI [-0.01, 0.04]) showed similar
471
patterns.
472
473
Figure 3. Side by side comparison of how attitudes toward police officers correlated with
474
attitudes toward policemen and policewomen. The sample is the first 2,300 participants to
475
complete the study.
476
477

MAN AS DEFAULT HUMAN? 26
Figure 4. Side by side comparison of how attitudes toward criminals correlated with attitudes
478
toward male and female criminals. The sample is the first 2,300 participants to complete the
479
study.
480
481
Regional endorsement of liberal values as moderator. Figure 5 displays regional
482
breakdowns for these effects toward traditionally ‘male’ groups. To test whether these effects
483
were moderated by the extent to which a society enforces traditional gender roles, we conducted
484
exploratory analyses on the correlation between the average difference in Group-Male attitudes
485
and Group-Female attitudes in each country in our dataset and the extent to which that region
486
endorsed liberal values (29). The extent to which a region endorsed liberal values was negatively
487
related to gendered attitudes toward politicians (r = -.46, 99.5% CI [-.81, .13]), police officers (r
488
= -.20, 99.5% CI [ -.68, .40]), and criminals (r = -.39, 99.5% CI [-.78, .22]). Individuals in
489
regions that endorsed liberal values more strongly showed smaller differences in their attitudes
490
for men versus women in traditionally male roles, although the magnitude of this relationship is
491
uncertain.
492
493
Figure 5. Regional differences in whether group attitudes are more strongly related to attitudes
494
toward the men or women of the group for (A) attitudes toward politicians, (B) attitudes toward
495
police, and (C) attitudes toward criminals.
496
497
‘Man’ as default in racial groups
498
For the following confirmatory hypothesis tests related to race, participants who were
499
members of the target racial group were excluded from analyses. As predicted, attitudes toward
500
Black people (rdiff = 0.07, 99.5% CI [0.02, 0.12]) had a stronger relationship to attitudes toward
501

MAN AS DEFAULT HUMAN? 27
Black men rather than Black women. See Figure 6. The pattern is similar when using the full
502
sample, rdiff = 0.08, 99.5% CI [0.05, 0.12].
503
504
Figure 6. Side by side comparison of how attitudes toward Black people correlated with attitudes
505
toward Black men and women. The sample is the first 2,300 people to complete the study that did
506
not identify as Black.
507
508
However, attitudes toward East Asian people did not have stronger relationships to
509
attitudes toward the men rather than the women of that group. See Figure 7. Specifically,
510
attitudes toward East Asian people were not more strongly related to attitudes toward East Asian
511
men or women, (rdiff = 0.01, 99.5% CI [-0.03, 0.05]). Notably, analyses using the full sample
512
showed that attitudes toward East Asian people were more strongly related to attitudes toward
513
East Asian men rather than East Asian women, rdiff = 0.03, 99.5% CI [0.00, 0.07].
514
515
Figure 7. Side by side comparison of how attitudes toward East Asian people correlated with
516
attitudes toward East Asian men and women. The sample is the first 2,300 people to complete the
517
study that did not identify as East Asian.
518
519
We decomposed this analysis by participant location to examine our a priori hypothesis
520
that individuals in the U.S. would view ‘woman’ as the default East Asian person. A
521
confirmatory hypothesis test among U.S. participants who were not East Asian women
522
demonstrated that attitudes toward East Asian people were more strongly related to attitudes
523
toward East Asian men than attitudes toward East Asian women (rdiff = 0.10, 99.5% CI [0.02,
524
0.18]). However, because there were fewer than 2,300 participants in this analysis (N = 1305),
525

MAN AS DEFAULT HUMAN? 28
this comparison was underpowered based on our initial threshold of N = 2,300 and is considered
526
exploratory. See Figure 8.
527
528
Figure 8. Side by side comparison of how attitudes toward east Asian people correlated with
529
attitudes toward East Asian men and women. The sample is the 1305 participants who completed
530
the study in the U.S. and did not identify as East Asian women.
531
532
Contrary to predictions, attitudes toward White people had a stronger relationship to
533
attitudes toward White women than attitudes toward White men (rdiff = -0.08, 99.5% CI [-0.14, -
534
0.02]). There were fewer than 2,300 non-White respondents (N = 2,260), thus, this comparison
535
was underpowered based on our initial threshold of N = 2,300 and is considered exploratory. See
536
Figure 9. Analyses were similar when participants who identified as “Turk” were categorized as
537
“White”, rdiff = -0.10, 99.5% CI [-0.16, -0.04]. Notably, this latter analysis meets our initial
538
threshold for confirmatory analyses.
539
540
Figure 9. Side by side comparison of how attitudes toward White people correlated with attitudes
541
toward White men and women. The sample is the 2,260 participants who completed the study
542
and did not identify as White.
543
544
Figure 10 presents regional breakdowns for the race data. We conducted exploratory
545
analyses to examine whether ‘man’ as default in racial groups was related to the extent a region
546
endorses liberal values. The extent to which a region endorsed liberal values had no clear
547
relationship to more gendered attitudes toward Black people (r = -.07, 99.5 CI [-.61, .50]), White
548
people (r = -.16, 99.5 CI [-.66, .44]), and East Asian people (r = .19, 99.5 CI [-.41, .68]). Notably,
549

MAN AS DEFAULT HUMAN? 29
the relationship to East Asian people ran in the opposite direction such that more liberal regions
550
were more likely to have gendered attitudes toward East Asians, but confidence bounds are very
551
wide, so the magnitude of these relationships is unclear.
552
553
Figure 10. Regional differences in whether group attitudes are more strongly related to attitudes
554
toward the men or women of the group for (A) attitudes toward Black people, (B) attitudes
555
toward East Asian people, and (C) attitudes toward White people.
556
557
‘Man’ as default among women participants
558
Exploratory analyses demonstrated that Black (N = 180) and White women (N = 2,152)
559
did not view ‘man’ as the default member of their respective racial groups. Among Black women,
560
there was no evidence that attitudes toward Black people had a stronger relationship to either
561
attitudes toward Black men or Black women (rdiff = 0.03, 99.5% CI [-0.18, 0.24]). Similarly,
562
among White women, attitudes toward White people were not more strongly related to attitudes
563
toward White men. On the contrary, it was more related to attitudes toward White women (rdiff = -
564
0.12, 99.5% CI [-0.19, -0.05]) as was found for non-White participants. See Figures 11 and 12. A
565
similar pattern was found when participants who identified as “Turk” were categorized as
566
“White”, rdiff = -0.10, 99.5% CI [-0.17, -0.03].
567
The latter effect is consistent with the unexpected finding reported above that among non-
568
White participants, attitudes toward White people had a stronger correlation with attitudes toward
569
White women than White men. Exploratory analyses revealed a non-significant difference in the
570
same direction among the 690 participants that identified as White men, rdiff = -0.06, 99.5% CI [-
571
0.18, 0.06].
572
573

MAN AS DEFAULT HUMAN? 30
Figure 11. Side by side comparison of how attitudes toward Black people correlates with
574
attitudes toward Black men and women. The sample is the 180 participants who completed the
575
study and identified as Black women.
576
577
Figure 12. Side by side comparison of how attitudes toward White people correlates with
578
attitudes toward White men and women. The sample is the 2,152 participants who completed the
579
study and identified as White women.
580
581
However, unexpectedly, exploratory analyses among East Asian women (rdiff = 0.16,
582
99.5% CI [0.02, 0.30]), attitudes against East Asian people were more strongly related to attitudes
583
against East Asian men. See Figure 13.
584
585
Figure 13. Side by side comparison of how attitudes toward East Asian people correlates with
586
attitudes toward East Asian men and women. The sample is the 485 participants who completed
587
the study and identified as East Asian women.
588
589
Discussion
590
The present research provides evidence for the extent and limitations of ‘man’ as default
591
in attitudes toward multiple social groups among a diverse sample of participants recruited via a
592
globally distributed laboratory network. Specifically, we found evidence suggesting ‘man’ as
593
default in attitudes toward Black people and politicians, but not police officers, criminals, White
594
people, or East Asian people. Notably, on average these effects were smaller than 0.1 limiting the
595
practical relevance of these effects.
596

MAN AS DEFAULT HUMAN? 31
However, the magnitude and direction of these effects varied by region (see Figures 5 and
597
10) and participant characteristics. For example, though male politicians were the default on
598
average when looking at the whole sample, they were not among participants in the U.S. or
599
China. Chinese participants, in fact, showed a flipped direction of the effect. Similarly, though
600
Black men were the default in the pooled sample, they were not among Black women or
601
participants in Nigeria (See Figure 10). Thus, ‘man’ as default in attitudes likely depends on
602
many contextual factors. We found some support for one contextual factor—the extent to which a
603
region endorses liberal values. Specifically, participants from regions that endorsed liberal values
604
were less likely to view men as default for all categories except East Asian people, but the
605
magnitude is uncertain due to endorsement of liberal values being assessed by proxy. Future
606
studies with individual-level assessment of endorsement of liberal values are required to assess
607
this potential effect more precisely.
608
Given past research demonstrating White men as default in the mental representation of
609
White people (3), the current effect that White women rather than White men were default in
610
attitudes toward White people was unexpected. Though there was regional variation in the
611
magnitude and direction of the effect (e.g., White men rather than White women were in default
612
in attitudes toward White people among participants in Canada, see Figure 10), future research
613
should investigate why White men were not the default in attitudes toward White people among
614
participants in the United States. For example, one possibility is that in the United States attitudes
615
toward White men but not White women or White people became more negative following media
616
coverage of White men killing unarmed Black people.
617
Though the sample was diverse in terms of the number of regions (N > 40) represented, it
618
comprised undergraduates and, therefore, may not represent the general population in any region.
619
For example, undergraduate samples may be younger and more liberal than a region’s population.
620

MAN AS DEFAULT HUMAN? 32
Additionally, the region with the most participants was the U.S. (N = 1422), so the opinions of
621
participants from that region might be overrepresented in the overall sample. Future research
622
should investigate the extent to which these factors (e.g., region, group membership, regional
623
endorsement of liberal values, and sampling) contribute to ‘man’ as default in attitudes.
624
Conclusions
625
In conclusion, we used a methodology based on the measurement of attitudes to assess the
626
extent to which ‘man’ is seen as default in three traditionally male social groups and three racial
627
groups. Although ‘man’ was the default for attitudes toward Black people, that, unexpectedly,
628
was not the case for attitudes toward White people. These findings speak to unique forms of
629
prejudice that women experience either because they have ignored societal expectations for
630
gender roles or because of their unique place in the intersection of race and gender.
631
Data availability
632
Data and final analysis code can be downloaded from the open science framework
633
(https://osf.io/7etnw/).
634
635

MAN AS DEFAULT HUMAN? 33
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636
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