In comparison to self-identification questions where open-ended responses are seen as the most inclusive alternative (Lindqvist et al., 2020), the categorization of others benefits from response options that explicitly remind participants that not all people identify as women or men.

Participants were

experiments

carefully

impacted

A probable explanation

finding t

xternal stuff.

he results are co

We found that only multiple categories elicited beyond-binary response

Experiment 2 indicated that participants categorize beyond the binary when response options include more options than women and men only. However, the free text option did not differ from the binary option. Thus, the multiple categories condition, with its explicitly stated non-binary options seems to act as reminders to participants. Furthermore, categorization within the binary was not skewed by the addition of multiple categories or the free text option, meaning that the ratio of women and men categorizations was still about 50/50. This did not systematically affect their overall pattern of responses in terms of woman and man categorizations.

n facial gender and binary categorization (i.e. the slope of facial gender) across the conditions

We treated the binary categories condition as a neu

(Difference = 0, CI =[-0.02, 0.03], BF01= 394.93). The effect of facial femininity on woman categorizations almost was the same in the free text and binary categories (Difference > 0.001, CI =[-0.02, 0.02], BF01= 394.93).

as often in the Multiple Categories and Binary Categori

corresponding to moderate evidence

women and e

Overall rates of b

Participant Flow

outside the binary

There was also a clear differenc

In the

Grey dots represent participants who only categorized faces as women or men

to participate in the study.

Figure

summing variations

summing variations of “other” and “non-binary” and dichotomizing this new index

binary categories, free text, and multiple categorie

The experiment used a between-participants design

The experiment used a between-participants design. The two conditions were the one-dimensional (control condition), and two-dimension

In short, the stimuli comprised a multiracial set of faces morphed to vary in terms of facial gender. We defined facial gender as the degree of the female face present in the morph. In other words, a 33 face was slightly tilted toward the man, a 50 face was an even mixture and a 100 consisted only of the woman’s face. Because there were 18 pairs morphed in 7 steps, the total number of faces was 126.

facial gender as the degree of the female face present in the morph

56 women, 47 men 2 participants did not indicate gender)

Study 2 compared a control condition consisting of standard binary response options to two alternatives: a third gender option (such as ‘non-binary’ or ‘other’) and an open text box for participants to type in their respons

However,

33 and 67.

ost participants display a non-linear S-shape and this was also the pattern of the group means.

arked “not woman” and “woman”; in the “man” continuum the anchors were marked “not man” and “man”. The separate continua were presented on different trials and the order of trials

only to

These were highly correlated (R = 0.86)

multiple

Design and procedur

28, N2d = 38)

The one-dimension condition included 33 participants

N1d

“woman” and “man”.

a

pairs

useful outcome

binary gender

L. DeBruine, 2018).

All participants were informed that participation was voluntary and gave written consent to participate in the study.

one-dimensional response options and two-dimensional response options. If one-dimensional scales influence participants to think of gender as binary and opposites and two-dimensional scales don’t do this, there should be a reduced categorical e

investigate the influencing effect of one and two-dimensional response options by investigating whether participa

faces

encountering a face

Categorical effects for continuous stimuli in any domain suggests

to 80% of participants

“woman” and “man”

esearch on how people perceive and categorize the gender of others has used both dimensional scales as well as discrete categories, but in both cases almost exclusively treats gender as a binary catego

however,

Both the initial and later challenges to the gender binary in

transsexual”

for example,

“androgynous”,

later group of challenges to t

In treating gender as a psychological trait, for example,

have been encouraged to includ

see Carleton et al., 2022; Cronin et al., 2022; D’Agostino et al., 2022 for some recent example

Such measurement invisibilizes

assigned sex

Stefan Wiens

accentuated

In other words

0000-0002-8393-5316

Impact

explanation for the difference between free text and multiple categories

We found that only multiple categories elicited beyond-binary responses. Compared to binary control, neither changed the pattern of categorizations of women and men

meaning that the ratio of women and men categorizations was still about 50/50

categorize beyond the binary when response options include more options than women and me

facial femininity and woman categorizations (i.e. the slope of facial femininity

For example, does categorization of faces as non-binary systematically decrease “woman” categorization. We therefore investigated inclusive response options changed participants overall tendency to categorize women and men.

Categorizations by Participants

fit the data to Bayesian mixed-effects

were aggregated

n the free text condition, this included various variations of “other” and “non-binary”.

ummint

only

stimuli were identical

completed the study i

The experiment used a bet

These were adapted from common ways to measure participants categorization of their own gender to other categorization research. Study 2 compared a control condition consisting of standard binary response options to two alternatives: a third gender option (such as ‘non-binary’ or ‘other’) and an open text box for particip

articipants were more likely to categorize faces beyond the binary when using a multiple categires including “non-binary” and “I don’t know” than when using a free text option

cisgender

The results are, however, consistent with previous work suggesting faces are perceived categorically.

gorize their own femininity and masculinity independently of each other

ment 1 indic

Difference > 0.001

he total number of faces was 124

Binary categorization repre

results do no include

Participants were randomly allocated into one of the two response options conditions.

face twice on two different continua,

Observing categorical effects for any stimuli suggest that people treat that stimuli as two separate categories, even if varies for gender

highlighted

BSRI

hese practices

The experiment used a

Measures

gender percieved categorically? (Res

stronger in the one-dimensi

categorical

condition

highlight

“woman” a

earch Question 2

like the early wave of challenge to the gender binary, study 1 does not capture the full diversity of gender. Moreover, given this diversity categorization of individuals as beyond the traditional binary framework is emerging as a an area of research unto itself. However, being a relatively unexplored domain, effective methodologies for measuring these categorizations are not well-established. This study aims to address this gap by identifying more accurate and comprehensive methods for gender categorization analysis.

Measuring gender ca

categorize faces categorically

according to some response options decided by the participants

e of the free text response is that it makes room for hostile participants to fill in nonsense categories, such as “helicopter

The BSRI defined gender as a psychological trait and the people possessing those traits were still seen as either women or men.

Such binary can be reflected in reposnse options that posit male and female as opposites by placing them at the ends of a single dimension or providing woman and man as the only options to choose from.

study

n (Research Question 1) and whether this categorical perception was heightened when gender was measured as unidimensional construct compared to when it was measured as a bidimensional construct (Research Question 2). In Study 2 we measured perceptions of gender measured as discrete categories. We investigated whether and how often participants categorized faces beyond the binary when categorizing f

The present research

Binary and unidimensional response options, for example, may bias participants toward conceiving of gender as a binary category. Non-binary, or bidimensional response options, on the other hand, could have an opposite effect

significant

inclusive response

unidimensional and bidimensional

Using Bem’s scale, the Bem Sex-Role Inventory (BSRI), a person could score highly on both femininity and masculinity, or

is

ditionally, if for example masculine faces, are more likely to be categorized as non-binary, this would in practice bias overall categorizations toward women

nly “young” or “old” would miss the variation of ages

ording facial features such as skin smoothness, jawline, and hair length used to determine gender iden

esearch on how people perceive and categorize the gender of others still almost exclusively treats gender as a binary categor

dditionally, there is substantial variation in gendered features even within the binary gender categories.

face perception

ever, it is not immediately clear that the best way to measure self-categorization also applies to categorization of others

Later recommendations have suggested ways to measure gender as a discrete category

was

national geographic

en as exclusively women or men

even as it implicitly challenged that framework

BSRI

significant

This challenged the prevailing norm of thinking about gender as a strict and mutually exclusive binary

example

not mutually exclusive and need to be measure separately

and such measurements do and do not affect participants’ responses

practices which are becoming widely adopted

calls have gone out urging

Hyde et a

The study highlights the importance of careful consideration of response options in gender categorization research.

esponses

more inclusive response optio

ants

=

dentify beyond the binary

vestigated inclusive response options changed participants overall tendency to categorize women and men. This could happen if, for example, more masculine

er Categorizations by Pa

taching package: 'gridExtra'

most still categorized faces a

ure @ref(fig:descript

Descriptive statistics were used to summarize th

All other responses were removed

“I don’t know” and “other”

he free text condition consisted of an open text box.

These were the binary categories, multiple categories and free text and conditions

recommendation is to incl

showed fairly strong tendencies toward

a condition.

high degree of individual variation, and some participa

Figure@ref(fig:descriptives-tw

f participants respond only to the morph of faces, the lines should be a straight diagonal

We investigated whether participants accentuated gender compared to the morph level of the faces. To answer this question

nsion condition, participants rated gender based on a single continuum with the anchors marked “woman” and “man”. In the multiple dimensions condition, participants rated each face twice on tw

Each person completed a total of 126 trials (i.e. they categorized every face in the stimuli set)

6.66

ategorization of others benefit from response options that explicitly reminds participants that not all people identify as women or men

or slider scales

neurological processes underlying gender perception, which may require more sophisticated techniques such as fMRI and EEG

hese findings are somewhat c

. Specifically, the results suggested that participants only use beyond-binary options to categorize faces when such options are provided explicitly. Free text answers or continuous scales did not affect participants binary gender categorization. Additionally, response options which did not present women and men as opposing categories did not induce participant’s perception of gender in faces to be less accentuated

First

Second

Research Question 3

se()` has grouped output by 'fem', 'scale'. You can override using the `.groups` argument.

In Study 2 response options conditions, such that response option conditions consisted of a single dimension, which ranged from “woman” to “man” and “multiple dimension” which ranged from “not woman” to “woman” and “not man” to “man”

3636364

d man should be skewed near facial femininity = 50. In other words, a face with 33.33% facial femininity would be rated as less woman than that. Therefore, we examined the differences between the two conditions at facial femininity = 33.33% and 66.67%,

y 2 investigates whether perception of gender is accentuated towards the extremes (Research Question 3), and whether a binary measurement increases this accentuation toward the extreme (Research Question 4).

tudies which attempted to address these two potential sources of error. Study 1 investigates whether people use response options beyond the binary when they are able (Research Question 1) and how this influences categorizations of women and men (Research Question 2). Study 2 investigates whether perception of gender is accentuated towards the extremes (Research Question 3), and whether a binary measurement increases this accentuation toward the extreme (Research Question 4)

ribution of Binary responses was affected by the inclusion of non-binary response options. In other words, did the inclusion drastically change categorizations of women and men? This could manifest as a main effect of condition as an interaction between condition and morph level

Second, we

This suggested fairly clearly that androgynous faces were overwhelmingly likely to be categorizd beyond the binary is this analysis convincing? Is this effect interesteing at all?

But looking past all that, what do you think a bout this type of figure to illustrate the individual level data?

us hypothesis testing results in the exploratory stage, parameterestimates resulting from these analyses might be inflated

ground speed

because the value of the missing TRUE or FALSE, x