Data on behavioral autogynephilia in cis women from /r/IAF suggests similar rates as in trans women

[Epistemic status: The data examined in this post is unreliable, and the post itself is a bit silly/tongue-in-cheek. The main point in this post is that people should be less overconfident about autogynephilia in cis women. Previously, I have expressed skepticism that autogynephilia is as prevalent in cis women as it is in natal males, disbelieving that it is normal female sexuality, and I remain skeptical of widespread autogynephilia in cis women, but I think it’s a position that needs to be criticized seriously (especially since some cis women, like Alice Dreger, believe that it is common) rather than dismissively, hence my defense.]

Behavioral autogynephilia is arousal to performing feminine behaviors. The general autogynephilia scale measures it using items like sitting in a feminine way or speaking with a feminine voice, but canonical examples given of behavioral autogynephilia might also include knitting, being a cheerleader, or similar. Behavioral autogynephilia is often rare in cis women, and so it is often used as an example to prove that autogynephilia is not normal cis female sexuality, e.g. here:

My own arguments against the claim that autogynephilia frequently occurs in natal females were more general and not directed at Moser’s survey. I wrote, for example, that the notion that typical natal females are erotically aroused by—and sometimes even masturbate to—the thought or image of themselves as women might seem feasible if one considers only conventional, generic fantasies of being a beautiful, alluring woman in the act of attracting a handsome, desirable man (or woman). It seems a lot less feasible when one considers the various other ways in which some autogynephilic men symbolize themselves as women in their masturbation fantasies. Examples I have collected include: sexual fantasies of menstruation and masturbatory rituals that simulate menstruation; giving oneself an enema, while imagining the anus is a vagina and the enema is a vaginal douche; helping the maid clean the house; sitting in a girls’ class at school; knitting in the company of other women; and riding a girls’ bicycle. These examples argue that autogynephilic sexual fantasies have a fetishistic flavor that makes them qualitatively different from any superficially similar ideation in natal females.

(emphasis added)

The exact rate has so far been unknown, though. However, recently, /r/ItsAFetish ran a poll to find the rate:

agp_iaf

Rates of behavioral autogynephilia among women, according to a poll by /r/IAF. Archived results.

It may be useful to consider the rates found in trans women for comparison. For this, there is a survey on /r/AskTransgender that may work:

agp_mtf.png

Rates of autogynephilia on /r/AskTransgender as measured by a number of different questions.

For behavioral autogynephilia, the relevant question was “Enacting stereotypical feminine behaviors (painting nails, knitting, etc.)”. Thus, apparently, in cis women the rate is 7%, while in trans women, the rate is 11%. This means that there is a difference of 7%/11%~0.6.

Due to the content of /r/ItsAFetish, it is unlikely that the participants have been confused about the meaning of the questions. This is relevant because in other surveys, such as those which find that most women report being aroused by the thought of being women (1, 2), it is often hypothesized that most women are not actually aroused by the thought of being women in the same sense that natal males are, but instead confused about the meaning of the question because they have never observed autogynephilia in males. This problem is unlikely to come into play here.

There seem to be a number of conclusions that can be drawn here:

  1. Behavioral autogynephilia is not typical cis female sexuality, but it is also not typical MtF sexuality.
  2. Presence of behavioral autogynephilia does not strongly distinguish between cis women and MtFs.
  3. If you strongly disbelieved one of the above two statements – and I suspect a lot of people arguing against autogynephilia in cis women disbelieved statement 2 – then you should be less confident about your assertions about autogynephilia.

Age of onset as the origin of discrete types of gender dysphoria?

[Epistemic status: currently speculative, but probably easily testable when I get the time]

In a previous post, I created a model where different factors (GNC and AGP) influenced a single dimension of gender issues. This model yielded roughly speaking two types of trans women, one of which was homosexual and GNC, while the other was AGP. This split happened as a result of Berkson’s paradox, the phenomenon where if there are multiple causes that influence some common factor, and you select for said factor, the causes end up negatively correlated in the selected sample.

The Problem

However, my model was fundamentally flawed, because I needed to “fake” the parameters with unrealistic values in order to create the two clusters. In particular, I believe I set the rate of homosexuality higher than realistic, the rate of AGP among homosexual men too low (it’s probably more like 5%), the connection between AGP and gender dysphoria too weak, the degree of femininity among homosexuals too high, the influence of femininity on gender dysphoria too high, and the overall rate of gender dysphoria was likely also too high. This doesn’t really change the point in my previous blog post, since I get essentially the same connection between AGP transsexuality and femininity if I use more realistic parameters, but it does have implications for the theory of gender dysphoria.

Namely: we appear to observe two different kinds of gender dysphoria, and this dichotomy requires some theoretical explanation. Berkson’s paradox, which I relied on in the post to explain it, is too weak. It consists of the fact that selecting for the existence of some traits leads to a negative correlation between the traits, but it tends to more lead to a spectrum than to a dichotomy.

berksons_typology.png

Illustration of attempting to achieve the trans typology through Berkson’s paradox. Each point represents a person, and the assumption is that people transition when their total gender issues – defined as the sum of the two etiological contributors – exceeds some threshold. While it will lead to a strong observable negative correlation, it will still be a spectrum, and both types of trans women will have elevated degrees of both etiologies.

If the etiologies were incompatible – that is, if the shape of the distribution above was an L shape, rather than a round O shape – then the center of the distribution would be removed, and we would end up with two discrete types. This would require that the etiologies were negatively correlated, and indeed negative correlations between the etiologies themselves (i.e. AGP ruling out GNC homosexuality) exist, but are also too weak (and indeed my model also included such negative correlations). So what are we to do?

There’s a different solution: What if the different types contribute to gender issues independently? That is, rather than adding the etiologies together to get the estimated degree of gender issues, we could take the maximum; this can end up cleanly yielding two distinct types:

modified_berksons_typology

In this modified situation, trans women of one type do not have any of the traits associated with trans women of the other type.

I’ve seen it argued that the psychological feelings arising from autogynephilia and HSTS-spectrum gender issues are fundamentally different, and that therefore this approach – taking the maximum – is more realistic, as it doesn’t really make sense to talk about “gender dysphoria” in general when there are multiple different kinds. However, I strongly disagree with this conclusion; both from theoretical concerns, and from just practical psychological concerns:

Consider, for instance, the process of “gender-questioning” that many autogynephilic transsexuals go through. In this process, one question that often comes up is the question of whether one is a “true transsexual” – and obviously things like gender nonconformity, childhood gender identity disorder, and so on, are all going to contribute to concluding that one is indeed a “true transsexual”, increasing the likelihood of transition. Thus, I think the additive model is far more realistic than the maximum-based model.

The Solution

So, what can we do? The title of course spoils this a bit, namely we can use the age of onset as the factor that gives us our “maximum”. Essentially, it seems realistic enough to assume (tautological, almost) that people ever develop gender dysphoria if they at some time develop gender dysphoria; so people’s life-total degree of gender issues can be understood as the maximum degree of gender issues that they have had at some point in their life. This gives us the maximum we have been looking for.

If we then further assume that different factors influence gender dysphoria differently at different life stages, we’re done! Specifically, if we assume that factors related to the HSTS spectrum of gender dysphoria (such as gender nonconformity) influence gender dysphoria more strongly earlier than later in life, while factors related to the AGP spectrum of gender dysphoria (such as, well, autogynephilia) influence gender dysphoria more strongly later than earlier in life, then this gives us something akin to the two types.

onset_typology

Required influences on gender dysphoria over time. Scare quotes around “HSTS” because HSTS is typically the term used to refer to the type of trans people, rather than the etiologies involved (which include e.g. gender nonconformity, but also other things).

The great thing about this assumption is that it is already broadly accepted as true. Specifically, it’s generally accepted that the vast majority of kids with gender identity disorder desist, indicating that whatever contributed to their gender dissatisfaction in childhood could not continue to affect them later in adulthood (and conversely, it’s generally accepted that HSTSs were GID kids when they were younger); and it’s of course also often accepted that AGP has a larger influence on gender satisfaction from adolescence and onwards, when libido becomes stronger.

The HSTS cluster of gender issues probably consists of multiple effects, and to have a model that encompasses all nuances in play, these multiple effects should be analysed separately. Any such analysis will currently be speculative, but I can give an example of what I mean:

onset_subtypes

Possibility for the change in contribution from different factors associated with HSTS. Here, cognitive gender confusion refers to a belief that one is female; attachment/ingroup factors refers to having a bad relationship with one’s father, or similar inverted with mother, or to idealizing girls and having a negative view of boys, gender norm enforcement refers to being bullied or otherwise harmed because one is targetted by other individuals for being gender nonconforming, and desire for masculine heterosexual men refers to the HSTS attraction to, well, masculine heterosexual men, that Bailey described in The Man Who Would Be Queen.

Most likely, the AGPTS cluster of gender issues could be similarly decomposed, but it’s less clear how to even begin with that. Autogynephilia itself does not seem to be the only thing in the cluster that contributes to gender issues, as e.g. bisexuality correlates with autogynephilia and appears to contribute independently of autogynephilia (at perhaps d~0.75). However, it seems implausible that bisexuality is the direct contributor, as there’s no clear reason why it would be, so it would need to be broken down further to be interpretable.

Duality

The observations above do not make the homosexual/autogynephilic typology obsolete. Rather, they suggest that there are two ways of viewing things. There are the etiologies of gender issues – various groups of traits that correlate internally and produce gender dissatisfaction. And then there are the onset clusters, which determine what sets of gender issues are able to get combined together, timing-wise, to lead to transsexuality.

onset_clusters

The two different perspectives, etiology vs onset, determine whether you are focused on what traits co-occur and influence each other, or whether you are focused on what traits trans people can end up having.

One perhaps-intuitive way to think about this is to consider the question of “building a trans woman”. Most natal males don’t transition, so in order to end up transitioning, they need some combination of unusual traits. No single trait can predict transitioning, so therefore it is insufficient to have only one trait, and we instead need to consider some combination of traits that are sufficient. If we abuse the liability-threshold model a bit, we can even come up with a scoring system; different traits contribute differently to one’s scores, and they also contribute differently depending on the onset age. In order to end up gender dysphoric, the set of traits must add up to a sufficient threshold of gender issues – which, based on the transition rate, might be perhaps 4 sigma (though perhaps it’s lower than that, say 2.6 sigma…).

build-a-tran

Different contributors to gender issues, visualized as arrows in proportion to their effect. To determine the numerical score of different contributors, I consider the correlation between the contributors and gender dissatisfaction.

The more contributors one adds, and the more extreme the contributors one adds, the more “unlikely” the individual one is writing down is. To illustrate the system, here is some sort of attempt at scoring myself using this model:

build-a-tailcalled

Polycausal overview of factors that plausibly influenced my gender dissatisfaction. To be honest, I’m slightly surprised that the model was so good at accounting for my gender issues.

When I was younger, the AGP had much less effect, and it’s also likely that the masculinity had much greater effect. This matches with me having developed gender issues late. However, if someone had been more feminine than me, then they might have started out with some milder gender issues, which then dissipated as the effect of femininity got smaller with time, but also increased as the effect of AGP grew. Thus, this model predicts that there can be some quite-complex patterns of evolution of gender issues over time.

Generally, as one adds more and more contributors in the model of an individual, the likelihood of all of these contributors existing in the same person drops. This is the main factor that puts order into the ways gender dysphoria can function; an individual can only have so many contributors to gender issues, so it is unlikely for them to develop gender dysphoria if they don’t have the big ones (such as autogynephilia for late-onset individuals).

Some additional complexities

The model currently assumes that if one ever exceeds the (4 sigma?) threshold of gender dissatisfaction, one becomes permanently transgender. This assumption is a bit unrealistic, and is directly contradicted by phenomena like desistance.

It seems to me that there is some sort of nonlinear effect that leads to similar consequences to this assumption, though the specifics of the effect seems to possibly differ by age. For instance, it’s often believed that it is impossible to treat full-blown gender dysphoria in adulthood; this might be due to some sort of commitment or self-reinforcing effect, where gender issues strengthen once they exceed the threshold. Meanwhile, in childhood, gender issues seem to affect one in different ways, e.g. leading to pretending to be the opposite sex in ways that plausibly contribute to gender issues in the future.

I have some more thoughts on the details of this which I might address in a later blog post, but if there’s any flaw in the model, this is a likely where it is.

It is also worth noting that some trans women may have had mild gender issues in childhood that were not enough to lead to full-blown gender identity disorder, and then later in life developed gender issues for different reasons. For the purposes of this model, the milder earlier gender issues are not very important, as they represent gender dissatisfaction due to different causes than the ones that later made them trans.

Implications

Right now, all of this is theoretical speculation, so the primary implication is probably that this should be tested. And to the degree it is tested, it may also be useful to start collecting data on differences in how traits affect childhood versus adulthood gender dissatisfaction.

The model also suggests some ways in which people can vary from the standard HSTS/AGPTS dichotomy. These probably aren’t going to massively reorganize things, but they might make the typology more nuanced. At this point I’ve already started reinterpreting things in the light of this model, and it seems quite promising.

It seems to me that this model might also be better able to account for trans men. In particular, women seem to face stronger gender norms, which implies a larger effect size for gender nonconformity on adulthood gender issues, and thus a greater degree of “blurring” the types.

Economics of transition, the liability-threshold model, and gender dysphoria

I recently wrote a blog post with a model of how people end up becoming trans, and in response I received an email asking about the foundation of the common “gender dysphoria” variable that all the different causes contributed to. I thought I would write a blog post about what the meaning of this is:

In economics, the simplest model we use to understand people’s behavior is the utility-maximization model. Under this model, people have a set of preferences, which is defined by their utility function, which assigns outcomes a number that represents the desirability of said outcomes. When people have a choice, they are then assumed to pick the option which leads to the highest expected utility.

We can apply this model to transgender topics. The core defining property of transgender people is that they transition, and ultimately transition is a decision to be made. If we apply the economic model, then we get the conclusion that people become trans when utility(transition) > utility(staying cis).

These utility functions are relatively complicated objects, especially because for our purposes, they represent expected utility, which means that they also include an element of belief about what is going to happen in each situation, and not just what is actually going to happen. Thus, utility(transition) is likely going to contain terms related to autogynephilia and passability, but also things related to whether one believes that transition is a good option for trans people in general. They also don’t just include one’s own psychological traits; for instance, utility(staying cis) might be higher if one has a successful established life (significant other, job, …) as one’s natal sex.

Thus, we might imagine that we can approximate utility(transition) = autogynephilia + passability + femininity – transphobic environment + …, and that one can approximate utility(staying cis) = masculinity + relationships + attractiveness as natal sex + …; with the different factors influencing one’s decision being included in the model.

The specifics of how these preferences work (e.g. whether they are based in pain or yearning, etc.) don’t matter from an economic point of view. There is perhaps a sense in which one can say that utility(trans) is likely to represent “positive emotions” related to transition, while utility(cis) is likely to represent “negative feelings” about being one’s assigned sex, but ultimately they’re treated symmetrically, and so the distinction is not very important. This may make it seem like the economic understanding is missing critical information, but depending on the purpose, this may be perfectly acceptable.

In the liability-threshold model used in behavioral genetics as well as other fields, it is assumed that for some binary condition (such as being trans), there exists a latent “liability” to develop this condition, such that when the liability exceeds some threshold, then one ends up with the condition. However, notice what happens when we combine this with the economic model above: utility(trans) > utility(cis), which is the condition for ending up trans, is equivalent to 0 < utility(trans) – utility(cis). This gives us a rather complicated variable, utility(trans) – utility(cis), which leads to transsexuality when it exceeds a certain threshold – exactly what the liability-threshold model needs! So, we can define liability(trans) = utility(trans) – utility(cis). In reality, this is going to be a bit more complex than what I’ve described above, as there may be nonlinearities, change across time, irrationality, etc., but it’s a good starting point.

Another thing to notice is that this notion of liability(trans) is very close to how gender dysphoria can end up defined in clinical settings. For instance, in the factor analysis of the GIDYQ-AA, items related to intention to transition, such as wanting HRT or SRS, have incredibly high factor loadings at around 0.95. From a psychological standpoint, it might be reasonable to try to figure out some narrower sense, which can apply to someone who decides not to transition despite discomfort, and which might not apply to someone who wants to transition while having relatively little discomfort, but there’s also a real sense in which the decision-oriented approach makes sense.

The mathematical consequences of a toy model of gender transition

Alternative title: “true transsexuals” as a statistical artifact.

Consider the following ultra-simplified model of gender dysphoria, inspired heavily by Blanchard’s typology:

simple_model

Assume further that people transition once they exceed some threshold of gender dysphoria. This model definitely doesn’t contain everything (e.g. it’s missing socioeconomic status, in reality there likely is a nonlinear homosexuality x femininity effect, …), but it may serve as a nice toy model. We can simulate the model in Python:

import numpy as np
import random
np.random.seed(12345)
random.seed(54321)
def generate_person():
	gd_noise = np.random.normal(0, 1)
	gender_dysphoria = gd_noise
	homosexual = random.random() < 0.04
	agp_rate = 0.15 if not homosexual else 0.03
	agp = None
	gd_agp = 0.0
	if random.random() < agp_rate:
		agp = np.random.normal(0, 1)
		gd_agp = 1.4 + 0.3 * agp
		gender_dysphoria += gd_agp
	mean_mf = 0 if not homosexual else 2
	mf = np.random.normal(mean_mf, 1)
	gd_mf = 0.5 * mf
	gender_dysphoria += gd_mf
	return gender_dysphoria, homosexual, agp, mf, gd_noise, gd_agp, gd_mf

SAMPLE_SIZE = 1000
TRANS_RATE = 200
samples = [generate_person() for _ in range(SAMPLE_SIZE * TRANS_RATE)]
samples.sort(key=lambda person: person[0])
trans = samples[-SAMPLE_SIZE:]
cis = samples[:-SAMPLE_SIZE]

The specific numeric parameters of this model are vaguely inspired by reality, but I changed most of them around a bit compared to my beliefs about what they were in order to make the resulting distribution of MtFs more like what we observe in various studies. Some assumptions of this model may be disputed; for instance, I believe that meta-attraction cannot account for all autogynephiles’ interest in men, and so some gay men are autogynephilic, but some people disagree with that. Generally, the point of this post isn’t that these specific parameters are necessarily right, but rather, to investigate some qualitative consequences of models with the general structure of the first diagram.

First, let’s get the standard sexual orientation vs autogynephilia numbers. They can be computed as follows:

def fmtpcnt(rate):
	return str(round(100*rate)) + "%"
n_hs = len([p for p in trans if p[1]])
print("Homosexual rate: " + fmtpcnt(n_hs/SAMPLE_SIZE))
print(" - AGP rate among HSTS's: " + fmtpcnt(len([p for p in trans if p[1] and p[2]])/n_hs))
print(" - AGP rate among non-HS TS's: " + fmtpcnt(len([p for p in trans if not p[1] and p[2]])/(SAMPLE_SIZE - n_hs)))

These were the main things I used for tuning the parameters of the model to match studies of trans women:

Homosexual rate: 10%
 - AGP rate among HSTS's: 26%
 - AGP rate among non-HS TS's: 89%

They don’t entirely match the rates that studies find, because it turns out to be hard to tune the model precisely while also preserving realism of the parameters. However, they’re arguably “good enough”. Note that due to the assumptions of the model, there’s no “misreporting” here; we know exactly how the data is generated, and this is based on the internal data in the model.

However, something interesting happens when we consider the amount of femininity by sexual orientation:

def fmtd(diff):
	return(str(round(diff, 2)))
std_mf = np.std([p[3] for p in samples])
print("Femininity among HSTS's: " + fmtd(np.average([p[3] for p in trans if p[1]])/std_mf))
print("Femininity among non-HS TS's: " + fmtd(np.average([p[3] for p in trans if not p[1]])/std_mf))

# Results:
#   Femininity among HSTS's: 2.81
#   Femininity among non-HS TS's: 0.93

While HSTS’s are noticeably more feminine than non-HS TS’s, and non-HS TS’s are arguably more masculine than they are feminine, even non-HS TS’s are quite behaviorally feminized compared to cisgender men.

This is a quite curious phenomenon, but it makes a lot of sense from a statistical standpoint. As most autogynephiles don’t transition, there is a strong selection effect among those that do transition to have traits that predispose them to additional dysphoria. This selection effect could select for even more autogynephilia, but it could also select for other traits, such as femininity.

In fact, it gets even more subtle – this model predicts that trans women have a magical gender identity, despite not even containing a term for that! More seriously, it is not just autogynephilia and femininity that will be selected upwards, but also the “noise term” representing other massively-polycausal factors that are not modelled (whether those be autism, neuroticism, personal aesthetic tastes, traumas, …).

std_mgi = np.std([p[3] for p in samples])
print("Magical gender identity among HSTS's: " + fmtd(np.average([p[4] for p in trans if p[1]])/std_mf))
print("Magical gender identity among non-HS TS's: " + fmtd(np.average([p[4] for p in trans if not p[1]])/std_mf))

# Results:
#   Magical gender identity among HS's: 1.99
#   Magical gender identity among non-HS's: 2.03

Thus, according to this model, trans women have their gender issues increased by two standard deviations caused by essentially-opaque factors that are not included in the model. From the inside, this likely feels like having an innate inexplicable gender identity that cannot simply be reduced to autogynephilia and masculinity/femininity. Indeed, if such a thing existed, then it would get “lumped in” with the noise term.

There’s also another way to think about this. Why do people transition? Can we list some different types of reasons? In order to address this, we first need to consider what we mean by “why”. Probably the most elegant definition is to say that people transition because of something if, without that thing, they would not have transitioned.

Since we have 3 different proximal causes of gender issues (autogynephilia, femininity, and noise), we have 23 = 8 different options for whether each of these three causes are what made the individuals in question transition. To organize them, I will use the letter ‘A’ to represent autogynephilia, ‘F’ to represent femininity, and ‘I’ to represent the noise term. Uppercase means that the individuals in question transitioned because of that cause, and lowercase means that they did not. Thus, for example, ‘AfI’ represents ‘classical autogynephilic transsexuals’, who do not transition because they are feminine, but do transition because of autogynephilia and because of other contributing factors.

First, the code to compute the distribution:

print("    Non-HS  HS")
threshold = (trans[0][0] + cis[-1][0])/2
for needs_agp, label_agp in [(True, "A"), (False, "a")]:
	for needs_gnc, label_gnc in [(True, "F"), (False, "f")]:
		for needs_mgi, label_mgi in [(True, "I"), (False, "i")]:
			def check(person):
				ok_agp = (person[0] - person[5] < threshold) == needs_agp
				ok_gnc = (person[0] - person[6] < threshold) == needs_gnc
				ok_mgi = (person[0] - person[4] < threshold) == needs_mgi
				return ok_agp and ok_gnc and ok_mgi
			rate_nonhs = len([p for p in trans if not p[1] and check(p)])/(SAMPLE_SIZE - n_hs)
			rate_hs = len([p for p in trans if p[1] and check(p)])/n_hs
			label = label_agp + label_gnc + label_mgi
			print(label + ": " + "{0:>3}".format(fmtpcnt(rate_nonhs)) + "   " + "{0:>3}".format(fmtpcnt(rate_hs)))

It yields the following results:

    Non-HS  HS
AFI: 45%   22%
AFi:  0%    3%
AfI: 42%    0%
Afi:  0%    0%
aFI: 10%   74%
aFi:  0%    0%
afI:  3%    1%
afi:  0%    0%

Here, there are three types that have non-negligible probability; ‘AFI’, representing those who transition for “all the reasons”, ‘AfI’, representing those who transition because of autogynephilia and other predisposing factors, but not femininity (which could be thought of as “classical autogynephilic transsexuals”), and ‘aFI’, representing those who transition because of femininity and other predisposing factors. The rates of these vary by sexual orientation, with the former two making up the majority of non-HS TSs, and the last one making up the majority of HSTSs.

If I modify the code to also show the degree of femininity in each type, then among non-HSTSs, the ‘AFI’ group is much more feminine (1.33) than ‘AfI’ (0.32). Thus, this model implies that there is a distinct subgroup of autogynephilic transsexuals who would not have transitioned if not for their femininity, and who are much more feminine than the classical group where preexisting femininity did not play a role in their transition.

Implications

This is a made-up model. As such, it does not have a direct relationship to reality. However, it illustrates some natural consequences of a wide class of models of gender issues, namely that even if autogynephilia is not linked with femininity, it is very possible for autogynephilic transsexuality to be.

One of the parameters of the model was that 0.5% of natal males transition. By some estimates, that’s about right, but by other estimates, that’s wayyy too high. I originally set it to lower numbers, but one consequence of this is that the selection effects get stronger, which lead to high autogynephilia rates among HSTSs. Roughly speaking, the transition rate is going to determine the selection bias, and therefore the degree to which people are going to transition for “all the reasons”, versus for specific reasons. As such, if the transition rate I’ve entered into the model is too high, this only strengthens the fundamental point I’m making about selection effects.

Typically, Blanchardians seem to portray AGPTSs as not being behaviorally feminized. This doesn’t seem to be justified by any studies (but is instead contradicted by all the studies I’ve seen), and as I’ve shown here, even within a Blanchardian framework it can be hard to make this work. It’s not impossible, of course, in that one could connect a number of nonlinear effects to cancel things out, but I have not seen any reasons to believe this to be the case.

This also gives a plausible explanation for how AGPTSs can end up feeling that the typology does not capture their experiences very well. According to the simulations, very few would have their gender issues solely originate in autogynephilia (the ‘Afi’ case), but would instead have many other contributors too, with many having femininity as a significant contributor.

It’s still conceivable that the classically-presented typology would be true, I guess, and that trans women split neatly into a group that is androphilic and behaviorally feminized, and a group that is not androphilic and not behaviorally feminized. However, I’d really like to know why we would go with that model, rather than a subtler one like the one above. And I don’t think “parsimony” is anywhere close to being a sufficient explanation for this, as logically speaking it’s more important that the dynamics that generate the data is parsimonious, than that the final distribution of the data is.

 

It’s A Hate Sub

So, /r/ItsAFetish is a thing, and it claims to be a subreddit that neutrally and fairly documents the existence of autogynephilia, particularly autogynephilia as a contributing factor to MtF gender dysphoria. I claim that is bullshit, and that they are engaging in a variety of behaviors, most notably Chinese Robber Fallacy, to portray autogynephiles and trans women in a negative way. So, who’s right?

To determine this while not committing chinese robber fallacy myself, I made a bot that gathered links to all /r/ItsAFetish posts that have been made during the week before I made this post. Next, I evaluated these posts on the following criteria:

  • Do they portray autogynephiles or trans women in general as predatory, dangerous, or evil?
  • Do they express disgust about autogynephilia, or otherwise treat it as bad or a personal failing?
  • Do they cherry-pick rare forms of autogynephilia that people for some reason tend to find particularly aversive? (These forms of autogynephilia are usually harmless, but e.g. if conservatives were constantly associating homosexuality with promiscuity and other things they found harmful, we would still consider this homophobically motivated, even though we think promiscuity is fine.)
  • Do they present autogynephilic transsexuals as “false trans”, non-dysphoric, or similar?
  • Do they misrepresent the situation portrayed to make it seem “worse” than it actually is?
  • Do they spread false stereotypes about autogynephiles (e.g. that autogynephiles are narcissistic)?

I’ve tried to be conservative in what I count, and of course these are only a limited segment of all the possible problems that could be in posts. Regardless, I found that more than 70% of the evaluated threads included one of these. Of the remainder, some ambiguously included these sorts of things, and the ones who didn’t were more often theoretical in nature (123).

None of the posts seemed to include positive messages about AGP; e.g. while there were many posts calling transvestic fetishism misogynistic, there seemed to be no posts encouraging transvestic fetishists to enjoy themselves. While some posts could not be classed under any of the specific categories I wrote, even the ones that were categorized as neutral often seemed to be engaging in chinese robber fallacy, and often seemed to have negative attitudes towards autogynephiles.

The most common forms of hate was portraying AGPs as predators (25%), cherry-picking (23%), or misrepresenting the situation (19%). In particular, there were quite a few people who implied that autogynephiles were likely murderers.

You can download a table with the posts that have been considered here; it includes the labels of the posts (0.5 means ambiguous, 1 means unambiguous), along with some reasoning.

Meta-attraction cannot account for all autogynephiles’ interest in men

Meta-attraction (also sometimes called “pseudo-bisexuality”/”pseudo-androphilia”) is an interest in men, not due to classical attraction to men, but instead due to autogynephilia, with the men generally playing the role of strengthening the autogynephilic feelings, making one “feel like a woman”.

Probably the most-accepted way of operationalizing/detecting the difference between meta-attraction and alloandrophilia is to consider arousal to gay male porn. Someone who is interested in men only in the context of feeling feminine is not going to be very interested in depictions of two masculine men engaging in sexual acts with each other, but someone who sees men as erotic in themselves, without any further context, definitely will.

For instance, in her book on page 135, Anne Lawrence criticized assumptions that trans women’s sexuality changes through gender transition, and cited a study which found that one trans woman who claimed her orientation had changed was still subjectively and physiologically aroused by lesbian but not gay male porn.

This is only n=1, but when asking around for other studies that supported this point, I was also recommended this study, which finds that among gynandromophophilic men (i.e. men attracted to trans women), autogynephilia but not androphilic subjective or physiological arousal patterns predicted bisexual identities.

To further hammer home the point, some time ago I made a simple poll on an autogynephilia-related subreddit, /r/MEFetishism, where I found that the bi/gay-identifying AGPs didn’t find gay porn arousing:

mefs

So, case closed? We can now conclude that bisexual and gay AGPs are meta-attracted, rather than classically androphilic? It might seem that way, but I think this is actually overestimating the prevalence of the phenomenon, and this is what I will try to show in this post.

Detour: the controversy

Meta-attraction is one of the parts of Blanchard’s trans typology that is considered very controversial. It’s criticized for being unfalsifiable, for assuming porn interests or arousal patterns are indicative of sexual orientation, and so on. I’ve never found this critique convincing.

On the other hand, while the standard critique of meta-attraction is bad, that doesn’t mean it can’t properly be criticized; a lot of the meta-attraction narrative really seems to lack data. For instance, the studies I linked above did not test bisexual trans women; the studies do not consider the cases where the subjective arousal pattern shows up as androphilic (in such cases, is the physiological arousal pattern also androphilic?), and so on. A lot of people seem to assume that meta-attraction just always applies, even in cases where trans women insist that personal experience (e.g. using gay male porn) makes them different from the others.

As far as I can tell, both sides are wrong. Meta-attraction is real and noteworthy, but there is no way it can account for all androphilia in AGPs, and the studies that exist on it don’t even indicate that it might. I’m not saying this in order to try to prove something about my own sexuality – I acknowledge that most of my interest in men is meta – but I think it’s important for understanding how this works.

The assumption that autogynephilia cannot exist in gay men has gotten very extreme in some circles; I’ve seen one person react with confusion about what the phrase “autogynephilic gay man” even means, despite the fact that it should be a pretty simple concept. Others have understood the concept, but still found it baffling, or have objected to it as being definitionally impossible. (You know there’s a problem when people try to wave away contradictory data by declaring it logically impossible. >.>)

Cracks in the narrative

Let’s start slow, with an existence proof, of someone who is exclusively androphilic and also autogynephilic. Blanchard himself has observed it in-person:

During the 15 years when I regularly interviewed gender dysphoric patients, I saw exactly one male who seemed to be truly homosexual and truly autogynephilic. Much more common are autogynephilic males for whom male sexual partners are interesting purely because they symbolize the autogynephile’s own femininity.

One counterexample is enough to disprove any universal rule, but it would perhaps be a bit much to expect such a rule to hold perfectly universally. That said, this already raises some questions of the causes of autogynephilia; since autogynephilia is rarer in gay men than straight men, it has been proposed that autogynephilia is caused by self-directed attraction to women, in some sense. However, androphilic AGPs are fundamentally incompatible with this theory.

Whatever. This is just one person. But it shows up in more places. In the literature, I can see quite a few old studies which appear to find androphilic PPG arousal patterns in autogynephiles. For instance, this study tests 33 transvestites, one of whom identifies as gay, and finds a range of PPG results, with one of them skewing androphilic. This study tests five “fetishistic transsexuals”, four of whom identify as androphilic-leaning bi, and one of whom identifies as homosexual, and finds that they on average have a bisexual arousal pattern, but with some spread, indicating that one might have a homosexual one. (They don’t report the range here, unfortunately.) I also remember finding another study with 5 autogynephiles, where one had an androphilic pattern, one had a gynephilic pattern, and the remaining on average had a bisexual pattern (but possibly with some internal variation), but I can’t find the link anymore.

This is tiny data, though. Recently, I’ve tried testing this myself, by showing people on /r/SampleSize various forms of pornography. Here, I found that the sexual arousal patterns of bisexual and gay autogynephiles did not differ much from those of bisexual and gay non-autogynephiles:

no_meta

Combined data from two surveys showing pornographic images and asking for ratings of sexual arousal.

If all of them had been meta-attracted, then like in the studies cited in the beginning of this post, the AGPs should have rated the FF porn high and the MM porn low. Instead, they had ratings that mostly match their sexual orientation, which shows that they are classically androphilic. It appears that meta-attraction will only be widespread in certain limited cases, such as heavily-AGP groups (presumably because people with other forms of sexuality will focus on that instead of going to AGP groups?), or in cases where it’s the only plausible explanation (“change” of sexual orientation with transition).

This really should be the end of it, in my opinion, at least until there is higher-quality contradictory data. However, when I present this, I regularly get a set of objections that I will try addressing too.

Inaccurate reporting?

Probably the most consistent objection is, what about social desirability bias? Maybe these AGPs are aware of Blanchard’s typology and the whole controversy surrounding meta-attraction, and report differently because of it.

I… don’t really think this is plausible, at all. These are just ordinary men that I recorded from an ordinary place on the internet; why would they know much more about it, and care much more about the associated controversies, than the AGPs recruited in other studies? E.g. why would they be more likely to be misreporting than the trans women in the study Lawrence cited, or the men from the GAMP study? It doesn’t really add up.

Furthermore, AGP men don’t appear to be misreporting on other domains. Probably the way that AGPs seem like they’d be most motivated to misrepresent themselves would be to present themselves as more feminine, yet I find no connection between AGP and self-reported femininity. If we can’t see AGP-motivated social desirability bias on something as central as femininity, why would we expect it anywhere else?

Another way we could look at it would be sexual history. People talking about meta-attraction often argue that meta-attracted AGPs have a gynephilic sexual history; e.g. Kay Brown focuses on that here. Yet the sexual histories of AGP bi/gay men appear similar to those of non-AGP bi/gay men:

sexual_history

Average sexual histories of AGP and non-AGP gay and bi men.

In the above diagram, I rescaled the number of male and female partners by taking the logarithm, as there are very high levels of sexual inequality, and so the numbers would be highly sensitive to outliers if we didn’t. For understanding group’s sexual history, it’s more relevant to know who they’ve end up with, than whether there’s someone who has had hundreds of partners of some sex.

Notice that this corresponds roughly to a “typical” AGP gay man having had 1.1 male partners and 0.5 female partners, and the “typical” gay man having had 1.4 male partners and 0.3 female partners. And for bi men, this corresponds to AGPs having had 0.6 male partners and 0.8 female partners, and non-AGPs having had 0.5 male partners and 1.1 female partners. The relatively low numbers are due to my sample being young, asocial, and due to the logarithm that weights people with a lot of partners much lower.

It seems hard to believe that the AGP men are misreporting their number of partners in an androphilic way, as that is a relatively clearly-defined question, asking about actual experiences rather than internal feelings of attraction. Thus, this seems to further hammer home the point that AGP gay men exist.

We can also consider things from another point of view: The usual way that meta-attraction is supposed to account for exclusively-androphilic identities in AGP men is in conjunction with an-allosexuality, where the autogynephilia is so strong that it “overshadows” other sexual interests. This implies a very strong degree of autogynephilia, yet the difference in degree of AGP tends to be even bigger than the difference in prevalence of AGP between straight and gay men, suggesting that AGP gay men are less AGP than AGP straight men. Similarly, AGP gay men are about as gender dysphoric, perhaps slightly less, than AGP straight men. These results are not very compatible with the idea that meta-attraction explains homosexual identity, as that would predict a greater degree of autogynephilia than otherwise. Furthermore, since meta-attraction predicts gender issues above and beyond other forms of autogynephilia, the lack of “extra” gender issues for AGP gay/bi men suggests that they aren’t really particularly meta-attracted.

(On the other hand, since AGP gay men do have more gender issues than non-AGP gay men, and to about the same degree as one would expect from their degree of autogynephilia, this indicates that the autogynephilia in gay men is “real” in some sense, in that it works analogously on gender issues to that in straight men; this means that it’s not just people who accidentally clicked on the wrong answer option.)

Finally, to hammer the point home, men (and, tangentially, lesbians but not straight women) have a large overlap in their aesthetic preferences and sexual preferences; straight men tend to consider women very aesthetically pleasing, while gay men tend to consider men very aesthetically pleasing. AGP gay men tend to agree with non-AGP gay men, considering men more aesthetically pleasing than women:

Screenshot at 2019-09-09 16-42-47

Participants were asked to rate whether they find male bodies or female bodies more pleasing from a nonsexual aesthetic perspective.

It’s not clear how significant this is, but to me it feels like it hammers home the point more convincingly.

Implications

It’s really not very significant or important that there might exist some bisexual autogynephiles. They’re still attracted to women, bisexuals aren’t that feminine, there’s really no change except making the sexual orientation range for AGPs wider. It does raise some cute questions, e.g. whether there exist “autobisexual” men (as far as I can tell, yes; autogynephilia and mimicry-autoandrophilia are uncorrelated or slightly positively correlated in bisexual men), and whether “autobisexuality” prevents transition (as far as I can tell, probably not; but it needs more research). However, in the grand scheme of things, such questions aren’t very important.

Perhaps one important question regarding bisexuals that this raises is how it can be that bisexual men are more autogynephilic than non-bisexual men. This appears to be a relatively big effect, in my experience (in one survey, I got d~0.66), and it’s easily explained if the bisexuality is meta-attraction, but hard to explain otherwise. Perhaps autogynephilia makes one more comfortable with acknowledging one’s bisexuality, or bisexuality makes one more comfortable with acknowledging one’s autogynephilia? I don’t know.

This also doesn’t invalidate the previous findings that meta-attraction exists; what I’ve found is that it’s not as widespread as previously assumed, but my results don’t really contradict previous studies, and they’re still perfectly-consistent with perceived changes in sexual orientation being due to meta-attraction. In fact, I would still assume that trans women who’ve experienced their sexual orientation “changing” are meta-attracted, rather than classically alloandrophilic.

However, the existence of autogynephilic gay men raises some questions. For instance, it seems to imply that “erotic target location errors” – i.e. a self-directed form of attraction to women – cannot explain autogynephilia in general. Now, this might just be that there are multiple distinct causes of autogynephilia, or that ETLE works more subtly than that. Since ETLE appears challenged from other angles, though, it might be a good idea to look into alternatives.

It also raises the question of whether the autogynephilic gay men are etiologically similar to non-autogynephilic gay men. For instance, gay men appear to be more feminine than straight men, likely because homosexuality is often caused by some sort of feminization. And indeed, in my Survey on Gender, Sexuality and Other Things, I found greater degrees of femininity in both non-AGP gay men (Glass’ Δ = 0.87, n=62) and in AGP gay men (Δ = 0.95, n=22) compared to straight men. As usual, I doubt social desirability bias is in play here, as everything else seems to check out.

Note that this does not imply a particularly large population of trans women who can be said to be “both types”, both HSTS and AGP. There’s still perhaps 90x more gynephilic autogynephiles than androphilic autogynephiles, so this probably at most accounts for ~1% of trans women. (This doesn’t even take into account that autogynephilia in gay men is weaker than autogynephilia in straight men.) And among those, most will probably not have the highly-GNC background that HSTSs do, so the androphilic AGP MtFs are probably not comparable to HSTSs.

All of this does mean that in the future, one should not describe all AGP interest in men as being due to meta-attraction. In fact, at least in my surveys, it appears that most AGP interest in men is classical alloandrophilia, rather than meta-attraction; however, this finding probably doesn’t generalize to AGP-dominated groups, and it might not generalize to AGP trans women (as meta-attraction becomes more viable when one lives as a woman, and transition might select against classical attraction to men if e.g. such attraction causes autoandrophilia).

Epistemic status

But… this really is just a bunch of internet surveys done by a hobbyist (me). It’s not the highest-quality form of evidence, but at the same time, we really don’t seem to have any contradictory evidence. Yes, some PPG studies found some bisexual- or androphilic-identifying AGPs exhibiting low physiological arousal to gay male porn, but these AGPs also reported low subjective arousal. There is currently no evidence that AGPs who are subjectively aroused by gay male porn are meta-attracted.

That said, if some contradictory PPG study came out tomorrow which did document this, showing well that AGPs who report subjective arousal to gay porn are not physiologically aroused by it, then I will be the first to admit that I was wrong. I just don’t see any good reason to expect this to happen, as I hope that I’ve shown well above. But really, any sort of replication would be great.

For the vast majority of the data I presented here, I didn’t collect it in the hope of disproving meta-attraction, but instead because I wanted to prove meta-attraction to finally be able to end the debate on this topic. However, due to getting unexpected results, I ended up seeing more and more problems with the meta-attraction model. But since it was collected to prove meta-attraction, it is if anything biased in favor of proving meta-attraction, not biased against meta-attraction.

If a cis kid was made to transition…

[Epistemic status: somewhat speculative, due to lack of data.]

One argument that’s sometimes made in favor of the existence of an innate gender identity is the case of David Reimer, a boy who was brought up as female due to damage to his penis after a circumcision. Later in life, he ended up gender dysphoric and transitioned back to living as male, but eventually committed suicide.

The Reimer case is likely not strong evidence, though. It’s only n=1 (obviously), and John Money made Reimer do things that could very easily be seen as abusive. Thus, it’s not a great case to rely on.

Instead, a better case to consider might be boys with cloacal exstrophy (a serious condition that among other things leads to an underdeveloped penis) who were raised as female. In the main study of such boys that I’m aware of, they found high levels of gender dissatisfaction, with the majority returning to live as boys, and 2 to 4 of the 5 living as girls wishing to be boys.

On first glance, this supports the notion of innate gender identity. However, one thing that’s already worth noting is that all of these kids were very masculine from young; thus, it also supports some sort of link between this gender identity and masculine behavior, a link that isn’t very compatible with narratives of repressing or hiding gender nonconformity.

In addition, I think if we read the study more carefully, we see some issues with the idea that it supports notions of innate gender identity. The kid who had the least gender issues, and was the most satisfied with being a girl, subject #1, appeared to be in many ways similar to the other kids. So why the different outcomes? Here’s my suspicion:

If you read the study carefully, only two of the subjects living as male, 9 and 10, spontaneously declared their “gender identity”. These, along with two other subjects who were not living as male, were the only ones who formed the idea of “I am male” independently, indicating that whatever is motivating the gender issues of most of the subjects, it’s more complicated than an internally-generated feeling of being male (as gender identity is usually but not always defined). This leaves open the possibility that for all the subjects, including these two, the gender issues came from a more-complex interaction between their behavioral masculinity and society.

Subjects 11-13 adopted a male identity after their parents told them of their medical status at ages 5-7. Since there are quite a few people who have gender issues in their childhood but get over them when older, this makes them imperfect examples. For instance, we could hypothesize that telling masculine girls that they are in some sense male will often lead to a desire to transition. For these subjects, I’d wonder how many of them would’ve desisted if they had not been told.

Subject 14 assumed a male identity after being told at age 18. This is probably old enough that the effect above cannot explain it; thus, I’d categorize this subject in a way similar to subjects 9 and 10.

Subjects 7 and 8 are really similar to subjects 9 and 10, except that their families aren’t supportive, limiting their ability to start living as male. Both for these, and for subjects 9 and 10, there’s another issue that’s worth considering; due to being natal males, they need to take estrogen medically, rather than having the body produce it naturally at puberty. Refering the desistance study again, it is worth noting that some masculine girls feel uncomfortable at puberty but eventually find that they like being girls:

The second factor the desisting girls associated with their decrease in gender discomfort was the feminization of their bodies, primarily the growth of their breasts. At first they reported that this was unpleasant. They felt embarrassed and uncomfortable, and felt it interfered with their freedom to move. However, before long their feelings shifted in a positive direction and they desired even more physical feminization.

♀ Desister #11
Before puberty, I disliked the thought of getting breasts. I did not want them to grow. But when they actually started to grow, I was glad they did. I really loved looking like a girl, so I was glad my body became more feminine.

One thing I would wonder is if the need to take the estrogen exogenously leads to more gender issues, as the effects of it are seen as more foreign and avoidable than if this is what the body naturally produces. As such, while subjects 7-10 are probably the most-unambiguously gender-dissatisfied of the bunch, the situation isn’t completely unambiguous.

Subjects 1 to 5 have their own set of ambiguities, though. The only info we had on how they did in adulthood was based on parent report, which raises the question of how accurate it is. However, the parent’s reports that the subjects are generally content is compatible with more-reliable observations that masculine girls with gender issues generally get over them when they grow up. (On the other hand, the kids in the cloacal exstrophy study were attracted to girls, while the masculine girls who tend to get over their gender issues tend to be attracted to boys.) For most of them, their gender issues were also somewhat limited in scope at the initial assessment, further supporting the possibility that they did fine in adulthood.

Subject 6 is really unclear, though. They appeared to be doing ok – not perfect, but ok – at the initial assessment, but after being told of their medical status, would not discuss the topic with any. However, they did comply with estrogen treatment. Due to lack of better info, I’d classify them with subjects 11-13, as having ambiguous gender issues.

Group Count Subjects
Ambiguously no gender issues 5 1-5
Ambiguous gender issues 4 6, 11-13
Unambiguous gender issues 5 7-10, 14

I’d be inclined to drop subjects 6 and 11-13 for being told at a young age, making them very difficult to compare to e.g. masculine natal females. This yields about half with no gender issues, and about half with clear gender issues, and I can’t help but point out that this is a similar to the rate of people who tend to identity as cis-by-default in surveys (this survey found 54% identifying as cis-by-default, 46% identifying as affirmatively cis).

[Epistemic status for the followup: questionable math, mainly for sanity-checking. The following math can probably be adjusted to “prove” anything by fiddling with the assumptions.]

How well does this fit with a model where masculinity interacting with society is the driving factor for these sorts of gender issues? I usually estimate there to be a D~2 gender difference in psychology, which implies that 15% of people are more like the opposite sex than like their natal sex. This is wayyy to much if just used directly, as this would suggest that 15%/2 = 7.5% of natal females become trans men.

However, doing this estimate directly would also be somewhat ridiculous, as the vast majority of the 15% would still be more feminine than the average boy, and because the gender issues appear to be much stronger among those attracted to girls than those attracted to boys.

Thus, the real question we need to know is how many lesbians are more masculine than the average man. The estimate of the difference between lesbians and straight women in masculinity/femininity varies depending on study, but let’s go with the gender diagnosticity difference from this study and assume d~0.5. This means that lesbians are d~1.5 more feminine than men. The estimated rate of lesbianism also varies, but let’s go with a middle ground answer of 2%.

By these estimates, about 7% of lesbians should end up with serious gender issues and transition to end up as trans men, which in total should make up a bit more than 0.1% of the natal female population, or, if we estimate trans rates to be about 0.3%, a bit less than half of the FtM population.

This is twice as high as the rate they truly make up (23%, according to the USTS), but there’s infinitely many places that the numbers and calculations can be tweaked, so I don’t think this problem with fitting it should be taken too seriously.

My main conclusion for this is that probably a lot of men would do fine living as women if they were raised as girls, but also that quite a few probably wouldn’t. This appears to very roughly match the cis-by-default self-identification situation, at least to around an order of magnitude or two (which, admittedly, is a pretty bad match). I don’t think that these results are as compatible with a universal innate immutable gender identity, as much as they might be compatible with more-complex mechanics, involving significant individual variation in how well any given man would do living as female.

EDIT 2019-08-02: This study also appears to find that 50% of boys with cloacal exstrophy who are raised as female end up non-gender-dysphoric, but I haven’t read it very carefully so I don’t know. Might be worth looking into.

Playing around with “gendermetricity”

[Epistemic status: silly statistical experiments. Might eventually turn into something useful but for now everything should be taken with a grain of salt.]

[Apology: this is a badly-organized post. The explanation of what gendermetricity and gendermetric correlations are comes in the middle of the post, rather than in the beginning. I find the results in the end really interesting and promising, but it takes a while to get there.]

I love behavioral genetics, because I find the way that it allows you to summarize complex and opaque information into simple variance components interesting and enlightening. For this reason, I got excited when I saw Gwern post a tweet with a link to a study that generalized this approach from behavioral genetics to neuroanatomy. Does this mean we can use this for other domains too?

For some background: typically, behavioral genetics have used the known similarities between monozygotic and dizygotic twins to infer to what degrees various traits are heritable, shared environment or nonshared environment. If more-genetically-similar twins are more phenotypically similar than less-genetically-similar twins, the trait in question is heritable. However, more recently, it has become possible to genotype extremely large numbers of unrelated individuals, which makes it possible to compare similarity without the individuals being related family-wise. This allows the technique of comparing degree of genetic similarity with degree of phenotypic similarity to work with non-twin samples, as long as they are big enough. This statistical tool is called GCTA (genome-wide complex trait analysis).

However, there’s nothing restricting you to genetic similarity. In principle, you can use any similarity metric you want, as long as it satisfies the conditions assumed by the GCTA statistics. This was what they did in the paper Gwern linked, replacing genetic similarity with neuroanatomic similarity, allowing them to study highly interesting questions of how strongly phenotypes can in principle be predicted from neuroanatomy, even though they haven’t yet discovered how to predict these neurotypes. They called this statistic morphometricity.

But if this works with genetics, and it works with neuroanatomy, then surely it works for just about anything! Gwern suggested gut microbiomes and leaf spectral imaging, but given my interests, my attention immediately shifts to personality, life experiences, or generally any sort of data that is sufficiently multidimensional that regressing directly with it becomes difficult.

As masculinity/femininity appears relatively high-dimensional, and as I get more and more interested in exploring massively high-dimensional data, I’m interested in this sort of tool for my surveys. However, the immediate question that comes to mind is, do I have the sample size needed? GCTAs are usually run with thousands of participants, whereas I typically have a few hundred (though I have a project in the works that might yield me thousands…), so it’s not looking promising. On the other hand, it seems that I have way fewer dimensions to work with, so perhaps this helps; after all, this is supposed to be less data-intensive than just plain linear regression…

After trying an failing for a while to translate their matlab code to Python, I decided to just follow Gwern’s advice and abuse the GCTA program to directly give me the results. I loaded it up with data from my survey on Gender, Sexuality and Other Things and gave it some test runs. Here’s some example results:

Demo Trait g^2 SE
all gender 56% 6 pp
women aap 53% 12 pp
women narcissism 47% 11 pp
men feminism 46% 9 pp
women gender issues 40% 11 pp
women self-mf 29% 11 pp
women age 27% 10 pp
all age 24% 6 pp
men age 24% 8 pp
all sexual orientation 21% 5 pp
men sexual orientation 21% 7 pp
men narcissism 20% 9 pp
men self-mf 19% 7 pp
women sexual orientation 13% 10 pp
all quality of life 12% 5 pp
women feminism 12% 10 pp
men gender issues 8% 6 pp
men agp 2% 4 pp

In the above, I used the GCTA program to look at demographics and traits and compute their “””gendermetricity””” (“””g^2″””) – i.e. its estimate for how much variance in the trait can in theory be predicted linearly using the masculinity/femininity items I included in the survey. SE denotes the standard error that GCTA estimated. Self-mf refers to self-assessed masculinity/femininity.

The above table is… not very promising for the usability of this tool. The confidence intervals are very wide (though that’s to be expected with my sort of sample size), there’s relatively little connection to how strongly something appears to be related to masculinity/femininity and how high its gendermetricity is (though this is not what the tools promise either – in principle, they’re supposed to detect any variance that can be predicted from combinations of the items, even if these combinations are completely orthogonal to masculinity/femininity), and it’s kinda opaque if just considered directly. It did have some ups, though, e.g. placing gender as being the most-gendermetric trait, and placing AGP as being one of the least-gendermetric traits, but given the other problems, I wouldn’t trust gendermetricity in these domains either.

GCTA is supposed to have a “genetic correlation” function, which should be usable for figuring out the degree to which the gendermetric variance in two variables is correlated. However, I couldn’t get it to work, and the problems I mentioned before made me a bit uninterested in spending too much effort on making it work.

However… gendermetricity is basically an estimate for how well linear regression can in principle be able to predict the traits in question. If we just ignore the “in principle” part, we can explore gendermetricity-like concepts by performing the relevant linear regressions directly!

Let z be a random vector containing the masculinity/femininity-related variables that we seek define gendermetricity using, and x (and y) be a random variable containing the trait that we seek to predict the gendermetricity of. Let x // z denote residualizing x for z. The gendermetricity of x is simply just the fraction of variance explained by z of x, which can be computed as var_z(x) = (var(x)-var(x//z))/var(x). Similarly, the gendermetric covariance of x and y must then be cov_z(x, y) = cov(x, y)-cov(x//z, y//z), and so their gendermetric correlation be cov_z(x, y)/√(var_z(x)var_z(y)).

To help with dealing with the amount of data I have, I use PCA to reduce the dimensionality of the masculinity/femininity test from 22 to 7. In addition, I residualize the variables in a “leave-one-out” manner, which is to say, I predict each individual with a model that has been fitted to all other individuals. To reduce noise variance, I test giving the regression different numbers of principal components as input, ranging from 1 to 7, and give the number that yields the highest gendermetricity. This yielded the following gendermetricities:

Demo Trait g^2
all gender 42,3%
all sexual orientation 20%
men feminism 13,5%
men sexual orientation 11,6%
women gender issues 9,6%
women self-mf 9,5%
men self-mf 8,2%
men age 8,2%
all age 4,8%
women aap 3,7%
women sexual orientation 3,6%
women narcissism 3,3%
men gender issues 2,5%
men narcissism 2,1%
all quality of life 1,0%
men agp 0%
women age 0%
women feminism 0%

This doesn’t look too bad, but more importantly, we can now compute gendermetric correlations! But first, what actually is a gendermetric correlation? The best way I can explain a gendermetric correlation between two variables X and Y is the following: Suppose there’s some stuff that makes X correlate with the masculinity/femininity test (i.e. X is somewhat gendermetric). And suppose there’s some stuff that makes Y correlate with the masculinity/femininity test. The gendermetric correlation is then a measure of how much these two “stuffs” is the same stuff. Now let’s take a look at some examples!

gmcorr-all-pca7.png

Correlation matrix among the full sample. The above-diagonal correlations are the gendermetric correlations, while the below-diagonal correlations are the residual correlations.

So, how do we interpret the above? There’s a number of things that could be said. First, note that the gendermetric correlation between sexual orientation and quality of life exceeds the [-1, 1] bounds that are typically expected of correlations. This is not because gendermetric correlations are somehow able to correlate more strongly than ordinary correlations; rather, it is because my math sucks. (I could have removed these effects, e.g. by just clamping them to the relevant range, or by not doing the leave-one-out thing in my regression, but I think they serve as a useful reminder not to take the statistics in this post too seriously.)

Consider the gendermetric correlation between sexual orientation and gender. It is very close to one, which makes sense when you break it down: The variance in gender decomposes into the gendermetric variance, which boils down to the fact that men are more masculine than women, and the non-gendermetric variance, which boils down to the fact that some women are masculine and some men are feminine. Meanwhile, the variance in sexual orientation decomposes into the same gendermetric variance where men are more masculine and women are more feminine, plus a bit of extra gendermetric variance where gay people are more GNC than the baseline, plus a lot of non-gendermetric variance due to not all queer people being GNC, and not all straight people being gender-conforming.

The gendermetric correlation tells you how much the gendermetric variance in the two variables is shared. Since the main difference in the gendermetric variances is that sexual orientation also contains some GNC gay people, the bulk of the variance (namely that men tend to be more masculine than women) is shared, and so the gendermetric correlation is high. (It’s probably worth adding that I wouldn’t be surprised if the 0.98 number above is an overestimate.)

The residual correlation is much lower. This correlation tells you how much the variables are still correlated after taking the gendermetric variance into account. That is, it tells you the degree to which the non-gendermetric variance is shared. As you can see from the diagram, it is much lower than the gendermetric correlation, and I can also inform you that it is lower than the usual correlation, as in this sample, gender and sexual orientation is correlated at r~0.42.

gmetric-all-pca7.png

Depicted: the gendermetricities for the traits mentioned before. It is only the gendermetric variance, and not all of the variance, that gendermetric correlations use to measure the connection between variables.

In the text above, I assumed the gendermetric variance was related to masculinity/femininity. This is likely in the case of gender or sexual orientation, but it doesn’t necessarily need to be the case in general. Since I allowed up to 7 dimensions from the masculinity/femininity test to be included in the regression, it is possible for the linear regression to form predictions that are not based on masculinity/femininity, but instead also on mixes, e.g. taking some “masculine” characteristics and some “feminine” characteristics and using them to form a new “trait”.

As an example, two traits that might be included in a masculinity/femininity test (but which weren’t included in mine) are Expressivity (caring about others) and Instrumentality (high agency and a strong sense of self). One might assume that gendermetricity computed using these traits only use either the traits directly, or use their difference. However, gendermetricity might also instead use their sum to predict things, which corresponds to Extraversion, a relatively ungendered trait.

Now that we understand gendermetricity (hopefully), let’s look at some more examples.

gmcorr-Female-pca7.png

Gendermetric and residual correlations for women. A number of other traits were also tested but found to be nonsignificantly gendermetric, namely attraction to women, mimicry-autogynephilia, every paraphilia in the survey except for the ones listed in the diagram (including a different variant of exhibitionism), feminism, dislike of own appearance, age, life satisfaction, exclusive attraction to women, number of female partners, and number of male partners. 

 

The first thing to note is that including self-mf (i.e. self-assessed masculinity/femininity) in a gendermetric correlation is in some ways strange. Gendermetricity is meant to capture masculinity/femininity, so what exactly happens when this gets combined with self-mf? Well, basically, we’d expect the gendermetric variance in self-mf to be just that, masculinity/femininity. However, there is going to be some additional variance in self-mf, both because any self-report measure has some noise, and because our masculinity/femininity measure might not be complete. Thus, a gendermetric correlation with self-mf tells us something about whether the gendermetric variance in a trait is due to masculinity/femininity, or due to something else (such as the extraversion example earlier).

Thus, what the above diagram suggests to us is that the gendermetric variance in attraction to men, self-sexualization, and gender issues in women is due to masculinity/femininity, but that the gendermetric variance in autoandrophilia and narcissism is partly due to something else. I believe that like for ordinary correlations, the gendermetric correlations have to be squared in order to yield the shared variance; this means that 28% of the gendermetric variance of autoandrophilia is, according to this measure, due to masculinity, while the remaining 72% isn’t.

Despite this, autoandrophilia appears to gendermetrically correlate really strongly with gender issues, even though these should be mainly about masculinity/femininity. This is another example of how you should take the results here with a grain of salt; it is impossible for the “real” gendermetricities to work like this, but the estimates do.

One thing that’s worth noting is that autoandrophilia gendermetrically correlates with androphilia. Furthermore, while gynephilia and lesbianism isn’t statistically significant gendermetrically, if I force it to compute the gendermetric correlation between those and AAP, I also find those to be negatively correlated with autoandrophilia. Thus, autoandrophilia is gendermetrically correlated with heterosexuality; this is despite the fact that I usually find it to be negatively correlated with heterosexuality. I’m not yet sure how to interpret this finding, but I find it very intriguing that we finally have an AAP/heterosexuality “””correlation”””, as the lack of this is one of the arguments against AAP as a concept.

gmetric-Female-pca7.png

Gendermetricities of the traits under consideration.

One odd thing is that narcissism, autoandrophilia, and gender issues are all gendermetrically correlated. I’m not sure what’s up with that, and it’s worth keeping an eye on whether this replicates. (Is this pattern predicted by the ROGD model? I don’t know.)

It is also interesting to observe that autoandrophilia is negatively gendermetrically correlated with self-sexualization, even though it is otherwise positively correlated with self-sexualization. This might also be worth keeping an eye on in the future.

If you think about it, the matrix for women appears to suggest a two-factor solution, with a “general gendermetric factor” that all the dimensions load positively on, and a “courtship-vs-GID factor” where self-sexualization/androphilia/narcissism load in the courtship direction, and AAP/gender-issues/self-mf load in the GID direction. This approach might be worth considering looking into (though that would require me to first figure out how to do “gendermetric factor analysis”, which appears to be easy enough but might be trickier than it looks).

I don’t know if it was a fluke, or what happened, but for some reason of the two exhibitionism items I had, only one, which I’ve labelled “exhibitionism 1”, was gendermetric. This exhibitionism item is related to flashing; its item text is “Exposing my genitals to an attractive stranger”. Meanwhile, the other exhibitionism item, “exhibitionism 2”, is about public sex, “Performing sex acts while stranger watch”.

On to men!

gmcorr-Male-pca7.png

The gendermetric-and-residual correlation matrix for men. Traits that were also tested for gendermetricity were life satisfaction and autogynephilia, which were found not to be significantly gendermetric, and number of male partners, which was found to be gendermetric in the obvious way (positive correlations with almost everything else) and thus excluded because the diagram was getting crowded.

This time, there is a strong, obvious structure in the graph that just screams that it wants to get noticed: There’s a gender nonconformity factor that involves self-mf, sexual orientation, gender issues, feminism, and disliking one’s own appearance (with all but the disliking-appearance dimension being completely gendermetrically correlated), and a courtship factor that involves liking one’s appearance, self-sexualization, being older, narcissism, and having had more female partners.

I think the first of these two factors is very cute; there appears to be a single general factor of gender nonconformity, rather than there being different forms of GNC that are relevant for different traits. (Alternatively, my data analysis is bad enough that I’m not able to detect different forms of GNC.)

The courtship factor is surprising to me. The masculinity/femininity test I’m using doesn’t have any items that are “obviously” related to courtship for men; there’s no “going to the gym” items, or anything similar to this. Presumably there’s an explanation for this that will become clear if I perform some sort of gendermetric factor analysis, but until then my best explanation is that gendermetricity is magic.

And it’s not even that the courtship-related variance it’s capturing is tiny. Here’s the gendermetricities of men’s traits:

gmetric-Male-pca7.png

Amount of variance explained by the masculinity/femininity test across a range of traits that were tested for men.

 

The number of female partners is the most gendermetric trait according to this analysis. It’s not that I’m complaining, because other than masculinity/femininity itself, courtship would probably be one of the most-relevant things for a masculinity/femininity test to capture. I just don’t understand how it does it.

One constrast between women’s and men’s correlation matrices is that for men, the residual correlations appear to often to be smaller than for women. I’m not sure if that effect is real, but if it is, it indicates to me that this approach works better for men than for women.

I think there’s four obvious followups to this post:

  1. Perform “gendermetric factor analysis”. It seems that this should allow us to extract highly-intuitive factors from the masculinity/femininity test, which might be useful for other things in the future. Plus, gendermetric factor analysis might help reduce some of the potential problems that can arise from overfitting in these cases. (When playing around with changing the number of principal components, it appears that the structure in the men’s gendermetricity matrix is just a result of the existence of the first two principal components. However, in the women’s gendermetricity matrix, the structure appears to require more principal components, despite appearing mostly 2D.)
  2. Expand the study of gendermetricity with more traits and better masculinity/femininity tests. Maybe we can discover even more structure within the traits, and at least we can verify the structure that is already found. Attractiveness is an obvious thing that might be worth including, as would sociosexuality.
  3. Apply these methods to other domains too; for instance, it would be interesting to see if the AGP/GAMP correlation is due to [attitudes to androgyny]metricity, or something similar for other correlations in sexuality. One complication is that in order to make this system work, the domain that is used must be multidimensional; otherwise the correlations will all be 1 or -1. At the same time, really it’s not the gendermetric correlation that needs to be used to see if some factor is a potential mediator, but instead the residual correlation.
  4. Improve the calculations of gendermetricity, e.g. by fixing the cases where gendermetricities greater than 1 or smaller than -1 are computed, or by figuring out a way to use the linear mixed model approach that e.g. the GCTA program uses.

Brief note on differences between the ROGD narrative and the transtrender narrative

In a picture:

rogd_vs_trender

Pictured: the stereotypes associated with stories labelled ROGD vs transtrender.

There are two narratives that have become popular among people critical of the trans community, and they have some surface-level similarity that I think might prevent people from noticing how different they really are. Briefly, both claim that there is a social trend of people taking on transgender identities, but they differ a lot in how they describe the nature of this trend. I think there’s some serious issues with both narratives, but I think it’s worth writing an article that clearly distinguishes them before writing a response to either of them.

According to the transtrender narrative, there are a lot of normal girls who pick up transgender identities in order to get attention, but who aren’t gender dysphoric and aren’t seriously transitioning. People talking about “transtrenders” are usually mainly worried about them making “true trans people” look silly. They do sometimes worry about “transtrenders” engaging in medical transition, but in these cases, they generally consider regret to be inevitable.

transtrender

Breakdown of issues pointed to by the transtrender narrative.

The ROGD narrative is different. Here, the idea still starts with relatively-normal girls who are in social groups that encourage taking on a trans identity. In the ROGD narrative, they’re also said to have a lot of mental health issues that they expect transition to fix. In addition, the followup is different: ROGDs start following the script that would be expected of trans men, discarding feminine behavior and putting a lot of energy into transition.

rogd

Breakdown of issues pointed to by the ROGD narrative.

The ROGD narrative isn’t worried about whether the trans community looks silly, but is instead worried about people ending up with expectations that transition will solve problems that it really doesn’t solve, that people who didn’t need transition will undergo medical interventions, and that the trans community might encourage suicide or self-harm.

In the above, I presented the narratives as being completely separate, but it can be far more continuous than that. There’s nothing contradictory in seeing these things as a continuum, as a progression, or as whatever else one might mix together.

Since the ROGD narrative is clearly the most alarming one, it’s also the one I intend to write a response to first. But that’ll have to wait until a later post.

Triangulating Autohomosexuality

Autogynephilia in natal males and autoandrophilia in natal females can be thought of as a notion of “autoheterosexuality”; a sexual interest in being the opposite sex. They are hypothesized to be variations on heterosexuality, in some sense applied to the self. (Or at least, autogynephilia is; autoandrophilia is a bit weirder.)

It seems like in theory some symmetric notion of autohomosexuality should exist. Not necessarily be common, mind you, but if it’s possible for gynephilic men and androphilic women to invert their sexuality in some sense, then the same should be possible for androphilic men and gynephilic women. That autogynephilia doesn’t go away with MtF transition is further evidence for this hypothesis.

Anecdotally, I’ve heard some cases of this too. I know a trans man who post-transition has sexual interests that are unambiguously like those of AGP cis men, and he says that pre-transition, he had similar interests, e.g. fantasizing about being the women he found attractive.

One characteristic of autoheterosexuality is that it often gets reified into arousal to the idea of being the opposite sex. It’s unclear to me whether autohomosexuals would reify their sexuality the same way, thinking of it as arousal to the idea of being their current sex, because this might merely be a side-effect of not being the desired sex. It’s also not fully clear to me what autohomosexuals would be into. For instance, it’s often proposed that they might masturbate to themselves in a mirror, but to me it also seems plausible that they might instead more be into taking on the appearance of other people of their sex that they find attractive (at least, I’ve had more success with that reification in some initial surveys on the topic).

Based on what we know or suspect about autogynephilia, I would propose the following characteristics for determining the validity of a putative autohomosexuality measure:

  • Since autoheterosexuality is more common in heterosexuals than homosexuals (at least in natal males… autoandrophilia is weird), we should expect autohomosexuality to be associated with typical homosexuality (though it would not necessarily be common among gay people).
  • If autogynephilia and autoandrophilia are hypothesized to be the same sorts of variance applied to different underlying orientations, then we should expect them to be strongly correlated in bisexual women. This expectation might also be applied to bisexual men, but on the other hand, it is often not clear how bisexual the bisexual men actually are, so it shouldn’t necessarily be expected there.
  • Similarly, autogynephilia is hypothesized to “run in families”; if this is the case, then we might expect the bisexual sisters of AGP men to also be AGP.
  • Autohomosexuality should have some elements in common with autoheterosexuality that makes it recognizably similar; but I wouldn’t require exact identity, and it might look like a bit of a stretch when it has actually been fully discovered. However, at the very least I would expect autohomosexuals and autoheterosexuals to “recognize” their own sexuality in each other when having them described, at least moderately better than chance.
  • There’s no clear reason why we would expect autohomosexuality to be more common than autoheterosexuality, so we probably shouldn’t expect this.
  • If the measure works for trans women too (and some AGP autohomosexuality measures might not), we should expect trans women to score highly. Similarly, if it applies to cis men, we should expect it to be in agreement with other autogynephilia measures.

One thing that can be noted is that these validation rules are based on the assumption that autogynephilia is a variation of attraction to women. Some instead argue that it is a form of feminized sexuality. I find this argument somewhat questionable, as I would think that it is androphilia that is feminized sexuality. I’ve seen some quite conflicting anecdotes and arguments that focus on the form female sexuality takes, and I don’t think there’s any clear conclusion here, so it’s difficult to just rely on experience. Instead, one way I would test this would be by looking at whether they are correlated with other forms of feminized behavior. IME this holds for attraction to men but not for autogynephilia, so therefore I wouldn’t think of autogynephilia as being a feminized sexuality.

Using these guidelines, we can try to evaluate a number of proposed autohomosexuality measures. They generally focus on autogynephilia in women, rather than autohomosexuality in general, as very few people seem to pay attention to autoheterosexuality.

1. Moser’s approach

In his paper Autogynephilia in women, Charles Moser took the Cross-Gender Fetishism scale and translated it to better apply to cis women. He then did a survey where he tested how many women answered it affirmatively.

Very little additional data was collected for Moser’s scale. A lot of his sample was heterosexual, and he got a significant amount of affirmative answers, so this could be interpreted as evidence that his scale doesn’t correlate with sexual orientation, or perhaps even correlates with heterosexual orientation. However, it’s not very clear, and more research would be needed to say for sure. Similarly, the other validity checks are also impossible to evaluate currently. As a result, Moser’s scale can only really be interpreted as a suggestion, rather than a validated approach.

Perhaps the most notable thing that is missing is a comparison to trans women’s responses. The first of his items might not generalize well to trans women, but the rest should probably work relatively OK.

2. Lawrence’s approach

Anne Lawrence criticized Moser’s approach for not using scales that reified the “attraction to being a woman” enough, and instead suggested a different set of items that strongly reify this concept.

Nobody has collected data with Lawrence’s items, but I once collected data with a similar approach. It did not pass many of the validity checks that I was able to run.

agp

Picture: comparison of the results from trans women and cis women on a scale much like Lawrence’s.

In particular, it did not correlate by sexual orientation; it and AAP were uncorrelated, perhaps with a negative trend, in bisexual cis women; and trans women had the same distribution as cis women. The main validity check that it passed was that in men, it matched the results from another autogynephilia measure.

I also tested a similar measure in men. Here, it seemed to partially pass the sexual orientation criterion. However, among bisexual men, it was negatively correlated with autogynephilia, and for some reason autoandrophilia was more common than autogynephilia regardless of sexual orientation. In women, this autoandrophilia also matched the more-standard ones I use well. On the other hand, trans men didn’t score super high, so it’s hard to say what to make of that. (It may be that autoandrophilia is not a primary cause of gender dysphoria on reddit, but instead that something else, e.g. masculinity, is. If that is the case, we should be able to identify this by finding that autoandrophilia in trans men is negatively correlated with this “something else”.)

reified-a_p-diagram

Picture: different degrees of autogynephilia and autoandrophilia in different groups of men.

3. Veale’s approach

In her Master’s thesis, Jaimie Veale changed Blanchard’s Core Autogynephilia Scale to be more relevant for cis women by asking whether they had ever been sexually aroused by imagining having “more attractive” physically female features. Her thesis is much more extensive than Moser’s paper, so this time we can evaluate some new things.

On page 66, she has a correlation table which found that her measure of autogynephilia was associated with attraction to men rather than with attraction to women. In general the associations were weak, and so it’s hard to say anything for sure, but it makes me question the validity of her scale.

A lot of the other validity checks were not examined, and so I don’t know whether they held, but that’s not surprising considering they’re relatively obscure. It might be useful to research this in future studies, though.

Veale found that trans women scored slightly but not much higher than cis women on her scale.

4. The Self-Attraction approach

Some people feel that attraction to oneself would be the way autohomosexuality works. On the one hand, I can sorta understand that, and I could totally see the counterfactual female!me be attracted to herself. In fact, there’s quite a few anecdotes of things that seem like autogynephilia and include a heavy element of self-attraction. But I’m not sure this is how it would work, and part of it is the evidence I got when I tried to test it.

self-attraction

Pictured: the average self-reported degree of self-attraction in my Survey on Sexuality, Masculinity and Femininity.

While there does seem to be an effect, where queer people report greater levels of self-attraction than straight people, the effect size is modest. As such, it’s not a very convincing case of passing the sexual orientation test.

I do not have the data to evaluate this approach on many things other than the sexual orientation test yet.

In another survey I tried a variant, asking about arousal by own body and sexual experiences (such as masturbation sessions) focused on admiring one’s own body. This yielded some more-promising results:

own-body-arousal

Pictured: results from Survey on Personal Sexual Arousal.

This suggests that perhaps the most-effective way to ask about this would be the third approach, asking about whether people have sexual experiences where they focus on their own body.

This general approach strikes me as similar to autosexuality, so perhaps this is something that needs to be researched.

5. The Mimicry-A*P approach

When talking with a trans man I know who is AGP, he suggested focusing on fantasizing about being other women, rather than on sexual interest in oneself. Before he transitioned, he had found it arousing to imagine having a body like the women he was attracted to.

This leads to the concept I call “mimicry-autohomosexuality”. Here, I ask something like “Picture a handsome man/beautiful woman. How arousing would you find it to imagine being her?”. This approach has seemed to pass quite a few tests.

mimicry-A_P-groups

Pictured: mimicry-A*P results from my Survey on Gender, Sexuality and Other things.

The expected ordering with sexual orientation is there; queer > straight. We also see trans women score higher than other groups, though this doesn’t apply to trans men for some reason. Among trans men, there was no statistically significant correlation between masculinity and autoandrophilia (r~-0.266, p~0.1). Among bi women, there was a strong correlation between mimicry-AGP and standard ways of asking about AAP (r~0.32; p~0.0003), and also between mimicry-AGP and mimicry-AAP (r~0.45). The mirrored correlations didn’t exist in bi men (r~-0.1 and r~0.23).

Mimicry-autoheterosexuality had adequate agreement with my standard way of measuring autoheterosexuality in men (r~0.6) and in women (r~0.63). However, I got much higher rates of affirmative answers for mimicry-autoheterosexuality in men (66% vs 45%) and slightly higher in women (54% vs 43%).

The range on this measure seems limited; for instance, trans women seem to tend to hit the ceiling, and so their degree of mimicry-AGP is likely underestimated:

agp_development

Pictured: response distribution for various groups.

One potential issue with mimicry-A*P is that people seem systematically more likely to endorse the variant that matches their gender than the variant that doesn’t. For instance, gay men were 1.4x more likely to endorse mimicry-AAP than straight men were to endorse mimicry-AGP, and straight men were 1.4x more likely to endorse mimicry-AAP than gay men were to endorse mimicry-AGP.

a_p-by-orientation

Pictured: amount of mimicry-A*P endorsed by various groups in my fourth porn survey.

There may also be other potential flaws; e.g. in women, I found mimicry-AGP to be correlated with narcissism (r~0.24, p~5E-4), even though I found no such connection in men. Mimicry-AGP also seemed correlated with femininity in women (r~0.13, p~0.03), despite no such connection in men or trans women. My conclusion from this is that most likely, mimicry-AGP picks up on additional things beyond just autohomosexuality.

Overview

Perhaps it might be relevant to create an overview of the different approaches:

Criterion 1 2 3 4 5
Queer higher than het ? No No Yes Yes
Correlated with AAP in bi women ? No ? ? Yes
Runs in same families as autohet ? ? ? ? ?
Surface-level similarity to autohet Yes Yes Yes Yes Yes
Same prevalence as autohet ? No No Maybe Kinda
High scores for trans women Likely Kinda Yes N/A Yes
Concordance with other autohet measures N/A N/A N/A N/A Kinda

Overall, I believe that the most promising approach is mimicry-AGP, but there may be value in considering other approaches and expanding the scales. In particular, it seems that it would be very valuable to discover that multiple different approaches agree with each other, as that could be useful for studying this in a more stable manner.