Contra Blanchard and Dreger on Autogynephilia in Cis Women

Some argue that it is not just males who can be autogynephilic, but instead that cis women are also autogynephilic too. In an interview, Blanchard countered:

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 mine.)

A similar argument was proposed by Dreger:

I’ve talked with Blanchard, Bailey, and also Anne Lawrence about this, and my impression is they all doubt cis (non-transgender) women experience sexual arousal at the thought of themselves as women. Clinically, Blanchard observed autogynephilic natal male individuals who were aroused, for example, at the ideas of using a tampon for menses or knitting as a woman with other women. I have never heard a natal woman express sexual arousal at such ideas. I’ve never heard of a natal woman masturbating to such thoughts.

One might think that before making this argument, Blanchard would’ve tested the relative frequencies of sexual interest in menstruating in autogynephilic males vs female in general, but he didn’t.

At some point I realized, hey, this idea is totally unfounded and probably wrong, so I should test it so we can stop running in circles. Here’s my results:

8

Bar charts from my porn survey on autogynephilia. Each row represents a different operationalization of autogynephilia. Each column represents a different group that was studied. I will focus on the third row and the second, third and fourth columns for this post. Participants were asked to answer “How arousing would you find the following…?” for a large number of sexual interests, relatively uniformly shuffled together.

I defined autogynephilic cis men as participants who said that they were men, not transgender, and endorsed “A little” or more arousal to “Imagining being the opposite sex”. I defined non-gynephilic cis women as participants who said that they were women, not transgender, and “A little” or less attracted to women, while gynephilic cis women were defined as having “Moderate” or more attraction to women.

As can be seen in the diagram, both gynephilic and non-gynephilic cis women endorsed more arousal to “Yourself menstruation (if you are male, imagining that you were able to menstruate and menstruating)” than autogynephilic men did.

Endorsement from all the groups on this item was extremely rare. This raises the question of how relevant Blanchard’s argument is in the first place, as it attempts to reason about the nature of autogynephilic using an extremely rare manifestation of autogynephilia. But regardless, Blanchard’s argument was not supported.

Limitations

 

My survey was very nonrepresentative. I posted it on /r/SampleSize, which is known to have much higher rates of autogynephilia in males than the general population. How this generalizes to female participants is unclear, but it’s probably a good guess that the rates of endorsement are elevated for them too. (Furthermore, one can raise some questions about the validity of the items used.)

This implies that my survey doesn’t really show the real rates in cis women, and so still leaves the problem that we don’t know how high the rates are. The solution to this problem is that Blanchardians should stop making up unfounded arguments that cis women are not autogynephilic. Instead, they should either stop arguing about it, or do what the people who argue that cis women are autogynephilic do and study it directly. (See 1234, and 5.)

Conclusion

I’ve gone through different takes on whether cis women are autogynephilic, ranging all the way from “yes” to “no”. My current take is agnosticism. Is that agnosticism really justified? Shouldn’t the answer be, “no, obviously”?

I notice several deep… “anomalies”, with the claim that autogynephilia is rare in cis women:

  • Ray Blanchard and Alice Dreger use very strange and contorted arguments to argue for it, even though they should know better.
  • Homeovestism, or something very much like it, appears to be common in women.
  • When using scales similar to what Lawrence suggested for assessing autogynephilia in women, one can get exceedingly high endorsement rates.
  • A number of people have claimed publically that autogynephilia is common in cis women to audiences that contain large numbers of women, without any pushback. For instance, Scott Alexander’s post even gave an extremely overt example of what autogynephilia means (so there can’t be much confusion), yet women in the comments didn’t go “hey, that sounds wrong”.
  • I know trans women whose cis female partners have claimed, to the protest of the trans women, that autogynephilia is normal female sexuality.
  • Many who disagree with it, such a gender critical women, seem very openly hostile to research being done on it, as if they were trying to hide the truth, and also attempt to counterargue using contorted arguments like that it is impossible by definition.

Can all of these be explained away? Yes, with some assumptions and legwork. Is “autogynephilia is fairly common in cis women, but some people are opposed to acknowledging it because it is inconvenient” a simple theory that can account for these anomalies without trouble? Also yes.

With these, one could almost ask whether my take on autogynephilias being highly prevalent in cis women should be “yes, obviously”. I still have some concerns that I want to look into before I endorse this though, namely:

  • I think that some of the overtly autosexual things in my list of A*P interests are unlikely to be as common in cis women as they are in cis men.
  • There is still some nonzero concern that cis women are misinterpreting the items given, though this concern is gradually shrinking due to factors that make the intent more clear.
  • Another theory that could well account for many of the anomalies would simply be that I am in a very autogynephilic corner of the world; men on sites like reddit or SlateStarCodex are much more autogynephilic than the general population, so why wouldn’t women be too? So the question is, do all of these findings apply to representative samples too?

I don’t think autogynephilia in women necessarily changes that much from a theoretical standpoint. Certainly it better allows for some magical innate gender identity theories, but it doesn’t prove such theories. Furthermore, due to women’s low sexual specificity, it doesn’t even particularly challenge ideas like erotic target location error.

I think it would help to not make up arguments without grounding, though.

Contra Serano and Lehmiller on Autogynephilia Prevalence

Serano just published a new review, claiming to “debunk” autogynephilia again. I’m not going to comment on most of it as it is just a repeat of some old and tired arguments, but one part stood out to me:

In addition to cisgender women experiencing FEFs, subsequent studies have shown that many cisgender people experience cross-sex/gender sexual fantasies as well. In a recent study of 4,175 Americans’ sexual fantasies, Lehmiller (2018) found that nearly a third of his subjects reported having sexual fantasies that involved being the ‘other sex’, and a quarter had fantasised about crossdressing.

Serano claims that Lehmiller has shown autogynephilic and autoandrophilic fantasies to be common here. However, this is not the case. Lehmiller did not use a representative sample, as he writes in his book:

This book is built around a massive survey of more than 350 questions taken by more than four thousand Americans, including persons from all fifty states. Although the sample is not necessarily representative of the US population, it does consist of an incredibly diverse group of individuals. Participants ranged in age from eighteen to eighty-seven and had occupations spanning everything from
cashiers at McDonald’s to homemakers to physicians to lawyers. The group included all sexual and gender identities, political and religious affiliations, and relationship types, from singles to swingers.

Rather, he ran his survey on social media:

In total, 4,175 adults age eighteen or older who were current citizens or residents of the United States completed my survey, most of whom had heard about it through a major social media channel like Facebook, Twitter, or Reddit. Given that this was the primary way people learned about my survey, the demographics of my sample tended to skew more toward the average social media user than they did toward the average American. For instance, the median age of my survey participants (thirty-two) was about six years younger than the overall median age in America.3 Likewise, my participants were more highly educated and more affluent than the average American. My survey did not disproportionately attract people of one sex, though—it was virtually a fifty-fifty split between those who said they were born
male and those who were born female.

Is that a problem? Yes; my experience with doing surveys on social media is that they tend to attract very high rates of autogynephiles/autoandrophiles, compared to what we would expect on the basis of representative surveys.

Because, yes, there are representative surveys on the rates of autogynephilia/autoandrophilia, and they give much lower rates than what Serano writes. To give two examples, this study finds a rate of autogynephilia of around 10%, and this study finds a rate of transvestic fetishism in males of around 3%.

I shouldn’t have needed to say this, but it’s wrong of Serano to ignore representative studies when discussing the prevalence of autogynephilia and autoandrophilia.

Serano also continues afterwards:

Second, the notion that FEFs have the potential to cause transsexuality is specious and not supported by the evidence (Serano, 2010, 2020). After all, almost a third of Lehmiller’s subjects experienced cross-sex/gender sexual fantasies (Lehmiller, 2018, p. 66), yet the vast majority of these people will never develop gender dysphoria or desire  to transition.

This again is a highly misleading argument. While these autogynephiles don’t transition, they have a large change in their affective gender identity (see e.g. this, finding effect sizes from 1.9 to 2.9), making them much closer to being trans than non-autogynephiles. Furthermore, autogynephilia can exist in different intensities and different types, which might also affect things.

In conclusion, one cannot trust Serano to accurately report the state of the evidence on autogynephilia.

A dataset of common AGP/AAP fantasies

Autogynephilia is a sexual interest in being a woman, and autoandrophilia is a sexual interest in being a man. However, what does this mean in practice?

There are a number of ways one can examine this. For instance, there exist many porn/erotica sites catering to autogynephiles, and they have been observed in clinical contexts, with their fantasies sometimes being recorded. However, I worry that these do not necessarily get at typical such fantasies, but instead get at more extreme and unusual variants, due to their greater selection effects.

To solve this, and to get more data on autoandrophilia, I did a survey asking about qualitative autogynephilic and autoandrophilic fantasies. More specifically, on /r/SampleSize I posted a survey titled “Can you look at some porn For Science? Survey #5” which asked about a broad variety of things, mostly of which were not related to this topic. Near the end of the survey, I asked people whether they found it arousing to “Imagine being the opposite sex”, and among those who answered anything other than “Not at all”, I asked the following open-ended question:


Fantasies about being the opposite sex

Optional. Above, you said that you would find it arousing to imagine being the opposite sex. I’m currently studying the nature of sexual fantasies about being the opposite sex, and as part of this it would be useful to know more about what exactly people fantasize about. So: If you were to fantasize about being the opposite sex, what sorts of things would you imagine?

I’m both interested in the scenarios you imagine (e.g. what sorts of sexual actions are in play, what sorts of environment and partners do you imagine, what sort of body type do you imagine having?) and in the perspective of the fantasy (e.g. who is the object of desire in the fantasy, do you imagine things from a first-person view, etc?).

Feel free to add any other information about experiences or feelings that you may consider relevant to this sexual interest. For instance, it would be interesting to know if you had any thoughts about what makes this sexual fantasy feel attractive to you.


About 500 cisgender women and about 1100 cisgender men completed my survey. Out of these, 96 cis women and 203 cis men answered my question about AAP and AGP fantasies respectively. The dataset, along with some extra variables that I thought it would be worth sharing, can be accessed here. (Note that a few of the participants opted not to have their raw answers shared, and so it contains only 290 data points.) In order to give an overview, I’ve run through the fantasies to try and list the most commonly described themes:

Disclaimer: There were a lot of sexual fantasies and I didn’t have a systematic way to code them, and I did it all by hand, so there may be some mistakes in the following list.

  • 33.5%: Heterosexual sex. (57 AGP, 28%, e.g. “I imagine a luxurious hotel with an handsome abd muscular men after a long diner.”, 39 AAP, 41%, e.g. “I mean not to write too porny but I’ve imagined having a dick and having fairly rough sex with a woman.”)
  • 24%: Masturbating. (45 AGP, 22%, e.g. “I imagine fingering myself”, 25 AAP, 26%, e.g. “I’m mostly interested in being able to feel the pleasure of masturbation with a penis”)
  • 20.5%: Homosexual sex. (41 AGP, 20%, e.g. “sex with my current girlfriend”, 20 AAP, 21%, e.g. “Having gay sex with my partner”)
  • 12%: Being dominant/powerful. (7 AGP, 3.5%, e.g. “I imagine myself sometimes as an attractive woman, sometimes as a normal woman, and since men are less picky about who they choose to have sex, just choose someone, invite them over and be dominant with them.”, 19 AAP, 20%, e.g. “Fucking someone while having a penis seems fun and powerful. “)
  • 11%: Implied heterosexual sex (e.g. mentioning “penetration” abstractly). (23 AGP, 11%, e.g. “being penetrated vaginally from a first person perspective.”, 11 AAP, 11.5%, e.g. “thrusting inside of someone’s genitalia”)
  • 11%: Blowjob. (9 AGP, 4.5%, e.g. “I watch reverse blowjob stuff sometimes.”, 17 AAP, 18%, e.g. “thrusting inside of someone’s mouth”)
  • 10.5%: Orgasming/sexual pleasure. (31 AGP, 15%, “World be fascinating to experience orgasms from the female perspective.”, 6 AAP, 6%, e.g. “I would fantasize about what having a penis would feel like. I like to imagine what my partner is feeling during sex.”)
  • 9%: Multiple partners (AGP only). (19 AGP, 9%, e.g. “I would imagine sex with multiple partners at once, giving and receiving, the gender of the partners ismt really important to me but normally if think about the fantasy its me with men.”.)
  • 7%: Caressing/fondling oneself. (26 AGP, 13%, e.g. “Playing with my boobs”, 1 AAP, 1%, e.g. “I imagine touching my strong, firm, well developed muscles and jerking off”)
  • 6.5%: Being submissive/overpowered. (21 AGP, 10%, e.g. “I would be a sexy little slut that gets used in all sorts of kinky ways.”, 3 AAP, 3%, e.g. “But also the reverse. Having a woman have power over me. The main focus is the penis.”)
  • 6%: Used strap on/packer to simulate penis (AAP only). (6 AAP, 6%, e.g. “I’m a gay woman and have engaged in the above with a strap on as the giving party.”)
  • 6%: Curiosity. (13 AGP, 6.5%, “I’m curious how sex as a woman would feel.”, 5 AAP, 5%, e.g. “Really curious about what it is like to have a penis.”)
  • 6%: Ejaculating (AAP only). (6 AAP, 6%, e.g. “I want to know what it feels like to ejaculate!”)
  • 5.5%: Crossdressing (AGP only). (11 AGP, 5.5%, e.g. “I like female clothing, so my fantasies often have myself and my partner(s) dressing as a woman. Mainly skirts, stockings and pink/purple stuff.”.)
  • 5.5%: Receiving a lot of sexual attention/being desired. (17 AGP, 8%, e.g. “seeing what it’s like to be a girl and receive all the attention”, 3 AAP, 3%, e.g. “Getting a lot of pretty girls that want to have sex with me, and being able to pleasure them just with my own body.”)
  • 5%: Casual sex (AGP only). (10 AGP, 5%, e.g. “I do fantasize about being a woman and how much of a slut I would probably be.”.)
  • 5%: Specific body characteristics (mentions concrete characteristics). (7 AGP, 3.5%, e.g. “Having smallish tits”, 7 AAP, 7%, e.g. “I (usually) imagine myself as a skinny man with the face similar to my actual face but having a beard.”)
  • 5%: Attractive body characteristics (e.g. fit). (17 AGP, 8%, e.g. “Everyone in my fantasies are healthy and generally fit.”, 2 AAP, 2%, e.g. “I would imagine being a fit man and pleasuring a woman from a first-person view.”)
  • 4.5%: Overall body size (small for AGP, big for AAP). (8 AGP, 4%, e.g. “I am a small woman who gets fucked in the vagina by a large man.”, 5 AAP, 5%, e.g. “I imagine being bigger than whatever partner I have. Being so big and tall that I can hug them and practically engulf them.”)
  • 4.5%: Stronger orgasms. (8 AGP, 4%, e.g. “I’m interested in how it would feel. Women supposedly have stronger orgasms, and it’s sensations I as a man don’t normally (or at all feel).”, 3 AAP, 3%, e.g. “I don’t know what it would feel like to have sex with that sexual organ. That means I can imagine it feeling better than anything I’ve ever experienced.”)
  • 4%: Only mentions a sexed characteristic and nothing else in the fantasy. (AAP only) (4 AAP, 4%, e.g. “having a dick”)
  • 4%: Anal sex. (5 AGP, 2.5%, “anal (not painful)”, 5 AAP, 5%, e.g. “my penis swinging while being anally penetrated.”)
  • 4%: Using sex toys. (13 AGP, 6.5%, e.g. “Using a vibrator / dildo”, 1 AAP, 1%, e.g. “I fantasise about using a fleshlight or fucking a man in the arse.”)
  • 4%: Answers that didn’t give any specific info or said that they did not have any A*P fantasies. (11 AGP, 5.5%, e.g. “I don’t know”, 2 AAP, 2%, e.g. “Literally everything just to see what it’s like as a man “)
  • 3.5%: Imagining being androgynous (e.g. GAM, …). (2 AGP, 1%, e.g. “I usually fantasize about being a woman while still having a dick.”, 6 AAP, 6%, e.g. “body type, like my own (I still have breasts as well) but with an average to large sized penis.”)
  • 3.5%: Cunnilingus. (6 AGP, 3%, e.g. “I think about having someone perform oral sex on me.”, 4 AAP, 4%, e.g. “I imagine going down on or fucking a woman”)
  • 3.5%: Transforming (AGP only). (7 AGP, 3.5%, e.g. “While I consider myself masculine, I am still very much attracted to femine features and actions, and would find becoming an attractive woman to be very arousing.”.)
  • 3.5%: BDSM (AGP only). (7 AGP, 3.5%, e.g. “The fantasy scenarios vary but generally revolve around some form of bondage, as I personally find female bondage infinitely more attractive than male.”.)
  • 3%: Clothing (AGP only). (6 AGP, 3%, e.g. “wearing sexy outfits and nylons, wearing dresses and heels”.)
  • 3%: Sex with someone genderbending (e.g. drag queen, GAM, …). (6 AGP, 3%, e.g. “Sometimes I just imagine being the opposite sex in a solo fantasy where I’m jerking off while enjoying my body, other times I imagine that my (female) partner had a dick and would penetrate me with it. “, 3 AAP, 3%, e.g. “Also sometimes I fantasize about being a man and having sex with a dragqueen.”
  • 3%: Easier orgasms (AAP only). (3 AAP, 3%, e.g. “I think sex as a man is easier to reach orgasm and I like to imagine what it would feel like to have that easy stimulation.”)
  • 3%: Sex (partner’s nature unspecified). (7 AGP, 3.5%, e.g. “Masturbation and having sex”, 2 AAP, 2%, e.g. “I think it would be interesting to experience sex with a dick.”)
  • 3%: Ejaculate (AGP only). (6 AGP, 3%, e.g. “And feeling them cum in my pussy, ass, and mouth as well. I’d also like to be came on.”.)
  • 3%: Focus on partner. (1 AGP, 0.5%, e.g. “In this scenario I believe I would be more aroused by my partner than the act itself.”, 5 AAP, 5%, e.g. “The most arousing part is imagining the woman’s pleasure rather than my own.”)
  • 3%: Mimicry-A*P. (5 AGP, 2.5%, e.g. “I mostly fantasize about being hot women I see on Instagram or in real life”, 3 AAP, 3%, e.g. “imagine what it would be like to be a man I’m attracted to fucking a girl I’m attracted to. “)
  • 2.5%: Multiple orgasms (AGP only). (5 AGP, 2.5%, “Multiple orgasms are interesting. “)
  • 2.5%: Feeling sexually attracted to someone. (2 AGP, 1%, e.g. “They have beautiful body parts and can really get into “the zone” when aroused.”, 4 AAP, 4%, e.g. “The amount of attraction I have towards a woman, like I feel like men would have more primal, untamable urges.”)
  • 2.5%: Being attractive (AGP only). (5 AGP, 2.5%, e.g. “I just feel like I’d be more attractive as a girl.”.)
  • 2.5%: Exhibitionism. (6 AGP, 3%, e.g. “Imagine initiating sexual situations including public sex”, 2 AAP, 2%, e.g. “fantasies: usually in public, with people watching. I’m the object of desire. 3rd person view.”)
  • 2%: Exaggerated sexual dimorphism. (5 AGP, 2.5%, e.g. “she is busty (d-cup+), BMI around 25-30, big ass, trimmed not shaved, glamorously beautiful, vulnerable eyes, exposed vulva.”, 2 AAP, 2%, e.g. “I imagine myself with a large penis and a muscular, vascular body with a beard.”)
  • 2%: Intimate/loving sex. (3 AGP, 1.5%, e.g. “The scenario changes according to my mood and but can include having passionate sex with someone I love.”, 2 AAP, 2%, e.g. “Even though I assume the male role in those fantasies, it’s usually from a 3rd person perspective, and boringly romantic, as opposed to anything too racy or kinky.”)
  • 2%: Being normal. (5 AGP, 2.5%, e.g. “In casual gay sex encounters I prefer strict top/bottom or dom/sub roles, usually but not exclusively with me being the bottom/sub. As a female in a straight male/female casual sex encounter it would be easier not having to navigate who is in what role (I am aware women can be dom but I would not be interested in that).”, 1 AAP, 1%, e.g. “I’m lesbian, and as progressive as the world is, it just seems easier to imagine being able to talk to women as a man rather than a woman. It’s more of the fear of being gay in my environment (the south and conservative parents) that have me imagine being a man having sex with a woman.”)
  • 2%: Fantasy comes up in dreams. (1 AGP, 0.5%, e.g. “I recently had a dream”, 3 AAP, 3%, e.g. “This mostly comes up in my dreams.”)
  • 2%: Acting flirtatiously. (5 AGP, 2.5%, e.g. “imagine teasing and turning on the opposite sex”, 2 AAP, 2%, e.g. “Sometimes I imagine I’m single and try to pick up a woman to have sex with.”)
  • 2%: Watching one’s own body. (7 AGP, 3.5%, e.g. “Haven’t really thought about it much. I was more thinking of the hypothetical “if i was a girl for a day I’d just play with my boobs in front of a mirror” thing lol”, 1 AAP, 1%, e.g. “I imagine in it third person, but like watching myself.”)
  • 2%: Impregnation. (3 AGP, 1.5%, e.g. “being impregnated”, 2 AAP, 2%, e.g. “imagining creampie-ing a woman and getting her pregnant”)
  • 2%: Merging or swapping bodies (AGP only). (4 AGP, 2%, e.g. “So in this dream, we decided to switch bodies so that we would have to meet again later to switch back. […] During sex I enjoy being very intimate, intertwined (literally sharing as much skin surface as possible and sensing breath and pulse) and feeling what the woman feels and I love the way women experience arousal and sex, so becoming her or merging bodies would be the next level in this.”
  • 1.5%: Attracting straight people (AGP only). (3 AGP, 1.5%, e.g. “Having sex with straight men that I’m attracted to.”.)
  • 1.5%: Rape (AGP only). (3 AGP, 1.5%, e.g. “often forced male-on-female”)
  • 1%: Friendships becoming sexual (AGP only). (2 AGP, 1%, e.g. “Friendships turning sexual. slender lesbian top. I read a lot of yuri romance and thus have an unrealistic idealized fantasy about lesbian romance and sexuality”.)
  • 1%: Masochistic emasculation fetish. (2 AGP, 1%, e.g. “I’d also like to be a cuck and watch as a man cum in my wife so I can eat out her used pussy.”, 1 AAP, 1%, e.g. “As previously mentioned, I’m into orgasm denial and chastity. This kink is logistically more feasible with dicks instead of cunts.”)
  • 1%: Extreme masochism (e.g. slavery, brainwashing, …). (2 AGP, 1%, e.g. “I could be sold into sexual slavery, forced to perform sexually. Maybe I’m trapped in a machine that forces orgasms. Maybe a mysterious monster is magically draining my intelligence and simultaneously stimulating me to keep me from resisting. After their torment, I’ll be reduced to a brainless fuckable objectified being, which is one of my fantasies for a partner as well.”, 1 AAP, 1%, e.g. “usually he’s a vampire for the purpose of being able to torture him more without him dying, because dying isn’t sexy. He doesn’t have sex with girls unless they rape him, and he never enjoys it. Most of the scenarios don’t involve sex at all though. There’s a lot I haven’t said, but it’s embarrassing.”)
  • 1%: Peeing (AGP only). (2 AGP, 1%, e.g. “Sexual touching and peeing”.)
  • 1%: Everyday activities. (3 AGP, 1.5%, e.g. “I typically imagine myself just being female in my day to day life”, 1 AAP, 1%, e.g. “I imagine waking up in a man’s body and spending the day as a man.”)
  • 1%: Feet (AGP only). (2 AGP, 1%, e.g. “I like girls feet so I have thought about what it would be like to be a girl with cute feet and tease guys with foot fetishes”.)
  • 0.5%: Voyeurism (AGP only). (1 AGP, 0.5%, e.g. “Getting to see inside of a women’s locker room / changing room”)
  • 0.5%: Corsets (AGP only). (1 AGP, 0.5%, e.g. “Corsets/extremely small waists”)
  • 0.5%: Watching porn (AGP only). (1 AGP, 0.5%, e.g. “If I were to engage in a sexual fantasy involving me becoming a woman, I would only indulge in solo sexual acts, such as masturbation or watching porn.”.)

Does porn exposure create peculiar sexual interests? RCT suggests no

One proposed cause of peculiar sexual interests is porn depicting the interests in question. There’s clearly a correlation between use of such porn and having such interests, but the immediate problem is that this correlation could just as well be due to causation in the reverse direction; there’s no doubt peculiar sexual interests will lead to use of porn depicting such interests.

To test the association, I posted a survey to /r/SampleSize titled “Can you look at some porn For Science? Survey #5”. Among a huge range of other things, the survey contained an opt-in section showing porn depicting a random out of three sexual interests: autogynephilia (a comic depicting the Marvel character Thor transforming into a woman, a captioned picture of a nude woman getting a massage with the captions explaining that she used to be a man, and a picture of a woman having sex shown from her point of view), bondage (three pictures which each depict a man and a woman tied up with rope), and feet (three stock photos showing male and female feet… yeah, I might’ve been a bit lazy in getting pictures for this sexual interest). Furthermore, before and after being shown the porn, participants were asked about their sexual interest in the kinds of things depicted by being asked to rate their arousal to the following:

  • Imagining being the opposite sex
  • Tying up your partner (using rope)
  • Being tied up by your partner
  • Caressing your partner’s feet

The hypothesis I am examining is whether exposure to the corresponding types of porn will cause an increase in the above interests.

As a sample size, in total I got 1052 male participants who opted in to seeing the porn and who completed all of the relevant questions. About one third of these were randomly assigned to each type of porn.

Initial sexual interests

The different sexual interests varied somewhat in their prevalence, as can be seen below:

tested_interests

Frequency of various degrees of endorsement of various sexual interests.

I think these rates are higher than what is typically seen in the general population, but it’s what I usually get on reddit. This seems to be because reddit is unusually paraphilic. In order to perform the analysis, I coded the degrees of interest using integers from 0 to 4. When people rated their arousal to the stimuli, they rated them using integers from 0 to 0 to 6.

Validity of stimuli used

First, it might be a good idea to examine the validity of the stimuli used. Below, I show the univariate regression slopes from a number of sexual interests (listed along the y-axis) to a number of stimuli (listed along the x-axis):

12

Regression slopes indicating how much an expressed sexual interest was associated with arousal to the given stimuli. The last three columns are a breakdown of the stimuli used for autogynephilia, as I perceived them to be more conceptually heterogeneous than the stimuli used for the other sexual interests. As comparisons, I’ve also added two extra sexual interests (“Sex with a man”, “Sex with a woman”), as well as corresponding stimuli (erotic images depicting either men or women).

As can be seen, each stimulus has reasonably high validity; for instance, they all exceeded a slope of 0.4 from the sexual interest they were intended to measure, and the highest slope for each sexual interest was for the stimulus intended to tap into the sexual interest. One problem with validity, though, was that the autogynephilic stimuli were also arousing to heterosexual men. This problem is probably to be expected, as a typical autogynephilic stimulus will depict a woman, which seems like it would be sufficient to be arousing to gynephiles too.

Basic results

When comparing the control group and the intervention group, we didn’t see much effect:

Interest Group Before After
Imagining being the opposite sex Control (n=725) 1.35 (1.36) 0.93 (1.15)
Intervention (n=378) 1.32 (1.38) 0.91 (1.12)
Tying up your partner Control (n=730) 2.04 (1.27) 2.08 (1.3)
Intervention (n=373) 2.01 (1.36) 1.96 (1.32)
Being tied up by your partner Control (n=730) 1.77 (1.35) 1.83 (1.36)
Intervention (n=373) 1.71 (1.39) 1.73 (1.39)
Caressing your partner’s feet Control (n=751) 0.97 (1.09) 0.97 (1.11)
Intervention (n=352) 1.0 (1.12) 0.79 (1.05)

The “Before” column shows the average interest before exposure to the stimuli, while the “After” column shows the average interest after exposure to the stimuli. I’ve written the standard deviation in parentheses after each result.

The main change is that for both the control and intervention group, interest in autogynephilia was reduced in the “after” condition compared to the “before” condition. I believe this is because I asked individuals who reported any interest in autogynephilia in the latter case to give a qualitative description of what autogynephilic things they were into; it seems this lead to some of them no longer reporting AGP interest. This problem makes some forms of data analysis less workable, but it should not be a major problem as it applied to both the control group and the intervention group.

To test whether the intervention had any effect, I computed the change in arousal before and after having the intervention.

Interest Group Change p
Imagining being the opposite sex Control (n=725) -0.41 (0.87)
Intervention (n=378) -0.39 (0.88) 0.719 NS
Tying up your partner Control (n=730) 0.03 (0.67)
Intervention (n=373) -0.05 (0.67) 0.061 NS
Being tied up by your partner Control (n=730) 0.05 (0.64)
Intervention (n=373) 0.04 (0.6) 0.798 NS
Caressing your partner’s feet Control (n=751) -0.01 (0.65)
Intervention (n=352) -0.18 (0.66) <0.001 ***

Out of these, the only significant effect was for “Caressng your partner’s feet”, but it was the opposite direction of what would be predicted by porn causing it. (Perhaps a result of me using poor-quality stock photos for the stimulus? Not sure.)

Heterogeneity

This tells us that on average, porn doesn’t cause peculiar sexual interests. However, possibly one might hypothesize that the effects differ depending on the individuals; maybe porn turns some autogynephiles non-autogynephilic, but also turns some non-autogynephiles autogynephilic.

One possible sign of heterogeneity would be if the intervention group has higher variation than the control group in their degree of change in sexual interest. This does not seem to be the case, though I think I need very large sample sizes to detect it through this means, so it’s not a great method.

Rather, let’s look at it in a different and probably more relevant way: Among those who report being “Not at all” interested to begin with, how interested are they afterwards? This tells us something about whether porn can create an interest in someone who doesn’t have it to begin with.

Interest Group After p
Imagining being the opposite sex Control (n=268) 0.15 (0.48)
Intervention (n=145) 0.1 (0.38) 0.247 NS
Tying up your partner Control (n=88) 0.31 (0.53)
Intervention (n=59) 0.22 (0.52) 0.309 NS
Being tied up by your partner Control (n=153) 0.25 (0.49)
Intervention (n=92) 0.17 (0.43) 0.183 NS
Caressing your partner’s feet Control (n=301) 0.14 (0.38)
Intervention (n=142) 0.1 (0.3) 0.231 NS

There was no evidence that porn exposure could cause sexual interests among those who did not already have them, and in fact all the signs pointed in the opposite direction.

Discussion

At first glance, this might look to be in contradiction with what other studies on boots fetishism (1, 2, 3) found. They found that by pairing an unconditioned stimulus (i.e. a stimulus that the subjects are already attracted to) with a stimulus of a boot, they could make the subjects get erections to boots in isolation. My primary worry with these sorts of studies is that possibly they don’t actually create a sexual interest in boots, but instead set up an expectation that boots will be followed up with an attractive stimulus, which might lead to erections upon seeing boots in anticipation of seeing the attractive stimulus. They did not ask the men whether they were interested in the boots themselves, but instead merely measured their penile arousal to the boots. In addition, they found that repeated exposure to the boots would extinguish the tendency to get erections to them, which seems different from how fetishism usually works (being seemingly stable over longer periods of time).

My survey implicitly included pairings with unconditioned stimuli; the autogynephilic stimuli were somewhat arousing to straight men, and the bondage stimuli were somewhat arousing to men regardless of orientation, presumably because in addition to containing the kinks of interest, they also contained men and women, at least one of which people typically find attractive.

One possibility is that these sorts of effects would only come into play with extended exposure to the porn. But why would someone get extended exposure without being into it in the first place? The main suggestion I’ve heard for this is if one already has ended up with one peculiar sexual interest, then one might end up “picking up” adjacent ones that fit well with the one one has, and thus tend to co-occur in the same erotic material. But this is a pretty speculative theory that lacks evidence.

There are some anecdotal observations of people getting new kinks when encountering a new form of porn. This result throws doubt on them, but it also throws doubt on the common alternate explanation, that people “discover” their kinks from such porn; if a discovery effect applied, then it seems like that should also be found by my survey. However, as my survey was 18+, it does allow early-life discoveries, as well as early-life modifications of one’s sexuality. Such effects are speculative, though. It also does allow the possibility that people’s sexual interests regularly change and people somehow rapidly discover the porn that matches their new interests, faster than I would be able to “catch” in my survey.

The subset of the data collected for the survey that is relevant to this analysis is available here. Note that some people opted not to have their results shared publically, so this dataset will not be quite the same as the one I performed this analysis on.

Using instrumental variables to test the direction of causality between autogynephilia and gender dissatisfaction

[Epistemic status: experimental. Half-way speculative with some input from data.]

TL;DR: I use instrumental variables (paraphilias and masculinity/femininity) to test the direction of causation between autogynephilia and gender issues, and find evidence that it goes mostly from autogynephilia to gender issues, but also to some degree from gender issues to autogynephilia.

Autogynephilia – a propensity to be aroused by the thought of being a woman – is extremely extraordinarily strongly associated with gender dissatisfaction and cross-gender ideation in males. One controversial hypothesis, which I personally believe, is that this association is causal, autogynephilia → gender issues. However, this claim is controversial, as the causal aspect has not been properly demonstrated yet.

… mainly because causal studies are hard! It’s not like we have any simple way of randomly assigning people autogynephilia or gender dysphoria to people and seeing what effects that would have, and even if we did, that would probably be considered unethical.

But there are alternative ways of testing causality than randomized controlled trials. An important one is through the use of instrumental variables. Essentially, suppose we want to test the causality X → Y. In that case, if we can find another variable Z, then we can use this variable to test the X → Y causality, as long as Z satisfies certain assumptions:

  1. Z must affect X; we are essentially using Z as a stand-in for experimenting on X. If you are familiar with randomized controlled trials, then think of those high in Z as being the intervention group (except we are letting Z do the intervention, instead of doing it randomly), and those low in Z as being the control group.
  2. Z may not affect Y through other means than through X.
  3. Z may not be confounded with X or Y; that is, there must be no unmeasured factors that affect Z as well as X or Y.

Or graphically:

instrumental_variables

Assumptions made by instrumental variables estimation. Red arrows indicate forbidden connections. U represents any unmeasured confounders that may make the analysis invalid.

So far, there are two potential instrumental variables I can think of for examining the question of autogynephilia’s causal relationship with gender issues:

  • Paraphilias all seem to correlate with each other. This makes sense if there is a “general factor of paraphilia” that affects all of them; thus we can use this general factor as an instrumental variable that affects autogynephilia, to measure the effect of autogynephilia on gender issues.
  • Masculinity/femininity can easily be thought to affect gender issues; if someone has a poor fit to gender norms, then it would make sense for them to become uncomfortable with their assigned gender.

So, that’s the theory, which I’ve been aware of for some time, but now I have some data that will allow me to start testing it in practice! My initial power calculations suggested that I would need a very large sample size (1000+) to have enough power to meaningfully examine this question, and it’s not super trivial to get this. However, I’ve sometimes done “porn surveys” where I show participants on /r/SampleSize some porn and have them rate it, and usually these surveys are very popular, easily achieving the needed sample size. Therefore I decided to include the questions necessary to test this in a porn survey that I was doing for other reasons (more on that later, hopefully), to achieve the sample size needed.

Model: Paraphilia → Autogynephilia

So, how do we measure this general factor of paraphilia so that we can test the direction of causality? Essentially, we look at a bunch of paraphilias unrelated to autogynephilia. These paraphilias will all have some degree of influence from the general factor, as well as some random unknown influence from other sources. Thus, each of them is a noisy indicator for the general factor of paraphilia. We can find out how noisy they are by looking at how much they correlate with each other; because if they correlate with the general factor at a strength of h, then their correlation with each other would be at a strength of h2. (There’s some additional math that handles this in a more nuanced way, but I won’t go into that here. If you want to read up on it yourself, the keyword is “structural equation models”.)

So, we can use a set of paraphilias to estimate the correlation between the general factor of paraphilia and any other variable, by looking at how much the paraphilias correlate with the other variable, and adjusting for the noise inherent in using a proxy. However, there is one big complication to this: paraphilias have more structure than just the general factor. In addition to the general factor, paraphilias also correlate with each other in more specific ways. Consider for instance submissive paraphilias; they tend to correlate more with each other than they do with random paraphilias. This becomes a problem, because if one picks too many paraphilias within a single narrow domain, one ends up measuring this narrow domain instead of the broader general factor of paraphilia. So, when selecting the paraphilias, I tried to make them as unrelated to each other as possible, with mixed success. Here are the paraphilia items I selected for testing the model:

  • Treating your partner roughly in bed, e.g. spanking, shoving around, biting, scratching, or pulling hair
  • Being tied up by your partner
  • Exposing your genitals to an unsuspecting stranger
  • Watching a video of yourself masturbating
  • Having an older sexual partner take on a dominant parent-like role in the relationship
  • Imagining having sex with an anthropomorphic animal (furry)
  • Caressing your partner’s feet

For each of the above, participants were asked how arousing they found it. There were also a number of other sexual interests in the list, including normophilic ones (e.g. “Having sex with a woman”), and autogynephilic ones, of which I will use the following items:

  • Imagining being the opposite sex
  • Wearing clothes typically associated with the opposite sex (crossdressing)
  • Picturing a beautiful woman and imagining being her
  • Wearing sexy panties and bras
  • Imagining being hyperfeminized, i.e. turned into a sexy woman with exaggeratedly large breasts and wide hips

The survey I’m basing this on was a porn survey, and so I couldn’t easily fit in a detailed gender dysphoria measure. However, I included a handful of questions in a masculinity/femininity test and in a disgust sensitivity measure:

  • As a child I wanted to be the opposite sex
  • I feel I would be better off if I was the opposite sex
  • (“How disgusting do you find the following?”…) Imagining yourself being the opposite sex

I try to call this by the imprecise term “gender issues” instead of saying “gender dysphoria” because these do not measure very strong gender issues. One big improvement that could likely be made in future surveys would be to use a better measure of gender feelings.

Anyway, I then set up the following model in a statistics program, and ran it on the data from the cisgender male participants in the survey:

model_paraphilia

Structural equation model assuming paraphilias as an instrumental variable for autogynephilia.

(Here’s a bit of a technical point, so it might be worth skipping over if you don’t care: This model contains a cyclic causal connection, which is not usually allowed in causal models. I fit it as follows: If we let C be the matrix containing the coefficients for the SEM, and V be the matrix containing the residual variances, then I compute the implied covariance matrix as (I-C)-1V(I-C)-1 T. This essentially treats observed covariances as being what you end up with when one reaches an equillibrium after the causal effects are iteratively applied.)

If I fit this model, I get these results. The output here is a bit technical, so I will try to summarize:

  • The model finds evidence for bidirectional causality, but mostly in the autogynephilia → gender issues direction. (Specifically, B~0.56 from autogynephilia to gender issues, and B~0.2 from gender issues to autogynephilia.)
  • The model is very definitely wrong (as decided by the χ2 test); this is to be expected with these kinds of models once one gets enough sample size, as obviously it is too simplistic to assume that there are only three major factors that account for the covariation between the traits. As people say, “all models are wrong but some are useful”.
  • The model is also kind of bad; the numbers labelled “NFI”, “TLI” and “RMSEA” are measures that essentially assume the model isn’t true, and try to quantify how bad the fit is. Generally you want the NFI and TLI to be in the 0.9’s, and the RMSEA to be 0.05 or lower, all of which this model fails to achieve. Future research should probably look into creating a model that isn’t this terrible.

It’s also worth testing how stable these results were, as some of the measures I included were kind of “funny”. For instance:

  • As part of the gender issues measure, I asked people how disgusting they found “Imagining yourself being the opposite sex”. This is a weird question, but if I drop it from the model, I get very similar results; B~0.49 from autogynephilia to gender issues, and B~0.21 from gender issues to autogynephilia.
  • One of the paraphilia items asked about ageplay in a way that might include a degree of “role reversal”, and role reversal could plausibly be associated with gender issues in some way. If I drop it, I get B~0.58 from AGP to gender issues, and B~0.18 from gender issues to AGP. If instead I allow it to have a residual correlation with gender issues, I find no effect. Thus role reversal is probably not problematic for this model, but it is hard to say for sure.
  • When people answer my questions, they answers get discretized into the specific categories I provide (e.g. agree/disagree), rather than me getting data from what we can only assume is a more continuous underlying distribution. If I control for this in an ad-hoc way, I get B~0.54 from autogynephilia to gender issues, and B~0.25 from gender issues to autogynephilia. I used an ad-hoc way to control for this, though, so in the future it should be examined in a more numerically justified way.

In conclusion, using paraphilias as an instrumental variable seems to support the causality going in both directions, but mostly from autogynephilia to gender issues.

Model: Masculinity/femininity → Gender issues

The concept behind this second model is that if someone has a poor fit into gender norms, it seems plausible that they would start feeling dissatisfaction with their gender, or at least openness to being the opposite gender. Thus, we can use masculinity/femininity as an instrumental variable for gender issues.

But first we need some philosophy on what masculinity/femininity even is. I want to eventually write a blog post going into more detail on this, but to keep it brief:

There are various psychological differences between males and females; for instance, males tend to be more horny. These are not necessarily the same as masculinity/femininity, and therefore I will call them “gender differences”. Some of these psychological differences, as well as some things that are not gender differences, end up included in expectations for men and women, and these expectations appear to be closer to what people mean when they use the phrase masculinity/femininity than the gender differences are. I have done some research to find some things that could plausibly be relevant for the concept of masculinity/femininity, and have come up with this preliminary list of items:

  • I prefer talking to people about their daily activities rather than their feelings
  • I like being well-dressed at all times
  • As a child I often played with girls
  • As a child I often played with boys
  • I would be interested in being a fighter pilot
  • I would be interested in working as a machinist
  • I keep myself well-groomed
  • As a child I played with toy weapons or objects meant to simulate them (e.g. gun-shaped sticks)
  • I am interested in medical shows
  • I do not enjoy watching dance performances
  • I am very sensitive and easily hurt
  • I am muscular
  • I have a curvy body
  • [Arousal to] Being treated roughly in bed, e.g. spanked, shoved around, bit, scratched, or pulled hair

I deliberately avoided aspects of masculinity/femininity that I perceived to be strongly overlapping with gender identity, such as whether one wears feminine clothes, or whether one considers oneself to be masculine/feminine, as I think that makes its connections to gender issues too tautological, and so plausibly makes the model invalid. The dataset includes some data related to this, though, so you can play around with it if you download it.

In the previous models, I defined traits by assuming that there is some underlying “true” trait that makes all of the items correlate with each other. I don’t currently think this can be done with masculinity/femininity; instead, I will treat these items as an “index”, so I say that masculinity/femininity is whichever way they affect gender satisfaction. Or graphically:

formative_model

Individual indicators are assumed to cause a synthetic variable that we label masculinity/femininity, rather than be caused by this variable.

This is called a formative model, and it has some disadvantages relative to the model we used previously. In the previous models, called reflective models, the model inherently prescribes some relationships between the items, making it able to be tested much more aggressively. In addition, reflective models automatically control for measurement error, whereas formative models don’t.

And I want to add: Currently, I don’t think we don’t have a good idea of what constitutes masculinity/femininity. Most existing scales, including my own, do not correlate all that much with what is informally referred to as masculinity/femininity. (To be more precise: They seem to correlate on the order of magnitude of 0.4. As a correlation between two separate variables, this is quite high for the standards of psychology, but these are not intended to be separate variables, they are intended to be a measurement. Usually we want measurements to share at least 70% of their variance with what is being measured, whereas a correlation of 0.4 implies that they share only 16% of variance.) I interpret this to mean that we don’t really know what masculinity/femininity is, and so in the future the concept of masculinity/femininity I’ve written about here may change. But in the meantime, let’s look at the results.

So, I fit the following model:

model_mf

Structural equation model assuming masculinity/femininity as an instrumental variable for gender issues.

The initial fit gave these results, which asserted that autogynephilia overwhelmingly affects gender issues (B~0.8), and that gender issues actually reduce autogynephilia (B~-0.24), but it contained some elements that I found dubious, so I modified the model:

  • For some reason, the model claimed that arousal to being treated roughly in bed was masculine, even though I had intended it to be added as a feminine item. This might be an artifact of item phrasing, in that the item I had found to be associated with self-perceived femininity was “My partner acting dominant in bed”, but I wanted something more specific for my current survey, and therefore replaced the item. If I delete this item, I get B~0.6 for autogynephilia affecting gender issues, and B~0.16 for gender issues affecting autogynephilia.
  • Another issue I have is that the masculinity/femininity factor ends up almost entirely defined by the “As a child I often played with boys” item. I am concerned that having the variable defined so narrowly might lead to problems, so I removed this item to have it be defined more broadly by the other items. Combining this with the other change yielded B~0.55 for autogynephilia affecting gender issues, and B~0.24 for gender issues affecting autogynephilia.

Doing those modifications yielded these results. Here, we can observe that the resulting model is not as bad as the model that used paraphilias as an instrumental variable, though it is still quite bad.

Overall, the results seem to agree with the results based on using paraphilias as an instrumental variable: the causality is bidirectional and mostly goes from autogynephilia to gender issues.

Combined Model

Autogynephilia and gender dysphoria might be related in three ways: one affecting the other, the other affecting the one, or confounding where they are affected due to some common factor. Due to the two instrumental variables we have, we can find the causal effect in each direction, and so whatever correlation remains must be confounding. (In theory – assuming that there aren’t any major problems with the models, even though there probably are…)

To test this, I simply fit a straightforward extension of the previous models to the data:

model_combined

Structural equation model that allows confounded relationship between autogynephilia and gender issues.

Fitting this model yields these results. This model finds that most of the connection between the two variables is either autogynephilia causing gender issues (B~0.5) or confounding (0.15), with only negligible causality from gender issues to autogynephilia (B~0.05). This is kind-of sketchy, as both the previous models agreed that there was some causality from gender issues to autogynephilia.

It’s hard to tell for sure what happened, but it seems to me that some of the assumptions were violated. Specifically, masculine/feminine traits seem to have correlated with paraphilic interests. Thus, to fix this, I let the masc/fem items freely correlate with the paraphilia items too. This yielded these results, where autogynephilia causes gender issues (B~0.49), gender issues cause autogynephilia (B~0.23), and there is little confounding (0.05).

Limitations

For all of this to be valid, the assumptions behind the models have to hold. There are a number of ways in which this might not be the case:

Using paraphilias as an instrumental variable for autogynephilia assumes a “factor model”; that is, it assumes that there is a latent factor which causes the covariance between the different paraphilias. As an alternative to factor models, some people think of things as being “networks”. For instance, perhaps people “start out with” some sexual interest, “pick up” adjacent sexual interests, and repeat. This would be compatible with conditioning models of sexual interests. In such a case, the relationship between autogynephilia and other paraphilias would be bidirectional causality, with the paraphilias strengthening each other.

Using paraphilias as an instrumental variable also assumes that there are no other paraphilias that affect gender issues. If, for example, submissiveness tends to affect them, then this assumption is invalid and the effect of autogynephilia on gender issues will have been overestimated. Even more generally, paraphilias have not been sufficiently demonstrated to satisfy the requirements for instrumental variables (though my initial examination into this look optimistic – more on that another time).

The masculine/feminine traits are a grab-bag of different personality traits that I have lumped together. The assumptions behind instrumental variables need to be established for all of them, and so far we don’t even have an argument for why it should hold for any of them.

The masculine/feminine traits included questions about appearance. It is known that people tend to have extremely inaccurate ideas of how attractive they look. This raises the question of whether they also have equally inaccurate ideas about other aspects of their appearance. If so, this might have implications for the masculinity/femininity measure, though for now it’s hard to say how strong those implications will turn out to be.

The place where I got this data, namely reddit, has very high rates of paraphilias, and of autogynephilia specifically. This is going to increase the causal estimate for autogynephilia → gender issues, as there is more variance in autogynephilia on reddit than elsewhere. It also has very high rates of trans people; I have excluded trans women from this analysis due to a number of problems with including them (changes in traits due to transition, unsure about the self-report accuracy, …), but excluding them also leads to some biases (underestimation of effect sizes, particularly ones linked to gender transition).

It might be worthwhile to look into whether the bidirectional causality can be attributed to only certain narrower subtypes of autogynephilia. The current survey asked about autogynephilia quite broadly, so it is hard to say much about this

There are thus lots of things that could productively be researched in the future.

Conclusion

This is by no means a perfect test, and I’m not sure people on either side of the issue are going to be convinced by it. (Certainly it will be interesting to see what people say.) It might be worth considering some intuition for both sides of the issue:

  • There’s a straightforward way that autogynephilia could cause a desire to be female, and that’s because it’d be hot. There are also more subtle ways, though; for instance, there’s evidence that paraphiles tend to get attached to their paraphilic objects of interest, perhaps in similar ways that romantic attraction operates. Furthermore, maybe engaging in autogynephilic fantasies and behavior helps “normalize” the concept of being the opposite sex to oneself, as one has to keep confronting oneself with it?
  • Some people think that sexual interests reveal hidden desires. I’m don’t think I believe that, but there are two other categories of explanations that I find more plausible: Autogynephilia questions like “How arousing would you find it to imagine being the opposite sex?” correlate almost perfectly with questions like “How often do you imagine being the opposite sex?”. Thus, plausibly, people infer their arousal to it on the basis of how often they fantasize about it. But a man who is comfortable with the idea of being female might be more comfortable fantasizing about it. And, many claim that a man who is distressed about having a male body would also avoid fantasies where he has this, and likely replace them with fantasies where he has a male body.

It should also be noted that these results could easily be overthrown. You need massive sample sizes to estimate the parameters accurately enough that they can be used for instrumental variables, and even this data is a bit too “close for comfort”.

To make it easier for others to research, I’m releasing the data used for this analysis. I can’t release all the data, as some people opted to not have their responses shared, but here is a subset of the data from those who opted in to having it shared. I will also eventually be releasing the full survey results, so I guess stay tuned!

Julia Serano’s new article is intellectually dishonest about the history and empirical support of the autogynephilia model

Julia Serano just wrote a new article claiming todebunk” Blanchard’s typology. Let’s take a look. (Archive with the version I’m addressing in case there are edits.)

Dishonesty about the history of autogynephilia

Julia Serano starts out with:

In my many years as a scientist, I have never before seen a theory so riddled with ad hoc hypotheses as autogynephilia.

This is certainly wrong; reality has a surprising amount of detail, and you would be surprised at just how many strong, well-established theories are filled with seemingly ad-hoc hypotheses. Here, Serano defines ad-hoc hypotheses as hypotheses added to the theory to prevent “anomalies not anticipated” from falsifying it. But let’s take the examples Serano gives:

The central tenet of autogynephilia theory is that there are two (and only two) types of trans women, each with a different sexual-orientation-related cause — “homosexuality” or “autogynephilia” — the latter of which Blanchard claimed arises as the result of a “misdirected heterosexual sex drive.” But if this is indeed the case, then how does one explain the existence of bisexual and asexual trans women, who are neither “homosexual” nor “heterosexual” in sexual orientation?

Claiming that Blanchard’s theory did not anticipate bisexual/asexual trans women, and fails to explain them except with ad-hoc hypotheses, is an outright lie. Blanchard developed his theory as a simplification of earlier 4-type theories, which explicitly contained types for bisexual and asexual trans women, and so of course he knew of their existence. This prevents them from being an “anomaly not anticipated”, and indeed many of his papers explicitly study them.

Well, Blanchard (1989b) proposed that the former group is not truly bisexual, but rather merely experiences “pseudobisexuality” — a concept Blanchard invented out of thin air in a classic display of bisexual erasure.

While Blanchardians may overuse this concept, it is not without merit; it has been supported by some initial research (1, 2, as well as informal research), and it is dishonest to pretend that it is not.

And Blanchard claimed that asexual trans women are not truly asexual, but rather represent instances where “autogynephilic disorder nullifies or overshadows any erotic attraction to women” (Blanchard, 1989a, p. 324). Subsequent studies have undermined both of these hypotheses (Veale et al., 2008a; Nuttbrock et al., 2011a)

Again Serano is being dishonest here; the claim that asexual trans women are not truly asexual is not something Blanchard pulled out of nowhere, but is instead supported by the fact that asexual trans women report a great deal of autogynephilia. Indeed, both the studies Serano cites found that a large amount of autogynephilia in the asexual group. (In the former study, Veale claims that “no transsexuals classified as autogynephilic reported asexuality”, but the trans women who were not “classified as autogynephilic” in her study (through a sort-of arbitrary statistical procedure she used) still reported a lot of autogynephilia.)

Serano’s distortions about the history and nature of autogynephilia theories are particularly striking when one considers that she immediately follows it up with accusing Blanchardian’s of distorting the history. Regardless, it is worth responding to her accusations here:

If someone refuses to relinquish their pet theory, yet are confronted with insurmountable evidence contradicting it, there are two obvious tacks they can take. The first is to simply ignore all of the counterevidence — I call this the “pretend it never happened” approach. One can see this tactic in a recent paper from J. Michael Bailey’s group (Hsu, Rosenthal, & Bailey, 2014) in which they attempted to expand upon Blanchard’s early autogynephilia research without ever once addressing or citing the multiple lines of counterevidence that had been published by that point, and which together disprove the theory (e.g., Bettcher, 2014; Moser, 2009, 2010a; Nuttbrock et al., 2011a, 2011b; Serano, 2010; Veale et al., 2008a; Veale, 2014; this body of work is further discussed in my previous essay, Making Sense of Autogynephilia Debates). For a further critique of Hsu, Rosenthal, & Bailey (2014), see Veale (2015a).

There are huge problems with this accusation, though. The paper Hsu et al cite does not address autogynephilia in trans women or Blanchard’s transsexual typology, and indeed they do not even cite Blanchard’s theories on transsexuality. Rather, they explore autogynephilia in cisgender men. For this purpose, the “multiple lines of counterevidence” that “disprove the theory” (not really, but that’s another story) are simply not relevant.

She also brings up another paper which supposedly engages in this:

A similar “pretend it never happened” gambit can be found in a recent review by Zucker, Lawrence, & Kreukels (2016), in which the authors invoke “autogynephilia” and Blanchard’s taxonomy, and lament contemporary researchers’ tendency to ignore the theory, going so far as to call this an “intellectual erasure in the discourse.” And yet, over the course of that seven-paragraph passage, they (like Hsu, Rosenthal, & Bailey before them) fail to reference or discuss any of the aforementioned research and reviews that refute the theory. Intellectual erasure in the discourse indeed!

But this paper doesn’t endorse Blanchard’s typology! Rather, they limit themselves to a more abstract correlational description, which would be endorsed by several of Blanchard’s critics, including Veale and Nuttbrock, both of whom have proposed theories based on this.

Ignorance about male autogynephiles

As a counterargument to the autogynephilia model, Serano brings up the fact that some men have autogynephilic fantasies, and comments:

I honestly cannot tell you why significant numbers of cisgender people seem to have “cross-sex/gender” fantasies. If I had to guess, I’d imagine that part of the appeal is simply novelty — after all, our sexual fantasies are quite often centered on circumstances or scenarios that are unlikely or unattainable for us in real life (Dubberley, 2013; Lehmiller, 2018).

This sort of idea places cis men’s autogynephilic fantasies in an entirely separate domain than trans womens. Yet a very important observation is that the men who are autogynephilic have a strong increase in gender dysphoria and desire to be a woman. Thus, a proper unified theory of autogynephilia and gender dysphoria will need to explain not just why trans women have such fantasies, but also why men with such a sexual interest are more likely to have various forms of subclinical gender issues.

Serano presents the situation as if Blanchardians were not aware of male autogynephilic fantasies, but considering that there have been several studies published on them – some of which she cites in this very post – it is quite dishonest. This is not an unexpected phenomenon, it is an extension of the concept of the paraphilia.

Serano’s nonsense scenarios

Serano proposes another argument in her article:

I have taken to calling this the “popsicle argument” for the following reasons: Back when I was a young child — before I became consciously aware that I was transgender — I ate a lot of popsicles. Like, a whole lot. Sometimes during the summer, I would eat up to three a day! So now I’m thinking that maybe it was all those popsicles I ate that caused me to become transgender. What, you don’t think that’s very plausible? Okay, well go ahead then, try to disprove it!
You can’t. It is impossible to 100% rule out this hypothesis. Sure, you can point to the overwhelming majority of people who eat popsicles but don’t ultimately become transgender (just as I can point to the overwhelming majority of people who experience FEFs or MEFs, but don’t wind up transgender), but that does not rule out the possibility that popsicles turn some tiny subset of popsicle eaters transgender. But at the same time, no matter how invested I am in my “popsicle argument,” I cannot rightfully claim that it is a valid scientific theory. Because for a theory to be deemed scientific, it must be falsifiable. Otherwise, it is merely conjecture. Or pseudoscience.

This is entirely unreasonable. Autogynephilia is probably by far the strongest correlate of male gender dysphoria, at least among the known correlates, while there is no reason to think that popsicles have any sort of link to it. Yes, there are some important questions about the direction of causality, but there’s a huge difference between something with a plausible causal mechanism and a strong statistical link, versus something with no proposed mechanism and no statistical link.

She then follows this up with a couple of scenarios.

In the first scenario, she gives the example of an autoandrophilic trans man, and asks whether I’d even entertain the possibility that the autoandrophilia was a cause of his gender dysphoria. Yes, dumb question, of course I would; you’d have to be dogmatic not to. I’ve spent a lot of time arguing that autoandrophilia is an important cause of gender issues in natal females.

Her next scenario is twice as silly; she gives the example of a gay boy who had homosexual fantasies and ends up believing that these fantasies caused him to be gay. As Serano points out, he was probably gay the whole time; and as I would point out, similarly, we would expect autogynephiles to be autogynephilic the whole time, regardless of their fantasies. Bizarrely, Serano turns it around and compares sexual orientation to gender identity. The point of her argument seems to be that ego-dystonic, subjectively “compulsive” autogynephilic fantasies do not support autogynephilia theory, but can instead by explained by her “FEF” model. However, let’s go back a bit to her explanation of why trans women would have “female embodiment fantasies”:

[…] Critics of autogynephilia have long argued that FEFs and MEFs are such an obvious coping mechanism for transgender people (especially those who are pre- or non-transition) to mitigate or overcome gender dysphoria. […]

But it does not make very much sense that someone who is uncomfortable with the fantasies would use them as a coping mechanism, especially not in the cases where they do not want to be women.

(As for her broader point that there’s nothing wrong with men being autogynephilic, I very much agree but this does not seem to be anything Blanchardians disagree with either, and it’s unclear where she got the idea. Heck, Blanchard was the one who introduced the distinction between benign “paraphilias” and harmful “paraphilic disorders” to the DSM.)

Serano’s own article is evidence for the Dregerian narrative

Julia Serano introduces the Dregerian narrative:

I will end this essay with what may be the most common of all pro-autogynephilia handwaves, namely, the assertion that transgender activists are irrational, overly sensitive, science-denying extremists who are engaged in a relentless censorship campaign against autogynephilia theory.

Of course with all the dishonesty we see in Serano’s article, there seems to be extremely good reason here to think that it applies, at least sometimes. Serano also brings up the closure of Zucker’s clinic, without mentioning that it was eventually found to be the result of unfair extremist activism, exactly as the Dregerian narrative points out.

Serano brings up a number of examples too:

If you are skeptical about this, may I ask what your stance is regarding other false and potentially damaging pseudoscientific movements, such as climate change denial, anti-vaxxers, or those who champion “race science”?

The problem here is that, due to her political bias, she is likely lumping genuine science in with pseudoscience like climate change denial and antivaxxing. For instance, I imagine that she would lump discussion of racial intelligence gaps under “race science”, yet researchers in the scientific field of intelligence research generally agree that genetic racial differences play a role in intelligence gaps. So we can definitely see that Serano easily ends up letting her politics influence her scientific beliefs.

One should be careful about going to far with the Dregerian narrative. One should always first and foremost focus on the actual arguments and facts. But at some point, when there is sufficient bias, it would be unreasonable not to ackowledge that there is some clear degree of political bias involved here.

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.