Plenty Of Fish Experiment: Study 1

Background

Badoo Study

In this first test, conducted on the dating site Badoo, I published the results of a subset of dummy profiles. I begin by reviewing contact and reciprocation parameters. I tested what is the relationship between the attractiveness of initiators and recipients of the initial messages and reply behaviors. How the reply probability of a message correlates with attractiveness of the sender and receiver. And how the reply probability depends on the extent to which the sender’s physical attributes match the receiver’s stated preferences. These variables allowed me to measure the mating skew that quantifies the degree of unequal partitioning of mating output among individuals, the female mating biases that generate higher mating success for a subset of males. And I could partially quantify the set of mating options.

Okcupid Study

In this quick second experiment conducted on the dating site OKCupid, my purpose was to test a new study to assess the mating pool of some dummy profiles estimated through compilation of incoming messages and potential matches’ offers. This matching feature works as a unimodal or binary “like” function (“yes”/”no” rating) and it’s a depth and single dimension for defining mating choice decisions.

Lovoo Study Part 1 / Lovoo Study. Part2

In this third study, my task was to provide a broader topography using a wide-ranging variety of dummy profiles. I have omitted the compilation of incoming messages and I focused on the quantification of the dating pool just through the matching system feature. In this experiment, I’ve inserted a wide range of profiles on the dating site Lovoo.

POF EXPERIMENT

It is important to understand users’ mating preferences. The message sending and replying actions of a user are strong indicators for what an individual is looking for in a potential date and reflect the user’s actual dating preferences.

In this serie of new studies I want to find out again how users’ online dating behaviors correlate with physical attributes using dummies within a other online dating site: Plenty of Fish. Since the dating sites to which I have consulted don’t want to provide me their data, such a log files and users’ profiles, and compiled it in a data warehouse, I’d need again work with fictitious profiles within a dating system. Athough I’m aware that a small sample size means smaller power and the positive predictive value for a research finding decreases as power samples decreases. Thus, other factors being equal, research findings are more likely high in predictive value in scientific fields that undertake large studies with big data, such as studies analyzing data sets obtained through collaboration with online dating web sites.

Now I’m searching for mate acceptance or response curves that give the probability that different male individuals (with a given phenotype within a attractiveness scale) will be accepted as mate by different potential female mates (along the attractiveness scale). When a female subject classifies a male prospective mate as acceptable (i.e above or below her thresholds of acceptance)?.

I am trying to find out which phenotypic stimulus of attractiveness that evokes a response exceeding this threshold. It is surprising, however, the paucity of theoretical and empirical work in humans (though there are a variety of zoological studies) addressing these probability densities functions for each phenotypic stimulus (i.e. attractiveness) that evokes a response exceeding the mate threshold by each male/female.

Method:

I will collect and analyze the same variables studied in my first experient on Badoo. The dummy profiles will be introduced on the same mating system, the dating site “Plenty of Fish”, over the same 1-week period. They were placed into the same geographical place, a big-sized city.

I wanted place my focus mainly in response rates, since last studies on OkCupid and Lovoo I could not research this parameter. Anyway I am going to gather several types of information about messaging statistics:

MESSAGING STATISTIC:

Messages received: Number of messages received per time active on site. (7-days).

Contacts initiated:  Number of individuals I’ve contacted through each dummy profile. I chose a sample of 150 users for this first experiment.

These messaged users are not randomly chosen. I chose a filtered sample of 150 female users for each dummy through advanced search. They were to meet the following parameters:

Age range: 18-35 years old.

City or Postal Code / Miles: 100 miles

Body type: “thin”, “athletic” and “average”. I discarded profiles self-identified as “a few extra pounds, “big & tall / bbw” and “prefer not to say”.

Within the subset of browsed female profiles, I chose as target those profiles that contained pictures from women that could be included within the upper half of facial attractiveness spectrum. This is, from average or medium-attractiveness until highly-attractiveness.

Contacts received: Number of people who contacted this person per time active on site.

Reply proportion at first message: Proportion of initial contacts from this person to which others replied. (reply rate).

Localitation: big-sized city A.

Data-collection period: 7- days.

Dummy profiles:

This first study is composed of 4 male profiles, 2 of them highly attractive (males Z and Y), 1 moderately attractive (male F, r= 6.93) and 1 medium attractive or average attractive (male B, r=5.18).

Recall that to ease attractiveness comparisons, I’ve sorted the ratings into five equal categories of attractiveness (highly-attractive: 8-10, moderately-attractive:6-8, medium-attractive:4-6, and below medium-attractive(< 4).

I also wanted to test if there could be some slight influence of occupational and educational level in the choosing pattern. To rule out unobserved factors correlated with professional occupation as the basis of attraction, I assigned higher occupational and educational levels to less physically attractive profiles. The highly attractive profiles were assigned with jobs of less professional category, while the rest of the profiles were configured with white collar professions (higher education level required and higher paying jobs.).

Some scholars might expect that it was expected that women would find that men of moderate and average physical attractiveness but with more resources, finances and education, such as finances and education, more attractive than men with more physical attractiveness and less financial resources. Or at least make up the shortfall in looks and get a similar dating success than the most appealing males.

Male Z:  Fitness/Kickboxing Instructor; Male Y: Nightclub Promoter. Male F: Economist; Male B: Doctor Ophthalmologist.

 

a) High-attractiveness dummy profiles (>8 points on a 1-10 scale):

  • Male Z :
Highly attractive male Z

Figure 1. Photos of male Z.

Male Z profile

Figure 2. Headboard of Male Z Profile

 

  • Male Y:
Highly attractive male Y

Figure 3. Photos of male Y.

Male Y Profile

Figure 4. Headboard of Male Y Profile

 

b) Moderately-high attractiveness dummy profile:

  • Male F:
Male F photos

Figure 5. Photos of male F.

Male F Profile

Figure 6. Headboard of Male F Profile

c) Medium-attractiveness dummy profile:

Male B photos

Figure 7. Photos of male B.

Male B Profile

Figure 8. Headboard of Male B Profile.

 

 Results:

 

a) High-attractiveness dummy profiles (>8 points on a 1-10 scale):

  • Male Z :
dsfdf

Figure 9. Screenshot displaying Mail Inbox after the 7-days period; “Meet me” section: Number of people who wants to meet this user; Number of visitors.

 

mariocontact

Figure 10. Screenshot displaying Contact History: Contacts Received.

Sin título

Figure 11. Screenshot displaying Contact History: Contacts Initiated. The number of people contacted for all dummies were 150. Note that the screenshot after the 7 day period shows 147 users, because 3 of them have removed their accounts.

 

Mail inbox = 122

Meet me = 52

Contacts received = 36

Reply proportion =86 out of 150.

Reply rate = 57.3%

 

  • Male Y
xx

Figure 12. Mail Inbox after 7-days period; “Meet me” section; Number of visitors.

b

Figure 13. Screenshot showing Contact History: Contacts Received.

a

Figure 14. Screenshot showing Contact History: Contacts Initiated.

Mail inbox = 121

Meet me = 50

Contacts received = 48

Reply proportion = 73 out of 150.

Reply rate = 48.7%

b) Moderately-high attractiveness dummy profile:

 Mail Inbox after 7-days period for Male F: Mail Inbox after 7-days period; "Meet me" section: Number of people who wants to meet this user; Number of visitors.

Figure 15. Mail Inbox after 7-days period for Male F; “Meet me” section: Number of people who wants to meet this user; Number of visitors.

dds

Figure 16. Screenshot showing Contact History: Contacts Received.

ddasfasd

Figure 17. Screenshot showing Contact History: Contacts Initiated.

Mail inbox = 27

Meet me = 11

Contacts received = 1

Reply proportion = 26 out of 150.

Reply rate = 17.3%

 

c) Medium- attractiveness dummy profile:

42ds

Figure 18. Mail Inbox after 7-days period; “Meet me” section: Number of people who wants to meet this user; Number of visitors.

ct

Figure 19. Screenshot showing Contact History: Contacts Received.

asd

Figure 20. Screenshot showing Contact History: Contacts Initiated.

Mail inbox = 9

Meet me = 3

Contacts received = 2

Reply proportion = 7 out of 150.

Reply rate = 4.7%

 

Table of results Study 1:

Sin título

Dating pool in meet me: The highly-attractive men has as mean 51 women interested (potential matches in “meet me” section) per week, 4.6 times more women interested than the moderately-attractive male (11 potential matches), and 17 times more women than the medium-attractive man (3 potential matches).

Contacts received: The four accounts combined received 87 unsolicited messages, and two best looking men monopolized 96.6 % of these contacts.

Reply rate at first message: The mean response rate for the two most attractive men is of 53%, quite higher than the response rate for moderately-attractive profile, (17.3%) and much higher than the average-attractive profile (4.7%). Or in other words, best attractive guys are reciprocated by more than 1 out of 2 women. While the moderately-attractive F gets around 1 out of 6 females, and the average-attractive around 1 out of 21 women.

Discussion

I must admit that response rates for the highly-attractive men were lower than I had expected. I had hypothesized before running this first test that this response proportion would be close to 75% or higher.

Correlating acceptance thresholds with the strength of choice, or choosiness, can make intuitive sense as individuals with higher acceptance thresholds (females, mainly more attractive ones) are selecting a smaller elite portion of potential male mates. So individuals with higher acceptance thresholds will also need to expend greater mate search effort to find a sufficient number of acceptable mates.

It would not be difficult to infer that these two males profiles within the top tier of attractiveness would be above the mate preference thresholds of the most of the sampled women.  So maybe there are a certain percentage of women that are sure that they are talking to real profile when they are contacted by a highly-attractive guy, but maybe other amount of girls think these two male accounts are catfishes.

On the other hand, it can not be observed an association between occupational status and messaging statistics since I’ve  not controlled for this variable. There could be a factor that affect slightly messaging statistics, although according to the results of having effect it should be quite irrelevant. In a forthcoming study I will study this factor in one of the male dummies used on this first research, but I’m going to change this factor and holding the physical attractive variable constant, we’ll be able to come closer to understanding the true effect of the ocupational status.

I will not discuss the results of this first study in depth, since I prefer to wait for a more comprehensive data base. In future studies I will continue conducting tests using different male dummies,varying the location, analyzing more exhaustively response rates (reply rates at second message), and also researching on women’s profiles. So I would urge readers to waiting for succeeding studies, which I hope all of them will be more clarifiers.

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Posted in mate choice, online dating, pof experiment, reply rates | Tagged | 8 Comments

Estimating The Mating Pool Size. Part 2: Female Profiles

Background

Female mating skew 2: Supported by online dating experiment

In this first test, conducted on the dating site Badoo, I published the results of a subset of dummy profiles. I begin by reviewing contact and reciprocation parameters. I tested what is the relationship between the attractiveness of initiators and recipients of the initial messages and reply behaviors. How the reply probability of a message correlates with attractiveness of the sender and receiver. And how the reply probability depends on the extent to which the sender’s physical attributes match the receiver’s stated preferences. These variables allowed me to measure the mating skew that quantifies the degree of unequal partitioning of mating output among individuals, the female mating biases that generate higher mating success for a subset of males. And I could partially quantify the set of mating options.

Quantification of dating pools through an online dating system.

In this quick second experiment conducted on the dating site OKCupid, my purpose was to test a new study to assess the mating pool of some dummy profiles estimated through compilation of incoming messages and potential matches’ offers. This matching feature works as a unimodal or binary “like” function (“yes”/”no” rating) and it’s a depth and single dimension for defining mating choice decisions.

Estimating the mating pool size. Part 1: male profiles

In this third study, my task was to provide a broader topography using a wide-ranging variety of dummy profiles. I have omitted the compilation of incoming messages and I focused on the quantification of the dating pool just through the matching system feature. In this experiment, I’ve inserted a wide range of profiles on the dating site Lovoo. The first part of this study was already published. Now is the turn to analyze the data from female profiles.

Data:

I will collect and analyze the same variables studied in the male case. The dummy female profiles will be introduced on the same mating system, the dating site Lovoo, over the same 1-week period. They were placed into the same geographical place, a big-sized city.

Although additional variables are clearly relevant to assess the mating pools, such as the quality of potential mates within them, my goal is going to focused in measurement of the size of the mating pool. As I explained in the previous post, it was not possible to develop a index of quality of the mating pool, because I could infer social desirability on the basis of the evaluations of other users in the dating site (ratings).

On the other hand, the thumbnails photo galleries are not going to be submitted on this post, because of a logistical problem. As readers will see below, the huge number of male members composing each mating pool makes infeasible this task. So I apologize for not being able to provide the thumbnails galleries for the second part of this test.

Initially I thought of dividing the attractiveness scale into 5 equal categories:high (8-10), medium-high (6-8), medium (4-6), med-low (2-4), and low (<2).  But I finally decided  to merging the last two categories into a single range labeled as low-attractiveness (<4).

Dummy profiles:

According our previous poll,  we have 2 girls , A(8.2) and F (8.8), sorted into the high category of attractiveness (ranging from 8 to 10 on a 10 points scale). And 4 girls, B (7.18), C(6.82), D (7.40) and E(7.74) sorted into the medium-high category.

a) High-attractiveness dummy profiles (>8 points on a 1-10 scale):

Girl A (rating: 8.2 points):

Female partner A

Female partner A

Girl F (rating: 8.8 points):

Female F

I will introduce into the system 2  control profiles within the “high”category, increasing my confidence in the measures reliability. I tried to get homemade pictures (taken from Facebook) of anonymous girls who had modeled occasionally,  so they could be objectively classified within the top-tier of the “high”category. These are the 2 control female profiles:

Girl Z:

FotorCreated

Girl Y:

FotorCreat44ed

2) Medium-high attractiveness dummy profiles:

Girl B (rating: 7.18):

Female partner B

Female partner B

Girl C (rating: 6.82):

Female C

Female C

Girl D (rating 7.40):

Female partner D

Female partner D

Girl E (rating 7.74):

clara

Female E

Since in my listing of mismatched couples had no girl rated below 6, I had to choose two new women that may be in the medium and low category.

3) Medium-attractiveness dummy profile (4-6). :

Female M

Female M

d) Low-attractiveness female (<4 points):

Female L

Female L

 Results:

1) High-attractiveness dummy profiles:

Girl A:

crisblog

Girl F:

rociblog

Girl control Z:

asd

Girl control Y:

ogblog

2) Medium-high girls:

Girl B:

elsablog

Girl C:

laurablog

Girl D:

esterblog

Girl E:

clarablog

3) Medium-attractive girl:

hellenblog

4) Low-attractive girl:

unblog

 

Table of results:

Results for female profiles:

tabla

Results for male profiles (see first part of the study):

Dibujo

Conclusions

Mating inequality between sexes:

Bateman’s principles explain sex roles and sexual dimorphism through sex-specific variance in mating success, reproductive success and their relationships within sexes (Bateman gradients).

Different decision rules for determining mating preferences often lead to different mating options for the actively searching and the choosy sex (females). Sexual selection often leads to radical sexual dimorphisms, or dissimilarities between males and females, in appearance, behaviour, and reproductive role.

•The female dating size (1864.7 guys) outnumbered the men’s (35.68 girls) in 52 to 1.

•The most ranked women had a mating pool almost 16 times more larger than the most ranked men.

• The medium-high women had a mating pool almost 223 times larger than the medium-high men.

•The female medium category had a mating size almost 434 times larger than the male medium group.

• The low-attractive man had no potential mate, while low-attractive woman got a mating pool of 940 men.

• The low-attractive girl had a mating size (940) 7 times larger than the dating size for best-looking guys (130).

These huge inter-sex differences in pools of available mates perhaps reflect a lower proportion of women entering into the matching tool for searching mates.

This supports that females are more selective, given that mate search frequency is a corollary of selectivity. Other data sets also prove that women are much pickier in their communications, due to the relative rarity of a women initiating contact with men on the dating sites. Males send out more messages but receive fewer messages. And females are more likely to be contacted but less likely to reply to male messages.

That works as a feedback process in mating Leks, since that male searchers become less choosy over time because most of them are unsuccessful at finding mates whereas women, who are much more discriminating than men. And this wide availability of male potential mates (huge dating pools), counter-selects females to evolve more resistance to (i.e., decreased attraction) and increasing their mate thresholds. So cyclic antagonistic co-evolution ensues.

Measuring the Mating Skew:

Mating skew is a index for quantifying the degree of unequal partitioning of mating output among individuals of the same sex.  This empirical test show that, the mating pool were much more variable in males than in females, resulting in a steeper male Bateman gradient, consistent with Bateman’s principles.

1) Mating skew for males:

• The most attractive men (dating pool mean=130 girls) received 91% of all mating options (dating pool mean for male group=142.7).

• The medium-high group got 6.3 % of the mating options.

• The medium group had 2.6 % of the mating optiones.

• The low category had 0%.

2) Mating skew for females:

• First, note that the pool size for the high category is only 1.01 times larger than the high-medium category, 1.24 larger than the medium niche, and 2.1 larger than the low category.

• The most attractive women (dating pool mean=2025 men) received 30.7% of all mating options (dating pool mean for male group=6588.5).

• The medium-high group got 30.3% of the mating options.

• The medium category had 29.7 % of the mating options.

•The low category had 14.2% of the mating options.

In this study we can see how sexes differ greatly in the distribution of mating options among same-sexed individuals. In men, highly attractive individuals of the group monopolizes the vast majority of mating output (mating skew is high), whereas mating pool is much more equally distributed in women (mating skew is low).

This supports the observation that, unlike female preferences, male ‘preferences’ are inclusive of a broad range in female variance. While females are the reproductively limiting sex, male preferences are so much more inclusive of female variance, women shows a higher requisite stimulatory threshold to induce her mating response. Females have an optimum mating rate that is lower than the males, with high-rate fitness optima, and correspondingly high male optimal mating rate).

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Estimating The Mating Pool Size. Part 1: Male Profiles

Background.

In my last post I began to assess mating options and dating pool in a online dating context for the couple B and 2 control profiles rated as highly attractive as a comparative sample.

We find that, consistent with predictions:

• The highly-attractive man has 93 women interested in him, almost 19 times more dating pool in a week than the male B (5 offers). Anyway we can conclude that the dating pool (number of women willing to have a relationship with him) for an medium man is almost ridiculously small.

To address our research questions about the mating size of all those individuals that were rated in the pool, I’ll start in this post by addressing several data sets obtained through a online dating called Lovoo for the male dummy profiles. So the data I use in this work derive from male profiles on this dating site in the 1-week period.

This dating site allows users to browse and search the profiles of others, and it also provides a matching mechanism. Its feature works much like quickmatch from Okcupid or winks from Match.

This is how the match feature from Lovoo works:

If people likes a user’s profile picture, they click on a heart symbol. If people don’t like the picture, they click on the X symbol. Each time a user gets a positive rating for his/her profile photo, the corresponding user will be displayed under the tab “who likes me”. People can choice and accept someone playing “match” or browsing profiles using the “search function”

The primary quantity I consider in computing mating statistics on here is not the number of messages received but the number of distinct people whom a person is selected on match feature (number of “like you”). This places the focus not on how many messages a user obtained or your reply rate to initial contacts from this person, but rather on mate choice decisions or number of user’s choice an individual on the match feature. I consider it’s a valid track to capture the dimension of its popularity or desirability, and this is main key variable in my analysis.

Using these data analysis from this research, someone could correctly modelling and measuring dating pool, choosiness and preference functions, response curves that give the probability (y axis) that a focal individual will mate with a potential mate with a given phenotype (x axis). Moreover it would be interesting to know if online daters could base decisions either on a sample of mates (sampled-based decisions) or on a threshold of comparison (threshold-based decisions).

Method

Measurement of the size of the mating pool:

As I said, in an online dating context, the number of mating opportunities can be computed as the sum of unsolicited messages recollected quickmatch, winks or “likes you” and answered messages.

The primary quantity I consider in computing mating statistics on here is not the number of messages received or answered, but the number of distinct people whom a person is selected on match feature (number of “likes you”).

This places the focus not on how many messages a user obtained or his/her reply rate to initial contacts from this person, but rather on mate choice decisions or number of user’s choice an individual on the match feature. I consider it’s a valid track to capture the dimension of its popularity or desirability, and this is main key variable in my analysis.

Lovoo offers a sending kiss feature, which cost some credits, and a Fans/followers feature, where is displayed the number of users that have marked as “favourite” to that profile. This has little relevance to our research, but it will be shown to readers because it’s part of the image displayed on each profile.

I have thought about omitting those two variables for different reasons:

a) Received Messages

This unimodal or binary “Like” function (“yes”/”no” rating) acts as a depth and single dimension for defining mating choice decisions.  Furthermore there is usually an small degree of overlap of this two different features (mainly en male daters when some of them are viewing a female profile through the browsing tool). Some of the daters choosing a profile and clicking them as “like”, then contact him/her by sending a message.

b) Reciprocated messages.

In all online dating sites there are a substantial amount of fake profiles, which can interfere somehow to explore the relation of the some data to the underlying population. By example, I figured out that it could affect reply rates decreasing likelihood of a response, mainly in those plots of sent messages directed towards top tier of attractiveness groupings, where is most concentrated the fake profiles.

The unsolicted messages, quickmatches/wink/liks and other parameters are little affected by the introduction of these fake accounts, because most of these fake profiles are virtually generated for the website itself for luring customers, and these fakes profiles dont send messages or “likes you”. They are just visual baits. But these fakes will not respond to messages sent by real users, decreasing their response rates.

Moreover, experimenting with this parameter involves systematically send a lot of messages from each dummy profile, which would take a lot of work. Furthermore, on this website the messaging function is limited, since each user can only send 5 messages per day, so for sending, for example, 100 messages, it would take 20 days.

Measurement of quality of the mating pool:

I’d need to define women’s social desirability on the basis of the evaluations of other users in the dating site (ratings). In this way I would be able to analyze the results plotted in evenly-spaced “attractiveness groups” But I found several technical problems to address this issue. Studies of online dating, building our confidence that the rated attractiveness measures provide adequate proxies of dating market value.

Unlike okcupid, where you must upgrade to A-list to acces to your quickmatch list, Lovoo shows the full list of people who cliked “like you” but it is stored as blurred portraits. It’s required sign up for a vip membership to find out to reveal the profiles list. Although the “like you” list shows the users as blurred portraits, this would not hamper my analysis, while each one of these profiles was assigned with a rating. This is a limiting statistical requirement for a proper rendering of the quality of each available mate.

I realized that I could not quantify mate desirability with profile ratings from opposite-gender daters. This website does not enable a system-generated rating tool that presents users with a series of profiles for rating them a scale of attractiveness. The former okcupid version had this tool available, but the ratings were hidden from the public.

I’m aware that Badoo, website where I did my first study, displays these attractiveness scores. But this site asks its both sex users to rank one another based on a 10-points rating system. That greatly affects the accuracy of the ratings.

Besides the inherent overall lack of control the medium seems to inextricably exert on ratings, the evidence such as this has lead me to realize that opposite-sex ratings are better than both-sex ratings.

For example, when I’ve submitted photographs of objectively highly attractive males (homemade pictures drawn from social networks, since professional photos would be identified as belonging to a fake profile), I noted that generally their ratings were much lower than expected, yielding a cumulative middle ground. Either a lot of man gives to gorgeous male pictures a low rating of punishment or sanction. While some girls vote them really high and others girls vote them into a ground slightly above-middle.

Paradoxically, my highly-handsome male dummies were perceived as a highly interesting catch for females (their inboxes were packed full of messages with unsolicited messages and replies from women diary), but their ratings was not appropriate to their aesthetic level.

I found an great obstacle to use again Badoo, it was that to sign up to the new website version , each user have to enter a phone number. This preclude to create multiple dummy accounts within this site. So I decided work with Lovoo, because it’s a free website and I could sign up there multiple dummy accounts quickly.

Anyhow I must apologize to my readers for being unable to perform the assessment of quality of these mating pools. However I have tried to give an output to this question.

I’ve decided to submit as screenshots all prortraits belongs to users stored into the “likes you” list. This will not solve the inability to perform a statistical analysis of physical attractiveness of these lists of users. Firstly, it was required to reveal blurred profiles by playing myself into the match tool, selecting positively all users that appeared in the selection set. When there is a match, the system notifies it and reveals the images of those users. Thus I could view the profiles withing the “like you” List.

However this does not mean that readers are going to be able to judge accurately these groups of photographs. If we define photographic accuracy as the degree to which an observer would consider the photograph to provide a good enough approximation of the person in it. Keep in mind that:

1) There are possible incongruities between photography and reality. Anyway we already know that.

2) Physical appearance in one only profile photo differ from their appearance perceived on an overall basis viewing a set of photos of that same person. For example, note that I always show a collage of photos set for defining each one of our studied individuals, where readers can quite accurately assess their facial and body attractiveness (for obtaining a current representation of their physical appearance). This will not happen here. Although as author of the study I was able to take a look at many of these profiles, it was not feasible to collect all the account photos, and then create photo collages. Then we will have discrepancies concerned with the physical characteristics of the person portrayed in their profile photos. In a user with facial portraits will be appreciated slightly only her facial appearance, and in user with body picture only her body figure.

3) Furthermore, profile portraits will be displayed in reduced format, for technical reasons. Note that the “like you” listings are exposed as of photos that automatically scroll vertically through a long screen background. And those dummies with a bigger size pool (high-attractiveness), contains a great number of female profiles in their matches list, making it difficult to capture in one only snapshot all those users. The solution was to decrease the screen size and assemble different screenshots.

4) Some of online users do not upload photos, but they can choose people in the match game. So some of the profile portraits are blank.

Dummy profiles:

According our previous poll, we have 4 guys (A, B, D and E) sorted into the medium category (ranging from 4 to 6 on a 10 points scale):

Medium males:

Male partner A

Male partner A

Male partner B

Male partner B

Male partner D

Male partner D

Male partner E

Male partner E

and 2 guys (C and F) into the medium-high niche (ranging from 6 to 8 on a 10 points scale):

Medium-high males:

Male partner C

Male partner C

Male partner F

Male partner F

I will introduce into the system 2 profiles within the “high”category:

High males:

Highly attractive male Z

Highly attractive male Z

Highly attractive male Y

Highly attractive male Y

and 1 profile unattractive guy, below of medium category (< 4 points):

Low attractive male:

FotorCreated

Low attractive male

Results:

1) Medium males:

Male A:

blog6Profile pictures of his potential mates (2 women clicked on heart symbol):

Dibujortdft

Male B:

BLOG3

Profile pictures of his potential mates (1 woman  clicked on the heart symbol):

sdds

Male D:

blog5

 

Profile pictures of his potential mates (10 women  clicked on the heart symbol):

dd

Male E:

blog8

Profile pictures of his potential mates (2 women clicked on the heart symbol):

ddastg

2) Medium-high males:

Male C:

blog

Profile pictures of his potential mates (6 women clicked on the heart symbol):

Dibujoddd

Male F:

BLOG2

Profile pictures of his potential mates (12 women clicked on heart symbol):

12

3) High-attractive males:

Male Z:

blog7

Profile pictures of his potential mates (137 women clicked on heart symbol):

neubergernuevoneuberger

neuberger

Male Y:

BLOG4

Profile pictures of his potential mates (123 women clicked on heart symbol):

micharet2micharet

4) Low-attractiveness male:

aas

Results Table:

Dibujo6Note: For proving (or disproving) the validity of the dating pool of each user, we would have to involve collecting of interactions of every single dater included in our sample of potential partners. So we should send messages to each one of them for scrutinizing the likelihood of reciprocated exchange (not just at point of a first reciprocated message). I guess most readers who are familiar with online dating, will know that frequently they are unable to communicate with the matches they thought they were getting.

Discussion

• Each man received at least one “like you”, except the unattractive guy.

• The mean of the mating pool size for the high group is of 130 women.

• The mean of the mating pool for the medium-high group is of 9 women.

• The dating pool mean for the medium group is of 3.75. Note that male D, rated as 5.5 points (10 girls), received 71.4 % of all matches of his group. His mating pool resemble to the male F (6.93 points; 12 girls), and overcomes the mating pool of the male C (6.34 points; 6 girls). It seems that according to his mating size, its valuation was underrated on the previous survey.

• The mating size for the best looking guys is almost 15 times larger, in a week, than the mating size for the medim-high group.

If we try to explain a modal tendencies in the online population, the mating system for men seems hopelessly skewed (ie. it becomes almost a lottery in order to find a receptive female for those males who are not a 9 or 10 in terms of physical characteristics (high-attractiveness group). Since guys below of top tier of attractiveness have a narrow range of mating opportunities. Women set up a highly restrictive preferences for their ideal daters.

Although we do not know how many women have viewed every male profile in the matching tool, ie we can not establish the acetance rate. We know the number of women who have marked as potential partner to these male suitors, but not how many women have rejected them, rendering a skew which hinders assumptions of pair-matching.

Assuming that female attractiveness on Lovoo gets close to a “Normal Distribution”, where most women tends to be around a central value, at all levels of attractiveness, primarily sought out the most attractive male daters as potential partners. The modal category of sent “likes you”, regardless of the choosers’ level of attractiveness, was to the highest attractiveness male group. Although I could not run a qualitative analysis for each dating pool, taking a quick glance to their profile portraits, I’d say that male prospects have become so skewed against all but the most select cohort, that all that is left for the typical male (medium and high-medium guys) are just a few mating opportunities , ranging mainly, from medium and low-medium attractiveness , lower than their statistical equivalents. While the attractive man has no potential mate.  Which makes me keep asking myself as these lucky guys have gotten such a mating success in their real lives.

The dating pool for two best looking guys is quite varied; I’d dare to say that the vast most of their girls are located within the medium and high-medium spectrum, and few of hotties. The presence of women below-medium is scarce. So this tendency to aim for the most desirable partners declined somewhat with one’s own desirability, resulting in tempered vertical preferences as one moves down the desirability scale.

In the next post, I will address the patterns of mating options for the female dummy profiles, in this same real online dating system.

TO BE CONTINUED.

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Quantification Of Dating Pools Through A Online Dating System.

Background

And my last post, I exposed different photographs of couples drawn from real facebook profiles belonging to anonymous individuals. The main idea is that all these examples of couples randomly selected had a sharp and easily noticeable difference in the beauty/physical attractiveness between of two members of couple.

FFF

Most of studies of mating and mate choice have commonly relied on surveys or census data of married, cohabiting, or dating couples and therefore omit important pre-relationship dynamics. However experimental tests of ecological hypotheses are few. By beginning with established relationships, such studies miss initial romantic gestures that hold valuable clues for partner preferences and the origins of relationship stratification.

In my first research, I developed an online dating test to analyze solicitations and contact patterns for all active daters on a popular online dating site (Badoo) in a mid-size metropolitan area. These data provided me the opportunity to analyze men’s and women’s decisions in the earliest stages of relationship formation and allowed us to test several hypotheses about gender, partner preferences, and mate selection.

Purpose:

The purpose of this new study is to assess the dating pool for all these male examples. The dating pool is indexed as the approximate number of eligible mates (number of mating opportunities – number of opposite-sex individuals who perceive that target as a potential prospect for mating-) and quality of the pool of their potential mates. We should use these two approaches if we wish estimate the accurate index of mating opportunities.

For descriptive analyses of the correlates of men’s and women’s desirability, I’ve standardized the ratings on 1-5 scale towards a score transformation on a 1-10 scale (see Table). To ease attractiveness comparisons, I’m going to sort the ratings into five equal categories of attractiveness (high: 8-10, medium-high:6-8, medium:4-6, med-low:2-4, and low:0-2).

For now, in this first post I will focus on measuring the number of eligible mates for the male B, ranked as medium attractive: 5.18 score.

Male partner B

Male partner B

It was also desirable to establish the comparison with different female profiles. I used to his female counterpart in real life into the mating system, so we could get a proper notion of asymmetry or symmetry in dating pools for gender. It would have been more appropriate to use a girl closest to his statistical equivalence and ranked as medium. Although this girl B would be ranked within the medium-high in attractiveness ( 7.18 score), I took her profile for practical reasons and because it allows us to draw comparative conclusions between the scope of socio-sexual desirability between two individuals that make up a couple in real life. (Despite dissonance in physical appearance).

Female partner B

Female partner B

I’m going to work again in this context, considering that performing a field research (for example by measuring courtship interactions in any outside shared environment: bars/clubs, etc) is not the scope of this humble blogger and that online dating provides an ecologically valid or true-to-life context for examining all this questions.

Method:

I ran a direct experiment on a online dating site: Okcupid. I created two dummy profiles using the photos of the couple B (I’ll leave the research with the rest of couples for upcoming posts). Whereupon I could collect data from the two partners introducing their profiles within a real mating framework. Then I placed the pairs in same location. The profiles were active for a week, and the final counts are indicated by data collection during this period. It is important to note that I kept online to each profile only for a few minutes per day. This can have marked consequences on the number of visitors and number of incoming (unsolicited) messages. I guess the vast majority of people searching profiles for contacting would rather view profiles online than offline. I sopongo that there will had less impact on the number of quickmatch offers received.

In an online dating context, the number of mating opportunities can be computed as the sum of unsolicited messages recollected, quickmatch offers and answered messages:

Number of eligible mates: unsolicited messages + quickmatch offers + answered messages (reply rate).

Unsolicited messages:

A person’s popularity is indexed by the number of received messages: average number of people who initiate contact with him or her for the time he or she is active on the site. I think that this measure serves as a reasonable proxy for overall attractiveness, as we expect that more attractive people will, on average, receive more unsolicited attention than less attractive people. But rates of initial contact differ sharply by gender. Given this difference combined with the greater number of men on the site, women tend to be contacted much more often than men. Therefore this is a more valid to calculate the female dating pool, since women tend to browsing male profiles and send messages much less frequently. So this index is not representative enough to capture the magnitude of dating pool size for a given male target.

The differences in how women and men use this technology highlight just how entrenched gendered strategies in intimate relationships remain. Women are still more likely to follow traditional gendered scripts and expect men to initiate contacts,

Answered messages (reply rate):

The success of a user in online dating depends on his or her ability to garner a response from a potential date. The proportion of people who reply to one’s initial contacts is another potential proxy for attractiveness. I have omitted this index, since it does take more effort for experimentation, since it is necessary systematically browsing opposite-sex profiles and send then a significant number of messages (≥100 messages for an acceptable sample size) from each one of these fictitious profiles. In any case, although this won’t give us an exact size of the dating pool, it will serve to get a comparative idea of the scale of desirability for each one of the profiles online.

Quickmatch offers:

Finally, I consider the quickmatch feature, which let users choose potential targets. Quickmatch shows to daters the picture and profile information of a potential match. Then each user can either click them if a dater likes this person or skip them.

This feature showcases the target’s photos at the top of the page, and offers an easy way to scroll through them. The reminder of the profile is located underneath the pictures. The user is then encouraged to choose the targets on a binary scale, yes or not.

Women use to be discouraged from sending messages to contact male partners. Of course, just as in offline dating contexts, online “winks”, “quickmatch” (in the Okcupid case) or “like you” (Lovoo, Badoo, etc)  may serve as means for women to demonstrate interest with low rejection risk while letting the man continue to feel like the initiator.

Users also can click on the “like” button when they are browsing matches. And there is also an option within each profile screen to click on another “like” button placed below of “send a message” button.

I wanted to introduce two control profiles in the mating system consisting by two individuals with a modelesque appearance. So these control profiles could serve as benchmarking. I proceeded to create these two new control dummy profiles, made with portraits taken again from real facebook anonymous accounts:

highly-attractive male profile:

Highly attractive male.

Highly attractive male.

Highly-attractive female profile:

Higly attrative female

Results:

I will compute the number of eligible mates: quickmatch offers + unsolicited messages. As I said above, I did not sent out messages from these dummy profiles, so I will not be able to know the number of replied messages (number of persons who replied to each user) and response rate (proportion of initial contacts from this person to which others replied). The screenshots for each of the profiles studied after one week of testing are the following ones:

Screenshot displaying number of visitors, received messages and quickmatch offers for male B.

Screenshot displaying number of visitors, received messages and quickmatch offers for male control.

Screenshot displaying received messages and quickmatch offers for female B. The visitors count is not displayed since I accidentally clicked on this button and this count returned to 0.

Screenshot displaying number of visitors, received messages and quickmatch offers for female control.

Table of collected results:

YHH

I defined as dating pool the number of people interested in contact or be contacted by a user. In this study is constituted by the unsolicited messages count, which means the number of messages received per the week that each user was active on site, or number of people who contacted this person for this week. And quickmatch offers means number of people who showed interest in this profile, and considered to this user as a suitable potential mate.

• The highly-attractive man has 93 women interested in him, almost 19 times more dating pool in a week than the male B (5 offers). Anyway we can conclude that the dating pool (number of women willing to have a relationship with him) for an medium man is almost ridiculously small.

• The highly attractive woman had 2.5 times more men interested in her (1377 offers) in a week than the medium-high female B (544 offers). The mating pool difference among women is much lower, but keep in mind that the difference in physical attractiveness between these girls is small.

• The dating pool of girl B (544) outnumbered her real partner B (5) in 108 times more greater.

• The best looking man received almost 22 fewer offers than the best looking girl. In any case, the size of his dating pool is quite aceptable.

I guess my readers are wondering about that occurs with those other individuals that were rated in the previous post. And mainly, what happens with the other variable, the quality of the pool of their potential mates? I will leave these issues for the next posts.

TO BE CONTINUED

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On Mismatches Between Couples: Why Do We Sometimes See Physically Attractive Girls With Average Or Unattractive Male Partners?

She's Out of My League (2010)

She’s Out of My League (2010)

This partially contradicts part of my thesis, for those who have previously followed some of my previous posts, with my own online experiments and my compilation of meta-analysis. Most of the empirical and theoretical evidence establishes that females are more selective in all their mating considerations. Where female sexual choices will always tend towards small male breeding populations. In more colloquial terms, what this means is that male/female ‘leagues’ are asymmetrical, with male ‘rank’ being bottom heavy in distribution, while female ‘rank’ being top heavy.

The problem with these ‘leagues’ is that they are asymmetrical (meaning that there is a higher probability of a female attracting any given statistical subset of ranked males, than the reverse), rendering a disproportionate scarcity of receptive females for lower ranked males in mating leks (i.e.  nightclubs/bars, online dating sites, speed dating events)

So I’m thinking about writing a future post where I could address this disassortative mating issue. According the online dating & speed dating data, mainly we will see conventionally men dating less attractive female partners, frequently, irrespective of independent status indicators. And cases of the reverse dynamic would be vanishingly rare. Then, which are the specific reasons for this kind of couple mismatches, being female partner more beautiful than their boyfriends? My intuition tells me that most of this type of romantic pairings are arising out of mating leks context. But I’ll leave that speculation for upcoming posts.

Do equity considerations influence observers’ impressions of a romantic couples? In the present post, I collected some mismatch young couples drawn from social networks, where a mate value discrepancy occurs because, from my own perception, there is a mismatch in the value of mates between partners (average-looking guys having a romantic relationship with attractive women.)

In order to research this notion, I need to test this possibility by examining the readers’ impressions of these romantic partners who are mismatched in physical attractiveness (female partner will be more physically attractive than her male partner). In this situation, observers instinctually categorize the opposite-sex member of the couple as a potential mate and the same-sex member of the couple as a competitor for the potential mate’s affection.

I would wish to ask my readers their assessment for each individual on physical attractiveness on a scale of 1 to 5, with 1 being not attractive and 5 being very attractive. I need to verify that the female mate is more attractive than her current male partner. Blog readers are going to view photos of dating couples. So I would be grateful if you could rate these individuals on physical attractiveness.

In upcoming essays I will examine this matching dynamic in an future experiment in the context of online dating (okcupid site). I’ll create male dummy profiles using the pictures of these guys . I hypothesize that these average-looking men will get a very low success rate (both in number of interested contacts and  the physical quality of them, far below their real girlfriends) in the online dating context.

I would appreciate your ratings. Thanks.

Couple A

 

Couple A

Couple A

Female partner A

Female partner A

 

Male partner A

Male partner A

Couple B

Couple B

Couple B

Female partner B

Female partner B

Male partner B

Male partner B

Couple C

Couple C

Couple C

Female partner C

Female partner C

Male partner C

Male partner C

Couple D

FotorCreateddd

Female partner D

Female partner D

Male partner D

Male partner D

Couple E

Couple E

Couple E

Female partner E

Female partner E

Male partner E

Male partner E

Couple F

 

Couple F

Couple F

Female partner F

Female partner F

Male partner F

Male partner F

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Women Appraise Male Attractiveness in a Glance

On the above video-clips we can watch how a highly attractive guy captures massively the attention of females in large mixed groups at nightclubs.

Humans frequently make real-world decisions based on rapid evaluations of minimal information; for example, should we talk to an attractive stranger at a party? Little is known, however, about how the brain makes rapid evaluations with real and immediate social consequences. To address this question, Cooper et al (2012) scanned participants with functional magnetic resonance imaging (fMRI) while they viewed photos of individuals that they subsequently met at real-life “speed-dating” events. Neural activity in two areas of dorsomedial prefrontal cortex (DMPFC), paracingulate cortex, and rostromedial prefrontal cortex (RMPFC) was predictive of whether each individual would be ultimately pursued for a romantic relationship or rejected.

Activity in these areas was attributable to two distinct components of romantic evaluation: either consensus judgments about physical beauty (paracingulate cortex) or individualized preferences based on a partner’s perceived personality (RMPFC). These data identify novel computational roles for these regions of the DMPFC in even very rapid social evaluations. Even a first glance, then, can accurately predict romantic desire, but that glance involves a mix of physical and psychological judgments that depend on specific regions of DMPFC.

The pattern we can trivially observe is overall female preference for potencial male partners with whom a woman shares extraordinary physical/sexual chemistry. The fusiform face area, the lateral occipital cortex and medially adjacent regions, is activated automatically by physical appearance and it serves as a neural trigger for pervasive effects of attractiveness in social interactions.

We must consider two aspects of mate choice evolved in tandem 1) traits that evolved in the “display producer” to attract mates and, 2) corresponding neural mechanisms in the “display chooser” that enable them to become attracted to these display traits.

Fisher et al (2002) discusses a fMRI brain scanning project on human romantic attraction, what it is believed is a developed form of “courtship attraction” common to avian and mammalian species as well as the primary neural mechanism underlying avian and mammalian mate choice.

Their work hypothesizes that courtship attraction is associated with elevated levels of central dopamine and norepinephrine and decreased levels of central serotonin in reward pathways of the brain. It also proposes that courtship attraction is part of a triune brain system for mating, reproduction and parenting:

1)The sex drive evolved to motivate birds and mammals to court any conspecifics.
2) The attraction system evolved to enable individuals to discriminate among potential mating partners and focus courtship activities on particular individuals, thereby conserving mating time and energy.
3) The neural circuitry for attachment evolved to enable individuals to complete species-specific parental duties.

Recent studies have investigated what constitutes beauty and how beauty affects explicit social judgments. By example, Olson and Marshuetz (2005) found that those who are physically attractive reap many benefits and wider variety of mate choices. Since little is known about the perceptual or cognitive processing that is affected by aesthetic judgments of faces and why beauty affects our behavior. In their study, these authors show that beauty is perceived when information is minimized by masking or rapid presentation. Perceiving and processing beauty appear to require little attention and to bias subsequent cognitive processes. These facts may make beauty difficult to ignore, possibly leading to its importance in social evaluations.

They began by asking whether attractiveness can be perceived from minimal amounts of visual information. To answer this question, authors asked participants to rate faces that were presented under severely impoverished viewing conditions.

Although participants reported that they could not accurately see the faces, their ability to “guess” about the attractiveness level of the faces was surprisingly accurate.

In a second experiment, they asked whether the presence of an attractive face biases subsequent cognitive processing. This was tested in a priming task in which rapidly presented face primes were followed by positive or negative word targets. They reasoned that if attractive faces are encoded with little effort or attention and bias subsequent cognitive processing, then RTs to congruent words (e.g., positive words) should be faster than when the same words are preceded by an incongruent (e.g., unattractive) face. The results were that attractive upright faces prime positive words.

This finding could be interpreted as the generation of an implicit attitude (Fazio, 2001) when an attractive face is presented. No priming effects were observed for inverted faces or unattractive faces. The lack of RT speeding when a positive word was preceded by an inverted attractive face suggests that it is attractiveness, per se, rather than some uncontrolled low-level visual attribute, that led to the performance benefit observed in experiment 2. Why unattractive faces did not speed processing of negative words is less clear, although they speculate that unattractive faces do not induce negative emotions.

The generality of the attractiveness effect was tested in a third experiment . This experiment replicated the priming effect of attractive faces but found no priming effect for attractive houses, suggesting that attractive faces may induce emotions, whereas other attractive stimuli may not, or at least may not in the same manner. Other types of attractive stimuli, such as abstract art (Duckworth, Bargh, Garcia, & Chaiken, 2002) or animals (Halberstadt & Rhodes, 2003), have been shown to bias cognition, but such processes may be slower, requiring more time (Duckworth et al., 2002), attention, or effort, than attractiveness judgments for face stimuli, which appear to be easy and rapid. An alternative explanation for these findings is that the attractiveness of houses is not extracted as rapidly as it is from faces. Future studies can address this issue by using houses in a masking task similar to that reported in experiment 1.

There are a number of interesting issues not addressed in their article. For example, they did not find any significant interaction of participant gender with the gender of the stimuli. Another interesting issue is the question of how variables like sexual orientation might interact with facial attractiveness and gender.

The specifics of what particular feature or property of the face stimuli contribute to a positive or negative attractiveness judgment cannot be determined from this study. Other researchers have reported that facial averageness (Rhodes, Sumich, & Byatt, 1999) and facial symmetry are critical features (Grammer & Thornhill,1994; Rhodes et al., 1998), as well as sexual dimorphism (Johnston, 2000) or feminization (Perrett et al., 1998).

In summary, Olson and Marshuetz propose that facial attractiveness is assessed rapidly and from small slivers of visual information. These attentionally undemanding judgments bias other cognitive processes that may be the result of changes in affect upon viewing the “rewarding” (Aharon et al., 2001; O’Doherty, Winston, Perrett, Burt, & Dolan, 2003) attractive faces. These findings suggest that the positive benefits that attractive people garner may be due to processes that influence decisions with little awareness or intention, and that the beauty bias may result from a host of low-level visual and emotional effects.

The conclusions reached by Fletcher et al (2014) in a research based on judgments from partners and observers, is that assessments of attractiveness/vitality perceptions (compared with warmth/trustworthiness and status/resources) were the most accurate and were predominant in influencing romantic interest and decisions about further contact. Second, women were more cautious and choosy than men—women underestimated their partner’s romantic interest, whereas men exaggerated it, and women were less likely to want further contact. Third, a mediational model found that women (compared with men) were less likely to want further contact because they perceived their partners as possessing less attractiveness/vitality and as falling shorter of their minimum standards of attractiveness/vitality, thus generating lower romantic interest. These novel results are discussed in terms of the mixed findings from prior research, evolutionary psychology, and the functionality of lay psychology in early mate-selection contexts.

Finally I would like to comment a study of Fisher and Cox (2009) where authors explored women’s receptivity with respect to romantic relationship type and length, and investigated how male attractiveness influences this receptivity. Their findings suggest that women are willing to consider the most attractive men for all types of romantic relationships. In addition, short-term relationships yielded the highest rates of receptivity, which suggests that this relationship type provides a trial period for potential long-term mates and consequently represents a compromise between purely sexual relationships and long-term, committed relationships.

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The Match Between Potential Daters’ Attractiveness Is The Most Important Predictor Of Romantic Desire

facial-attractiveness

Gomez et al implemented a multi-agent model that allows an assessment of the relative contributions of selectivity and matching on ratings of attractiveness.

 

Gomez, P. & Erber, R. (2013). Is selectivity an aphrodisiac?

 

Abstract:

In a recent article, Eastwick, Finkel, Mochon, and Ariely (2007) reported data to indicate that selectivity might be an important factor in determining romantic desire. Using a speed-dating paradigm, they found that individuals who, on average, rated potential dates as highly desirable were likely to receive lower average ratings from their dates, as evidenced by what they termed as negative generalized correlations. However, the dyadic correlations were positive, suggesting that, across pairs, desire was somewhat reciprocated. Eastwick et al. go as far as to claim that “… daters somehow broadcast their unselectivity… ” (page 318), which we find to be a deeply dissatisfying explanation. We present an alternative and more principled approach in order to account for the disassociation between the generalized and dyadic correlations. We implemented a multi-agent model that allows an assessment of the relative contributions of selectivity and matching on ratings of attractiveness. The model suggests that the match between potential daters’ attractiveness is the most important predictor of romantic desire. We believe that Eastwick et al’s (2007) article is just another example of a dangerous pattern in social psychology research: spectacular claims are made on the flimsiest of evidence.

 

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