Blackpill The wall in a nutshell

[InMemoriam]

The effect of aging on facial attractiveness: An empirical and computational investigation
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Article: https://doi.org/10.1016/j.actpsy.2021.103385

ABSTRACT:

How does aging affect facial attractiveness? We tested the hypothesis that people find older faces less attractive than younger faces, and furthermore, that these aging effects are modulated by the age and sex of the perceiver and by the specific kind of attractiveness judgment being made. Using empirical and computational network science methods, we confirmed that with increasing age, faces are perceived as less attractive. This effect was less pronounced in judgments made by older than younger and middle-aged perceivers, and more pronounced by men (especially for female faces) than women. Attractive older faces were perceived as elegant more than beautiful or gorgeous. Furthermore, network analyses revealed that older faces were more similar in attractiveness and were segregated from younger faces. These results indicate that perceivers tend to process older faces categorically when making attractiveness judgments. Attractiveness is not a monolithic construct. It varies by age, sex, and the dimensions of attractiveness being judged.

That is due to biological factors:

One can see how faces cause a surge of activity in the pleasure centres.
When cells in one part of the brain are activated, those cells use up energy.
To replace their energy, the body diverts blood to this part of the brain so that
extra oxygen is available to utilize food stores: the brain part becomes
literally flushed with blood.
This response is the basis of most brain scans, which work not by measuring
the brain activity itself but by spotting which parts of the brain are pumped
up with blood – these parts are assumed to have been getting a good workout
from whatever the person was doing or thinking in the brain scanner.
(Incidentally, there are also devices that measure blood flow to the genitals,
and such devices might well reveal many men’s motivation in viewing
images of pretty women.1)

For heterosexual men watching faces, it is clear
that attractive women’s faces produce heightened activity and a rush of blood
to the pleasure centres. Handsome male faces just do not make the grade –
they have little effect on the pleasure centers. So when straight men see
attractive or unattractive male faces there is no change in activity in blood
flow to their pleasure centres. Hence the brain activity is related not to
beauty but to wanting (lusting), and to the effort men will make for access to
female beauty.2


1. Introduction

Facial attractiveness has an important impact on social interactions. Studies investigating the “beautiful is good” and the complementary “ugly is bad”/“anomalous is bad” effects demonstrate how a person’s physical attractiveness affects beholders’ attitudes, judgments, behaviors and brain functioning (Dion, Berscheid, & Walster, 1972; Eagly, Ashmore, Makhijani, & Longo, 1991; Griffin & Langlois, 2006; Hartung et al., 2019; Jamrozik, Oraa Ali, Sarwer, & Chatterjee, 2019; Workman et al., 2021). Attractive people are regarded as good people, while unattractive people are imbued with negative character traits. The age of a person, as a particularly salient feature of faces, influences the perception of attractiveness. The negative consequences of aging on perceived attractiveness are well-documented (Ebner et al., 2018; Foos & Clark, 2011; North & Fiske, 2015). However, are these negative attitudes modulated by the beholder’s age and sex? The present study applies empirical and computational methods to further examine age and sex differences in aesthetic assessments of older faces. In addition, we investigated whether this effect varies depending on how attractiveness is queried.

1.1. Age and sex differences in the perception of facial attractiveness The age and sex of the perceiver, and the age and sex of the face being viewed, play important roles in attractiveness judgments. Older people are generally perceived more negatively in physical terms than younger people (North & Fiske, 2015). For instance, older faces are rated as less attractive in physical appearance than young faces by young, middle-aged, and older perceivers (Ebner et al., 2018; Foos & Clark, 2011). Elderly faces, compared with younger faces, are also perceived as less likeable, distinctive, energetic, growth-oriented (Ebner, 2008), and competent (Palumbo et al., 2017). Properties of faces that contribute to attractiveness (e.g., symmetry) are thought to signal desirable aspects of mate quality and “good genes” (Grammer et al., 2003; Rhodes, 2006). Youth is strongly associated with health and fertility, since younger people are expected to have more time to reproduce. These evolutionary accounts offer one reason why many regard young people as more attractive than older people. However, previous observations on effects of sex and age of the perceiver, and of face sex, have been mixed. In an earlier study, younger and older participants rated younger and older faces on attractiveness and other dimensions (e.g., likeability; Ebner, 2008). Older faces were perceived as less attractive, especially by young raters. Older perceivers rated all the faces as more attractive than young raters. In contrast, Foos & Clark, (2011) reported young and middle-aged perceivers rated younger faces as more attractive than older faces, whereas older perceivers rated faces from three ages quite similarly. As face age increased, women more than men rated older faces as less attractive. Recently, Ebner et al. (2018) extended their earlier research and found that although older and middle-aged perceivers rated faces as more attractive than young perceivers, young perceivers rated young relatively higher than middle-aged and older faces. They also found that age influenced attractiveness more negatively for female than male faces. These empirical studies do not consider different ways of processing faces: categorization and individuation (Fiske & Neuberg, 1990). Categorical processing is a core component of intergroup bias (Hughes et al., 2019) and older people are often stereotyped negatively (North & Fiske, 2015). For the present study, we applied a network science approach to examine the structural properties of face preference networks for the different groups, in part to determine if older faces are treated more categorically than if their attractiveness is distributed along a continuum. Several hypotheses about the influence of the perceiver’s age in judgments of attractiveness have been proposed. Ebner et al. (2018) suggest that age effects on attractiveness are influenced by different social goals and may reflect in-group biases (i.e., faces from own-age group are perceived as more attractive). Another account suggests that older perceivers have more experience in face processing and thus may develop a more nuanced appreciation of attractiveness for all ages (Foos & Clark, 2011). Individual face preferences are shaped by life experiences (Germine et al., 2015). On this view, older perceivers who, on average, have greater exposure to more and varied faces might harbor a richer set of face prototypes and preferences. The powerful forces of sexual selection have forged different strategies to increase sex-specific mating and reproductive success in humans (Buss & Schmitt, 2018). Men tend to value physical attractiveness more than women do in choosing mates, whereas women prioritize male social status and resources (e.g., wealth, prestige) more than men (Li & Kenrick, 2006; Rhodes, 2006; Whyte et al., 2021). Women’s fertility is limited by age. For men, women’s physical features (e.g., youth) provide cues to health and reproductive value. In addition to physical features, resource-relevant information that could potentially provide economic resources and protection for women and their offspring, is also important when selecting mates. Thus, physical attractiveness is theorized to be less relevant to women than it is to men. These mating strategies may also be used in the evaluation of faces. We propose that male perceivers are more likely to be influenced by face age than female perceivers when making attractiveness judgments, especially for female faces. Overall, we hypothesized that: First, perceived facial attractiveness is influenced by face age (H1). We expected younger faces to be rated more attractive than older faces; Second, the perceiver age affects attractiveness judgments (H2). We expected that older people would be less affected by face age than younger people; Third, perceiver sex affects how age modulates attractiveness judgments (H3). We predicted face age would affect men’s ratings for facial attractiveness more robustly than women’s, especially for female faces.

1.2. Effects of different aspects of attractiveness on aesthetic judgments The general notion of attractiveness incorporates different dimensions. Recently, Menninghaus et al. (2019) acquired free association, questionnaire, and semantic differential data to examine the differences between four related concepts: beauty, elegance, grace, and sexiness, and found that the concept of elegance applied to older women and men more than the other descriptors. As a concept whose meaning is influenced by cultural norms, elegance may be less reliant on physical features. In their study, however, participants were asked to categorize the age ranges (e.g., 20–29) that women or men are likely to perceive as beautiful, elegant, and/or sexy, without using actual faces or judgments of specific stimuli, per se. Vision is one of the most important channels of aesthetic judgment and experience, especially for facial beauty. We sought to extend their observation by examining if different aspects of attractiveness judgments apply specifically to faces when these judgments are made by young, middle-aged, and older men and women. We examined three concepts related to attractiveness: beauty, elegance, and gorgeousness. A recent study reported that beauty, the most used aesthetic concept, is closely related to ‘elegance’ and ‘gorgeous’ in semantic memory networks (Kenett, Ungar, & Chatterjee, 2021). The Oxford English Dictionary defines ‘gorgeous’ as ‘very beautiful and attractive; giving pleasure and enjoyment’. Though “gorgeousness” is not often used in studies of empirical aesthetics, given support from a large sample of participants in Kenett et al.’s research, we selected it as a concept of inquiry and as a contrast to elegance. In addition to these three terms, we also included liking judgments to further assess people’s preferences. We hypothesized that aging differentially affects judgments of attractiveness, when framed as beauty, elegance, or gorgeousness (H4). We predicted that age effects would be attenuated or even reversed for elegance than for beauty or gorgeousness ratings.

further scientific evidence supporting the blackpill

2. Methods

2.1. Participants A total of 191 participants was recruited via Amazon Mechanical Turk to complete an online survey administered through the Qualtrics platform (97 males; age: 46.57 ± 17.12 years; education: 15.28 ± 2.08 years). Using effect sizes of η2 = 0.137 computed from data reported in Foos & Clark (2011) that investigated a similar research question, a minimum sample of n = 32 participants per age group was expected to provide sufficient power (80%) to detect the interaction between sex and age of face being rated, and sex of rater. Data were excluded from 30 participants: 9 for response times falling outside the mean reaction time ± 2 standard deviations, 12 for failing more than two of three attention catch trials embedded throughout the survey, 1 for choosing not to report their sex, and 6 who were aged between 58 and 59 years (to differentiate middle-aged and older groups more clearly). Finally, 2 were excluded because they acknowledged that their responses were of poor quality. The final sample consisted of n = 161 participants (race/ ethnicity: 128 white, 10 Asian, 8 black, 5 Hispanic or Latinx, 1 American Indian, and 9 multiracial), of which 57 were young (36 males; age: 27.32 ± 2.97 years; range 21–33 years; education: 15.23 ± 1.95 years), 43 middle-aged (19 males; age: 47.63 ± 6.82 years; range: 36–57 years; education: 15.37 ± 1.70 years), and 61 older (25 males; age: 65.77 ± 4.24 years; range: 60–76 years; education: 15.33 ± 2.47 years). The age categories used in this study are based on previous studies (Ebner, 2008; Ebner et al., 2018; Foos & Clark, 2011; Voelkle et al., 2012) and official definitions (United Nations, 2019). This study was approved by the Institutional Review Board at the University of Pennsylvania. 2.2. Face stimuli The stimuli were comprised of 30 sets of faces, including 30 younger (range: 20–29 years), 30 middle-aged (range: 39–55 years), and 30 older faces (range: 60 years or older). We balanced the face sex and race/ ethnicity. Each set of faces consisted of three different ages for the same reference face (see Fig. 1 for sample stimuli and Table 1 for detailed information about the face stimuli). Face stimuli were selected and generated in the following way: First, 80 middle-aged faces were selected from the Chicago Face Database (CFD; Ma et al., 2015; http://www.chicagofaces.org/), which also provides researchers with information about each face (e.g., race, age, attractiveness). We then used the FaceApp software (https://www.fa ceapp.com) to generate 80 sets of younger and older faces based on the middle-aged faces from the CFD. Second, in order to standardize the stimuli, face images were 1) normalized to inter-pupillary distance using algorithms provided by the OpenCV computer vision library (https://opencv.org/)

Note. M - Male; F - Female; Ratings for the middle-aged faces (3.06 ± 0.58) and age information (39.69 ± 3.88 years) were provided by the Chicago Face Database (CFD; Ma et al., 2015). Information of younger and older faces derives from the results of our face norming tasks. * On average, 67.3% participants rated the 30 computer-generated younger faces as 20–29 years; 79.4% participants rated the 30 computer-generated older faces as age 60 or older. landmarks provided by the dlib machine learning toolkit (http://dlib. net/); 2) resized and cropped to 345 pixels (width) × 407 pixels (height); 3) placed onto a plain white background using the GIMP 2 software package (https://www.gimp.org/); 4) color corrected (Workman et al., 2021). Third, an independent sample of n = 129 participants (race/ ethnicity: 102 white, 14 black, 6 Hispanic or Latinx, 3 Asian, 3 multiracial and 1 chose not to report), of which 33 were young (23 males; age: 28.82 ± 3.71 years; range: 20–34 years; education: 14.64 ± 2.56 years), 59 middle-aged (25 males; age: 47.05 ± 8.14 years; range: 35–59 years; education: 14.41 ± 2.71 years), and 37 older (11 males; age: 65.00 ± 4.22 years; range: 60–73 years; education: 14.92 ± 2.51 years), was recruited via Amazon Mechanical Turk to rate the computer-generated younger and older faces for attractiveness (how attractive do you find the person in the picture?) and realness (does the picture look like a real person?) on a scale from 1 to 7. Participants were also asked to indicate the age range of the faces (how old do you think the person in the picture is? e.g., 20–29 years). 43 sets of faces were selected based on the following criteria: 1) higher rates of being perceived as younger (20–29 years) and older (age 60 or older); 2) highest mean realness ratings. Next, an independent sample of n = 27 participants (15 males; age: 26.81 ± 3.72 years; range: 22–36 years; education: 18.22 ± 2.64 years) was recruited via Amazon Mechanical Turk to judge whether each face from the three different ages belongs to the same person. Finally, the 30 sets of faces with the most accurate age group ratings were chosen for the main task (accuracy: 0.99 ± 0.005). 2.3. Procedure In the main task, participants were asked to rate the faces for beauty (how beautiful is this face?), elegance (how elegant is this face?), gorgeousness (how gorgeous is this face?) and liking (how much do you like this face?) on a scale from 1 to 7. Images were presented in randomized order. There was no time limit so ratings were acquired in a self-paced fashion. Finally, participants responded to a short socio-demographic questionnaire. The experiment lasted approximately 20 min.

3. Results

3.1. LMEMs results 3.1.1. Hypothesis 1: perceived facial attractiveness is influenced by face age Linear mixed models examined the effect of age on facial attractiveness, with overall attractiveness as the dependent variable and face age (Younger|Middle-aged|Older) as a fixed factor. We included random intercepts for stimulus and subject. Face age significantly affected facial attractiveness judgments, with younger faces rated as more attractive than middle-aged faces , and middle-aged faces as more attractive than older faces . In addition, younger faces were liked 3.1.2. Hypothesis 2: the perceiver age affects attractiveness judgments A linear mixed model examined how the aging effect varies crossgenerationally, with overall attractiveness as the dependent variable, face age, and perceiver age (Younger|Middle-aged|Older) as fixed factors, and random intercepts for stimulus and subject. This model revealed a significant interaction between face and perceiver ages. Post-hoc pairwise comparisons indicated that, for middle-aged and older faces, a significant difference between age groups was not detected . Younger faces were seen as more attractive by younger than by older perceivers, Older perceivers were less influenced by the age of the viewed face than middle-aged and younger perceivers. 3.1.3. Hypothesis 3: the perceiver sex affects how age modulates attractiveness judgments A linear mixed model examined how the aging effect varies as functions of perceiver and face sex, with overall attractiveness as the dependent variable, face age (Younger|Middle-aged|Older), and face and perceiver sex (Female|Male) as fixed factors. Random intercepts for stimulus and subject were included and, for subject, slopes were allowed to vary according to face age. There was a significant interaction between face age, face sex, and perceiver sex. To better understand this interaction, we conducted post-hoc pairwise comparisons. For older male faces, there was no significant effect of perceiver sex. For older female faces, ratings from men were significantly lower than those from women raters,Age had a stronger influence on men’s ratings than women’s ratings for female faces. 3.1.4. Hypothesis 4: aging differentially affects judgments of attractiveness, when framed as beauty, elegance, or gorgeousness

4. Discussion

The present study used empirical and computational network science methods to investigate the effect of aging on attractiveness and to examine how this effect is modulated by the perceiver’s age, sex, and dimensions used to make attractiveness judgments. Using highly controlled stimuli, and replicating earlier observations, we found that older faces were perceived as less beautiful, elegant, and gorgeous, and they were liked less. Further, young people rated young faces as more attractive than did older perceivers. Older female faces received lower ratings from male perceivers than female perceivers, suggesting that the age of faces influenced men’s ratings for attractiveness more robustly than it does for women making ratings; Finally, beauty, elegance, and gorgeousness ratings were affected differently by age. While the ratings for all these attractiveness descriptors diminished with age, elegance was affected least. We also observed a relative categorical perception of older faces in that they were viewed more similarly to each other (i.e., they clustered closer together) than the other two groups of faces in face preference networks, which could make it easier for older faces to be subject to negative stereotyping. Alternatively, it’s also possible that negative biases towards older individuals make people less inclined to distinguish them. Consistent with these interpretations, older faces were more segregated from and located further away from younger faces compared to middle-aged faces in the networks, again suggesting older faces were more distinct from younger faces in facial beauty. Perceivers showed negative biases towards older faces, rating them as less beautiful, gorgeous, elegant, and liked. Face preferences are regarded as adaptations for mate choice since attractive traits signal mate quality (Grammer et al., 2003; Rhodes, 2006). The human brain may have evolved to favor these traits (Chatterjee, Thomas, Smith, & Aguirre, 2009; Rellecke et al., 2011). Thus, an evolutionary mechanism might enhance perceptual sensitivity towards younger faces. Alternatively, younger people may simply have less exposure to and experience with older faces. Faces of one’s own age group are better recognized and remembered than faces of another age group (own-age bias, OAB; Bartlett & Leslie, 1986; Ebner et al., 2013). Either way, older faces were judged as less distinct from each other and treated more categorically when making attractiveness judgments. Despite commonalities, the structural properties of the networks varied across perceiver age, sex, and dimension of attractiveness. Faces in the older perceivers face preference network were more segregated than those of younger perceivers. As perceiver age increased, older faces were seen as more distinct in attractiveness. These dynamic changes may reflect that our face preferences are updated by experiences and exposures to faces across the lifespan. Considerable research has demonstrated that environmental factors, including cumulative environmental exposure and different environments, contribute to age differences in human cognition (Siew et al., 2019; Wulff, De Deyne, Aeschbach, & Mata, 2021; Wulff, De Deyne, Jones, & Mata, 2019). Individuals continue to learn as they get older. Older people are assumed to have acquired more knowledge (e.g., broader vocabulary) than younger people, which subsequently leads to the concepts becoming more distant and further apart from each other in their mental representation (Cosgrove et al., 2021; Wulff, De Deyne, Aeschbach, & Mata, 2021). This may account for the pattern observed in the older adults’ semantic network and the similar segregated effect in face preference networks. Research on face preferences also emphasizes the substantial role of experience/environmental factors in shaping our notions of attractiveness (Germine et al., 2015). The cumulative exposure to faces has important implications for individual face preferences. Older people have been generally exposed to more faces and have more diverse experiences compared to younger and middle-aged perceivers. Regarding different environments, people interact more with peers in daily life. These cohort effects may contribute to older viewers being less influenced by the age of the viewed face and more discriminating with older faces in attractiveness. Taken together, we propose that differences in face experience may account for the age-related changes in perception of attractiveness that we report. Older people’s experiences and preferences cover a greater span of time. Men, more than women, segregated faces into clusters by age and sex. The homophily analysis also showed that men more than women were likely to associate same sex faces together. Finally, men viewed faces from different ages and sexes as more organized and more segregated, suggesting they make more distinctions between faces when judging facial beauty. These observations confirm the hypothesis that men are more sensitive to features of physical attractiveness than women, they are more likely to treat face attractiveness categorically, and their sensitivity is further pronounced when judging women’s faces. Sex-specific mating strategies might be reflected in these perceptions of facial attractiveness. Men tend to prioritize women’s physical attractiveness, healthiness, and youth, which are theorized to ultimately increase reproductive success and off-spring quality. In contrast, women are thought to value men’s status and resources more than attractiveness (Li & Kenrick, 2006; Rhodes, 2006). Empirical data also corroborate that these mate preferences translate into actual mating behavior (ConroyBeam & Buss, 2018; see also Buss & Schmitt, 2018). Such sex differences in preferences for physical appearance are likely important drivers of differences in perceptions of attractiveness between men and women. However, these strategies are confined to theorizing about heterosexual mating contexts. We do not know if these results would generalize to non-heterosexual individuals. Finally, there was a stronger association of the dimension of elegance with older than younger and middle-aged faces, and with female than male faces. Elegance, as a descriptor of attractiveness, seems to alert people to finer distinctions in attractiveness for older than younger faces. The overall decrease in attractiveness judgments by age is muted for elegance compared to beauty or gorgeousness is consistent with the view that the notion of elegance goes beyond physical attractiveness, and signals non-physical properties (Menninghaus et al., 2019). We speculate that elegance incorporates cultural norms of attractiveness that are not tethered to physical features as tightly as for beauty and gorgeousness. We extend previous findings for aging effects to different aspects of attractiveness and revealed differences in the processes people use when judging attractiveness of older faces. However, our study has a few limitations. Different effect sizes were observed for the three network measures in face preferences networks. This probably indicates that one data source is better than the other for these psychometric networks. Future studies are needed to replicate and strengthen our findings. In addition, age-related differences may result from generational or/and developmental differences. We suggest that face preference is influenced by face experiences across the lifespan. But it is hard to quantitatively measure individual difference in face experiences. Whether our findings are the effect of specific generational cohorts or actual aging and accumulation of experience is difficult to determine. Our study was also conducted in the US. American culture may disproportionately value youth. Perhaps these aging effects would be mitigated in cultures with different attitudes towards the elderly.

5. Conclusions

In summary, we replicate and extend the basic finding that people are judged to be less attractive as they age. However, attractiveness judgments are modulated by the age of the perceiver, the sex of the perceiver, and the dimensions of attractiveness judgments being made. Older perceivers are less influenced by the age of the viewed face than are the other groups. Men, more than women, distinguish between faces when judging attractiveness, especially when looking at women. Finally, attractiveness is not a monolithic construct. Aging has less of an effect on judgments of elegance compared to beauty and gorgeousness.

TL;DR. who is high as fuck right now

1.Male sexual arousal from visual pornographic material is greater than female sexual arousal from pornographic material. Heterosexual men get much more aroused by seeing sexual images of women than by seeing sexual images of men. Heterosexual women get mildly aroused by both! Chivers, M.L., Rieger, G., Latty, E. & Bailey, J.M. (2004) A sex difference in the specificity of sexual arousal. Psychological Science 15: 736–744.

2. Aharon, I., Etcoff, N., Ariely, D., Chabris, C.F., O’Connor, E. & Breiter, H. (2001) Beautiful faces have variable reward value: fMRI and behavioral evidence. Neuron 32: 537–551.

 

[NirvanaFan1988]

pynk floyd?

 

[InMemoriam]

pynk floyd?

 

[yez]

View: https://youtu.be/x7NhPcRcT9U

 

[InMemoriam]

View: https://youtu.be/x7NhPcRcT9U

 

[BornToLose]

Walls me.

 

[Izayacel]

Woman 41 + still smv mog most Man just bc of muh Tits and Ass. The Wall is just Cope and mostly applies to Man

 

[Immolator]

info obtained have a nice day

 

[InMemoriam]

info obtained have a nice day

 

[InMemoriam]

Woman 41 + still smv mog most Man just bc of muh Tits and Ass. The Wall is just Cope and mostly applies to Man

yep foid serialization is almost non expiring you know gilfs, not to mention artificial beautification

 

[Izayacel]

Brutally over for most Man

yep foid serialization is almost non expiring you know gilfs, not to mention artificial beautification

 

[Hopium]

 

[InMemoriam]

 

[El Enemigo]

The Wall mostly applies to Man

Elab?

 

[Izayacel]

Elab?

Man get older and loose Any resemble of SMV that they already barely had .

except chadlites and such of course.

Old Man arent desirable and just betabux at best , While older Woman gets their Tits Cummed , in and gets Money for that because of her Tits .

The Sexual Powers are just in favor of Woman , Really . They can be fat in their 40 s and still are able to fuck EASILY , while Man just dont.

 

[El Enemigo]

Man get older and loose Any resemble of SMV that they already barely had .

except chadlites and such of course.

Old Man arent desirable and just betabux at best , While older Woman gets their Tits Cummed , in and gets Money for that because of her Tits .

The Sexual Powers are just in favor of Woman , Really . They can be fat in their 40 s and still are able to fuck EASILY , while Man just dont.

If anything I'd say most men are already born into the wall like one of those titans from attack on titan jfl

 

[cvh1991]

Man get older and loose Any resemble of SMV that they already barely had .

except chadlites and such of course.

Old Man arent desirable and just betabux at best , While older Woman gets their Tits Cummed , in and gets Money for that because of her Tits .

The Sexual Powers are just in favor of Woman , Really . They can be fat in their 40 s and still are able to fuck EASILY , while Man just dont.

 

[Emba]

Brutal waul

 

[subhuman]

all i am. and all that i was ever gonna be. its just a travesty

 

[Izayacel]

Man get older and loose Any resemble of SMV that they already barely had .

except chadlites and such of course.

Old Man arent desirable and just betabux at best , While older Woman gets their Tits Cummed , in and gets Money for that because of her Tits .

The Sexual Powers are just in favor of Woman , Really . They can be fat in their 40 s and still are able to fuck EASILY , while Man just dont.

 

[Autismus Maximus]

Man get older and loose Any resemble of SMV that they already barely had .

except chadlites and such of course.

Old Man arent desirable and just betabux at best , While older Woman gets their Tits Cummed , in and gets Money for that because of her Tits .

The Sexual Powers are just in favor of Woman , Really . They can be fat in their 40 s and still are able to fuck EASILY , while Man just dont.

Fucking brutal, especially when you age like shit and get shit knees and heart problems. May as well just rope at 40.

 

[Izayacel]

Fucking brutal, especially when you age like shit and get shit knees and heart problems. May as well just rope at 40.