Social Psychology claims: “Partner’s Attractiveness Is More Romantically Appealing To Men Than To Women”. A Comment On Eastwick’s Blog.

Unmasking the greatest fallacy ever told

When reading the blog of the prestigious social psychologist Paul Eastwick, I can not stop shuddering with some of the extravagant conclusions of social psychology on this keynote topic. The manifest negligence might be due to academic censorship (Academia is not tolerant of controversy that challenges conventional assumptions about the fitness landscape),  weakness/futility of empirical studies used, or both.

Here’s one of my own findings in which I have a high degree of confidence. In a meta-analysis I conducted about five years ago, we examined whether a partner’s attractiveness was more romantically appealing to men than to women. We acquired a large collection of published and unpublished datasets (k = 97, N = 29,780) that spanned a variety of paradigms in which men and women reported on partners they had (at a minimum) met face-to-face. Overall, we found that the sex difference in the appeal of attractiveness was not significantly different from zero…..] (Evolving Blog. “Going on the record via preregistration”).

The apparent mindset of social psychologists is contingent on biased findings.

These results don’t support predictions previously presented consistent with contemporary sexual selection theory and endorsed by ecologically valid research (e.g. online dating) : Females are more selective (i.e. given that females are more sexually choosy) in choosing mates and vary less in their tendency to be selective than do males.

Upfront, the semantic construction of this topic is wrong. This concept should be replaced by for the appeal for physical appearance/morphology (i.e epigamic traits). Therefore the cornerstone would be relationship between phenotype (more concretely morphological features) and mating success; which further determines the shape, direction and intensity of sexual selection. Romantically appealing of attractiveness is a circular/redundant concept. It’s like saying romantically attracted to attractiveness, or romantically appealing of sex-appeal.

Therefore there is no such thing as romantic appealing for attractiveness. Attractiveness is not an intrinsic quality, but the result of a perceptual process derived from the interaction between the epigamic characteristics of the emitter and the sensory properties of the receiver (mate chooser). The product of this interaction is an aesthetic judgement/assignment of an attractive value to a certain configuration of morphological cues/signals. The second step is a decisional process. It describes the construction of the decision variable which integrates a value ( assigned attractiveness ) of prospective mates into a quantity that is used by a decision rule to produce a choice.

What sex gives more importance to physical appearance? It is as simple as answering the question on what sex has a more pronounced mating bias with respect to aesthetic features, or what sex is more selective based on physical traits. In other words: after the acquisition of visual information and its cognitive processing, what is the difference across sexes in assigning an attractiveness value to prospective mates?.

So what we are looking for is to quantify the covariance between secondary sexual characters and mating success for women and men, and later we can compare the differences between sexes. Or said in more colloquial terms, trying to quantify how each gender weighs and values opposite-sex physical traits and the degree of discrimination based on those same aesthetic characteristics when they are choosing mates.

Do both sexes rate equally morphology on opposite-sex? or perhaps, does one of them rate more harshly? Obviously the answer is the second one. And of course it’s not the male sex, like most social psychologists claim, contrary to the most solid principles on evolutionary biology.

The Darwin-Bateman theory of the sex roles, extended by Robert Trivers, formed the theoretical bedrock of the emerging field of behavioural ecology. Social psychology get stuck at this step, never progressing to detailed and testable models of cognitive adaptations that exploit mating cues to make real mate choices.

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). Empirical tests of these principles come clearly under appropriate scrutiny. Ecologically valid data sets prove that mate choices are more variable in males than in females, resulting in a steeper male Bateman gradient, consistent with Bateman’s principles.

Not providing qualitative support for Bateman’s principles, social psychology research demonstrates how current approaches can generate a misleading view of sex differences and roles. Online data and speed dating (despite its shortcomings) 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).

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.

I would appeal to work in mating Leks with high ecological validity and discard studies as serveys (i.e. summarized preference), laboratory-based research, such as the use of confederates, or asking participants to rate how likely they would be to go on a date based upon a hypothetical situation.

If you consider that females are the reproductively limiting sex (rate limiting in reproductive success) – which manifests in *all* dimensions of mate choice (in other words, females are more selective in all their mating considerations, including epigamic cues) – then one obvious implication of this is that, given sufficient latitude -i.e. mating leks- (ie. relieved of systemic constraint, which would otherwise limit their choices), female choices will always tend towards a narrow male morphotype distribution.

Online studies support predictions previously presented consistent with contemporary sexual selection theory and endorsed by ecologically valid research: Females are more selective (i.e given that females are more sexually choosy) in choosing mates and vary less in their tendency to be selective than do males.

In terms of online dating interaction, there is a strong agreement in the literature that females receive much more initial contacts and get higher reply rates when they send messages to desirable males. In line with the above, males also receive significantly fewer replies, messages and matches in general, whereas females can expect a lot of reciprocation. (Bapna et al. 2013, Fiore et at 2005, Fiore et al 2010, Hitsch et al 2010, Hitsch et al 2010b, Kreager et at 2014, Lewis, K 2013, Scharlott et at 1995, Xia et at, 2014, Shaw Taylor et al 2011. ). Women are rather picky in their decision of who might be their potential date. 

Log files from online dating 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.

Females are more selective, given that message frequency is a corollary of selectivityCupid on Trial: A 4-month Online Dating Experiment Using 10 Fictional Singletons  

Measures based on between-sex average variance in mating success alone provide at best a partial picture of sexual selection. It is necessary to obtain data where can be scaled the intra-sex variance and the quantification of mate choices monopolization across phenotypes, measuring sexual selection on phenotypic traits.

If we try to explain a modal tendencies in the online population, the mating system for men seems hugely skewed:  Estimating The Mating Pool Size. Part 1: Male Profile  , but unlike female preferences, male ‘preferences’ are inclusive of a broad range in female variance:  Estimating The Mating Pool Size. Part 2: Female Profiles.

Males have evolved as more tolerant of female morphological variance (i.e female physical attractiveness) than the reverse, given that females are rate-limiting in reproductive success.

On Tinder, a dating App, the data plotted exposed a probability density function for mating like a Pareto type I/power law: Tinder Experiments II: Guys, unless you are really hot you are probably better off not wasting your time on Tinder — a quantitative socio-economic study. These mating distributions for males granted by skewed female preference functions give a qualitative understanding of the real distribution given the Gini coefficient near to G=1 or α = 1 for a Lorenz curve:  Tinder Experiment. Inferring Population Preference Functions Using A Simple Binary Task Choice on A Dating App

Differential of matches achieved by average looking females and males on Tinder: Tyson et al 2016 and B. Seefeldt 2014).

Female status as ‘rate-limiting’ necessarily skews male:female prospects accordingly, destroying any notion of symmetry that Eastwick et al are supposing: Female Mating Skew II: Supported By Online dating Experiment . The prevalence of this agrees with a severe mating imbalance.

Females are selecting a smaller elite portion of potential male mates: Plenty Of Fish Experiment: Study 1 , Plenty Of Fish Experiment: Study 2  , Plenty Of Fish Experiment: Study 4. Females Profiles. Plenty of Fish Experiment: Study 3

If a large majority of men are interested in dating an average looking girl (judging from their immense mating pool available) while almost no woman (or none) is willing to dating an average-looking man. How can someone dare to say that physical traits are more romantically appealing to males?. In other words, a conventionally looking woman is attractive to the vast majority of the male population, while a conventional looking man is unattractive for the overwhelming majority of the female population.

Let’s see another study where females receive much more initial contacts, more matches/reciprocity and get higher reply rates: Quantification Of Dating Pools Through A Online Dating System.

Sexual equity arguments (or worse, those pointing to the greater male selectivity) belie even a cursory appreciation for dimorphic sex-biology, where determined/antagonistic skews in sensory-sexual valence are differentially deemed by dimorphic bio, with females designated the more valent sex.

If you want to observe differences in mating success between male and female phenotypes in nature. Which sex reject more certain opposite-sex conspecifics in favour of others?


Laboratory, questionnaires, speed dating and couples: Unsuitable empirical tests for to analyze mate preference functions.

[ ……..and it did not matter whether the study examined initial attraction (e.g., speed-dating, confederate designs) or established relationships(e.g., dating couples, married couples).”(Evolving Blog. “Going on the record via preregistration”

Neglect of the topic limits the ability to formulate empirically grounded models of sexual selection, to understand how mating decisions are made in different contexts and to be able to define a human mate choice model mathematically. There are strong anomalies in the literature: while surveys (summarized preferences) are futile, laboratory settings models are not being realistic and plausible, and other widely used settings (e.g. speed dating), even being more realistic, have little ecological validity.

They are not synthesizing real and empirical data from the wild, not withstanding strong empirical tests, and not contributing to the logical integration of proximate and ultimate approaches to mating decisions.

First, summarized preferences are pitfalls without out-cognitive modelling; then the conclusion will outline a normative and descriptive alternative. Here “mating preferences” come from self-reported marks on item likert scales, and they are not reliable/verifiable. Marks on scales are uninformative about for the knowledge of mate preference functions, and it’s a cue-cataloguing a rather weak method for characterizing our adaptations for mate choice.

The self inference (often intentionally adulterated), by means of intensity paper-marks, of the importance given by our brain to each one of the epigamic cues is absolutely useless. Mate choice is a processing-information mechanism by computational rules, which can only be tested through behavioural execution during the actual assessment/choice process (in an ecologically natural environment).

Regardless of the validity of surveys (I.e questionnaires are not reliable/verifiable and lack validity for a number of reasons. Participants may lie; give answers that are socially desired and so on), the ability to introspection on the cognitive machinery used to choose a partner is limited, since any decision process involves a heuristic mechanism that can not be unravelled through the placement of a cross with a pencil on a scale. What translation/interpretation is supposed to have these scores in terms of a preference function? What phenotypes of the opposite sex are accepted and which are rebuffed? What are the mate responsiveness or discrimination against potential natural morphotypes? Where the acceptance threshold would lie? Which would represent the peak of preference?etc.


Speed dating:

Nonrepresentative Sampling: samples are systematically different from the population. (Self-selection bias, decision to participate may be correlated with traits that affect the study, making the participants a non-representative sample). Consider the aversion to a context where a highly selective individual (i.e. females as choosy sex) must face blind dates.

First, because of the uncertainties for a person with reasonable options, to resort to a stressful method of interaction with male strangers, against the advantages of selecting potential partners with some less restrictive environment, with a much larger prospect size, and in a more comfortable way than in blind dates (e.g bars/nightclubs, online dating) The small proportion of women who decide to sign in such events is undoubtedly unrepresentative of the general population.

Those individuals who are highly motivated to try a last redoubt where to find a couple (i.e. usually those who find more obstacles to find receptive partners into more conventional mating leks, typically individuals who get less mating reciprocity) are over-represented.

Uncommon/unnatural environment mating Lek: Speed dating is a real staging , but extremely infrequent for most people. In speed dating, people are placed in situations they would rarely encounter in everyday life. While the majority of young people are immersed relatively frequently in mate-searching environments such as single bars/nightclubs and to a lesser extent on dating websites/apps.

There are few dating companies outside of the USA, UK and Australia. In Europe, for example, only in the largest cities there are some speed dating events available for single people. It has to be a biased example of women with less choosiness and who have had a scarce mating success over more favorable environments. Which, as a woman, is already an indicator that we are talking about some hypothetical subset of below-average attractive women with a low niche of opportunity, since most women enjoy a sufficient number of male prospects (abundant mating pool).

Speed-dating studies cannot be considered similar to what a person might encounter at a bar or party, since that many artificial constraints reduce their ecological validity.

In short, speed dating / blind dates have a low “generalizability across people”. Therefore we can not generalize from the people who participated in the speed dating to people in general.

The pool of participants immersed on blind dates/speed dating samples is self-selecting. Speed dating/blind dating and laboratory experiments are not representative of typical human mating behavior. Only if these artificial settings would converge beside naturalistic methods, one could be confident of the results.

In any case, the speed dating data agree with the asymmetry described above, where women are more selective:

1) Belot and Francesconi (2006) reported data on approximately 1800 women and 1800 men who participated to 84 speed dating events. As emerged in many previous psychological studies [Trivers 1972], women are much choosier than men: On average, women choose 2.6 men and see 45 % of their proposals matched, while men propose to 5 women and their proposals are matched in only 20 % of the cases. About 36 % of men and 11 % of women do not get any proposal. And 38 % of men and 46 % of women do not choose anyone.

2) Berlin Speed Dating Study (BSDS) made a set of carefully controlled experimental speed-dating sessions run at Humboldt University in Berlin: The mean offer rate of men was 41%, and 31% among women.

3) Kurzban & Weeden (2004) analyzed the percentage of yeses that a person received from members of the opposite sex, a measure of desirability in this context. On average, men were chosen by 34% of women (S.D.=21%), and women were chosen by 49% of men (S.D.=22%)

4) A a set of speed dating events conducted by the Department of Psychology at the University of Groningen (Jessica Pass, 2009),  found their study was able to replicate earlier findings on mate acceptance in a speed-date setting. Consistent with previous findings (e.g., Kurzban & Weeden, 2005; Todd et al., 2007), women were choosier than men: on average, female participants accepted 26.3% of their dates, compared to 49.3% for male participants (t(90) = 5.21, p < .01).

Laboratory Settings

Artificial trials: Surveys where a photograph-evaluation (either a binary / dichotomous judgment or on numbered likert-type scales) contexts is required , but where the evaluator are not going to be able to interact with the rated subjects. These kind of settings are artificial and distant from real life. It is more important to ensure that a study is high in psychological realism ( i.e. how similar the psychological processes triggered in an experiment are to psychological processes that occur in everyday life.

– Humans use multiple morphological cues to assign an attractiveness value to prospective mates. On this study Eastwick and Smith are using a single criteria of choice (facial portraits). But it happens that mate choice is multivariate, humans select mates by several criteria. Mate choice is based on multiple traits. Males and females may utilize different criteria, reflecting their sex roles. A study with some ecological robustness should examine the preferences for mates varying in several traits: facial attractiveness, body attractiveness, body size/height, since humans make discriminations regarding all of these traits. The stimuli (photographs/videos) displayed should contain a complete full-body picture or a set of images containing both facial portrait and body portrait, and indicating the height of each individual. A sample of facial stimuli where not even the trunk of potential partners is shown would be fully deficient, and again lacks any ecological validity.

Moreover, when you ran a preference function test, you’re scoring with a likert scale “how much are preferred. This leads to a high degree of vagueness/confusion because we still don’t know which phenotypes would be chosen as potential partners.

Therefore acceptance thresholds should have been tested through binary choice: repeated sequential sampling of rejection/acceptance responses (binary); i.e. who is preferred and who is not. This task tend to reduce the burden of ambiguity entailed by a simple rating likert scale. (e.g. a person can be scored by someone with an “5” being chosen/accepted as mate; and another people can score like a “7” to some individual but reject him/her, since its acceptance threshold lies above 8).

External validity is improved by use of field settings or online dating data recollection. These photograph-evaluation contexts on laboratory experiments are conducted in artificial situations and that it cannot be generalized to real life.

This means that the kinds of psychological processes triggered would differ widely from those of a real mate choice lek, reducing the psychological realism of the study.

If individuals are aware that they are not immersed within a real situation of mate choice, with possible gains/benefits and costs associated with their selection process, there will be an absolute lack of ecological and external validity of the configuration. Therefore, describing an experimental mating situation to participants and then asking them to respond normally will produce responses that may not match the behavior of people who are actually in a real mating lek. We cannot depend on people predictions about what they think that they would do in a hypothetical situation, or what they want to convey to the researcher; we can only find out what people will really do when we construct a situation that triggers the same psychological processes as occur in the real world, and with stimuli similar to those found into naturalistic environments.

Furthermore the presence of some type of Hawthorne effect is likely when performing these types of experimental configurations, where the subject is fully aware that his behavioral actions are being monitored by some researcher, filling out questionnaires, etc. In mating real-life settings (e.g. online dating, street/bars-nightclubs courtship approaching) where people could not possibly have known that an experiment is being conducted, and with a full ecological validity of the evaluation process (acceptance/rejection has real consequences) sexual dimorphism emerges.


Eastwick dismiss the data collection of online dating because they consider that they lack internal validity (due to the non-standardization of photographic stimuli):

Nevertheless, these purported demonstrations of the physical attractiveness sex difference are not especially definitive. These studies all examined a naturalistic context in which users could decide which photos of themselves to share with dating site users, and this element of the procedure opens the opportunity for several possible confounds to emerge…A clearer test of the attractiveness sex difference that avoids such confounds would entail the use of standardized stimuli that depict real people but do not allow the stimuli themselves to choose how they want to appear in the photograph” (PW Eastwick, 2018).

This is a minor and easily remedied problem. Making the appropriate adjustments by collecting profiles that meet certain validation criteria (eg, a minimum number of photographs: a single photograph may be unreliable with respect to the actual appearance, but several pictures showing face and body tend to have a fairly high precision and verisimilitude with respect to in vivo physical appearance).

Anyway, the tendency to publish online mainly better-looking photos is sexually isomorphic, and any margin of error with respect to reliability/verisimilitude regarding real appearance versus photographic appareance gap can be neglected when we’re trying to quantify between-sex mating success variance.


Dating couples/ Married couples:

Mate preference should be distinguished, both conceptually and empirically, from mate choice. Preference comprises the sensory and behavioural components that influence to mate differentially with certain phenotypes, whereas choice is the pattern of mating that is influenced not only by preference, but also other factors such as mates availability, costs of choice, etc..; which can not be deduced using this method, and that we only know the features of one only chosen partner.

It is useful to extract measures from preference functions that correspond to biologically meaningful properties for mate choice behaviour.

Social structure constrains mating preference because it defines the social spaces in which such interactions take place. Social structure is said to define the exposure to mating opportunities (i.e., potential persons with whom to interact). Choice is highly dependent on the own attractiveness of an individual, as well as on ecological and social factors. Individuals are expected to vary considerably in condition, and this can create substantial differences in their ability to express a preference.

Ecological conditions might influence the amount of time and energy required for finding prospective partners and for mate sampling. Social factors, such as population density and operational sex ratio, are also be of importance. Thus, constraints influence choosiness, which in turn affects the expression of the preference functions.

Due to the limitations for extracting mate preference-based measures from marriages/dating couples, researchers have developed a host of proxy measures that depend of asking in surveys on generalized aspects of partner satisfaction and relationship quality. As with any questionnaire, participants may provide the answers that they feel they should. Moreover, because the data are quantitative, it does not provide in-depth replies. Studies must involve direct observation strategies.

As, again, we’re only concerned with near moments of *sexual/romantic choice* (given its necessary evolutionary implications), so in any observation of long-term mate pairings, direct benefits can be a *huge* confounding variable if not controlled for(ie. where such pairings are not observing anything about sexual choice). If we don’t follow from common premises, we are fated to disagreement.

We should take great care when designing studies of mate choice if our goal is to project our conclusions to natural populations or to make quantitative predictions about how mate choice translates into selection on epigamic traits. If either is our aim, we need to rely on field studies or experimental studies conducted under settings that closely mimic those in the wild.

Even though prior studies of this sex difference were underpowered, the sex difference was there in our new study: r(Men) = .41, r(Women) = .28, q = .13, 95% CI (.18, .08). There is no chance that the prior studies were powered to find a sex difference as small as what we found. But it was hiding in there, nevertheless.” (Evolving, “Two Lessons From a Registered Report”).

Honestly I do not think that trying to compare r coefficients is useful or enlightening, and less taking into account as it belongs to regressions from data obtained by disparate experimental methods. To clarify results, it would be necessary to plot mating success data (number of people of the opposite sex who choice/rejects an individual as mate, within the sub-population collated), on relative frequency histograms /Probability Density Functions for males and females. Or even better histograms displaying mating success according to a previously established ranking of physical attractiveness (to avoid possible reasoning endorsing spurious correlations on mate choice and non-morphological characteristics, i.e. direct beneficts)

First, the correlation analysis satisfaction versus partner attractiveness is based on obtaining data through surveys, which again enlist volunteer data/surveys of insignificant samples make for spurious argument. Second, surveys are not a reliable indication of data (ie. there is consistent evidence of falsified reporting – and every indication that males embellish, while females understate), especially when it disagrees so strongly, not only with what sexual evolution predicts, but what other controlled testing reveals of male/female mating preferences, and the resultant asymmetry). There are a plenty of factors (not related to mate attractiveness) that can influence relationship satisfaction and there is a tendency with Likert scales for people to respond towards the middle of the scale, perhaps to make them look less extreme.

The statistical analytical result presented is based on calculation of correlation coefficients of the cartesian product romantic desire versus attractiveness (which is analogous to mate preference function, being the romantic desire/preference the dependent variable and attractiveness being the independent variable). But coefficient r measures the strength/direction of a linear relationship between two variables on a scatter-plot. It happens that while male functions are mainly fairly linear, the same usually does not occur for female mate function, which are skewed nonlinear functions. So, it may provide false results for non-linear relationships.

When plotted on linear axes, directionally female skew functions assumes a familiar J-shaped curve which approaches each of the orthogonal axes asymptotically.

IGreater selective bias female in mating leks corresponds fully to screening of aesthetic signals (ignoring spurious correlations), and it is not accompanied by weighting of non-aesthetic benefits. Which comes to indicate the greatest importance given by females on the male appearance, that in reverse, like social psychology claims.


 Strategic female duplicity

1. If men and women truly differ in the extent to which they believe attractiveness to be important in a partner, what factors interfere with the application of these ideals when they evaluate partners in real life? (Evolving Blog. “Going on the record via preregistration”).

This would only support that there are a great disparate quantity between what females claim (not what they believe), and what they demonstrate.


2. If there is essentially no difference between men and women in how much they actually prefer attractiveness in a real life partner, what sorts of social-cognitive biases might produce the sex difference in how much people think they prefer attractiveness in a partner?
(Evolving Blog. “Going on the record via preregistration”).



Male evolutionary agendas are more served by strategic knowledge availed by high aptitudes for logic and rational inference, as this lends more strongly to active utility in competitive environments where evolutionary success is incumbent upon contesting scarcity (especially reproductive/female access).

On the contrary, relaxed competitive rigors in female evolution have largely deprived females of such demands, instead selecting for passive strategies in the manipulation of male proxy (lending more to emotional appeals of ad-hominen tactics with respect to argument).

Haven’t you ever wondered why females are so acutely hypersensetive to any form of criticism as a general case?.

It is because female evolutionary success is more entangled in extraneous (socially or individually mediated) proxy benefits (an anxiety which was fixed by selective pressure during reproductive intervals when females were critically vulnerable to ecological stress), and are thus fanatically defensive of any information (true or not) that can potentially marginalize this ‘proxy’ through the effects of reputation (e.g. labeled as extremely shallows, etc.).

Females are not so quite obtuse in introspective capacity they first appear (Hadjistavropoulos et al, 1994). They can intuitively infer that the vast majority of males are getting a raw deal in the fitness landscape due female choosiness, and thus fear potential reprisals (even if only in a passive aggressive form of denying them mate-independent welfare privileges, which would otherwise temper their sexual choices with a more pragmatic bent which is scarcely in evidence today).

Thus, tendencies to preserve information and statu quo assymetry which expidite strategic pluralism (in the form of a cryptically imbalanced mating dynamic with respect to female mating skew), is an optimization of female evolutionary success.

In fact, the basis of any difference resolves to the fact that female biology is rate-limiting in reproductive success (hence their lower optimal mating rate), justifying their greater mate selectivity, contrary disposition, characteristic/strategic duplicty, etc.

Everything else is a popular fiction – spurious PR, designed to spin female agendas into something more noble than base reality.


Relationship satisfaction isn’t a putative index to the actual pre-mating preference functions.

This paper found the expected sex difference in a sample of N = 458 married couples. In brief, they found that women’s attractiveness predicted men’s satisfaction at r = .10, whereas men’s attractiveness predicted women’s satisfaction r = -.05. That’s an r(difference) of .15—still pretty small, but not zero (p = .046).” (Evolving Blog. “Going on the record via preregistration”).

On the other hand, when an indirect metric is used to infer preferences (such as relational satisfaction surveys) the result will be futile.

Obtaining a score, even if it was relatively honest, will not tell us anything about the original pre-mating preferences. Since a myriad of factors will affect the relationship satisfaction status. And if only the factors relative of attractiveness were at stake, we would find a plethora of viable options resulting in different levels of pairing satisfaction for wide range of mate attractiveness

If a female pair assortatively with respect to quality (i.e. attractiveness) and although the male partner may not match her ideal preference, he will be the closest that she can achieve owing to the limited number of perfect males left available for long-term relationship. So that female could be ‘satisfied’ with her partner because he is the best that she will get;

By the other hand, female preference is quality/condition-dependent and a few females choose trade-off physical appearance; she pairs with an average-looking or unattractive male because they may confer different benefits than the more showy males For example, highly attractive males are high-risk’ for investments and less attractive prospects can provide higher levels of faithfullness and paternal care to offspring.

Otherwise, we could expect those females paired with the least ornamented males to be dissatisfied with their partners, or maybe satisfied as they are the result of active choice and match a female preference.

Moreover, most females would prefer to pair with the most attractive males and consequently are those that lose out in the scramble competition. In this scenario, we might expect many females to be dissatisfied with their partners.

But, since the sample contains young couples, the paired women should be relatively satisfied insomuch as they are still youngs and they are not under pressure to mate with a low-quality male. So they could have remained unmated and to keep searching.

How should actually preferences be measured? Preference underlie mate choice, and we should know the chooser probability of favoring or spurning a certain morphotype. In other words, we would like to know, how often the same individual, given the same experience and same conditions, would mate with a particular phenotype given a large number of opportunities.

We can’t rewind time and replay life tape again and again, so we are faced with an inherent trade-off. If we measure preferences (for paired people) based on current partner (e.g. long-term couples), we can measure them only once per individual.

Using only one realized mate choice (partner) limits our ability to measure a wide range of phenotypes and the chooser responsiveness to morphological variation.

Furthermore, a pair bonding changes the individual experience and motivation. Therefore it would also be futile to try to artificially collate in a laboratory experiment (lacking the minimum ecological validity) the mate preferences for already paired individuals.

We must use direct essays of preference, which will be predictive of a mating outcome in nature. In this case we can measure response to a lot of stimuli (e.g. through data mining from dating sites or carrying out field research: interactions-courtship).

How do we measure mating choice and preference functions rigorously, and how do we compare its strength between the sexes?

Online dating research provides a high ecologically valid context ( although investigators use online data inefficiently – just logistic regressions for some parameters without appropriate graphics and data analisis or fails to notice statistically significant relationships).

Field courtship interactions are a great promising terrain yet to be explored. The few field studies carried out (Clark & Hatfield, Guéguen, Hald, Voracek et al), were focused to assess only the acquiescence rate to sexual offers, and this methodological details are not appropriate to quantify dating success on phenotypic traits derivated of courtship attemps.

References

1. Bapna, R., Ramaprasad, J., Shmueli, G. and Umyarov, A., “One-way mirrors and weak-signaling in online dating: A randomized field experiment”, International Conference on Information Systems (ICIS 2013): Reshaping Society Through Information Systems Design,3, 2013, pp. 2748-2762.

2. Belot, M., & Francesconi, M. (2006). “Can anyone be the one? Evidence on mate selection from speed dating”.

3. Eastwick, P. W., & Smith, L. K. (2018). “Sex-differentiated effects of physical attractiveness on romantic desire: A highly powered, preregistered study in a photograph evaluation context”. Comprehensive Results in Social Psychology.

4. Eastwick, P. W. Evolving (2018). “Two Lessons from a Registered Report”.

5. Eastwick, P. W. Evolving (2018). “Going on the record via preregistration.”

6. Fiore, A. T., and Donath, J. S., “Homophily in online dating: when do you like someone like yourself?”, In CHI’05 Extended Abstracts on Human Factors in Computing Systems, ACM, 2005, pp. 1371-1374.

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8. Jon Millward (2012). Cupid on Trial: A 4-month Online Dating Experiment Using 10 Fictional Singletons.

9. Hadjistavropoulos, T., & Genest, M. (1994). The underestimation of the role of physical attractiveness in dating preferences: Ignorance or taboo? Canadian Journal of Behavioural Science / Revue canadienne des sciences du comportement, 26(2), 298-318.

10. Hitsch, G. J., Hortaçsu, A. and Ariely, D., “Matching and sorting in online dating” The American Economic Review, 2010a, pp. 130-163.

11. Hitsch, G. J., Hortaçsu, A., and Ariely, D., “What makes you click? -Mate preferences in online dating”, Quantitative marketing and Economics, 8(4), 2010b, pp. 393-427.

12. Human Mating Blog (2014). Female Mating Skew II: Supported By Online dating Experiment.

13. Human Mating Blog (2015). Quantification Of Dating Pools Through A Online Dating System.

14. Human Mating Blog (2015). Plenty Of Fish Experiment: Study 1, Study 2, Study 3 and Study 4.

15. Human Mating Blog (2018). Tinder Experiment. Inferring Population Preference Functions Using A Simple Binary Task Choice on A Dating App.

16. Kreager, D. A., Cavanagh, S. E., Yen, J. and Yu, M., “Where Have All the Good Men Gone?” Gendered Interactions in Online Dating, Journal of Marriage and Family, 76(2), 2014, pp. 387-410.

17 Kurzban, R., & Weeden, J. (2005). HurryDate: Mate preferences in action”. Evolution and Human Behavior, 26(3), 227-244 

18. Lewis, K., “The limits of racial prejudice”, Proceedings of the National Academy of Sciences, 110(47), 2013, pp. 18814-18819

19. Meltzer, A. L., McNulty, J. K., Jackson, G., & Karney, B. R. (2014). Sex differences in the implications of partner physical attractiveness for the trajectory of marital satisfaction. Journal of Personality and Social Psychology, 106, 418–428. doi:10.1037/a0034424.

20. Pass, J. A. (2009). “The self in social rejection” Univ.

21. Scharlott, B. W. and Christ, W. G., “Overcoming relationship-initiation barriers: The impact of a computer-dating system on sex role, shyness, and appearance inhibitions”, Computers in Human Behavior, 11(2), 1995, pp. 191-204

22. Seefeldt, B. – (2014) “Tinder Interaction Messages”. – Department of Computer Science.

23. Shaw Taylor, L., Andrew T. Fiore, A. , Mendelsohn, G.A. “Out of My League”: A Real-World Test of the Matching Hypothesis“. 2011.

24. Tyson, G., V. C. Perta, H. Haddadi, and M. C. Seto. 2016. “A first look at user activity on tinder”. ., Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on (pp. 461–466). IEEE.

25. Xia, P., Jiang, H., Wang, X., Chen, C. and Liu, B., “Predicting user replying behavior on a large online dating site”, In Proceedings of 8th international AAAI conference on weblogs and social media, 2014.

26. Worst-Online-Dater (2015). Tinder Experiments II: Guys, unless you are really hot you are probably better off not wasting your time on Tinder — a quantitative socio-economic study.

 

 

Posted in mate choice, online dating, reply rates | Tagged , | 18 Comments

Comment On The Use Of Functional Magnetic Resonance Imaging (fMRI) To Evaluate The Visualization Of Sexual Stimuli

Recently I published a post replying to a user of this PUA apologist website. My comment is quite succinct and concise. Nobody there could give a convincing counterargument that would make it worthwhile another more extensive/clarifying subsequent answer. See the whole discussion over here:

 

TayF3rdLBC

Senior Member

UPDATE
Three for one, baby! Reader Will passes along another study that used MRIs to peer deep into male and female brains to discover the elemental neural processes at work when an attractive member of the opposite sex is in view.

Apologies (not too sorry) for this off-topic. Not sure if CH or anyone else has read this (probably). But it’s *science* that shows that guys are biologically wired to be *motivated* (read: boner) for visual ques (read tits and an ass) moreso than girls. This is an MRI being done on the brain that shows the amygdala is fired moreso in guys than girls when sexyness is visually seen.
This can be interpreted as how guys don’t care so much about status because the blood is rushed to our amygdala based on visual…. Not comparative social relations (such as power). Girls thus have more blood focusing on other parts of there brain such as which guy will give me higher status in terms of my social context.

Quoting the study results,

The emotion control center of the brain, the amygdala, shows significantly higher levels of activation in males viewing sexual visual stimuli than females viewing the same images, according to a Center for Behavioral Neuroscience study led by Emory University psychologists Stephan Hamann and Kim Wallen. The finding, which appears in the April edition of “Nature Neuroscience,” demonstrates how men and women process visual sexual stimuli differently, and it may explain gender variations in reproductive behavior. […]
The fMRI scans revealed significantly higher levels of activation in the amygdala, which controls emotion and motivation, in the brains of the male subjects compared to the females, despite the fact that both males and females expressed similar subjective assessments of their levels of arousal after viewing the images.
Hamann and Wallen had a separate group pre-select the images to ensure they would be equally arousing to both males and females.
“If males and females found the pictures equally arousing, you would assume they would have similar patterns of brain activation,” said Hamann. “But we discovered the male brain seems to process visual sexual cues differently.”
The scientists’ discovery also is consistent with an evolutionary theory that natural selection spurred the development of different sexual behaviors in males and females.
“There is an advantage for males in quickly recognizing and responding to receptive females through visual cues,” explains Hamann. “This allows them to maximize their mating opportunities, which increases their chances for passing on their genes.”

Another CH truth lovingly caressed by SCIENCE. And this is a humdinger of science, because it directly measured brain activation rather than indirectly through surveys or behavioral analysis.

Men are more viscerally aroused by female looks than are women by male looks. Men, therefore, can neither rely on their looks to get and keep women, nor excuse their failure with women based on their looks. Game, aka applied charisma, is about exploiting that soft space between a woman’s subjective assessment of her own arousal and her actual, primal arousal. As always, don’t listen to what women say, watch what they do. And nothing watches as closely as an MRI looking right into her friggin noggin.

 

TyrionLannister

TayF3,

False analogy/False equivalence. You try to confuse mate preference functions/mate choice with *sexual arousal*.

If you try to prove something, it would be, at best, that women have a neuronal activation to a lesser degree with respect to certain visual stimuli. Underlying therefore the hypothesis of an asymmetry / dimorphism in neuroanatomy in relation to the type of aforementioned stimuli. Which does not even imply, sub-iudice, that a lower neuronal activation is derived towards a lower phenotypic expression.

Beyond whether sexual arousal may be lower, and if sexual arousal experienced may is correlated or not with activations of neural zones in females and males, I’d rather outline ways in which one could answer the broader question of ”who” a woman choice as male partner, and “why” she behaves the way it does, and what are the characteristics in which she is making her decision. Which has nothing to do with this paper.

In any case, it could be interesting to know if there is a broad or limited neural activation differential among males chosen as mates to rejected male in an ecologically valid process. If this activation differential were narrow, we could wonder what physiological mechanisms, not encoded as detectable neuroelectric impulses, operate in this female phenotype of behavioral discrimination.

What matters is the measurement of mating choice, or mate preference functions, which is carried out if we adopt a ‘mate choice’ design, where individual preference functions are derived from their responses to sequentially presented stimuli which vary in ornament value (i.e. physical attractiveness) into an ecological valid real context.

Real context: If you could measure fMRI ,in a discreet way, when people are selecting potencial partners/dates on online dating webs or in a nightclub. Hence all of them would be an ethological valid environment. Mate choice is known to be context-dependent.

We know that women discriminate in terms of epigamic traits (facial attractiveness, body size/attractiveness, etc) and that they are more selective than men. Less tolerant to stimuli close to the population mean. There is a low average responsiveness to masculine stimuli, and a much higher differential respecting the value assigned to physical stimuli above the acceptance mate threshold regarding to those unattractive ones.

And yet this study has further serious problems even if we only want to measure *sexual arousal* and not *mate choice*. By example:

-*Lack of external and ecological validity* on fMRI studies (i.e. people lying inside an laboratory fMRI scanner while looking at pictures of naked individuals, and with whom they will not even interact), since means that the methods, materials and setting of the study must approximate the real-world that is being examined. Contexts between naturalistic settings and the fMRI scanner environment may potentially confound neuroimaging findings.

– *Extremely small samples of stimuli*, which is as detrimental to *statistical power* as the use of small samples of participants.

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Theoretical Reflections 3: Hypothetical Models Of Mate Search. Sampling and Choice Rules.

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Sexual selection depends on differential patterns of mate preference and choice. Little attention has been paid, however, to the manner in which individuals acquire information about the quality of potential mates and how this information is used to form mating decisions. Different decision rules for determining mating preferences often lead to different fitness consequences for the actively searching and choosy sex.

We can compare the expected fitness consequences of two alternative decision-making strategies: a best-of-n strategy (whereby searching individuals choose the best mate from a sample of size n) and a strategy based on sequential sampling (whereby the searching individual establishes a critical mate quality and continues searching until encountering a mate at or above this quality). For the same distribution of potential mate qualities, the sequential-search strategy generates higher expected fitness gains than the best-of-n strategy. This is in contrast to earlier conclusions (e.g., Janetos 1980; Halliday 1983) that the best-of-n is a dominant strategy for mate choice. In the models presented here, the cost of mate search is included; earlier models neglected this important aspect of mate choice, and this difference accounts for the different conclusions.

The sequential-search model establishes a critical mate-acceptance level that equates the cost of sampling one additional potential mate and the expected fitness gain from one additional search. As the cost of search increases, the critical threshold decreases. The basic sequential-search model can be extended to include time discounting, finite time horizon, systematic search, learning, variable search costs, and mate responsiveness. In each extension, new critical thresholds for mate acceptability can be established. In some cases, such as an extension to include a finite time horizon, the critical threshold is anticipated to undergo a monotonic decline as search progresses: searchers become less choosy over time if they are unsuccessful at finding mates.

The role of the arithmetic mean and variance of fitness among potential mates can be explored within these simple models. As the mean and variance of mate quality within the population increases, the critical threshold for acceptability increases. The basic model and its extensions are subject to easy empirical testing. These models will prove valuable in establishing the link between aspects of individual behavior and population-level selection processes.

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Theoretical Reflections 2: Preference Functions, Acceptance Thresholds in Mate Choice: Mate Discrimination.

Variation in preference functions. Each of the 18 preference functions depicts investment of resources in potential mates ( y axis) versus phenotypic variation in potential mates (x axis). Five different “shapes” of preference function are depicted in each of the columns. The first row is a reference example of each preference function shape – Threshold, Categorical, Linear, Stabilizing and Disruptive. Row 2 (f–i) depicts variation in the slope, row 3 (j–n) depicts variation in the horizontal position, and row 4 (o–r) depicts variation in the vertical position of each preference function. Solid lines and circles show the reference example and dashed lines and circles show variation in each preference function. Column 1 (a and j) depict a threshold preference function where the y axis is the probability of mating. All potential mates with trait values greater than the acceptance threshold are mated, whilst all potential mates with trait values less than the acceptance threshold are not mated. This preference function can only vary in the value of the acceptance threshold, that is, horizontal variation (j). There is no variation in the slope as this is a step function and no variation in vertical position as the y axis is bounded between 0 and 1. Column 2 (b, f, k, o) depicts a categorical preference function where phenotypic variation in potential mates falls into 2 distinct classes. Horizontal variation in the position of this preference function is viewed as a switch in the direction of the x axis without variation in the slope or vertical position (k). Dotted lines (f, k, and o) show connected elements of the same preference function.

 

Basic mating models adapted from zoological theory predict that decisions in mate choice may be determined either by ‘best‐of‐n’ preference functions or by sequential rules incorporating acceptance thresholds.

However, in humans, it seems that more complex determinations that incorporate versions of both protocols can be found. To understand the functions of co‐occurring protocols, I return to appeal to the scientific community for to promote the study of functions preference and mating decisions in human systems.

By example, we just know women prefer males with epigamic cues of attractiveness witch are above a certain tolerance threshold.

Regarding facial cues, females could avoid mating with a male, the attractiveness of which does not exceed a minimum gestalt value. If their preferences were absolute, they hold to this criterion even when no other men are present and regardless of body cues (size, height, somatotype, etc). Thus, mating decisions could not be simply based on amalgamated-source protocols (i.e. facial attractiveness x body cues).

If we have got plotted male attractivenes on a chart and female responses in a human population; later we could found that male traits show a median of Mt and ranged from M1 to Mx, while female acceptance thresholds for male cue showing a median of Z and ranged from Zmin to Zmax (for stabilizing functions, and without Zmax for open linear functions). The distributions of thresholds can be analyzed through skew/kurtosis.

In longitudinal studies we can find out if choosiness/thresholds hold or decline slightly with age, and if the global mate functions vary or remain stable over the lifespan. I guess both the male and female traits show significant repeatability within individuals.

The specific distribution of  acceptance thresholds would allow to know how sensory bias protocol work; skew in the distribution of thresholds; index of presence/absence of directional selection;shape of preference function.  If median threshold values will be similar or different to the male trait distribution/frequency, and the distributions of female thresholds. I figure out male trait distribution overlap very little with them. I mean, I think nearly most male phenotypes would not be acceptable to the great majority of females in the population.

Let’s see if in the future, not very distant, this type of studies begin to be implemented.

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Theoretical Reflections: Influence Of Phenotypic Experience On Plasticity In Preference Functions And Choice Thresholds.

An individual’s prior experience of sexual signals can result in variation in mate preferences?. It has important consequences for the course of sexual selection. One possibility is that the plasticity, if it occurs, would take place over the mate preference functions, and another more plausible alternative is that it modifies the acceptance thresholds. But still not even anyone has measured the functions of preference in humans.

Although in practice it would be unfeasible: two hypotheses about the evolution of experience-mediated plasticity in mate preferences would be tested: mating assurance and mismating avoidance. By example exposing females to life-time treatments that varied their experience of male attractiveness cues. Isolating them conveniently after birth and to raise them in a specially designed environment.

Treatments previous to the mate choice paradigm would consist on breeding women with (1) abundance of attractive males, matching preferred phenotypes, (2–3) men deviating either slightly above or below the average current population male phenotype, and (4) without males.

What would happen in this hypothetical scenario? I guess women experiencing preferred signals would show the greatest selectivity. However, maybe experience would not have no effect on preference fuction, which is a fixed algorithm. I don’t know if females raised on the stage (2-3) and (4) would choose unattractive males when they were presented to them.

These latets results would support the hypothesis that evolution has favored plasticity in mate preferences that ensures that mating takes place when preferred mates are rare or absent, while ensuring choice of preferred types when those are present. Another alternative is that the acceptance/choice thresholds are set in an innate way (congenital reference bio-template) and function as an absolute algorithm, and not relative comparison of morphotypes. We also have the possibility that the reference bio-template is constructed through the continuous visual experience of facial and body morphototypes that we are contemplating from birth.

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Proposed Academic Paper: Evaluation Of The Utility Of Cosmetic Surgery In Ist Effect On Facial Beauty/attractiveness Inferred Through Mating Success/outcomes.

Importance: Primary reasons why patients pursue aesthetic facial surgery are to increase their physical attractiveness; however, there is minimal literature about the effect of aesthetic facial surgery on attractiveness assessment. The procedure is usually the following: full-face/profile photographs, are taken before and after aesthetic surgery treatment, and then are randomly distributed in projector carousels (Panel perception setting) and shown to assessment panels consisting of laypeople, aesthetic surgeons orthodontists, dental students, parents of children undergoing orthodontic treatment, etc. Each judge rate the facial attractiveness of the pretreatment/posttreatment views of each patient using a visual analog scale/Likert-scale.

Objectives: Effective mate attractivility for opposite-sex individuals must be tested. Patients must be immersed on a experimental set-up of mate choice tests with photographs/video clips as stimulus in various real online contexts. This will allow to objectively and quantitatively evaluate the degree of perceived facial gestalt change and improvement in attractiveness following aesthetic facial surgical procedures. Facial beauty perception is a Gestalt process, where attractiveness perceptions are the products of complex interactions among various stimuli. Therefore facial beauty is depending on the nature of combined dimensions, and summarized the manner of information integration in visual spatial patterns. Our brains generate whole forms, particularly with respect to the visual recognition of global figures as human faces, instead of just collections of simpler traits/elements (eyes, nose, mouth, chin, jaw, etc.). Holistic processing is important in facial attractiveness judgment. Then what influence does the (surgical) modification of some facial structures have on the holistic/gestalt evaluation of the face?.

Design: Prospective evaluation on mating success of preoperative and postoperative photographs of a number of consecutive patients who underwent aesthetic facial surgery. The photographs of these patients are registered with their respective accounts within a dating website. Comparation of online dating activity parameters and quantification of mating success before and after aesthetic surgery treatment. Online dating activity allow to analyze unsolicited messages receibed, reciprocated messages and binary acceptance thresholds (mate choice interface which users make binary decisions. Likewise some methods should be implemented to determine the intrinsic quality/attractivenes for each mating pool by patient. Also computer tools could been proposed for supporting surgery planning. These tools present images of the possible effects of the surgery based on 2D images (or 3D scans), morphed with manual interfaces. As long as the 2D photos were completely realistic (do not resemble an artificial composite), could be used in the present project to evaluate the convenience / utility of incurring an aesthetic intervention.

Participants: Patient inclusion criteria consisted of primary facial surgical procedures with a minimum 6-month follow-up period, use of standardized photographs, and no cosmetic procedures in the intervening period.

Main Outcomes and Measures: Evaluative statistical treatment on mating scores after facial aesthetic surgery and detectability of mate success changes.

Results: In most studies, aesthetic facial surgery was effective in reducing the apparent age of patients but did not consistently improve their rated attractiveness. In any case, this paper enables us to detect whether partial improvements would lead to a gelstat increase in perceived facial beauty/attractiness, and if this would be reflected in an increase in sexual attractiveness in a mating context.

Posted in cosmetic surgery, Gestalt facial attractiveness, mate choice, online dating, Uncategorized | 1 Comment

“Mate Function Preferences”. Draft For Review.

1. INTRODUCTION

Over the last decade there has been a great deal of interest in variation of mate preferences for sexual attractiveness. Such variation is important as it has consequences for the rate and direction of sexual selection (Turner & Burrows 1995). Population level variation has been used extensively to examine the evolutionary history of female mate preference and its coevolution with male traits (e.g. Wilkinson et al. 1998). In contrast, variation in preference between individuals is less well studied. Yet individual preferences can provide important insights into the selective forces that shape mating decisions, since preference is predicted to be highly sensitive to both the costs of choice and the benefits derived from it (Pomiankowski 1987; Houle & Kondrashov 2002). Variation in preference among individuals can also be used to investigate the mechanisms underlying preference by measuring associations with other traits (e.g. Hingle et al. 2001a).

Most studies of female mate preference have focused on variation at the population or group level (reviewed in Jennions & Petrie 1997; Wagner 1998). However, extrapolation of such findings to the level of the individual can prove misleading, as individual preferences may differ widely in shape or form (Wagner et al. 1995; Wagner 1998). Selection may generate adaptive variation in individual preference if a female benefits from having preferences different from the population mean, for instance when the optimal strength of preference is dependent on the context of mate choice or the qualities of potential mates (Qvarnström 2001; Badyaev & Qvarnström 2002). In addition, selection can generate variance via the quality of the choser, if, for example, function preferences or choosiness are condition-dependent (Tomlinson & O’Donald 1996; Fawcett & Johnstone 2003). Despite its importance, only a few studies have successfully investigated individual preference variation (e.g. Wagner et al. 1995), while others have suffered from deficiencies in experimental design and choice of preference measure (reviewed in Wagner 1998).

Preference should be distinguished, both conceptually and empirically, from choice. Preference comprises the sensory and behavioural components that influence to mate differentially with certain phenotypes, whereas choice is the pattern of mating that is influenced not only by preference, but also other factors such as availability and the costs of choice (Jennions & Petrie 1997). This limits the utility of typical experiments that assess ‘preference’ when individuals are given a choice between simultaneously presented stimuli. This design forces to choose, which may misrepresent how individuals respond to the full range of phenotypes (Wagner et al. 1995; Wagner 1998).

If we adopt a ‘no-choice’ design, individual preference functions are derived from their responses to sequentially presented stimuli which vary in ornament value (Wagner 1998; Shackleton et al. 2005). In order to be exploited fully, no-choice tests need to assay choosers with a range of natural phenotypes. Studies that simply employ a few stimulus (typically syntetic composites and/or with extreme values of ornamentation) cannot accurately measure the strength of directional selection or detect stabilizing (e.g. Gerhardt 1991; Ritchie 1996; Hunt et al. 2005) or disruptive preference functions (e.g. Sappington & Taylor 1990; Greene et al. 2000). They also have limited power to resolve differences in the preference of individuals (Wagner 1998). It is also clear that the accuracy of preference functions will increase with the number of levels of a given ornament for which a chooser’ response is measured. However, care needs to be taken in repeated sampling of mating decisions to assess changes in receptivity and/or preference through time.

Ideal framework for studying preference are therefore those which perform easily distinguishable specific behaviours that indicate mating intent, such as exposing single people to solicitations of courtship/dating or active rejection of unwanted suitors.

2. DEFINITION OF MATE CHOICE.

Distinguishing preference and choosiness

A commonly adopted framework for describing mate choice is to distinguish preference from choosiness (e.g., Cotton et al. 2006 ; Jennions and Petrie 1997 ; Widemo and Sæther 1999 ). This demarcation is important as it differentiates innate tendencies toward specific mates (i.e., preferences – choices that would be made if cost was no object) and the actual mating bias that results depending on the amount of effort that a choosing individual is willing or able to invest in mate choice (i.e., choosiness; Cotton et al. 2006 ).

Jennions and Petrie (1997) provide a detailed description of terminology in which mate choice is defined as the pattern of mating that arises from “mating preferences.” “Mating preferences” are further divided into “preference functions” and “choosiness.” “Preference functions” are defined as the order in which an individual ranks potential mates. In contrast, “choosiness” is the amount of resources invested into choice, principally mate search effort and mate assessment effort. As mate search effort is coupled with variation in acceptance thresholds (i.e., individuals with higher thresholds will need to expend more effort searching for mates; see Acceptance thresholds above), variation in acceptance thresholds is positively correlated with “choosiness” in this definition ( Jennions and Petrie 1997 ).

In an approach echoing that of Jennions and Petrie (1997) , Cotton et al. (2006) expanded the description of preference functions to further define the “form” and “strength” of a preference function. The “form” of a preference function can take many shapes, for example, directional, stabilizing, or disruptive ( Figure 1a–e ) and is thus analogous to the ranking of potential mates. For a positive directional preference function larger trait values are ranked highest (and vice versa), for a stabilizing preference function intermediate trait values are ranked highest, and for a disruptive preference function extreme trait values are ranked highest. In addition, Cotton et al. (2006) defined the “strength” of a preference function as the rate of change, or slope, of the preference function, that is, how much higher an individual is ranked for a given phenotypic difference (e.g., Figure 1f–i ).

3. MATE CHOICE PARAMETERS:

3.1. Preference functions

Preference function: The order in which potential mates are ranked. The relationship between a phenotypic trait in potential mates ( x axis) and the reproductive resources invested in a mate ( y axis).

beheco_aru142_f0001

Hypothetical preferences of individual females. Despite substantial differences in the forms of the preferences, in all cases females would prefer trait value B to trait value A.

The concept of preference functions, and the term “preference function” have been widely and consistently used to describe patterns of mate choice (e.g., Basolo 1998 ; Gerhardt et al. 2000 ; Jennions and Petrie 1997 ; Ritchie 1996 ; Wagner 1998 ; Figure 1 ).

Preference functions have been particularly influential in the study of mate choice as the concept spans both empirical and theoretical approaches. The mathematical interpretation of preference functions has been widely used to model variation in choice, of which many examples can be traced back to the influential work of figures such as Lande (e.g., Lande 1981).

On the y axis of a preference function is a variable describing the will of invest in reproduction with each potential mate ( Bonduriansky 2001 ). On the x axis of a preference function is a phenotypic trait expressed by potential mates.

Furthermore, phenotypic variation in potential mates can often be complex in nature, and this can be depicted in multivariate preference functions (e.g., Backwell and Passmore 1996 ; Brooks et al. 2005 ; Candolin 2003

In summary, preference functions are incredibly useful for the description of choice because a wide range of preferred traits and expressions of choice can be depicted within the same framework. When viewing preference functions, a preference is shown whenever variation in liking/willingness to mate ( y axis) is dependent upon phenotypic variation in potential mates/attractiveness ( x axis).

Multivariate preference functions

We could considered only the effect of manipulating one visual character at a time. In reality, selection seldom operates on a single trait independently of other traits, and combinations of traits could have effects on individual fitness that cannot be predicted from consideration of the effect of varying a single trait in an experimental study (Lande and Arnold 1983).

Indeed, nonlinear selection analysis (Lande and Arnold 1983; Phillips and Arnold 1989) has formally shown that combinations of traits can have multiplicative effects on fitness via the action of correlational selection (e.g., Brodie 1992; Blows et al. 2003; LeBas et al. 2003).

The resulting pattern of selection operating on a suite of traits can thus be complex (e.g., Blows et al. 2003; Blais et al. 2004; McGlothlin et al. 2005) and impossible to predict from univariate analyses alone. Interestingly, correlational selection, in which two or more traits components influence attractiveness multiplicatively, has been invoked as a possible cause of directional, concave sexual selection (LeBas et al. 2003; McGlothlin et al. 2005).

3.2 Form/Shape

The shape of a preference function:

  • Threshold
  • Categorical
  • Linear
  • Stabilising
  • Disruptive
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Types of mate preference functions.

3.3. Strength

Preference strength/choosiness: Variation in the slope of a preference function—in general reference.

slope01log56

3.4. Acceptance thresholds

A step preference function in which potential mates with trait values greater than the threshold are accepted and all others are rejected.

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3.5. Responsiveness

Responsiveness (an aspect of choosiness).

Receptivity: Average response to potential mate.

responsivenesss

 

3.6. Discrimination.

Discrimination (an aspect of choosiness). Another approach to the description of “choosiness” has been to describe choosiness as an outcome of “responsiveness” and “discrimination” ( Bailey 2008 , 2011 ; Brooks 2002 ; Brooks and Endler 2001 ; Ritchie et al. 2005 ).

“Responsiveness” can be defined as a measure of motivation to mate, or the mean response of a focal individual to potential mates, and can thus be represented by a wide variety of traits, for example, courtship intensity, response latency, or association time. Variation in responsiveness can be depicted as a vertical shift in the position of the preference function ( Figure 1o–r and see also Bailey 2008 ).

In contrast, “discrimination” has been defined as the variation in response to different individuals. This can be calculated, for example, as the standard deviation of all responses ( Brooks and Endler 2001 ) or the difference between a response to the most preferred stimulus and the average response to all stimuli ( Gray and Cade 1999 ), though either method can yield similar results ( Bailey 2008 ).

 

  • Linear preference function:

F2.medium

 

  • Stabilizing preference function

Preference function components. Mate preference functions are shown for two populations or individuals (black dotted and blue solid lines). Female preference functions can be described by three aspects. Peak: Which male trait values are preferred, indicated by the trait value that elicits maximal response. Choosiness: How much deviation from the peak in male trait is tolerated, indicated by the width of the function. Responsiveness: The degree to which females respond to the trait, indicated by the height of the function. Choosiness and responsiveness combine to determine preference strength. Of note is that some ecological factors will change preference function width (strength) and others will change what’s preferred (peak). When both occur, this can result in clustering.

“Responsiveness” and “discrimination” can vary when there is no choice. Each of the 8 preference functions depicts motivation to mate ( y axis) versus phenotypic variation in potential mates ( x axis). Arrows indicate mean motivation to mate, that is “responsiveness” sensu Brooks and Endler (2001) . The distance between dashed lines indicates variation in responsiveness to mates, that is, “discrimination” sensu Brooks and Endler (2001) . Plots in the left panel illustrate how variation in responsiveness (a vs. b), discrimination (a vs. c), and both responsiveness and discrimination (a vs. d) can reflect variation in mate choice. Corresponding plots in the right panel (e, f, g, and h) illustrate analogous variation in these traits without any expression of choice.

  1. Selectivity: Variation in the response to potential mates
  2. Permissiveness: A response to a signal that is normally unattractive.

3.7. The slope of a preference function

The slope of a preference function can be defined as the difference in reproductive resources invested, including the likelihood of mating, per unit change in trait value of potential mates (e.g., Murphy and Gerhardt 2000 ). The slope of a preference function can be described for both linear and non-linear preferences through regression coefficients (e.g., Basolo 1998 ; Hunt et al. 2005 ; Murphy and Gerhardt 2000 ; Wagner et al. 1995 ; Figure 1g–i ).

a.jpg

The slope of a preference function can also be described for categorical traits as the difference in resources invested in potential mates belonging to different classes (e.g., Fisher and Rosenthal 2006 ; Qvarnström et al. 2000 ; Tinghitella et al. 2013 ; Figure 1f ).

b

There are two aspects of variation in a preference function (i.e., slope and horizontal position) each describe a distinct aspect of choice. In layman terms, the horizontal position of a preference function can be viewed as describing “what” is preferred. For find out choosiness/preference threshold subjects must make dichotomous decisions about whether an opposite-sex individual is attractive or not.

For example, the location of a peak of a stabilizing preference function is the most preferred trait value and the acceptance threshold describes the range of potential mates that will be accepted. For a categorical trait, variation in the horizontal position of a preference function is equivalent to varying the category of trait that is preferred without varying the slope or vertical position of the function (Figure 1k).

In contrast, the slope of a preference function can be viewed as describing “by how much” something is preferred, that is, by how much are preferred mate types favored relative to other mate types. Whereby subjects are asked to make judgements (i.e. scoring a likert scale) about a the same sequence of randomly varying stimuli.

These 2 pieces of information that describe mate choice echo previous approaches to the definition of mate choice that differentiated innate biases an individual might have toward certain mates from the manifestation of those biases (e.g., Cotton et al. 2006 ; Jennions and Petrie 1997 ). Consequently, the most appropriate term for referring to any variation in the horizontal position of a preference function would be “preference,” that is, which type of mate does the individual have a “preference” for. This term has previously been used in the same context when describing the location of a peak of a stabilizing preference function (e.g., Lande 1981 ; Rodriguez et al. 2013b ). “Choosiness” might then be used to refer to variation in the slope of a preference function (e.g., Ratterman et al. 2014 ). An important corollary of defining “choosiness” as the slope of a preference function is that, irrespective of taxa and the shape of a preference function, the absence of choice could universally be described as whenever “choosiness” is equal to zero. In summary, an individual could be described as exhibiting a “preference” for certain mate types (the horizontal position of a preference function), but the extent of those preferences will depend on “choosiness” (the slope of a preference function).

4. CHOICE DESIGNS

a) Simultaneous or sequential

There are simultaneous stimulus presentations. Stimuli are presented to a focal individual at the same time. Methods can vary depending upon the type of animal taxa and the investigator conducting the work. The responses of females can be scored dichotomously (who’s preferred- yes or no for each stimulus) or on a continuous scale (How desirable/attractive is it; the score rated with each stimulus).

In sequential test, each stimulus is presented individually to females. For example, we might measure responses to potential mates of different physical appearance, where all stimuli are presented sequentially. Responses to a series of sequentially presented stimuli can then be measured, and preference functions can be derived by examining the relationship between female response and stimulus trait value (e.g. Basolo 1995).

b) Choice/No-choice test.

An important way in which experiments testing mate preferences can vary is in the number of options the subject is presented with during the test, which we refer to as the “choice paradigm” or “choice design.” Tests can use either no-choice or choice designs ( Wagner 1998 ).

  • In a no-choice test each subject is presented with a single stimulus. Several no-choice trials may be performed using the same subject. these are referred to as sequential choice tests
  • In contrast, in a choice test each subject is given a choice between multiple stimuli presented simultaneously.

On a binary test, this variable would be the probability of an individual mating and finding out aceptance threholds. By the other hand, “Likert” scales are also usefully represent actual investment/willingness to invest in mating with a mate. Research must have a method of ascribing quantitative value to each indivual sampled, to make it amenable to statistical analysis,where a numerical value is assigned to each potential mate on a 1 to 10 scale ( or 1-5 scale, 1-7 scale.)

c) Population level

Population-level preference functions are the most commonly used method to analyse mate preference. Typically, a group of females within a population are tested only once for their response to a range of male attractiveness levels and a statistical model is fitted relating female response as a function of male signal. For genetic analysis this approach has limited value because variation in preference within and among females is unaccounted for. Therefore, a population-level function can in fact contain multiple female preference phenotypes.

d) Individual level

Individual preference functions measure a female’s response to an array of male attractiveness levels. Typically, a single female is repeatedly tested for her response to a randomized series of attractiveness levels and preference functions are estimated using either polynomial regression or cubic spline methods.

It is important that females are tested multiple times for their response to the same stimulus to allow variances to be calculated around mean response levels and variation among individuals to be compared.

In a experimental design for the analysis of individual preference functions for attractiveness, each individual is tested for her response to sample of stimuli with n trials conducted for each stimulus. This example is an absolute preference function, as females were not given a choice between males.

 In the example in part b, genetic variation in female preference functions. Each green line is the female preference function for an individual. Note that although many functions are overlapping, there is significant variation among genotypes within this population.

In the example in part b, genetic variation in female preference functions. Each green line is the female preference function for an individual. Note that although many functions are overlapping, there is significant variation among genotypes within this population.

5. Material and methods

a.1. Selection of stimuli:

They should be analyzed attractiveness assessments made to static images and dynamic video clips of the same persons:

  • Photographs: ratings of static images.

 

  • Video clips: ratings made to dynamic images. Which provide richer samples of appearance. Presence of much more information in the videos (multiple views, neutral and smiling expressions and speech-related movements)

– Recruitment of single participants. A great number (Ns) of male and female participants are recruited. They are photographed and videotaped, digitally processed. On a separate process independent judges (number Nr) rate the facial/body photographs and clips. Judges are asked to rate the attractiveness of Ns sample on a 1-10 numeric scale.

a.2) Another alternative would be to use a face/body database, which include standardized photographs of male and females subjets of varying ethnicity between a widre range of age. Extensive norming data are available for each individual model. These data include both physical attributes (e.g., face size) as well as subjective ratings by independent judges (e.g., attractiveness).

– Use of databases: If it is not feasible to recruit a large number of participants for our study, or the available databases do not contain such a large number of individuals, the set of stimuli may be replaced by non-standardized real-world profile pictures downloaded from the public domain. (e.g online dating webs).

– The use of profile pictures retrieved from online dating websites. Non-standardized images varying composition, face size and background cues.

a.2 Rating/sorting of stumuli.

Pictures/videos of participants are rated/sorted for attractiveness by judges recruited. Judges of both sex rate each image on a 10-point rating scale from ‘‘not at all’’ to ‘‘extremely’’ attractive.

1. Binary attractiveness rating. Probability function.

On a higher level, we seem to regard attractiveness in very binary terms(yes, or no).

A Np sample of subjects (unmated participants) interesed in meeting a potential partner, are recruited.

Study asks to make judgments about a sequence of randomly varying stimuli. It must adapt the non-choice sequence task to ask a pertinent question about mate selection, in which participants will rate each picture in a sequence, and will make also binary decisions (attractive or unattractive) in a simplified, real-world context.

It’s a design of binary task mimicking the selection interface (currently popular in online dating websites):

“Are you interested in dating this person? If you want to have a date with him/her, then tap on the “Yes” icon. You must swip left on “No” button if he/she is not attractive enough as potential partner.

When you are playing binary task, if you and another member both Like each other or slide each others photos into the ‘yes’ pile, then we’ll let both of you know. If either of you slide the photo to the ‘No’ pile, then nothing happens—if you don’t heart someone, they’re not hidden from you forever.

If you and another user like each other, we’ll let you both know by sending a notification when the experiment is finished. Moreover your contact info (i.e phone number) will be provided to each person into your mutual list.”

Participants are assayed for individual mate preference by scoring rejection or acceptance towards all oposite-sex stimuli (two males from each eyespan class). Once a response for a given stimuli had been determined, this one is removed and other subject is shown.

Participants must be assayed for individual mate preference by scoring rejection or acceptance of all the photographic stimuli. Once a response for a given picture had been determined, the picture is removed, and the next one is shown.

2) Attractiveness assessment. Preference Function.

A trial start with a picture is shown randomly from the set of opposite sex potential individuals. In an unspeeded task participants give a score on a 1-10 scale and then assest as attractive or not and the next face followed immediately.

Individual-level (left column) and population-level (right column) female preference functions for overall male attractiveness.

 

Experimental Design

In systems where species encounter mates sequentially and the costs of lost mating opportunities may be high, n-choice tests may overestimate the strength of mate selection. But the converse may also be true; in lek breeding animals (e.g humans), individuals may seldom encounter a single courter. Thus, no-choice paradigms may underestimate the strength of selection in lek breeding species.

In the case of a no-choice paradigm, the receiver likely compares the signal it receives to some internal template (although this template may not even be fixed, e.g., Taylor and Ryan 2013). In a n-choice test, the receiver may compare several signals to an internal template or it could bypass an internal template and compare the signals directly to each other.

The employment of multiple approaches within a specie can provide a richer understanding of perceptual processing and the evolution of mating signals.

We should take great care when designing studies of mate choice if our goal is to project our conclusions to natural populations or to make quantitative predictions about how mate choice translates into selection on male traits.

If either is our aim, we need to rely on field studies or experimental studies conducted under settings that closely mimic those in the wild.

In turn, if a role for mate rejection costs is demonstrated, it will yield insights into the economics of mate choice. For example, perhaps the high cost of rejecting a mate in males, where variance in mating success can be so high, accounts for the tendency of male choice to vary little between choice and no choice formats

If we can understand the impact of some set of environmental factors on the strength of choice, we have arguably gained some level of understanding of the factors that shape the evolution of choice.

Plasticity

Experience of sexual signals can alter mate preferences and influence the course of sexual selection. It is likely that patterns of experience-mediated plasticity in mate preferences that can arise in response to variation in the composition of mates in the environment.

The precise characters conferring male attractiveness is a composite trait that cannot be totally captured by simple measurements of single characters. That is to say, even if individual traits that are subject to sexual selection are heritable, this need not imply attractiveness in total is heritable and can evolve.

We don’t know if humans have been selected to adjust preference selectivity according to the variability of potential mates in their social environment, as well as to the presence/absence of preferred mates.

However it seems like a dead end since it does not seem feasible to control the life-time experience prior to the measurement/evaluation process for human subjects.

It is likely that experience-mediated plasticity in mate preferences can influence the strength of selection on male signals and can result in evolutionary dynamics between variation in preferences and signals that either promote the maintenance of variation or facilitate rapid trait fixation.

If we could control for patterns there would test hypotheses about potential sources of selection favouring experience-mediated plasticity.

Manipulated signal experience on experiment with the following treatments:

-Absence of stimuli

– Exposure to unattractive stimuli.

– Exposure to attractive stimuli.

-A mixture of attractive and unattractive stimuli.

In some cases, a female’s mate preferences may depend on experiences with conspecifics in the social environment prior to making a mating decision, providing evidence for socially cued anticipatory plasticity (SCAP) in mating preferences.

Features that comprise the attractiveness complex are intricate parts of an n-dimensional feature space. This feature space is organized such that all features point in the same direction. Attractiveness thus follows the redundant signalling hypothesis.

We do not assume innate beauty detectors; we rather propose that the brain has an innate tendency and basic rules on how to create beauty templates, which then are filled up during ontogeny. When the media raise attractiveness standards by prototyping beauty, then unreal expectations to mate quality (beauty) will emerge. If the template created is more attractive than reality, no mate selection can occur on realistic grounds, leading to a high proportion of singles.

a)‘Norm-based coding’ (Rhodes, Brennan & Carey, 1987), where averaging a large number of faces in the brain derives the norm.

b) ‘Density alone hypothesis’, where the Gestalt is a point by-point representation in a multidimensional space.

c)‘Template’ hypothesis, which suggests that the brain analyses the single parts with templates and then reintegrates them.

Assessment of facial attractiveness could depend on a genetically specified template or it could reflect general pattern-learning mechanisms. Early visual experience appears critical to the normal processing of facial configuration later in life (Le Grand

et al. 2001). Exposure to faces and prototype abstraction during early life may also have long-lasting effects on face processing including judgements of facial attractiveness. Thus, differences in the types of face an individual is exposed to may lead to subtle differences in the facial prototypes extracted, which in turn may bias attractiveness judgements towards exposed facial characteristics.

 

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