“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
hfghfth.jpg

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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How to measure human mate preferences: Introduction.

Darwin was the first to propose that female mating preferences can result in selection on male morphology and behaviour. Since then, particularly over the last two decades, many studies have confirmed that individuals prefer some opposite-sex trait variants over others and that potencial mates with preferred traits have enhanced mating success .

Sexual selection by mate choice can therefore be important in the evolution of the opposite-sex secondary sexual traits. More recently, attention has focused on the factors that influence the evolution of these preferences.

Three general experimental approaches must be adopted to study the evolution of mating preferences:

1) A comparative approach can be used to examine historical associations between phenothypic traits, opposite-sex preferences, and environmental factors. For example, statistical associations across populations between preferences and traits have been used to evaluate whether female preferences and male traits have co-evolved.

2) The benefits of an average preference of a population can be examined. For example, in populations where individuals tend to prefer one trait variant over another, the potential benefits of this preference can be evaluated by examining the fitness consequence of mating with mates with different trait values.

3) Selection on mating preferences can be measured directly by assessing the relationship between the strength of a preference and fecundity and survivorship.

For some questions about the evolution of female preferences, a comparative approach is the most appropriate method . When examining the adaptive significance of female preferences, however, historical associations between female preferences and other variables are merely suggestive; such correlations can arise because of causal relationships or because the preference and the other variable of interest are both correlated with some other variable.

In contrast, examinations of the adaptive significance of population-level preferences can help to determine whether selection currently acts on mating preferences. There are, however, at least two important drawbacks with this approach.

a) First, there may be adaptive variation in preferences; some females might benefit from having preferences different from the population average. Under such circumstances, examining the benefits of population-level preferences may lead to a misleading view of the factors influencing the evolution of preferences.

b) Second, selection on preferences is likely to be multifactorial; more than one direct source of selection is likely to act on preference functions, and indirect selection can act on preferences due to phenotypic correlations with other traits under selection. As a result, the examination of a single benefit of a population-level preference may provide an incomplete view of the factors influencing the evolution of preferences.

An alternative approach is to measure how selection acts on mating preferences within natural populations. Such studies would allow us to assess the fitness consequences of variation in preferences, the relative importance of different sources of selection, and whether correlations with other traits result in indirect selection on preferences.

For example, we could assess the relative importance of direct selection on preferences due to variation in survivorship and fecundity and indirect selection on preferences due to variation in offspring viability.

No studies have attempted to assess how selection acts on mating preference functions in humans within natural populations.

To be continue.

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Mating and sexual conflict. Part I

 

F1.large

Schematic illustration of the gender load. Because of differences in the optimal life-history trade-offs between the sexes, male (dashed line) and female (solid line) fitness functions typically differ. Whenever the evolution of SD in a trait expressed in both sexes is constrained, males (dark grey distribution) and females (light grey distribution) will be unable to reach their optimal sex-specific phenotypes. This results in a population fitness (W*) that is lower than that achieved when both sexes are free to evolve to reside on their adaptive peaks (Wmax). (Source: Göran Arnqvist, Midori Tuda, 2009)

There is currently controversy about definitions of sexual conflict, what it is, and how important it is in adaptation.

Paragon, one of the best posters on human mating/sexual selection, clarified some time ago some of its conclusions, and reviews some of the many recent developments relating to it:

“Premises: Female pessimal forms of social monogamy were a universal feature of all nascent classical civilizations. This is because all civilizations are density dependent, and thus only the most efficient mating systems are demonstratedly successful in achieving the requisite population yields.

Given that females are more sexually choosy (as well as more strategically passive), such female pessimal(where passive female choice prevails) solutions are most indicated in their efficiency. Thus, over time, evolution should favor viable female pessimal systems. Some people will point to female ‘liberation’ in contemporary occidental societies as proof that such assumptions do not hold. But given nascent trends of sub replacement fertility (where unbeholden/female choice confounds all other explanations), the of occidental populations will become increasingly evident at a rate of population momentum, and strategic displacement in frequency dependent selection.

Premises: Reproductive fitness ultimately reduces to an initial, limiting condition of female choice(passive: where outcomes in male competition are more strongly indicated, or active: where outcomes in sexual conflict are more strongly indicated). So, any solution which entails mediation of human reproduction (ex.theoretical policies in sustainable population dynamics), must necessarily weigh against options in female choice.

Premises: The lek paradox resolves through solutions in mutation-selection balance and intralocus sexual conflict, which retain constant variability in male sexual quality, thus preserving margins of selectivity even under conditions of more active female choice.

Premises: Strategic symmetry breaking(tending towards a more active form of female sexual choice – see ‘eating their cake and having it too’) has rendered conditions of artifical female scarcity (posing stochastic problems of large population replacement, evidenced in nascent indications of sub replacement fertility), analogous of local mate competition.

And just like in the case of LMC, evolution must resolve problems through their mechanism of imbalance (necessarily infringing upon prevailing options in fem sex choice).
Premises: Sufficient density in network reciprocity (correlated with large population size) will always pose problems in social prosperity, such that emergent efficiencies cultivate selfish replicator invasion vectors (which propogate through an unbounding of female choice) that break symmetry in the fitness landscape through strategic dynamics in balancing selection (like a bistable homeostatic switch), lending preponderance towards short term male fitness strategies and conditions of effective female scarcity in the more active expression of female choice.

So, any population acutely following from a more passive state of female choice to a more active state, will incur problems of stable population replacement (ie. sub replacement fertility) which must resolve through solutions which weigh against increased options in female choice.

Intervention is thus incumbant upon systematic regulation of reproductive fitness, through policies in stable population replacement (which, again, must necessarily infringe upon ‘floating’ options in female choice).

Further, any such control would need to hold globally, as any errant population would receive a competitive advantage, vectoring invasion by selfish replicators, which can then spread to all neighbouring populations (much like feminism has done). But, because such intervention requires infrastructure not yet in place, it will take time and resources to erect.

And if this cannot be accomplished before adaptive capacities are exceeded through efficiency demands in freefall replacement debt, remaining evolutionary solutions will entail the rapid unravelling of status-quo – western civilization, insofar as it is has come to exist, will very quickly lose all recognizable cohesion.

Premises: Adaptive capacity in human systems are meaningfully density dependent. It should be intuitively obvious that surplus population growth is easier to accomodate/abide than population decline, as population growth/explosion only becomes a problem where it begins to critically stress carrying capacity, while critical efficiencies in adaptive capacity are far more sensetive to declining trends in population.

Note: population explosion and decline pose potential problems which depend on the same limiting condition – female choice. Therefor any solution must necessarily weigh against that choice.

Consider:

At the global level the proportion above age 60 is likely to increase from its current level of 10 per cent to around 22 percent in 2050. This is higher than it is in western Europe today. By the end of the century it will increase to around 34 per cent, and extensive population ageing will occur in all world regions. The most extreme levels will be reached in the Pacific OECD (mostly Japan), where half of the population is likely to be age 60 and above by the end of the century, with the 80 per cent uncertainty interval reaching from 35 to 61 per cent. Even sub-Saharan Africa in 100 years is likely to be more aged than Europe today. The trend of our median proportion over age 60 is almost identical to that of the UN long-range projections2 up to 2050, but shows significantly stronger ageing thereafter. This confirms recent criticism that conventional projections tend to underestimate ageing6,7. The extent of and regional differences in the speed of population ageing “the inevitable consequence of population stabilization and decline” will pose major social and economic challenges.

– NATURE |VOL 412 | 2 AUGUST 2001 |www.nature.com
Summary:

Ascendence of Monogamy:

For much of recorded history, all large human populations were organized such that a culture of strong social monogamy prevailed attitude of female sexual choice.

The reason for this, is because in any environment where life conditions are sufficiently harsh, offspring survivability is reduced where paternal investment is minimal/erratic, thus according improved strategies in paternal investment and co-operative group care a selective advantage.

Given longer reproductive intervals acting upon selectivity, female sexual choice will naturally cluster/concentrate within small neighbourhoods of high quality males, posing stochastic fitness problems the more it is the case that *effective mate availability* deviates from an ideal 1:1 sex-ratio.

Thus, over time, evolution would expose competing human populations to selective pressures, favoring strategies which tended towards a viable1:1 ratio.

From this, a complex organization of co-operative specialty would emerge in socially monogamomous populations, where long term strategies in paternal success would efficiently maximize population yields by trading off opportunities in female choice for a more equal dispersion of effective mate availability (and an optimal utilization of male work in paternal investment).

This would be accomplished by strategies which exploit inferior female competencies, as well as burdens in reproductive liability, thus compelling them (deprived of sufficient welfare state contingencies) to trade sexual fidelity in exchange for male proxy(material benefits, etc) on behalf of themselves and their offspring.

Thus, paternity would be levered by males as an effective strategy in mate exclusivity, corresponding high selective value in reproductive fitness where initial conditions could be successfully met.

Such populations would go on to successfully outcompete rival systems of organization, and social monogamy – the precursor to civilization -was born.

http://en.wikipedia.org/wiki/Evolution_of_Monogamy

“Thierry Lodé argued that monogamy should result from conflict of interest between the sexes called sexual conflict. Organized from territory defense and mate guarding, monogamy appears as a response of male for the control of female sexuality”

Decline:

Emergent problems in unregulated social prosperity (technological advancement in social utility following from increased organizational complexities and economies of scale), cultivated selfish replicator strategies, such that they found an invasion vector through unforseen efficiencies – where they could thrive in the spontaneous symmetry breaking of the fitness landscape.

These strategies unbounded(‘liberated’) female sexual choice, breaking fitness dependencies in male long term strategies, lending to exacerbated conditions of effective female scarcity.

And this is why the most libertarian(occidental) populations – those which accord females the greatest lattitude of sexual choice – are also those becoming mired in sub-replacement fertility at a rate of evolutionary hysterisis, where popular scrutiny is confounded by migration dynamics and population momentum.

Considering the relative wealth of these populations, arguing family planning (which has existed for thousands of years) borne of some remote concern in diminishing carrying-capacity, proffers explanations with are neither indicated, nor plausible(multilevel selection in the form of overpopulation anxiety would have to be sensetive to local carrying capacities in order to operate advantageously – where evidence fails to agree).

You do not have to understand much about the subtleties of evolution to intuitively grasp that any dynamic which persists in skewing effective female availability towards a sufficiently small subset of males, must likewise incur stochastic ‘problems’ of large population replacement, perturbing stability (given that populations are ultimately density dependent on network reciprocity) through balancing selection in male fitness strategies(long term/short term).

Aftermath:

Male complicity in female ‘liberation’ was motivated primarily by blind expedience (to statisfy harranguing mates and female relations -“Here you go, now shaddap!”), and by sexual opportunism (via selfish replicators).

But, it is highly doubtful that *any* male appreciated the full
implications of the eusocial time-bomb they were planting.

A time-bomb which must ultimately resolve through a bottlenecking population, and free-fall inefficiencies (ie. as civilization crumbles) which will combine to exacerbate the work demands in paternal investment, according males with high paternal-care aptitudes a selective advantage in reproductive fitness (unlike today).

Over time, this will compel males to invest in a long-term mate (out of economic necessity), and the more entangled they become in such long term strategies, the more they will tend towards concerns of mate exclusivity/mate guarding (insuring their great ‘investments’).

The *only* effective strategies for mate exclusivity/mate guarding are female pessimal, as success relies upon a (male) consensus deterrence of female abandonment – the ball and chain will then be on the female foot (literally, if need be)!

So, the irony is that the ill-gotten gains of female liberation will not be confiscated by the nice-guy loser types who females love to mock, but by the anti-social males they most sexually covet – once this happens though, it will *radically* begin to change the evolutionary context (fitness landscape) of what it *means* to be a high quality male(ie. a primacy of physical attractiveness will not longer hold, much to the chagrin of females everywhere).

Monogamy will again hold strategically proponderant such that life conditions burden females with significantly greater liability, thus compelling them(deprived of sufficient welfare state contingencies) to trade sexual fidelity in exchange for male proxy benefits on behalf of themselves and their offspring.

Thus, paternity will be levered by males as an effective strategy in mate exclusivity, corresponding high selective value in reproductive fitness where initial conditions can be successfully met.

Populations will explode, and round-and-round we go(only this time, as in every iteration, female pessimal strategies should be better adapted to resisting invasion by selfish replicators, and thus conditions of ‘liberated’ female choice, through a stricter *regulation* of reproduction).

Some people appeal to ‘unforseen technological efficiencies’ as the savior to their sexual dystopia.

But, as the frequency dependent dynamics of best response strategies (male short term free-riders/selfish replicators) combine with the density dependent dynamics of sub replacement fertility (for which there exist only female pessimal solutions), the efficiency requirements of status quo will begin to increase geometrically, beyond critical thresholds of stability.

The great female optima experiment will crash and burn spectacularly. Some people claim that fragmented societies will simply revert back to smaller communal societies of joint-care in offspring – and that may indeed happen in isolated pockets.

But, such communities will be at a selective disadvantage for the same they were thousands of years ago (and for the same reasons occidental populations are in the nascent stages of depopulation as we speak), and evolution will once again, inevitably, favor the success of social monogamy (cue female angust). (Paragon 2009).

Therefore sexual conflict is a conflict between the evolutionary interests of individuals of the two sexes. A ‘conflict of evolutionary interests’ is equivalent to a potential to generate sexually antagonistic selection.

“The expression ‘sexual conflict’ encapsulates the capacity of individuals of one sex to inflict damage on individuals of the other sex. A conflict between the evolutionary interests of individuals of the two sexes’—and ongoing use by evolutionary biologists, this damage is in terms of genetic fitness, so that all instances of sexual conflict are by definition underlain by sexually antagonistic selection. (Lessells 2006).”

This may or may not result in overt behavioural conflict between males and females, depending on the form of the conflict and on how the evolutionary conflict is resolved. In terms of what we actually observe, it is theoretically possible either for one sex to win and the other to lose, or for some intermediate compromise.

Sexual conflict results ultimately from the fact that reproductive partners are genetically different; a mutation in one partner will not be present in the other, unless they are sibs in which case the probability of sharing the mutant allele is still below 1.0. Owing to their different genetic interests, for a given trait the two sexes may have different optima (yielding highest fitness prospects). Having different optima for certain character traits need not involve a conflict of interest between the sexes, provided the two optima can be achieved simultaneously (e.g. by sex limitation).

For instance, sexual selection often operates to increase male size relative to female size. There is no obvious conflict of interest between the sexes, provided that the two optima can be achieved simultaneously, because the fitness of one partner is independent of the strategy played by the other partner. Conflict requires some interaction or common activity between males and female (such as mating or parental investment (PI)) which generates the constraint that the ideal optima for each sex cannot be achieved simultaneously (e.g. only one outcome is possible). So, an individual’s fitness is both a function of its own strategy and its partner’s strategy.

The mean fitness of each sex must be equal in sexually reproducing species with a sex ratio of 1.0. Nevertheless, an individual with a mutant trait that increases its direct fitness in an interaction involving sexual conflict will, by definition, decrease the fitness of an individual of the opposite sex with which it interacts. If the trait spreads, counter selection may generate retaliatory changes in the other sex.

Sexual selection is a selective force defined by Darwin arising from competition between members of one sex for the other sex. Sexual conflict is not equivalent to sexual selection, it is a form of evolutionary conflict that may, or may not, be generated by sexual selection.

For instance, male–male competition may lead to suites of male adaptations (e.g. relating to mate-searching) that have no influence on female fitness. Like parent–offspring conflict or sib conflict, sexual conflict is a potential for generating selective processes, not the selective process itself. The selective pressures it generates may become part of, or modify, the action of sexual selection. Thus, sexual conflict is not equivalent to ‘sexually antagonistic coevolution’, though this may be a product of it.

This distinction is important: we first need to define over what parameter space conflict can occur (i.e. to define the *battleground*), and to distinguish this clearly from the question of how conflict may be resolved. Confusion can often arise from failure to distinguish between *battleground* and *resolution* models. Resolution models typically require many assumptions about strategic possibilities and trade offs, and typically generate a rich diversity of results. Battleground models typically make few assumptions about individual strategies, and serve to show over what parameter space conflict can occur.

Sexual selection arises ultimately from anisogamy, and a primitive form of sexual conflict may have occurred during the early evolution of anisogamy, such that early ova (proto-ova) might profit by fusing with other proto-ova rather than with proto-sperm. The intensity of sexual selection relates to relative PI, operational sex ratio (OSR) or potential rates of reproduction.

To be continued

 

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Plenty Of Fish Experiment: Study 4. Females Profiles.

The methodology and statistical variables are the same performed in Studies 1, 2 and 3 for male dummies, although here only are going to be collected the reply rates at first message. Geographical location is not changed.

I will not be able to assess the number of matches (number of people who showed interest in this profile) on the “meet me” section, since the display does not show the cumulative number when more than 99 match votes are reached. All female profiles sampled reached more than 99 votes.

Localitation: big-sized city (Spain).

Data-collection period: 7- days.

Contacts initiated: Number of males I’ve contacted through each dummy female profile.

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

Age range: 18-35 years old.

City or Postal Code / Miles: 100 miles

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

Therefore male targets were only mesomorphs men with athletic body. Within the subset of “athletic” male profiles, I chose as target those profiles that contained pictures from men that could be included within the upper half of facial attractiveness spectrum. This is, from average or medium-attractiveness until highly-attractiveness.

Dummy profiles:

This fourth experiment is composed of 3 female profiles ( age range= 20-22 years old). One of them highly attractive (female Z, r> 8 ), one moderately attractive girl (female C, r= 6.82), and one medium-attractive girl ( female N, r≈ 5). It has not been inserted any female dummy  into the below-medium attractiveness spectrum.

 

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

  • Female Z:
FotorCreated

Figure 1. Photos of female Z.

 

Headboard

Figure 2. Headboard of female Z profile .

 

b) Moderately-high attractiveness dummy profile (r=6,82):

  • Female C:
Female C

Figure 3. Photos of female C.

Figure 4. Headboard of female C profile.

Figure 4. Headboard of female C profile.

c) Medium-attractiveness dummy profiles (r≈ 5):

 

  • Female N:
34679127280

Figure 5. Photos of female N.

hy

Figure 6. Headboard of female N.

 

 

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

 

  • Screenshots:
INBOX

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

Figure 8. Screenshot showing Contact History: Contacts Received.

Figure 8. Screenshot showing Contact History: Contacts Received. The screenshot after this period displays 443 male contacts, but this account was contacted by 447 men, since 4 male contacts quit within this 7- days period.

mensajes enviados

Figure 9. Screenshot showing Contacts Initiated. The number of males contacted were 100.

 

  • Parameters compiled:

Mail inbox = 547

Meet me = +99

Contacts inicitated = 100.

Contacts received = 443 (she was contacted by 447 men, but 4 male contacts quit within the 7- days period)

Reply proportion =100 out of 100 sent messages.

Reply rate = 100%.

b) Moderately-high attractiveness dummy profile (r=6,82):

  • Screenshots:
ddd

Figure 10. Screenshot displaying Mail Inbox after the 7-days period; Mail Inbox. “Meet me” section; Number of visitors.

enviados

Figure 11. Screenshot showing Contact History: Contacts initiated. The number of males contacted were 100. Note that the screenshot after the 7 day period shows 95 users, because 5 of them has removed their accounts.

recibidos

Figure 12. Screenshot showing Contact History: Contacts Received.

 

  • Parameters compiled:

Mail inbox = 545

Meet me = +99

Contacts inicitated = 100.

Contacts received = 450.

Reply proportion =95 out of 100 sent messages.

Reply rate = 95%

c) Medium-attractiveness dummy profiles (r≈ 5):

  • Screeshots:

    recibidos 204

    Figure 15. Screenshot showing Contact History: Contacts Received.Parameters compiled:

  • Parameters compiled:

Mail inbox = 247.

Meet me = +99

Contacts inicitated = 60.

Contacts received = 204.

Reply proportion =43 out of 60 sent messages.

Reply rate = 71.6%

TABLE OF RESULTS:

table

Figure 16. Table of results.

Contacts Received

• The highly-attractive girl and the moderately attractive girl got practically the same amount of messages (447 and 450 messages respectively). Not only there is no male bias towards a greater preference for the best attractive girl, but even the moderately attractive girl earns a slightly higher output, although not significant. The most attractive girl and also the moderately attractive girl earned (M=448.5 messages) 2.2 times more messages than the medium attractive profile (M=204).

• The most attractive girl and the moderately attractive girl scored (M=448.5 messages) 9.3 times more messages than the most attractive men (M=48.2). And the moderately attractive girl (M= 450 messages) earned 180 times more messages than the moderately attractive men (M=1.6 messages). The medium attractive female profile (M=204 messages) got 127.5 times more messages than the medium attractive males (M=1.6 messages).

Reply rate at first message:

 
• The best attractive girl is reciprocated by the 100% of men. The moderately-attractive girl reciprocated by the 95% of males contacted. Girl C get al most the same reply rate than the most appealing girl. The medium-attractive girl is reciprocated by the 71.6% of men contacted.

• If we compared these results with male profiles, we see that the most attractive guys had a response rate of 51.7%, the moderately attractive males got a rate of 12%, and the medium attrative profiles had a reply rate of 3.48%.

 

This study do not examine the extent of mating succes for a below-medium attractiveness female profile. It would be interesting to find out the outcomes for a larger set of female profiles and including some unattractive girl. Moreover it has not carried out to determine the rate response to second messages. Obtaining these data would allow us to compare them with those available to male profiles. Thus further research would be needed for a more complete perspective.

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Sexual Selection For Female Mate Choice.

Several mechanisms have been put forward to explain mate choice:

1) Direct phenotypic effects

Female preference for a male attractiveness can evolve as a result of direct phenotypic benefits if the morphology reflects the ability of the male to provide material advantages, such as a high-quality territory, nutrition, parental care or protection.

There is considerable empirical support for this mechanism on some animal taxa. Female choice might also evolve as a result of resistance to direct costs imposed by males.

2) Sensory bias.

Female preference favouring a male ornament can initially evolve under natural selection for other reasons. Males evolving traits that exploit this bias then become favoured by mate choice. There is increasing phenotypic evidence that some male traits initially evolved through female sensory biases, but the evolution of female sensory bias itself requiresmore testing.

Image4

Figure 1. Sensory bias model. Female preference should evolve first, folowed by the evolution of male traits.

3) Fisherian sexy sons

If there are genetic components to variance in female preference and male trait, a female choosing a male with a sexy trait bears daughters and sons that can both carry alleles for a sexy trait, and for the preference for it. This genetic coupling might lead to self-reinforcing coevolution between trait and preference.

Direct critical testing of this mechanism is difficult, but molecular genetics offers new possibilities (see main text).

slide_29

Figure 2. Fisher’s Runaway process. If females exhibit preference for a male trait and selection does not act on females, then their sons and daughters will carry genes for both the preference and the trait. This creates a genetic correlation between the preference and trait. And leads to geometric increase until further increase in the male trait is opposed by natural selection.

4) Indicator mechanisms (‘good genes’ or ‘handicap mechanisms’)

It suggest that attractive male traits reflect broad genetic quality. Inherent in such mechanisms is the maintenance of genetic variation, the ‘paradox of the lek’, and parasite- and pathogen-mediated mechanisms have been suggested as potential solutions. In addition, other advantageous genes and relative freedom from deleterious mutations might lead to high male condition and expression of sex traits .

Female preference for such traits can provide genetic benefits to those of her offspring that inherit favourable alleles from their father.  The resolution of the lek paradox remains a crucial area for sexual selection research.

img017

Figure 3. Good genes models require a mechanism for maintaining heritable variation in offspring viability: Recurrent deleterious mutations, and parasite-host coevolution maintains parasite resistance. Handicap models refer to male traits that can only be displayed by males in good condition. These can be “honest” indicators of male condition.

5) Genetic compatibility mechanisms.

As well as additive genetic benefits reflected by indicator traits, there might be non-additive benefits from choosing a mate with alleles that complement the genome of the chooser. Examples have been found for instance in major histocompatibility complex genes, and compatibility advantages might be one adaptive reason for multiple mating by females.

Conclusions

The evolution of mate choice is based either on direct selection of a preference that gives a fitness advantage (mechanisms 1–2) or on indirect selection of a preference as it becomes genetically correlated with directly selected traits (mechanisms 3-4)

In addition, rather than favouring any particular display trait, mate choice might evolve because it conveys non-additive genetic benefits (mechanism 5).

These mechanisms are mutually compatible and can occur together, rendering the evolution of mating preferences a multiple-causation problem, and calling for estimation of the relative roles of individual mechanisms. Several diagnostic differences among the mechanisms suggest ways in which they can be tested by quantitative genetic analyses.

 

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Plenty of Fish Experiment: Study 3

Method:

This article aims to enlarge the two previous analytical studies (See study 1 and study 2) and increase our database. Therefore upon completion of this third study of male profiles, we will count with a more proper data base of assessing different issues and to discuss the broader issue of an objective assessment of mate choice in an online dating framework.

The methodology and statistical variables are the same performed in Study 2 . Only the geographical location is changed. 

Localitation: big-sized city C.

Data-collection period: 7- days.

Dummy profiles:

This third study is composed of 6 male profiles, 1 of them moderately attractive (male C (r=6.34), 3 medium attractive ( male D, r=5.50; male B, r=5.18; male E=4.66; male A, r=5.36)  and 1 below- average attractive (male W, r<4).

 

a) Moderately-high attractiveness dummy profile:

  • Male C
Male partner C

Figure 1. Photos of male C

Figure 2. Headboard of male C profile.

Figure 2. Headboard of male C profile.

 

a) Medium-attractiveness dummy profile:

  • Male D.
Male partner D

Figure 3. Photos of male D.

b

Figure 4. Headboard of male D profile.

 

  • Male B
FotorCreated9

Figure 5. Photos of male B.

bperfil

Figure 6. Headboard of male B.

  • Male E
Male partner E

Figure 7. Photos of male E.

perfil

Figure 8. Headboard of male E profile.

  • Male A
Male partner A

Figure 9. Photos of male A.

 

aperfil

Figure 10. Headboard of male A profile.

 

c) Below-medium attractiveness dummy profile:

  • Male W
FotorCreated

Figure 11. Photos of male W.

fperfil

Figure 12. Headboard of male W.

 

 

Results:

a) Moderately-high attractiveness dummy profile:

  • Male C

1) First screenshot (third day):

sdfsdf

Figure 13. Screenshot displaying Mail Inbox at 3rd day; “Meet me” section: Number of people who wants to meet this user; Number of visitors.

1a

Figure 14. Screenshot displaying Contact History: Contacts Inicitated. The number of contacts inicitad is 150. Note that the screenshot shows 149 users, because 1 of them has removed her account.

2b

Figure 15. Screenshot showing Contact History: Contacts Received.

Mail inbox = 9

Meet me = 5

Contacts iniciated = 150

Contacts received = 2

Reply proportion at first message =  7 out of 150.

Reply rate at first message = 4.7 %

2) Second screenshot (seventh day):

2mensaje

Figure 16. Screenshot displaying Mail Inbox at 7th day; “Meet me” section: Number of people who wants to meet this user; Number of visitors.

Sin título

Figure 17. Screenshot displaying Contact History: Contacts Inicitated. The number of people contacted with this dummy were 150. Note that the screenshot shows 146 users, because 4 of them have removed their accounts.

2a

Figure 18. Screenshot showing Contact History: Contacts Received.

 

Mail inbox = 4

Meet me = 6

Total contacts received = 2

New contact received = 0

Reply proportion at second message = 4 out of 150.

Reply rate at second message = 2.7 %

 

b) Medium attractiveness dummy profile:

  • Male D.

 

1) First screenshot (third day):

a

Figure 19. Screenshot displaying Mail Inbox at 3rd day; “Meet me” section: Number of people who wants to meet this user; Number of visitors.

150

Figure 20. Contact History: Contacts Inicitated

respuestas

Figure 21. Screenshot showing Contact History: Contacts Received.

Mail inbox = 12

Meet me = 13

Contacts initiated = 150

Contacts received = 5

Reply proportion at first message =7 out of 150.

Reply rate at first message = 4.7 %

2) Second screenshot (seventh day):

panel completo

Figure 22. Mail Inbox at 7th day; “Meet me” section; Number of visitors.

2mensaje

Figure 23. Screenshot showing Contact History: Contacts Received. Note that 1 female users has removed her account.

147

Figure 24. Screenshot showing Contact History: Contacts Initiated. The number of people contacted with this dummy were 150. Note that the screenshot shows 146 users, because 4 of them have removed their accounts.

Mail inbox = 3

Meet me = 15

Total contacts received = 5

New contact received = 0

Reply proportion at second message =3 out of 150.

Reply rate at second message = 2 %

  • Male B

1) First screenshot (third day):

b3

Figure 25. Mail Inbox at 3rd day; “Meet me” section; Number of visitors.

B1

Figure 26. Screenshot showing Contact History: Contacts Inicitated.

b2

Figure 27. Screenshot showing Contact History: Contacts Received.

Mail inbox = 2

Meet me = 0

Contacts initiated = 100

Contacts received = 0

Reply proportion at first message =2 out of 100.

Reply rate at first message = 2 %

2) Second screenshot (seventh day):

panel2

Figure 28. Mail Inbox at 7th day; “Meet me” section; Number of visitors.

b5

Figure 29. Screenshot showing Contact History: Contacts Inicitated. The number of people contacted for this dummy were 100. Note that the screenshot shows 98 users, because 2 of them have removed their accounts.

b4

Figure 30. Screenshot showing Contact History: Contacts Received.

Mail inbox = 3

Meet me = 0

Total contacts received = 1

New contact received = 1

Reply proportion at second message =2 out of 100.

Reply rate at second message = 2 %

  • Male E

1) First screenshot (third day):

a

Figure 31. Mail Inbox at 3rd day; “Meet me” section; Number of visitors.

b

Figure 32. Screenshot showing Contact History: Contacts Inicitated. The number of people contacted with this dummy were 100. Note that the screenshot shows 99 users, because 1 of them have removed her account.

c

Figure 33. Screenshot showing Contact History: Contacts Received.

Mail inbox = 3

Meet me = 2

Contacts initiated = 100

Contacts received = 0

Reply proportion at first message =3 out of 100.

Reply rate at first message = 3 %

2) Second screenshot (7th day):

2a

Figure 34. Mail Inbox at 7th day; “Meet me” section (Note that the screenshot shows now 1 user wants to meet this person, because 1 of them have removed her account); Number of visitors.

2c

Figure 35. Screenshot showing Contact History: Contacts Inicitated. The number of people contacted with this dummy were 100. Note that the screenshot shows 98 users, because 2 of them have removed their accounts.

2b

Figure 36. Screenshot showing Contact History: Contacts Received.

Mail inbox = 0

Meet me = 2

Total contacts received = 0

New contact received = 0

Reply proportion at second message =0 out of 100.

Reply rate at second message = 0 %

  • Male A

1) First screenshot (third day):

a2

Figure 37. Mail Inbox at 3rd day; “Meet me” section; Number of visitors.

a3

Figure 38. Screenshot showing Contact History: Contacts Inicitated. The number of people contacted with this dummy were 100.

a4

Figure 39. Screenshot showing Contact History: Contacts Received.

Mail inbox = 3

Meet me = 1

Contacts initiated = 100

Contacts received = 0

Reply proportion at first message =3 out of 100.

Reply rate at first message = 3 %

*Note: This dummy account was deleted by the system after I did the first screenshot (for reasons unknown to me), so I could not collect further data.

c) Below-medium attractiveness dummy profile:

  • Male W

1) First screenshot (third day):

fpanel

Figure 40. Mail Inbox at 3rd day; “Meet me” section; Number of visitors.

f1

Figure 41. Screenshot showing Contact History: Contacts Inicitated. The number of people contacted with this dummy were 100.

f2

Figure 42. Screenshot showing Contact History: Contacts Received.

Mail inbox = 2

Meet me = 0

Contacts initiated = 100

Contacts received = 0

Reply proportion at first message =2 out of 100.

Reply rate at first message = 2 %

2) Second screenshot (7th day):

fpanel2

Figure 43. Mail Inbox at 7th day; “Meet me” section; Number of visitors.

f3

Figure 44. Screenshot showing Contact History: Contacts Inicitated.

f4

Figure 45. Screenshot showing Contact History: Contacts Received.

Mail inbox = 1

Meet me = 0

Total contacts received = 0

New contact received = 0

Reply proportion at second message =1 out of 100.

Reply rate at second message = 1 %

 

Table of results (study 3):

Estudio3

 

Overall Results Table for males profiles:

ddgr

 

Discussion

 

Dating pool in “Meet me”:

 
The highly-attractive men has (M=50.75) women interested (potential matches in “meet me” section) 6 times more women interested than the moderately-attractive males ( M= 8 women), more than 11 women interested than the medium-attractive males (M=4,4). The below-medium male did not get any like in meet me feature.

 
• Contacts received:

 
The most attractive men have (M=42.8) 17 times more messages in a week than the moderately-attractive men (M=2.5), and 27 times more messages than the average-attractive males (M=1.6). The below-medium guy did not get any contact received. The two best looking men monopolized 91.3 % of the contact received.

 
•Reply rate at second message:

 
The best attractive guys are reciprocated on average by 1 out of 2,3 women. The moderately-attractive group are reciprocated by around 1 out of 35 women. The medium-attractive and below-attractive groups get the same reply rate at second message, and they are reciprocated by 1 out of 100 women.

 

Posted in matches, mate choice, Mutual Match, online dating, reply rates, Uncategorized | Tagged | 17 Comments

Plenty Of Fish Experiment: Study 2

Introduction

For studing reciprocation, some dating sites such as OkCupid (see OkTrends Blog) or AYI (see Businessinsider) offer us reply rates at the first message, that is, percent the users that respond to an initial message. Furtherore when most academic papers are analyzing what characteristics are associated with contacting and being contacted by others or with replying to another’s initial message, they work generally with contacts replied at the first message too. So one tends to make the mistake of confusing response rates as first message as mate acceptance rates, which it’s wrong. Mate acceptance is not equal to rates of initial contact in which one sent a message to the other and got just a response.

Obviously most recipients refuse to send a response to those senders who don’t meet their physical requirements, so they ignore those they don’t find worthy. Say that you get an e-mail from someone, and you can tell immediately that you have no interest in communicating with that person.

So most people don’t reply at all, ever. Just delete the message. In Internet-speak, this tactic is completely understood to mean “Not interested at all, ever.”

But other users send a first short response to undesirable sender due to a protocol in terms of courtesy.

Others send a short reply saying, “Thanks for writing, but I’m not interested.” Then they delete the person’s message. If the person continues to write, they don’t answer ( if the person persists, use the blocking feature on their message system for example).

By the other hand, some people may even extend the messaging exchange with an unattractive prospect, mainly users who usually receive few or no messages from their potentially preferred partners.

But when an inicial user contacted realizes that the prospect just isn’t a match, the frequent action to stop of keeping sending more messages. Other action is to send a last message to say: “I need to stop now. I’ve enjoyed chatting with you, but I don’t think we’re a match. I don’t want to waste any more of your time. Best of luck in your search.”

So how can we distinguish real mate acceptance of reply rates at first message? or even reply rates at several messages?. Hitsch et al. (2010) explain a reliable way to measure mate acceptance rates, or as they define, matches:

[…Since we can track only the users’ online behavior, we do not know whether two partners ever met offline or eventually got married. However, our data do allow us to observe whether users exchange a phone number or e-mail address, or whether an e-mail contains certain keywords or phrases such as “get together” or “let’s meet.” We therefore have some indirect information on whether the online meeting resulted in an initial match, i.e., a date between the users. We therefore define a match as a situation where both mates exchange such contact information (i.e., for a match it is not enough for a man to offer his phone number; we also require that the woman respond by sending her contact information)…]

In other words, we can define as match or mutual acceptance when users are sharing personal information (emails, phone numbers, social networking, Skype ID, etc) with the purpose of set up an date. They further argues that:

[…For some users we observe multiple matches. We use each of these as a separate event when we describe the sorting patterns below. Matches are very different events from first-contact decisions: Table 4 shows that of all first contact e-mails sent by men, 4.3 percent lead to an eventual match…].

In other words,  match or mutual acceptance is definied if two users are sharing personal information (emails, phone numbers, social networking, Skype ID, etc) with the purpose of set up an date. They also observed a statistical pattern for all exchanges that could be defined as a match. The mean number of exchanges required until the relationship resulted in an offline date were six messages:

[…Typically, a match is achieved only after multiple rounds of e-mail exchanges: the median and mean numbers of e-mails sent back and forth before a match is realized for conversation initiated by men are 6 and 11.6, respectively…]

d

Source: “Matching and Sorting in Online Dating” ( Hitsch et al, 2010).

Thus, Hirsch et al. are providing an interesting correlation to studying those databases where researchers can not access into the contents of the messages exchanged. This makes it possible to obtain reliable measures of acceptance rates in online dating experiment. Kreager et al (2014) allude to these correlations in their own online dating study:

 

[…As the number of reciprocated responses increased, the percentage of messages in each category declined, so that only 3% of men’s, and 7% of women’s, sent messages resulted in more than five exchanges. The last category captured the mean number of exchanges (six) required until the relationship resulted in an offline date by Hitsch et al. (2010a). Obviously, the likelihood of any given message resulting in a reciprocated exchange and eventual date is extremely small..].

 

Method:

In the second study I’m going to measure response rates at the second message. It would be too laborious perform a message exchange for each pairwise to determine whether each initial recipient is willing to provide to each sender her phone number and set a date. I assume this estimate is quite interesting, and would give us a response rates data
fairly close to real rates of mate acceptance.

The dummy profiles will be introduced on the same mating system, the dating site “Plenty of Fish”, over the same 1-week period. They were placed into the other geographical place, a big-sized city named B.

MESSAGING STATISTIC:

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

Contacts initiated:  Number of indivuduals I’ve contacted through each dummy profile.

These messaged users are not randomly chosen. I chose a sample of 100 female users for each dummy through advanced search. They were to meet the following parameters:

Age range: 18-35 years old.

City or Postal Code / Miles: 100 miles

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

Within the subset of browsed female profiles, I chose as target those profiles that contained pictures from women that could be included within the upper half of facial attractiveness spectrum. This is, since avarage or medium-attractiveness until highly-attractiveness ( 5 to 10 oints on a 1-10 scale).

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

Reply proportion at first message: Proportion of initial contacts from this person to which others replied one.

Reply proportion at second message: Proportion of initial contacts from this person to which others replied twice.

Localitation: big-sized city B.

Data-collection period: 7- days.

Dummy profiles:

This second study is composed of 4 male profiles, 2 of them highly attractive (males Z and Y; r(>8), 2 moderately attractive: male F (r= 6.93), male C (r=6.34).

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

I assigned higher occupational and educational levels to less physically attractive profiles. The highly attractive profiles were assigned with blue-collar occupations, while the rest of the profiles were configured with white-collar professions (see study 1).

Male Z:  Fitness&Kickboxing trainer; Male Y: Nightclub Promoter; Male F: Architect; Male C: Mathematical.

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

  • Male Z :
Highly attractive male Z

Figure 1. Photos of male Z

Male Z Profile

Figure 2. Headboard of Male Z Profile

  • Male Y:
Highly attractive male Y

Figure 3. Photos of male Y

 

Male Y Profile

Figure 4. Headboard of Male Y Profile

 

b) Moderately-high attractiveness dummy profile:

  • Male F
Male partner F

Figure 5. Photos of male F

Male F Profile

Figure 6. Headboard of Male F Profile

  • Male C
Male partner C

Figure 7. Photos of male C

perfil

Figure 8. Headboard of Male C profile.

 

Results:

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

  • Male Z :

1) First screenshot (third day):

100 enviados al comienzo

Figure 9. Screenshot showing Contact History: Contacts initiated.

22 contactos iniciales

Figure 10. Screenshot showing Contact History: Contacts received.

Mail inbox = 65

Meet me = 23

Contacts iniciated = 100

Contacts received = 22

Reply proportion at first message =  65-22 = 43 out of 150.

Reply rate at first message = 43 %

2) Second screenshot (sixth day):

final mensajes enviados

Figure 11. Screenshot showing Contact History: Contacts initiated. The number of females contacted were 100. Note that the screenshot after the 7 day period shows 99 users, because 1 of them has removed her account.

final contactos d

Figure 12. Screenshot showing Contact History :Contacts Received.

Mail inbox = 62 (9 of them were first responses. So 9 female users responded to the first contact but after the first screenshot). I won’t send a second message to these delayed 9 users, since the possible answers will be out of data collection period of 7 days.

Meet me = 36

Total contacts received = 32

New contact received = 10

Reply proportion at first message revised =  43 +9 = 52 out of 100.

Reply rate at first message = 52 %

Reply proportion at second message = 43 out of 100. (*) The data is unreliable because 9 users who responded to the first message did too late and I could not record their behavior for a second message.

Reply rate = 43% (*)

3) Contats received and Meet me at seventh day:

final contactos 42

Figure 13. Screenshot displaying Contact History: Final Contacts Received

Meet me at 7 day.

Figure 14. Screenshot disyplaing Meet me at 7 day.

Meet me = 56.

Total contacts received = 42.

 

Male Y :

1) First screenshot (third day):

20 contactos

Figure 15. Screenshot showing Contact History: Contacts Received:

69 y 16 meet

Figure 16. Screenshot showing Contact History: Contacts Inicitated. The number of Contacts Initiated was 100 (I made a wrong screen capture).

Mail inbox = 69

Meet me = 16

Contacts initiated = 100

Contacts received = 20

Reply proportion at first message =49 out of 100.

Reply rate at first message = 49 %

2) Second screenshot (sixth day):

98 mensajes enviados finales

Figure 17. Screenshot showing Contacts Initiated. The number of females contacted were 100. Note that the screenshot after the 6 day period shows 98 users, because 2 of them have removed their accounts.

23 contactos final

Figure 18. Screenshot displaying Contact History: Contacts Received.

Mail inbox = 45

Meet me = 28

Total contacts received = 23

New contact received = 3

Reply proportion at second message = 45-3 = 42 out of 100.

Reply rate at second message = 42 %

3) Contats received and Meet me at seventh day:

f2

Figure 19. Screenshot displaying Contact History: Contacts Received at 7 day.

f

Figure 20. Screenshot displaying “Users who want to meet you”.

Meet me = 45

Contact received = 45

b) Moderately-high attractiveness dummy profile:

  • Male F

1) First screenshot (third day):

18 mensajes

Figure 21. Screenshot showing Contact History: Contacts Inicitated.

contactos iniciales

Figure 22. Contact History: Contacts Received.

Mail inbox = 18

Meet me = 8

Contacts initiated = 100

Contacts received = 3

Reply proportion at first message =15 out of 100.

Reply rate at first message = 15 %

2) Second screenshot (sixth day):

100 mensajes finales

Figure 23. Screenshot showing Contact History: Contacts Initiated. The number of females contacted were 100. Note that the screenshot after the 6 day period shows 99 users, because 1 of them have removed her account.

Contacts recieved

Figure 24. Contact History: Contacts Recieved

Mail inbox = 5

Meet me = 10

Total contacts received = 4

New contact received = 1

Reply proportion at second message = 5-1 = 4 out of 100.

Reply rate at second message = 4 %

3) Contats received and Meet me at seventh day:

There were no changes,

Meet me=10

Total contacts received = 4

 

  • Male C

1) First screenshot (third day):

1b

Figure 25. Contact History: Contacts Received

1

Figure 26. Contact History: Contacts Inicitated

Mail inbox = 14

Meet me = 2

Contacts initiated = 100

Contacts received = 3

Reply proportion at first message =11 out of 100.

Reply rate at first message = 11 %

2) Second screenshot (sixth day):

2 a

Figure 27. Contact History: Contacts Inicitated

2b

Figure 28. Screenshot showing Contact History: Contacts Received. Note that the screenshot after the 6 day period displays 1 users, because 2 of the inicital contacts have removed their accounts.

 

Mail inbox = 1

Meet me = 3

Total contacts received = 3

New contact received = 0

Reply proportion at second message =1 out of 100.

Reply rate at second message = 1 %

3) Contats received and Meet me at seventh day:


f

Figure 29. Screenshot showing final number of Likes in “Meet Me” at 7 day.

Meet me = 4

There were no changes in contacts received.

 

Table of results:

 

dd

(*)= Reply proportion at second message = 43 out of 100. This data is unreliable because 9 users who responded to the first message did too late and I could not record their behavior for a second message.

 

Dating pool in meet me:

The highly-attractive men has as mean 50.5 women interested (potential matches in “meet me” section) per week, 7 times more women interested than the moderately-attractive males ( mean: 7 women).

Contacts received:

The four accounts combined received 97 unsolicited messages, and two best looking men monopolized 99.6 % of these contacts.

Reply rate at second message:

The mean response rate for the two most attractive men is of 45.5%, quite higher than the response rate for moderately-attractive profile (2.5%). Or in other words, best attractive guys are reciprocated by almost than 1 out of 2 women. While the moderately-attractive males are reciprocated by around 1 out of 40 women.

In the third study I will complete the database, mainly experiencing with several profiles within the medium-attractiveness spectrum and one below-average attractiveness man.

 

 

 

 

 

 

 

 

 

 

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