Disgust Response and the Menstrual Cycle: Empirical results

1

Running head: DISGUST AND THE MENSTRUAL CYCLE

Disgust Response and the Menstrual Cycle

K. Rebecca Cooling

Australian Catholic University

Author note

Word Count: 4696

DISGUST AND THE MENSTRUAL CYCLE 2

This research paper acknowledges the concept and

hypothesis as the idea of Dr. Helen Aucote, and any future

publication of this work will include Dr. Aucote as first

author. Further, we acknowledge the assistance of Dr. Leah

Kaufmann in the programming of the GNAT part of the

questionnaire, and Dr. Xochitl De la Piedad Garcia’s

assistance in the use of the computer software programs

Inquisit and SPSS.


Abstract

Disgust involves both automatic and conscious evaluative

processing, so using a combination of explicit and implicit

disgust measures, we sought to predict higher conception risk

by the measurement of the perceptions toward disgusting things

associated with sexual interaction in pre-menopausal females.

We hypothesised that disgust for the bodily fluids involved in

sexual interaction would be downgraded, and that disgust for

lower-genetic value reproductive mates would be upgraded

(Tybur, Lieberman, Kurzban, & DeScioli, 2013) to collectively

predict higher conception risk. Specifically, we measured

explicit disgust using the Three Domain Disgust Scale (TDDS;

Tybur, Lieberman, & Griskevicius, 2009), and implicit disgust

using a Go/No-Go Association Task (Nosek & Banaji, 2001).

Menstrual cycle position was defined using probability for

conception risk (Wilcox, Dunson, Weinberg, Trussell, & Baird,

2001) in an undergraduate university sample N = 50 females (M

= 24.1 years, SD = 6.65 years). Following forced-entry

regression analysis, hypothesised predictors were not

statistically significant in this sample; however, the Moral

Domain of the TDDS was a statistically significant correlate

with high conception risk t(44) = 2.437, p = .019, 2 =.17.

Finding was not consistent with previous menstrual cycle and

disgust studies, but adds to empirical data for moral disgust

and ovulatory shift hypothesis.

Keywords: menstrual cycle, disgust, three domain disgust scale,

GNAT, implicit measures, pathogen disgust, ovulatory shift

hypothesis, high conception risk


Disgust response and the

menstrual cycle

Disgust represents a range of aversions experienced by

humans, perhaps more complex than distaste. Distaste is an

oral and facial reaction to a bitter flavour, evidenced in

other animals (Berridge, 2000; Curtis, 2012). Disgust is part

of the behavioural immune system (Prokop & Fancovicova, 2013;

Schaller, 2006), allowing discrimination between stimuli that

may inherently contain disease-causing pathogens or parasites

(Kelly, 2011). Ultimately, it is an emotion of withdrawal and

avoidance (Ekman, 2008). Despite this apparently

discriminating emotion, it must be allowed to downgrade,

somehow, for sexual reproduction to occur (Angyal, 1941; Tybur

et al., 2013). Perhaps not unsurprisingly, if sexual arousal

is present, the disgust response is weakened (Borg & de Jong,

2012; Stark et al., 2005; Stevenson, Case, & Oaten, 2009),

rendering bodily fluids – which would generally elicit extreme

disgust (Rozin & Fallon, 1987) – as less than harmful.

Defining disgust

Extreme disgust: for saliva, sweat, semen, and emissions

from the reproductive organs – capable of the transmission of

any number of microorganisms that may lead to disease. Disgust

has a typical physiologic profile, marked by the facial ‘gape’

expression, a slightly increased core body temperature,

lowered heart rate, and increased quantities of saliva with

specific constituents for protein retention and immune

reaction (Stevenson et al., 2012). It also has a number of


locations in the brain, some of them disgust-specific

(Ciaramelli, Sperotto, Mattioli, & di Pellegrino, 2013).

Disgust is also gender-specific, with females generally

exhibiting more disgust than males (Al-Shawaf & Lewis, 2013;

Rohrmann, Hopp, & Quirin, 2008) although this may be dependent

on the type of disgusting stimuli (Tybur et al., 2009), or on

the context (Shidlovski & Hassin, 2011).

The context and the Ovulatory Shift Hypothesis (OSH)


The specific context of this study is the way in which disgust

relates to the menstrual cycle, leading up to ovulation, in

female participants without any intervention from hormonal

contraceptives. For the purposes of this study, we define this

context in terms of conception risk, based on the probability

table formulated by Wilcox, Dunson, Weinberg, Trussell, and

Baird (2001). High conception risk (HCR) may be considered to


be a number of days prior to ovulation, where the probability

of conception increases, reliant on the felicitous position of

the female oocyte in-transit through the fallopian tube. This

physiology is a result of hormonal and neuroendocrine

interaction within the female reproductive system (see Figure

1). The behavioural differences that occur coincidentally with

this female physiological change are collectively referred to


as the ovulatory shift hypothesis . This hypothesis has been expanded

by a subsequent and ongoing body of literature (Beaulieu,

2007; Thornhill, Chapman, & Gangestead, 2013; Wallen & Rupp,

2010). The OSH outlines the various behaviours adopted both

unconsciously and consciously that increase the female’s

chances of obtaining the best possible reproductive mate

choice. This is facilitated by subtle changes of symmetry,

body movement, and sensory-system coordination, to assist in

reproductive mate choice (Aitken, Lyons, & Jonason, 2013;

Gangestead, Thornhill, & Garver-Apgar, 2010; Pfaus, 2009;

Pipitone & Gallup, 2008).

The current literature relating to the OSH also indicates

some evidence for a relationship between the menstrual cycle

and disgust. However, research into disgust is made more

complex due to the individual variability of items that elicit

disgust, and also by the various theories of disgust

classification – and subsequently, the techniques of

psychometric measurement. The three main empirical findings

that relate the menstrual cycle directly with a measurement of

disgust are laid out in Table 1.

Specific empirical relevance

Fleischman & Fessler (2011) hypothesised that the higher

progesterone levels of the latter part of the menstrual cycle

coincided with higher disgust sensitivity, and found empirical

support for this. At this stage of the menstrual cycle, the

body’s immune system is lowered through the action of

progesterone to prevent immune rejection of any newly

fertilised oocyte. Therefore, an increase in disgust


propensity may prevent contact with objects or organisms that

may carry disease. Comparatively, in the earlier part of the

menstrual cycle (days 6-14), Shidlovski & Hassin (2011)’s

lower disgust response was found in higher-fertility

participants who had been primed with words relating to a goal

of motherhood. Participants in their study who were in the

latter part of the menstrual cycle showed higher disgust to

the same elicitors, even with the priming goal. Fessler &

Navarrete (2003) found higher disgust in females during the

early part of the menstrual cycle, but the findings were not

statistically significant. Despite these seemingly paradoxical

findings, the different disgust sensitivities can be

interpreted according to the different threats posed to the

individual (Curtis, 2012).


Table 1

Disgust response and the menstrual cycle: the current

literature

Menstrual

stage

Disgus

t

outcom

e

Disgust

measure/s

Disgust

stimuli/s

Reference Significant

Statistics

Pre-

menstrual

Curtisa

images

with

Likert-

type

scale;

Revised

Padua OCDb

Photographs

of disgust

elicitors;

imagined

hypothetica

l

situations

of

contaminati

on

Fleischma

n &

Fessler,

2011

r (79) =

0.25, p

= 0.02

Fertile:

days 6-14;

Non-

fertile:

days 0-5

and 15-28

IAPSc

images

with 1-9

scale; 1 =

not at all

to 9 = to

a great

extent

Priming of

‘motherhood

’;

(IAPS)

babies with

runny

noses,

dirty

nappies.

Shidlovsk

i &

Hassin,

2011

t(36)=2.66,

p<.05,

2=.21


High

conception

risk

DSd,

modified

Age-

disparity

sexual

intercourse

; incest.

Fessler &

Navarrete

, 2003

B=3.14,

F (1,307)

=1.01,

p >.05,

=.11,

Note. a Images rated for disgust intensity; (Curtis,

Aunger, & Rabie, 2004); b Revised Padua Obsessive-Compulsive

Inventory (Revised Padua OCD; Burns, Keortge, Formea, &

Sternberger, 1996); c International Affective Picture System

(IAPS); d Disgust Scale (DS); (Haidt, McCauley, & Rozin,

1994).

Disgust, automatic evaluation, and the avoidance reaction

Tybur, Lieberman, and Griskevicius (2009) used the

evolutionary model to define different disgust sensitivities

according to the various elicitors’ threat to survival.

Pathogen disgust refers both to actual and imagined stimuli

that may contain disease-causing microorganisms, indicated by

cues of texture and smell. Sexual disgust refers to aversions

for sexual interactions that do not contribute positively to

reproductive health, such as bestiality. Moral disgust refers

to socially undesirable actions that may threaten the

cohesiveness of the social group (Tybur et al., 2009). In

further describing disgust, Tybur et al. (2013) suggest that

there are many environmentally different situations requiring

discrimination between stimuli that have inherent survival

value (like food that is safe to eat or a desirable sexual

partner), and stimuli that represent inherent threat (such as


rotting food or the sexual advances of an undesirable mate).

The authors further suggest that disgust activates a non-

conscious evaluative process, which leads to the palpable

feeling of disgust and the behavioural reactions of withdrawal

and avoidance (Tybur et al., 2013). This evaluation also may

take into account other information such as hunger or sexual

arousal. Together, these processes of evaluation and

discrimination stimulate the same bodily outcome of disgust,

if the threat is great enough. This means that there is one

reaction, and three domains of disgust elicitor, according to

the type of survival threat.

Disgust and mate selection

Tybur et al.’s (2009; 2013) model of disgust can be

applied to the female menstrual cycle, especially where this

pertains to the OSH, and to days of higher conception risk.

One example of this would be reactions toward incestuous

relationships, both between siblings who are genetically

related and those who grown up with extended cohabitation.

This is otherwise known as the Westermarck effect (Antfolk,

Karlsson, Backström, & Santtila, 2012). Indeed, a recent study

assessing females’ phone records found that females

communicate less with fathers during periods of high

conception risk, and the authors suggest females avoid

suboptimal sexual unions when they are more likely to conceive

(Lieberman, Pillsworth, & Haselton, 2011). Although not

directly related to disgust, this study does show a

behavioural phenomenon that includes non-conscious avoidance


during HCR. Essentially, avoidance may be the social outcome

of the affect response, experienced more keenly during HCR.

Sexual motivation studies have found other instances of

the avoidance or approach phenomenon during HCR. Firstly, pre-

ovulatory females not only show an increased preference for

males with the scent of symmetry, a biomarker for health

(Thornhill et al., 2013), but they also show a lowered

olfactory threshold for the easier detection of specific

odours (Navarrete-Palacios et al., 2003). One example of this

is the odour that may be linked to Major Histocompatibility

Complex (MHC). MHC recognition in the body involves a set of

genes that play a major role in immune response. The joining

of male/female gametes in reproduction benefits from a pairing

of dissimilar MHC genotypes, as this results in heterozygous,

and therefore more robust-immunity offspring (Penn & Potts,

1999; Wedekind & Füri, 1997). MHC recognition and olfactory

preference was reversed in a study by Milinski and Wedekind

(2001), where females who were cycling normally showed a

statistically significant preference for MHC dissimilar

scents. Comparatively, those females who had hormonal

contraceptive intervention preferred scents from genetically

similar MHC markers. Thus, the days of HCR may evidence an

increased ability to perceive genetically diverse, healthy

prospective mates, greater disgust response toward their polar

opposites, and accordingly an activation of appropriate

avoidance or approach behaviours.

Dual systems theory


To assist in the evaluation and solicitation of optimum

reproductive mates, and in withdrawal and avoidance of

genetically inferior ones via the digust response, females

also may benefit from greater attribution of verbal-memory,

and attentional resources during the HCR stage (Protopopescu

et al., 2008; Vogt, Lozo, Koster, & De Houwer, 2010,

respectively). This may be especially true where evaluation is

undertaken with limited conscious awareness (Roberts, Newell,

Simoes-Franklin, & Garavan, 2008). Such evaluations may

demonstrate dual systems theory (Macapagal, Janssen, Fridberg,

Finn, & Heiman, 2011). The first system is a pre-conscious,

intuitive information processing system, employed to make

quick decisions based on the most current sensory, emotive,

and motivational cues, such as sensations of disgust or sexual

arousal (Macapagal et al., 2011). This may then be assisted by

the second, more rational and deliberate system; which takes

into consideration past, present, and future consequences to

exercise cognitive control over decision-making (Macapagal et

al., 2011). The combination of these dual systems has

similarities to Tybur, Lieberman, Kurzban, and DeScioli's

(2013) theory regarding computational processes that assess

when to avoid substances that ordinarily elicit disgust, and

when to ignore the automatic affect-related avoidance –

especially where this means the difference between eating and

survival, or sexual intercourse and reproductive success.

These computational processes, or the dual decision-making

systems, are the target of our disgust research.

HCR and the menstrual cycle


In sum, there are two poignant concepts that inform our

choices for the current study. Firstly, the two main aspects

of disgust that may relate to behavioural choices of approach

or avoidance during days of HCR: disease-related disgust

elicitors and whether sensitivity to these increases or

decreases; and sensitivity of the female’s disgust propensity

for suboptimum reproductive mates. Secondly, the two systems

of information processing that may relate to behavioural

choices during days of HCR: the pre-conscious evaluation

leading to more automated decision making; and the slower,

rational system that makes decisions based on the summation of

consequences.

To address the concept of behavioural choices regarding

disgust elicitors related to sexual interaction, we sought to

measure over the course of the menstrual cycle. This choice

was based on the finding that sexual arousal occurs more

readily prior to ovulation, and the increased proclivity for

sexual arousal during HCR may therefore interrupt the disgust

response (Duchaine, Cosmides, & Tooby, 2001; Haselton &

Gangestead, 2005). It was also based on the theory that

females carry the greater burden of nurture for any offspring

resulting from sexual interaction (Fessler & Navarrete, 2004).

If sexual interaction occurs with males who have lesser

genetic value (such as genetically related or compromised

individuals), any resultant offspring may be more costly in

terms of nurture, due to genetically-reduced health (Haig,

1999).


To address the dual systems of information processing, we

selected two types of measures. Firstly, an explicit self-

report measure assessed disgust responses according to Tybur

et al.’s (2009) three domains: the Three Domain Disgust Scale

(TDDS), which has good reliability and validity (Olatunji et

al., 2012). Secondly, an implicit measure was chosen to asses

the automatic responses to disgust elicitors related to

pathogens and sexual behaviours; a Go/No-Go Association Task

(GNAT; Nosek & Banaji, 2001). The GNAT has been used in

previous studies relating to the understanding of implicit

motivations for sexual behaviour (Macapagal et al., 2011;

Roberts et al., 2008). The GNAT specific to our study was a

lexical evaluation task, relating sexual behaviours to an

evaluation of good or bad, and relating bodily fluids

associated with sexual interaction to an evaluation of good or

bad.

The hypothesis

Specifically, we hypothesised that in explicit

measurement, higher values in the Sexual Domain and lower

values in the Pathogen Domain would predict higher conception

risk. We further hypothesised that in implicit measurement, a

higher likelihood of an evaluation of ‘good’ for bodily

fluids, and ‘bad’ for sub-normal sexual behaviours would also

predict higher conception risk. This hypothesis was restricted

to females whose menstrual cycles were not influenced by

hormonal contraceptive methods, due to the evidence that

suggests the behavioural changes during the days of HCR are


either absent, or reversed in women with hormonal

contraception (Alvergne & Lummaa, 2010; Roberts et al., 2008).

Method

Participants

In all, 114 (M = 24.1 years, SD = 6.65 years) females

from the undergraduate population at Australian Catholic

University (ACU) participated in the study, either in exchange

for course credit or for entry into a raffle to win one of six

$20iTunes giftcards. 64 participants were excluded from the

study due to their use of hormonal contraception, leaving a

total of 50 participants (M = 24.7 years, SD = 7.5 years).

Measures

Disgust measures were the Three Domain Disgust Scale

(TDDS), and a Go/No-Go Association Task (GNAT). The following

summary describes each part of the questionnaire in the order

the participant completed them, and the operationalisation of

each measure within the study.

Demographics. Participants were asked their age in years

and their current living arrangements.

The TDDS. (Tybur, Lieberman, & Griskevicius, 2009) The

TDDS is a 21-item questionnaire that requires responses based

on explicit feelings of disgust toward a variety of concepts.

It is scored according to the three domains of disgust as

theorised by Tybur et al. (2009); Pathogen Disgust, Sexual Disgust,

and Moral Disgust. Each domain has seven questions referring

directly to that domain, and the participant responds

according to a 6-point scale where 0 is Not at all disgusting and 6


is Extremely disgusting. The measure takes one item,

consecutively, from each of the domains in order Moral, Sexual,

and Pathogen (See Appendix A). The Pathogen Disgust items measure

the participants’ feelings toward disgust elicitors that are

associated with disease-causing pathogens: bodily secretions,

feces, blood, and insects that are in close proximity with, or

are perceived to be associated with disease (e.g., “Stepping

on dog poo”; and “Seeing a cockroach run across the floor”).

The Sexual Disgust items measure the participants’ feelings

toward sexual acts; both those in the more widely understood

repertoire of human sexual expression, and those that may be

considered more extreme (e.g., “Bringing someone you just met

back to your room to have sex”; and “Performing oral sex”).

Moral Disgust items ask questions about the more abstract

notions of social interaction rules, such as “Stealing from a

neighbour”; and “Deceiving a friend”. Responses are scored to

give a total for each of the three domains.

Each of the items of the TDDS have been independently

analysed for psychometric reliability; both internal

consistency and external validity. Exploratory factor analysis

(principal components analysis, with oblique transformation;

and parallel analyses) produced a consistent model of three

components, which supported Tybur et al.’s (2009) division of

the items into three domains (Olatunji et al., 2012). The

reliability and validity analyses also showed the TDDS has

reasonable test-retest reliability and internal consistency

(Pathogen disgust domain r = .74, p < .01; = .81; Sexual

disgust domain r = .79, p < .01; = .86; Moral disgust domain


r = .48, p < .01; = .91) and it shows a strong association

with a measure of contamination fear (when assessed for

convergent validity with the Padua Inventory Contamination

Fear subscale) and a non-significant correlation with a

measure for depression (when assessed for divergent validity,

using the Center for Epidemiological Studies – Depression

Scale; Olatunji et al., 2012).

The GNAT. (Nosek & Banaji, 2001). The GNAT used by this

study was a four-block semantic GNAT (see Table 2 and Appendix

B), assessing the implicit attitudes towards target categories

Bodily Fluids, and Sexual Behaviours, and target attributes of good

and bad. Specifically, one block pairs the target category

“BODILY FLUIDS” with the target attribute “GOOD”; another

block pairs “BODILY FLUIDS” with “BAD”; another block pairs

“SEXUAL BEHVIOURS” with “GOOD”; another block pairs “SEXUAL

BEHAVIOURS” with “BAD”. Each GNAT block had specific

instructions and a short practice session preceding it, and

then the categories and attributes were randomly selected so

any one of the four blocks may appear. The target words, and

the target category and attribute, were displayed on the

computer monitor the participant used to access the

questionnaire (see Figure 2, example on the left). The screen

was black, with white type for the words. The target category

and target attribute was displayed in uppercase type at the

top left and right of the screen (respectively), and the

stimulus was presented in lowercase type in the centre of the

screen. To respond, the participant pressed the space bar on

their computer’s keypad. The stimuli were presented for 900ms


and the inter-stimulus interval (blank screen within the

stimulus area) was 100ms.


Figure 2. Example of GNAT screen (left) and feedback to

participant’s incorrect or untimely response (right).

The participant could record a ‘Go’ response by the

pressing of the space bar within the set timeframe; and a ‘No-

Go’ response by doing nothing within the timeframe. If the

participant incorrectly responded, or did not respond quickly

enough, a red ‘X’ would appear in the stimulus area (see

Figure 2, example on the right). Signal detection and

subsequent data collection was based on the accuracy with

which the participant correctly responded: a ‘Go’ response

when the stimulus word was from the specific target category,

or from the specific target attribute; and a ‘No-Go’ response

(i.e., no key is pressed) when the stimulus word was not from

the target category or attribute lists, listed in Table 2. A

further 12 words were added as ‘distracter’ words.


For example, if the target category was ‘BODILY FLUIDS’

and the target attribute was ‘GOOD’, and the stimulus word was

‘spit’, or ‘joy’, then the participant’s correct response

would be to press the space bar; a ‘Go’ response. If a

distracter word appeared, such as ‘vinegar’, or a word from

the BAD list, such as ‘regret’, then the participant’s correct

response would be to not press the space bar (i.e., no action

at all); a ‘No-Go’ response. Distracter words, or words from

the opposite attribute list, collectively become ‘noise’ and

needed to be discriminated by the participant from the

‘signal’ or target category word.

Table 2

Categories and attributes for the Bodily Fluids and Sexual Behaviours GNAT


Categories Attribute

s

Target Distracte

r

Good Bad

Bodily

Fluids

Word

leng

th:

Shor

t

Long

spit oil joy bad

cum milk hug sad

sperm bleach good sick

sweat water bliss cramp

semen vinegar proud regret

saliva detergent amaze ugly

Sexual

Behaviou

rs

Word

leng

th:

Shor

t

Long

incest theft joy bad

bondage hiding hug sad

flashing fighting good sick

stripping grimace bliss cramp

S + M defamatio

n

proud regret

bestiality hibernati

ng

amaze ugly


The GNAT is scored using the number of correct responses

for each block. Sensitivity to the target (accuracy), and the

participants’ timeliness in discrimination of the target from

the noise gives an indication of each participant’s evaluation

of the target as being good or bad. Each block gives a total

of 80 pairings of targets and stimulus, and a single attitude

score is calculated for each pairing: sexual behaviours/good;

sexual behaviours/bad; and bodily fluids/good; bodily

fluids/bad. The difference between these scores gives a value

of the strength of association of the target and the attitude

for each participant.

Menstrual Cycle Information. Participants were asked to

recall the beginning of their menstrual cycle, using a visual

calendar to assist in their recall (Fessler & Navarrete,

2003). Assessment of participants’ responses of number of days

since beginning menstruating was compared against a

conception-probability table formulated by Wilcox, Dunson,

Weinberg, Trussell, and Baird (2001). Using this table,

participants’ self-reported day in cycle was recoded to the

equivalent probability value (see Table 5 for sample

frequencies).


Contraception. Contraception use tends to negate

participants’ inclusion in studies using menstrual cycles as a

variable, (Beaulieu, 2007; Fessler & Navarrete, 2003;

Fleischman & Fessler, 2011; Protopopescu et al., 2008),

because it eliminates the cycling of hormone levels, and

therefore may change behavioural variability associated with

HCR (Fleischman & Fessler, 2011). Participants needed to

select from a drop-down choice for oral contraceptives type,

and a second drop-down list to choose if the non-oral

contraceptive was a barrier, hormonal or combined method (See

Appendix C for questions in full).

Procedure


The questionnaire was written using Inquisit 4.0.3

(Computer software; 2013); was hosted online by Millisecond,

and administrated by the ACU Sona Research Participation

System, following approval by the ACU Human Research Ethics

Committee. Once participants indicated their desire to take

part, the information letter gave full details of the study,

and of their rights as participants. It also informed them

their completed, submitted survey constituted their informed

consent. The questionnaire began with age and living

arrangement questions; then the 21 items of TDDS (Tybur et

al., 2009). The participants then completed the four-block

GNAT, which began with specific instructions on how to respond

to the on-screen stimuli (See Appendix B); and also

instructions and a trial at the commencement of each GNAT

block. At the conclusion of the last GNAT block, participants

were taken to the final part of the questionnaire to complete

details about the length of the previous two menstrual cycles,

and their contraceptive use. Sample size was set according to

the number of predictors (5) and was set at 90 to obtain a

medium effect size. With the lower than expected sample size

(N=50), the study had limited power to detect an effect.

Results


Contrary to our hypothesis, the explicit measures of

Sexual Disgust and Pathogen Disgust, and the implicit measures

of good/bad evaluations of Bodily Fluids and Sexual

Behaviours, were not statistically significant predictors of

HCR, and only accounted for 14% of the variance R2 =.14 , F

(5, 44) = 1.49, p > .05, but showed a medium effect size; 2

=.17 (See Table 3). Further, the explicit measure of Moral

Disgust positively predicted conception risk. Descriptive

statistics are represented in Table 4, and the frequency

distribution of the conception risk of the sample is in Table

5. Results of evaluation of assumptions led to the

substitution (M+2SD) of one outlier (Case 6; MD = 17.21; R2 =

-0.003 after substitution). Assumptions of multicollinearity

were met; even though there was significant correlation

between the domains of the TDDS. This correlation was

attributed to its measurement of the same construct. Due to

issues of non-completion, there were some missing values in

the GNAT data, which was assumed to be missing completely at

random and represented less than 5% of the GNAT data. GNAT

data also showed a number of false alarms consistent with

higher-than-chance results (negative d-scores: 11.5% of SEXUAL

BEHAVIOURS/BAD; 7.8% of BODILY FLUIDS/BAD), suggesting that

this aspect of the measure may not have worked reliably in

this sample. Neither age, nor living arrangements, was found

to have a significant effect on regression outcome. Analysis

was conducted with available data, using SPSS 20.0 for Mac.


Table 3

Multiple regression analysis of predictors for conception risk with associated

correlation statistics

Unstandardise

d

Coefficients

Standardis

ed

Coefficien

ts

t p r

B SEB

Constant.004 .020

.186 .85

3

Pathogen disgust.000 .001 -.023

-.12

6

.90

0

.141

Sexual disgust.000 .001 -.144

-.78

8

.43

5

.101

Moral disgust.002 .001 .442*

2.28

2*

.02

8

.331

*

Sexual behaviours

good/bad-.005 .007 -.098

-.66

0

.51

3

-.06

3

Bodily fluids

good/bad.002 .007 .062

.413 .68

2

.016


Note: R2 = .144. * p <.05.

Table 4

Means and standard deviations of the predictors in the sample

Measure M SD

Three Domain Disgust

Scale ScoresPathogen domain total 26.76 6.32Sexual domain total 23.28 7.76Moral domain total 26.42 7.93

GNAT score differencesSexual behaviours:

Good - Bad

0.27 0.63

Bodily fluids: Good -

Bad

-0.12 0.68

N = 50

Table 5

Frequency distribution of conception risk probabilities for the current sample,

showing from lowest to highest probability of conception risk (Wilcox et al., 2001)

ProbabilityNo. of

Participants

%

.0000 4 7.7

.0010 1 1.9

.0020 1 1.9

.0040 2 3.8

.0070 1 1.9

.0080 1 1.9

.0090 5 9.6

.0100 5 9.6

.0110 3 5.8

.0130 1 1.9


.0170 1 1.9

.0250 2 3.8

.0320 2 3.8

.0400 1 1.9

.0440 2 3.8

.0500 3 5.8

.0610 5 9.6

.0720 2 3.8

.0750 3 5.8

.0810 4 7.7

.0840 2 3.8

.0860 1 1.9

Discussion

The hypothesis for this study was that decreased disgust

for pathogenic elicitors, especially those bodily fluids

associated with sexual interaction, and increased disgust for

subnormal sexual behaviours should together predict higher

conception risk; which should be shown both in the explicit

and implicit measures of disgust. The data did not support the

hypothesis. However, in the explicit measure, a statistically

significant correlation was found between disgust for

elicitors from the Moral domain, and high conception risk

(HCR). Although unexpected, this finding may add support to

the research not only for the link between morality and

disgust, but also for the concept of moral disgust as a

separate construct from other emotions such as anger or fear

(Chapman & Anderson, 2013). Moral disgust has been linked with

sexual aversions – especially incestuous interaction – and

this has been shown to be more prevalent during HCR (Lieberman

et al., 2011). Indeed, the finding of increased moral disgust


during HCR adds an interesting element to the ovulatory shift

hypothesis (OSH; Gangestead et al., 2005), and may be

explained by the increased desire to affiliate with social

groups (Schultheiss, Dargel, & Rohde, 2003). This means that

the proximity to peak fertility may foster a desire to align

oneself with social groups likely to provide positive support,

and to subscribe to their moral standards in case conception

and pregnancy occur. Tybur et al. (2013) acknowledge that

their specification of the Moral domain of disgust necessarily

involves interconnection with the other domains, (Pathogen and

Sexual), both in the use of the language to describe

elicitors, and in the similarity of facial-muscle involvement

and other physiology involved in the disgust reaction. The

finding of the explicit measure of this study adds further

detail to the research on disgust, and to the OSH.

Given the size of the sample, interpretation of the

results of the implicit measure must be taken with caution.

The ability of the GNAT to measure automatic evaluative

responses to a target is not in question (Mitchell, Nosek, &

Banaji, 2003) but the number of false alarms in the GNAT for

this study may indicate a measure of imprecision, and decrease

the internal reliability of the results. However, the results

indicate no association of HCR with the hypothesised targets:

neither associating the bodily fluids encountered with sexual

involvement with ‘good’ (and therefore low disgust response),

nor associating subnormal or deviant sexual interaction with

‘bad’ (and therefore high disgust response). This means that

on days of higher conception risk, females are no more likely


to find the bodily fluids associated with sex as not

disgusting, and aberrant sexual behaviours as more disgusting,

than females on days of low conception risk.

More than half the participants were excluded from the

analysis due to their use of hormonal-intervention

contraception methods. This may represent an opportunity for

further analysis, in line with Peterson, Carmen, and Geher's

(2013) recent study on the correlation between mating

intelligence (the ability to coordinate successful

solicitation behaviours in order to obtain an optimum

reproductive mate) and the menstrual cycle. Their study found

that women on hormonal contraception evidenced no

statistically significant difference in the strength of the

effect between the menstrual cycle and mating intelligence,

compared to normally ovulating women. They suggest that

hormonal contraceptive intervention may only soften the

influence of menstrual cycle variability on mating

intelligence. Further, those females on hormonal contraception

in the sexual arousal and disgust study did not show

statistically different responses for levels of disgust

following the priming of sexual arousal (Borg & de Jong,

2012). The use of disgust-response data obtained from

participants with hormonal intervention may be efficacious for

future investigations.

Curtis’ (2012) latest summation of disgust is that it is

less specific and less complex than some theories suggest.

Curtis’ (2012) view is that rather than separating the emotion

into its various elicitor-based domains, focus could instead


be on a threat for which disgust – and its typical avoidance

behaviour – is suited: avoidance for parasites; both the

microorganisms, and given the social nature of our species,

the social ones as well. In fact, Curtis (2012) emphasises

avoidance as being the main point of disgust, rather than the

multitude of types of elicitors. However, even if this is the

case, the puzzle still remains as to how humans are able to

allow sexual interaction, given that it includes the very real

possibility of transmitted parasitic opportunism.

In conclusion, the sample of this study has brought a

small measure of empirical knowledge to add to the body of

results in the measurement of disgust. In the sample of this

study, an empirical relationship between a decrease in disgust

for sexually-related bodily fluids and the time leading up to

ovulation was not found. Sexual aversion for suboptimum

reproductive mates during the days of higher conception risk

was also not shown in this sample, but has been suggested by

the data of previous research. The main finding of this study

was an increase in the propensity for moral disgust during

days of higher conception risk, and we suggest this may be due

to a female’s desire to subscribe to the norms of the social

groups they exist in, so as to promote and protect both their

own health, and that of any future offspring.


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APPENDIX A

The Three-Domain Disgust Scale

Tybur, Lieberman, & Griskevicius, (2009).

(Embedded within the Inquisit programming script for the

online questionnaire)

The following items describe a variety of concepts. Please

rate how disgusting you find the concepts described in the

items, where 0 means that you do not find the concept

disgusting at all and 6 means that you find the concept

extremely disgusting.

1. Shoplifting a candy bar from a convenience store

2. Hearing two strangers having sex

3. Stepping on dog poop

4. Stealing from a neighbor

5. Performing oral sex

6. Sitting next to someone who has red sores on their arm

7. A student cheating to get good grades

8. Watching a pornographic video

9. Shaking hands with a stranger who has sweaty palms

10. Deceiving a friend

11. Finding out that someone you don’t like has sexual

fantasies about you

12. Seeing some mold on old leftovers in your refrigerator

13. Forging someone’s signature on a legal document

15. Standing close to a person who has body odor

16. Cutting to the front of a line to purchase the last few

tickets to a show


17. A stranger of the opposite sex intentionally rubbing your

thigh in an elevator

18. Seeing a cockroach run across the floor

19. Intentionally lying during a business transaction

20. Having anal sex with someone of the opposite sex

21. Accidentally touching a person’s bloody cut


APPENDIX B

GNAT INSTRUCTIONS (embedded within Inquisit programming

script)

Description: The purpose of the GNAT task is to identify words

that appear briefly in the centre of the screen as targets or

not. The target categories are displayed in the top left and

right corners of the computer screen, throughout trial blocks.

For example, the target categories in this practice GNAT are

"FRUIT" and "GOOD" or "BAD". Thus, if the word that appears in

the centre of the screen is "BANANA" (a fruit) or "HAPPY" (a

good word), you should press the <SPACEBAR> as quickly as

possible. If, however, the word that appears in the centre of

the screen is "ANT", you should identify this word as

belonging to neither of the target categories, and make no

response. The target words belonging to each target category

are intended to be straightforward as in the above example.

INSTRUCTION AND EXAMPLE

A word will appear in the middle of the screen. You will then need to identify the word as a TARGET (i.e., it belongs to EITHER of the target labels named in the top left and right corners of the screen) or not. You will need to do this as quickly as possible.> If you decide the word is a target, press the SPACEBAR key. > If you decide the word is NOT a target, make NO RESPONSE andthe next word will appear (quickly).Example of target present and target absent trials


NOTES

You will receive feedback if you make a mistake X = Incorrect.Trials are very short, so you need to respond AS QUICKLY AS POSSIBLE.If you are too slow, your response will not be scored as correct.There are 80 trials in a block, so if you make a mistake, try not to get distracted. Just keep trying to identify TARGET words.

APPENDIX C

Menstrual cycle and contraception information (embedded within

Inquisit programming script).

Think back to when you last had a period. Can you remember the

day that it started? That might have been a while ago –

perhaps you can think of the day that it finished, and then

count back to the date it started.

This might be a stretch for your memory, but now see if you

can think of the period before. Might you be able to make a

close-as-possible guess when it started – or perhaps when it

finished and count back the number of days to its start?


When was the last time that you had sexual intercourse with

another person?

a) In the last 12 hours

b) Between 12 and 24 hours ago

c) 1 to 2 days ago

d) 3 to 7 days ago

e) More than a week ago

f) I have never had sex.

Are you currently on the Pill?

a) Yes, I am on a triphasic pill (three levels of hormone)

b) Yes, I am on a monophasic pill (single unchanged dose of

hormone)

c) Yes, I am on a pill but I don’t know what type

d) I am not on the pill.

Are you currently using any other form of contraceptive?

a) Yes, I have been fitted with an intra-uterine device that

contains a slow-release hormone (i.e., “Minera”)

b) Yes, I have had a hormone pellet inserted into the back

of my arm (i.e., “Implanon” or “Nexplanon”)

c) Yes, I get an injection every three months (i.e., “Dep-

Provera”)

d) Yes, I use a vaginal ring that releases hormones (i.e.,

“NuvaRing)

e) Yes, I put on a contraceptive hormone patch on my skin

(i.e., “OrthoEvra”)

f) Yes, I use barrier methods (i.e., condoms, female

diaphragm)

g) No.

Disgust Response and the Menstrual Cycle: Empirical results

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