Running Head: INFLUENCE OF OBJECTIVE FACIAL FEATURES ON

Running Head: INFLUENCE OF OBJECTIVE FACIAL FEATURES ON TRUSTING

BEHAVIOR

The Influence of Objective Facial Features on Trusting Behavior

Jack Colvin

Department of Psychology and Neuroscience

University of Colorado at Boulder

Date for Defense: March 31st, 2021

Defense Committee:

Thesis Advisor: Leaf Van Boven, Psychology and Neuroscience

Honors Council Representative: Lewis Harvey, Psychology and Neuroscience

Outside Reader: Lawrence Williams, Marketing

Additional Member: Eric Pedersen, Psychology and Neuroscience

INFLUENCE OF OBJECTIVE FACIAL FEATURES ON TRUSTING BEHAVIOR 2

Abstract

First impressions have been repeatedly studied in psychology and yet while most people pay

attention to how one acts during the initial interaction, many judgements are made from the

visual appearance first. This study investigates how 11 unchangeable facial characteristics

influence trusting behavior without any prior interaction between the stimulus faces (N = 132)

and the trust subjects. It was predicted that larger eyes, as well as wider faces would be more

often trusted by unacquainted participants. Related facial features such as higher cheekbones and

longer foreheads, both of which would contribute to the length of a face, were hypothesized to

reduce the trusting behavior by the same participants. Further exploration into additional facial

characteristics such as the shape of the nose, shape of the chin, and fullness of the lips were

included in the study. Once the measurements were completed, the stimuli faces were analyzed

against pre-existing data. The results showed no significant relationships between the objective

facial characteristics and the trusting behavior, in contrast to some of the findings of previous

literature. This could be reflective of the difference between objective measurements and

subjective ratings and how each of those are able to be applied in social decisions.


Introduction

A quote by the philosopher Aristotle claims “Man is by nature a social animal; an

individual who is unsocial naturally and not accidentally is either beneath our notice or more

than human. Society is something that precedes the individual. Anyone who either cannot lead

the common life or is so self-sufficient as not to need to, and therefore does not partake of

society, is either a beast or a god,” (Aristotle, 1905). While Aristotle lived well over 2

millennium ago, human nature has not separated from the social world. In fact, countless social

interactions occur every day, all around the globe and these interactions can range from

situations as simple as ordering food off a menu or as complex as selecting a single applicant for

a job from thousands of interviews. When this interaction between two or more people is

positive, it promotes gain and advancement for one or both parties. This human phenomenon is

defined in psychology as prosocial behavior, the voluntary behavior of one individual intended to

benefit another (Davidov, Vaish, Knafo‐Noam, & Hastings, 2016; Eisenberg, Fabes, & Spinrad,

2006). However, in both of the scenarios provided, the prosocial behavior is only completed

because individuals place trust in another subject to fulfill the obligations that are expected,

whether that be delivering an insignificant burger or agreeing to a social contract such as a job.

Because of the incredible extent that our daily lives require other people, it is important to

understand why we choose to place trust in one individual over another.

A study conducted in 2019 has shown that children as young as 3 years old begin to

recognize characteristics of personality traits from a person’s facial appearance, though these

trait inferences are not yet implemented in social interaction. However, by the age of 5, children

are shown to alter their behavior based on how trustworthy they deem that individual

(Charlesworth, Hudson, Cogsdill, Spelke, & Banaji, 2019). For example, after the researchers


concluded that children could consistently identify which face they deemed more trustworthy,

they conducted another experiment in which children 5 years old specifically gave gifts to faces

that they considered more trustworthy. Coincidentally, characters in films, especially animated

movies where the body shapes and proportions can be manipulated, are typically portrayed

differently based on the role of the character. Disney movies capitalize on this ability and

routinely display the villain as a slender character with a longer face and small eyes, for example,

Maleficent from Sleeping Beauty, Hades from Hercules, Jafar from Aladdin, or even Scar from

the Lion King. This technique is able to help children immediately identify who the villain is in

the show without having as much ability to comprehend the actions and language used. The final

example even shows that the facial characterizations that children are able to identify as negative

can extend to anthropomorphic faces in animals as well. With how young of an audience that

these movies target, it goes to show that humans begin making judgements on faces very early in

life and use these judgements to characterize people. However, it is difficult to be able to

determine whether these characterizations are due to a natural tendency, or if the technique that

these animators are causing the common perception of the audience.

Interestingly, physiognomy, which is the study of faces, has developed multiple

conflicting positions on the reliability of personality inferences from facial characteristics

(Hassin & Trope, 2000; Todorov, Said, Engell, & Oosterhof, 2008). Tests of inferring

personality traits from looking only at facial constructions have been consistently studied for the

past century (Anderson, 1921) and results have also consistently maintained the fact that people

are able to perceive specific personality traits from faces across settings (Secord, & Bevan, 1956;

Berry & Brownlow, 1987). However, despite the fact that separate people are able to find a

consensus on which faces are more trustworthy, attractive, deceitful, kind, etc., these personality


traits are not reflective of the actual personality of the person as observed from their behavior

(Cohen, 2016; Alley, & Hildebrandt, 1988). Beginning in the 1990s, studies began to make the

claim that some traits identified through facial judgements were reflective of that individual’s

personality (Berry & Brownlow, 1989). The kernel of truth theory originally stated that

overgeneralized stereotypes may not be fully accurate representations for each individual in the

group, but there may be a small part of the stereotype that still applies to each varying person.

After beginning to see small effects in the study of facial characteristics, researchers began to use

the same theory in physiognomy (Penton-Voak, Pound, Little, & Perrett, 2006). It is with this

theory in mind that research on judgements of facial structures and characteristics has continued.

There are a few aspects of face judgements that are of interest for this study. First, it is

important to acknowledge how judgements are made and how quickly they are able to influence

perceptions of an individual. Previous studies by Alexander Todorov have confirmed that

individuals are capable of making personality trait judgements on faces that are only seen for as

little as 33 ms and these judgements do not increase in accuracy past 167 ms of exposure time

(Todorov, Pakrashi, & Oosterhof, 2009). Furthermore, the same study concluded that subjects

that are primed for making judgements on trustworthiness were capable of making those

judgements in less than the 33 ms threshold. In addition, Jones, Schild, & Jones (2020)

conducted a study testing the validity between manual facial evaluation and computer automated

facial evaluation. This study explored how manually placing markers on a face compared to

computer-automated marker placement and if one or both would be able to accurately measure

the face characteristics that were commonly studied. The results found that both computer-

generated points and manually placed points are correlated and both methods are reliable to use

in studies. Finally, another study analyzed whether personality trait judgements were determined


from aspects of appearance by an external viewer or if salient structures of the face alone such as

the geometry of the face were able to determine the trait judgements (Rojas, Masip, Todorov, &

Vitria, 2011). The findings stated that a holistic approach that analyzed all appearance

information was better equipped to accurately make personality trait judgements, but the

structural information of the face such as objective measures also produced enough information

to make trait judgements as well.

Another aspect of facial judgements to consider is which traits are able to be recognized

in facial characteristics and how those traits may influence selection decisions. Since some

prosocial behavior requires a sacrifice by the acting individual, in the case of the trust experiment

used in this study the sacrifice would be money, people want to avoid having wasted that

sacrifice without any result. To do this, individuals require trust that the money will not be lost.

Therefore, trustworthiness is an important factor in selection criteria. The physiognomic

attributes of trustworthiness as determined by external judgement are often correlated to sexual

dimorphism, facial width to height ratio, and baby-faced aspects such as large heads, large eyes,

and round chins (Berry, & Brownlow, 1989; Hassin, & Trope, 2000; Jones, Schild, & Jones,

2020; Stirrat, & Perrett, 2012). Stirrat and Perrett (2012) concluded that wider male faces are

more trustworthy for in-group subjects, but curiously, when faced with competition from an out-

group, the same authors in a study 2 years earlier found that males with wider faces were rated as

more deceitful and were judged to be especially untrustworthy when rated by the opposite sex

(Stirrat, & Perrett, 2010). Facial attractiveness has also been extensively studied in physiognomy

and has been shown to be highly correlated with trustworthiness (Xiao, Zheng, Zhang, Xin,

Chen, & Li, 2016). Thus, the ratings of trustworthiness in attractive individuals were increased

when judged by members of the opposite sex. The physiognomic attributes for attractive faces as


determined by Milutinovic, Zelic, & Nedeljkovic (2014) are generally smaller facial features

such as in the nose, face size, and lips. In addition, sexually dimorphic faces and symmetry

between both sides of the face are often stated to be perceived as objectively attractive.

Finally, the effect of attention has found significant results in previous studies but

remains an important area to study further. Previous research has concluded that attention,

whether internally motivated or caused by external forces such as an experiment cueing

mechanism, increases an individual’s perception of importance for the cued target (Mrkva & Van

Boven, 2017). This effect remains even when controlled for possible primacy and recency

effects. The research on the effect of attention was also established in a prosocial trust game

scenario (Grant, Pedersen, Ramos, & Van Boven, 2019) which contributed the attention and trust

data for the current study’s analysis. In an attempt to answer some of the questions posedby the

previous literature, this study analyzes the effect that particular features of stimuli faces have on

the decision to trust one picture over another along with the interaction that facial characteristics

might have with attention.

For the current study, there are 11 facial metrics that were hypothesized on. These

measures are Facial Width to Height Ratio, Upper Head Length, Face Roundedness, Face

Heartshapeness, Cheekbone Prominence, Cheekbone Height, Eye Shape, Eye Size, Distance

Between Pupils, Lip Fullness, and Nose Shape. According to several studies (Berry, &

Brownlow, 1989; Bull, & Gibson-Robinson, 1981; Manesi, Van Lange, & Pollet, 2016;

Rezlescu, Duchaine, Olivola, & Chater, 2012; Todorov, Said, Engell, & Oosterhof, 2008) that

make the claim that baby-faced aspects and individuals with larger heads and wider faces are

more trustworthy, the first hypothesis states that there will be a positive correlation between

trusting behavior and Facial Width to Height Ratio, Face Roundedness, Eye Size, Eye Shape,


and the Distance Between Center of Pupils, all of which are associated with the previously

identified trustworthy characteristics. Second, this study is also expanding the measures by

expecting additional significant relationships between trusting behaviors and Upper Head

Length, Cheekbone Height, and Face Heartshapeness. Because of the studies identifying facial

width to be associated with trustworthy faces, Upper Head Length and Cheekbone Height, which

are more related to face height, are predicted to result in negative correlations with trusting

decisions. Plus, Face Heartshapeness analyzes how sloped the bottom portion of the face is and

how quickly the face narrows, thus, since this variable contrasts with facial width, it is also

predicted that this variable will have a negative relationship as well. The third and final set of

metrics analyzed in this study are Cheekbone Prominence, Lip fullness, and Nose shape. These

characteristics are used as exploratory factors to further the investigation of face structure on

trusting behaviors.

To summarize, it is hypothesized the (1) Facial width to height ratio, Face roundedness,

Eye shape, Eye size, and Distance between pupils will each be positively correlated with trusting

behavior. (2) Upper head length, Cheekbone height, and Face heartshapeness will each be

negatively correlated with trusting behavior. (3) Lip fullness, Nose shape, and Cheekbone

prominence, which are used increase the base of knowledge for physiognomy, will not be

significantly correlated with trusting behavior. In order to test this, each of the characteristics

were measured for every face available. This study then analyzed the trusting behavior data that

was previously obtained (Grant et al., 2019), as well as the measurements that were taken, in

order to look for any correlations between the variables. The design of the study as well as all of

the hypotheses, measurement data, and analyses plan were pre-registered online (osf.io/qhkby).


This pre-registration was completed before any analyses were conducted and all the information

is open for viewing.


Methods

Faces

This study utilized a total of 144 stimuli faces that were taken of previous students at the

University of Colorado Boulder. The pictures were voluntarily taken in a research lab in

exchange for general psychology course credit. The ethnic distribution of the faces used were a

majority Caucasian but with multiple faces each representing Asian, Black, Latino, and Middle

Eastern ethnicities. Each of these stimuli pictures were taken before the start of the trust

experiment. All the volunteers for the photos were asked to not make any expressions, however,

a closed lip smile was allowed. All the pictures were taken in the same indoor location, so the

white wall background and standard overhead lighting all remained consistent. All pictures were

taken facing directly forward, aligned with the face and with no head tilt of the participants. The

pictures displayed the head, hair (no restrictions were placed on the hairstyle), and tops of the

participants shoulders. Due to the photos being taken with varying cameras and varying qualities,

all the pictures were digitally resized to be 500 pixels in width by 375 pixels in height for this

study. During the trusting behavior experiment, all the pictures were displayed on the same lab-

provided, desktop computers, so each participant also viewed the pictures with the same

conditions. An example of the participant’s view is display in Figure 3.

Previous Trust Experiment

The previously conducted experiment that these stimuli faces were used for was designed

to test if attention influences the perception of a target face and how this interaction can be

measured in an economic game (Grant et al., 2019). The experiment was conducted on a sample

of student participants at the University of Colorado Boulder. These participants were invited


into the lab with credit compensation to complete the roughly 30-minute computer experiment.

The students were given two credits in order to give a maximum time length of one full hour,

thus ensuring the participants finish the experiment. Since all participants were from the

introductory psychology course, the trust experiment participants primarily consisted of

Caucasian, first-year, undergraduate students about ages 18-20.

Trust Behavior Procedure

Before being allowed to enter the lab, subjects were deceived into believing they were

given money to spend during the experiment. Participants were then guided to begin the

experiment and given instructions by the lab-provided computers. After receiving the

instructions and verifying comprehension, the participants engaged in a three practice trials to

orient them and ensure understanding. After this, 25 recorded trials were completed as the

experiment. Each trial began with a stream of letters flashing on either the right or left side of the

computer screen (see fig. 2) The participants were directed to which side of the screen the letters

would be randomly assigned. In each stream of letters, the participant attempted to count the

number of X’s that appeared, in order to assure their attention is directed to that particular side.

Midway through the stream of letters, two randomized facial pictures appeared on either side of

the screen (see fig. 3) The pictures were used to have the participant decide who they are willing

to give money to. During each trial, the stimuli pictures were matched in gender so as to avoid

sex biases as a reason for giving money to one face over another. After the stream of letters had

finished, the participants were asked to choose one of the two people on the screen with the

theory that the participant will choose the person on the same side that their attention has been

drawn to.


This previous data from the Grant et al. experiment had to first be formatted to

accommodate the new measurements in the current study. All extraneous variables (participant

and trial, demographic information of the participants, subjective ratings, etc.) were nullified and

filtered out of the dataset. The only key points of data that were retained were the trusting

decision and the cued or not cued condition for each time the face was chosen. From this point,

the proportion that each face was chosen to be trusted out of the total times that the face was

shown was calculated and separated into 4 separate conditions: every time the face was shown in

a trial, only the times the face was cued in each trial that it was shown, only the times the face

was not cued when it was shown, and the difference between cued and not cued conditions. The

difference category was included to incorporate the effect of attention when running the analyses

against the facial characteristics.

Current Study Measures

A total of 11 different facial metrics were used for analysis and computed in the following ways:

Eye Shape was measured by eye height divided by eye width. Eye Size was measured by eye

height divided by face length. Distance between center of eye pupils was taken directly from the

measurement. Facial width to height ratio was measured by the face at the widest point divided

by face height. Upper head length was measured by forehead length divided by face length. Face

Roundedness was measured by the face width at mouth divided by face length. Face

Heartshapeness was measured by face width at cheeks divided by face width at mouth.

Cheekbone Prominence was measured by [face width at cheek minus face width at mouth]

divided by face length. Cheekbone Height was measured by [average mid-cheek to chin for right

and left sides of the face] divided by face length. Lip Fullness was measured by lip thickness

divided by face length. Nose shape was measured by nose width divided by nose length. These


facial metrics were calculated from a total of 15 different measurements (see figure 1) that were

found with the GNU Image Manipulation Program (GIMP; The GIMP Development Team,

2020). Each measurement was recorded by the number of pixels between the two points.

Because each face is different and no single point can be referenced across all faces, specific

instructions for each measurement were also created and continually referenced to thoroughly

eliminate the possibility of poor measures. The measurements and instructions are explained

below:

Face Length was measured from the center base of chin to center of hairline. Forehead

Height was measured from the same center of hairline point to the top Y-coordinate of the

observed eyebrows. Facial Width was measured in three locations, across the upper cheekbones

and below the eyes, across the middle cheekbones and across the width of the nose as, and across

the mouth. The first two width measurements can be observed on each face and measured

accordingly, however for the measurement across the mouth of the face, additional instructions

were created. The faces used for this experiment were taken without consistent facial

expressions, therefore while some faces have a blank expression, others are slightly smiling, and

their lips do not follow a straight line. Thus, the Face Width at Mouth was measured across the

Y-coordinate of the center of their lips, regardless of the shape of their mouth. Eye Height was

measured from the base of the bottom eyelid to the top eyelid along the outside of their pupil

which was generally the largest point of the eye. Eye Width was measured from the corner of the

outside of the eyeball to the observable inside point of the eyeball, the tear duct was not included

for the measurements. Both the Eye Height measurement and the Eye Width measurement were

measured on both eyes and averaged to find a single number. Distance between Pupils was

measured by the observable center of each pupil. Lip Thickness was measured from the center of


the top lip to the center of the bottom lip. Nose Width was measured across the widest point of

the nose, along the outside edge of each nostril. Nose Length was measured from the center of

the observable bottom of the nose to the observable top point in the nose, in the depression

between the middle points in Eye Height. Lastly, Left and Right Chin to Middle Cheek was

measured from the same center base point used in Face Length to the Y-coordinate of the bottom

of the nose along each side of the face, generally found below the ear lobes. Each of these

measurements were then used to compute the face metrics used in the analysis.

During the measurements, a few data issues were identified and resolved. These issues

consisted of 3 faces being labeled as the wrong ethnicity and since this issue was discovered after

the previous experiment had been conducted, the faces were eliminated from the current study’s

analysis. One male face was found to be included twice but since the faces were randomly

included in the experiment trials, this did not impact the results of the analysis. In addition, one

white female face was not included in the previous study and to remain consistent in the dataset,

this face was also removed from this study’s analyses. Furthermore, there were 10 faces that

could have caused potential problems in the data. These problems consisted of hair that covered

certain parts of their face or the stimuli faces were wearing glasses which may have changed the

eye shape and size. One face featured parted lips and showed teeth in the smile, thus interfering

with the lip thickness measurement. Each of these faces were excluded from the dataset.

The total amount of stimuli faces that were used for analysis is 132 and all data

manipulation and analyses were completed in RStudio (http://www.rstudio.com/). The data from

the attention study that was recorded is the trusting decision and the cued or not cued condition

when the face was chosen to be trusted. Linear regression analyses were conducted on each of


the 11 facial metrics individually and all together against the proportion of trusting behavior for

all faces, as well as the proportion of trusting behavior after the attention manipulation.


Results

Upon inspection of the facial characteristics, each of the measures appeared to be

normally distributed, thus a linear model seemed most appropriate for the data. Using the

Shapiro-Wilk’s test, it was determined the 2 of the 11 facial characteristics were significantly

non-normal, these measures were the distance between pupils, which was hypothesized to be

positively correlated with trusting behavior, and nose shape, which was hypothesized to not be

correlated with trusting behavior. A separate linear regression was run for each of the 11 facial

characteristics individually, against the proportion that the face was trusted out of all the times it

was shown, regardless of if the face was cued. These data show the basic relationship between

facial characteristics as measured with objective measures and trusting behavior which was taken

from how often participants of the previous study would choose to explicitly trust one face over

another face in the experiment. However, each of the characteristics resulted with non-significant

relationships: Facial Width to Height Ratio (b= –0.25, t(130) = -0.72, p = 0.47), Upper head

Length (b= –0.28, t(130) = -0.58, p = 0.56), Face Roundedness (b= –0.12, t(130) = -0.38, p =

0.71), Face Heartshapeness (b= –0.26, t(130) = -0.81, p = 0.42), Cheekbone Prominence (b= –

0.54, t(130) = -0.93, p = 0.35), Cheekbone Height (b= –0.21, t(130) = -0.37, p = 0.71.), Eye

Shape (b= 0.29, t(130) = 1.23, p = 0.22), Eye Size (b= 1.79, t(130) = 1.09, p = 0.28), Distance

Between Pupils (b= 0.0005, t(130) = 0.21, p = 0.83), Lip Fullness (b= 0.39, t(130) = 0.47, p =

0.64), Nose Shape (b= –0.11, t(130) = -0.79, p = 0.43). These results show that facial metrics are

not indicative of one face being trusted over another in this particular study (see figures 4-14).

Next, the same linear model analyses were run using the difference between the

proportion of trusting in the not cued face condition and proportion of trusting in the cued face

condition, as a method to include the effect of attention. For each facial characteristic, we again


found no significant relationships between any variables: Facial Width to Height Ratio (b= –

0.18, t(130) = -0.55, p = 0.58), Upper head Length (b= –0.42, t(130) = -0.94, p = 0.34), Face

Roundedness (b= –0.13, t(130) = -0.45, p = 0.66), Face Heartshapeness (b= 0.11, t(130) = 0.39, p

= 0.70), Cheekbone Prominence (b= 0.18, t(130) = 0.33, p = 0.74), Cheekbone Height (b= –

0.30, t(130) = -0.59, p = 0.55), Eye Shape (b= -0.13, t(130) = -0.61, p = 0.54), Eye Size (b= -

0.34, t(130) = -0.23, p = 0.82), Distance Between Pupils (b= 0.001, t(130) = 0.53, p = 0.60), Lip

Fullness (b= 0.68, t(130) = 0.90, p = 0.37), Nose Shape (b= –0.05, t(130) = -0.365, p = 0.72).

These results further indicate that facial characteristics do not share a relationship with trusting

behavior even after accounting for the effect of attention.

After acknowledging the initial results, additional analyses were run on multiple

combinations of related characteristics to determine if trusting behaviors were not identifiable

from a single objective feature, but instead could be found in the corresponding features that all

promoted a trustworthy face. First, a multiple regression analysis was run on all the

characteristics simultaneously against the proportion of trusted decisions in all faces (see table 1)

as well as the difference of not cued from cued faces (see table 2). Neither function produced any

significant relationships. A simple correlation test was run on each of the facial characteristics in

order to view how related the various facial measures were. The face characteristics were then

converted into z scores and using data from the previous correlation, the most related scores were

then averaged together. Two groups of highly correlated features were identified: Cheekbone

Height, Facial Width to Height Ratio, and Face Roundedness were averaged to create a face

shape and Eye Shape and Eye size were averaged to create a single eye measure. Finally, 6

additional linear regression analyses were run on the averaged features. The proportion of trusted

decisions on all faces was run against average face shape (b= -0.01, t(130) = -0.55, p = 0.58), and


average eye shape (b= 0.02, t(130) = 1.18, p = 0.24), as well as the proportion of trusted

decisions for the difference between cued faces and not cued faces was run on average face

shape (b= -0.01, t(130) = -0.60, p = 0.55) and average eye shape (b= -0.01, t(130) = -0.43, p =

0.67). A multivariate linear regression including independent variable interaction was ran on the

proportion of all faces (see table 3) against the averaged face shape (b= <-0.01, t(130) = -0.24, p

= 0.81), the averaged eye shape (b= 0.01, t(130) = 1.63, p = 0.29), as well as the interaction

between independent variables (b= <-0.01, t(130) = -0.15, p = 0.88). Another was also ran on the

cued difference (see table 4) against the averaged face shape (b= -0.01, t(130) = -0.81, p = 0.42),

averaged eye shape (b= -0.01, t(130) = -0.58, p = 0.56), and the interaction (b= 0.01, t(130) =

0.71, p = 0.48). There were still no significant interactions in any of the analyses conducted.

Discussion

Although this study did not find any significant relationships or interactions between the

face characteristics and trusting behaviors regardless of cueing, it nonetheless identified very

important results. The study of physiognomy has developed controversial findings, especially in

the past 3 decades, over how accurate initial judgements of personality traits are based solely off

of facial appearance. The results posited here are not significant but because of the inconsistent

findings in the past, these results are still able to support some of previous research that also

found no correlations between facial characteristics and personality traits (Cohen, 1973; Alley

1978). Furthermore, there are specific exploratory variables that now have a base of research to

extend from. There is also the possibility that this study committed a type 2 error and the null

hypotheses were actually false though the results showed for the null hypothesis to be accepted.

Therefore, this study would first require replication to ensure there was not a false negative

reported.


Implications

This study analyzed the trusting behaviors of university students when presented with a

prosocial situation. The analyses were conducted on 4 separate trust conditions: the proportion of

trusted decisions regardless of an external cueing mechanism, the proportion of trusted decisions

when the stimulus face is cued, the proportion of trusted decisions when the stimulus face is not

cued, and the proportion of trusted decisions when the effect of cueing is taken into account.

None of the relationships for the any of the facial characteristics were significant. Therefore, the

main hypotheses that Facial width to height ratio, Face roundedness, Eye size, Eye shape, and

the Distance between pupils would all be positively correlated with trusting behavior was not

supported in any of the trusted proportion conditions. This conflicts with multiple of the previous

studies because the characteristics identified to have positive effects with trustworthiness were

all specifically mentioned and accounted for with the exception of facial width which had

contrasting effects based on whether the interaction involved competition with an outgroup.

Nonetheless, the results here are consistent with the theory that facial characteristics do not have

correlational relationships with personality traits of a subject and also provoke additional

skepticism for the experiments that found significant results. The secondary hypotheses on the

relationships between Cheekbone height, Upper head Length, Face heartshapeness and the

trusting decisions of the previous data were also based on the previous studies as each of the

metrics were related to facial height and correspondingly will have a lessened facial width.

Therefore, it was predicted that these measures would have a negative correlation with trusting

decisions, though this hypothesis is also unsupported. Finally, there were 3 facial characteristics

that were used as exploratory variables and the relationship between them could not be

hypothesized. These variables, Cheekbone prominence, Lip fullness, and Nose shape were also


uncorrelated with trusting behavior for any of the conditions. However, due to the fact that these

variables lack previous research, more analyses and data must be collected before no effect can

be confirmed.

Limitations

The lack of significant relationships in the analyses prompted an abundance of

consideration of different techniques or designs that could have been implemented. One of the

main limitations that should be acknowledged is the difference between trait descriptions and

behavioral actions. Each of the previous studies that were mentioned before described how facial

features were identified to be correlated with subjective ratings of personality traits such as

trustworthiness or attractiveness. For example, the objective facial features corresponded with

external ratings of trustworthiness, though these ratings of trustworthiness do not necessarily

predict that other individuals will trust those faces. These previous studies found a significant

effect, but because this study analyzed the facial features correlation to actual behaviors instead

of subjective ratings, it may be the reason for conflicting results. Therefore, if this study was

redesigned to analyze the relationships between objective facial features and participant’s

subjective ratings of the facial stimuli, it may produce different results. In addition, if the

distinction between subjective ratings and active trusting behaviors is not made clear in past

studies, it could be one of the potential explanations for the controversial findings from the past

few decades.

Furthermore, by using the actual trusting behaviors in this study, other social factors need

to be acknowledged. For example, because each of the participants in the attention study were

deceived into believing that they would be interacting with the person in the stimuli pictures, the

participants may have altered their judgements based on how they see that stimulus person. With


traits such as attractiveness, individuals can either specifically choose one person over another

regardless of feelings of trustworthiness or attention cueing or the individuals may avoid a

person they deem to be attractive because the participant may be intimidated by the

attractiveness. In either case, the results would then be disturbed by other effects interfering and

thus the objective measures would not reflect the choice being made. Situations like this could

potentially create a false positive result and should be kept in mind for future studies.

In addition, there are several lines of research on prosocial behavior that inspect the actor

in a prosocial situation and not the target of the behavior. Each of the studies cited here

(Cañigueral, & Hamilton, 2019; Dawans, Fischbacher, Kirschbaum, Fehr, & Heinrichs, 2012;

Guo, Sun, Cai, Zhang, & Song, 2019; Guo, Sun, & Li, 2018; Jones, 2008) focus on the role of

the actor in prosocial situations and how traits such as shyness, intelligence, stress reactivity, and

other factors all impact the decision to engage in prosocial behavior. Therefore, the participant’s

personality traits may impact the trusting behavior as much as the objective face measures do.

Another issue with the participant’s trusting behaviors is that they are not able to mimic the

decision that each participant would make outside of the study. Thus, because the trust

behaviors were observed in a lab setting under a forced option situation, the results are not

representative of real-world actions and decisions.

There were also a few operational limitations to the objective face analysis that could

have created a false negative result. First, the stimuli pictures were not systematically captured.

Researchers in the original study instructed picture stimuli participants to smile without showing

teeth or not smile at all, however, since these people had the option to choose to take the picture

differently or even smile differently when they choose to smile, there are notable differences

between pictures. One of the studies in facial features analyzes the effect of eyes being open,


closed, or looking away (Manesi, Van Lange, & Pollet, 2016). These findings are important

because while people are asked to smile, they each squint or close their eyes in different

amounts, thus possibly leading to a misleading characteristic during measurement. As the

attention experiment also notes, expressions on the stimuli picture’s faces and the emotional

ratings of those pictures can influence giving behavior. Therefore, if social situations can impact

the emotional salience of each photo then the slight difference in expressions between the faces

may alter the trusting decision. Another issue with the faces used is that many of the pictures

were not taken with the same qualities and so each photo had to be manually adjusted to attempt

to decrease the amount of photograph discrepancy. While it is important to have differing and

distinct features between stimuli faces, having a standardized photograph pool to use as the

recipients of trust may contribute to observing an effect. Finally, the data on trusting behaviors

itself was taken from another study that had a different research questions and different

experiment goals in mind. Because of this, although the simple results are able to translate onto

this current study, there may be errors in study design as it relates to the current research

question.

Future Directions

As mentioned multiple times already, this area of study is controversial and has held

multiple conflicting views in the past century. This fact alone requires that more research is

conducted to test and retest the results that have already been determined. For future studies in

objective facial measures, there are multiple improvements to be made. First, using a new

stimulus set that is either computer generated faces, or controlled for expressions, facial hair,

photographic qualities, and equal ethnic group representation would be able to increase the

validity of the results. Additionally, this study can be divided into two separate future lines of


research with one targeting objective facial metric’s influence in applied behavioral observations

and the other focusing on how the same objective facial features are individually rated on

personality traits without the behavioral or social factors, thereby refining the research question

and implementation of quality experiments. Furthermore, the research gathered on

trustworthiness and attractiveness shows that the two are correlated, even though the two

different traits are shown to be related to different features. The correlation may be due to the

halo effect in which seeing a stimulus face as generally good may influence the ratings of

trustworthiness as well as attractiveness despite the two actually being correlated. Therefore,

additional exploration into perceived attractiveness and trustworthiness and how each trait is

related to facial characteristics would be important.

Conclusion

This study resulted in no significant relationships between the facial characteristics and

trusting decisions, however, there is still a lot of data that can be taken from these results. Having

no significant relationships conflicts with several contending studies that propose these same

measures are correlated with personality traits such as trustworthiness and attractiveness.

Because of this, it is important to replicate the findings of the previous research as well, as the

results of this study, in order to start bridging the gap between this controversial debate.

Physiognomy is an interesting topic and there is clear evidence of humans making automatic and

highly consistent judgements on faces; thus, it is imperative that these judgements are able to be

reasoned and supported, or otherwise argued and discarded as useless first impressions.

Regardless, there is a reason why humans have developed the ability to recognize and judge

faces with such refined skill and that is a research area that is waiting to be explored.


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Appendix

https://link-gale-com.colorado.idm.oclc.org/apps/doc/A471838782/PPNU?u=coloboulder&sid=PPNU&xid=b27fa5d7




Figure 1: Example of measurements taken for each face (the face shown here is an example from

the Chicago Face Database and was not used in the study). Each line represents a different

measurement. In cases such as the forehead height, nose height, or lip height, the example line is

offset to show a separate measurement, however the measurements used would be in the same

position as the facial height.


Figure 2: Example of what is seen by experiment subject for the stream of letters.

Figure 3: Screen showing instructions for faces displayed on the screen for subjects.


Figure 4: Correlation between the Proportion of Trusting Behavior regardless of cueing and Facial Width

to Height Ratio

Figure 5: Correlation between the Proportion of Trusting Behavior regardless of cueing and Face

Roundedness


Figure 6: Correlation between the Proportion of Trusting Behavior regardless of cueing and Eye Shape

Figure 7: Correlation between the Proportion of Trusting Behavior regardless of cueing and Eye Size


Figure 8: Correlation between the Proportion of Trusting Behavior regardless of cueing and the Distance

between the Center of the Pupils

Figure 9: Correlation between the Proportion of Trusting Behavior regardless of cueing and Face

Heartshapeness


Figure 10: Correlation between the Proportion of Trusting Behavior regardless of cueing and Upper Head

Length

Figure 11: Correlation between the Proportion of Trusting Behavior regardless of cueing and Cheekbone

Height


Figure 12: Correlation between the Proportion of Trusting Behavior regardless of cueing and Cheekbone

Prominence

Figure 13: Correlation between the Proportion of Trusting Behavior regardless of cueing and Lip Fullness


Figure 14: Correlation between the Proportion of Trusting Behavior regardless of cueing and Nose Shape


Estimate Standard

Error t- Value Pr(>ltl)

Intercept 2.74 5.47 0.50 0.617

Facial Width to

Height Ratio

0.48 1.05 0.46 0.649

Upper Head Length -0.19 0.63 -0.31 0.757

Face Roundedness -0.95 1.41 -0.68 0.501

Face Heartshapeness -1.95 4.89 0.40 0.691

Cheekbone Prominence 1.64 8.52 0.19 0.848

Cheekbone Height 0.03 1.06 0.03 0.976

Eye Shape 0.42 0.77 0.54 0.590

Eye Size -1.31 5.61 -0.23 0.816

Distance Between Pupils 0.00 0.00 0.25 0.805

Lip Fullness 0.76 1.08 0.70 0.485

Nose Shape -0.13 0.19 -0.70 0.487

Table 1: Multiple Linear Regression for All Characteristics and All Faces


Estimate Standard


Intercept 0.81 4.94 0.16 0.870

Facial Width to

Height Ratio

-0.70 0.95 -0.74 0.462

Upper Head Length -0.85 0.57 -1.50 0.136

Face Roundedness 0.90 1.28 0.70 0.483

Face Heartshapeness 0.06 4.42 0.01 0.990

Cheekbone Prominence 0.84 7.71 0.11 0.913

Cheekbone Height -1.29 0.96 -1.35 0.180

Eye Shape -0.79 0.70 -1.13 0.262

Eye Size 3.66 5.07 0.72 0.472

Distance Between Pupils -0.00 0.00 -0.06 0.953

Lip Fullness 0.79 0.97 0.81 0.421

Nose Shape -0.08 0.17 -0.50 0.620

Table 2: Multiple Linear Regression for All Characteristics and the Difference Proportion

Between Cued Faces and Not Cued Faces


Estimate Standard


Intercept 0.50 0.01 38.12 <.001

Average Face Shape <-0.01 0.01 -0.23 0.813

Average Eye Shape 0.01 0.01 1.06 0.290

Avg Face : Avg Eye <-0.01 0.02 -0.15 0.880

Table 3: Multivariate Linear Regression with Interactions for Average Face Shape and Average

Eye Shape with proportion of all faces

Estimate Standard


Intercept 0.04 0.01 3.08 0.003

Average Face Shape -0.01 0.01 -0.81 0.422

Average Eye Shape -0.01 0.01 -0.58 0.563

Avg Face : Avg Eye 0.01 0.01 0.71 0.481

Table 4: Multivariate Linear Regression with Interactions for Average Face Shape and Average

Eye Shape with proportion of cued difference.

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Running Head: INFLUENCE OF OBJECTIVE FACIAL FEATURES ON