White et al. Cognitive Research: Principles and Implications (2017) 2:23
DOI 10.1186/s41235-017-0058-3

Cognitive Research: Principles
and Implications

ORIGINAL ARTICLE

Open Access

Choosing face: The curse of self in profile
image selection
David White1,3* , Clare A. M. Sutherland2,3 and Amy L. Burton4

Abstract
People draw automatic social inferences from photos of unfamiliar faces and these first impressions are associatedwith important real-world outcomes. Here we examine the effect of selecting online profile images on first impressions.
We model the process of profile image selection by asking participants to indicate the likelihood that images of theirown face (“self-selection”) and of an unfamiliar face (“other-selection”) would be used as profile images on key social
networking sites. Across two large Internet-based studies (n = 610), in line with predictions, image selections accentuatedfavorable social impressions and these impressions were aligned to the social context of the networking sites. However,contrary to predictions based on people’s general expertise in self-presentation, other-selected images conferred morefavorable impressions than self-selected images. We conclude that people make suboptimal choices when selecting theirown profile pictures, such that self-perception places important limits on facial first impressions formed by others. These
results underscore the dynamic nature of person perception in real-world contexts.
Keywords: Face perception, Self perception, Impression formation, Interpersonal accuracy, Online social networks, Visualcommunication, Photography

SignificanceSelecting profile pictures is a common task in the digitalage. Research suggests that choosing the right image
may be critical – people’s first impressions from profile
photos shape important decisions, such as choices ofwhom to date, befriend, or employ. Surprisingly, theprocess of image selection has not yet been studied directly. Here, we show that people select profile picturesthat produce positive impressions on unfamiliar viewers.
These impressions are tailored to fit specific networking
contexts: dating images appear more attractive and professional images appear more competent. Strikingly, weshow for the first time that participants select more flattering profile images when selecting pictures for otherpeople compared with when selecting for themselves.
This phenomenon has clear practical significance: should
people wish to “put their best face forward,” they should
ask someone else to choose it.

* Correspondence: david.white@unsw.edu.au
1
School of Psychology, University of New South Wales Sydney, Sydney,
Australia
3
ARC Centre of Excellence in Cognition and its Disorders, Macquarie
University, Sydney, NSW, Australia
Full list of author information is available at the end of the article

BackgroundKey events in our professional, social, and romantic lives
unfold on the Internet. Approximately one-third of employers search online for information on job candidates(Acquisti & Fong, 2015), half of British adults that are
currently searching for a relationship have used online
dating (YouGov, 2014), and 1.79 billion people worldwide have an active Facebook account (Facebook, 2016).
As a result, we are continually forming first impressionsof unfamiliar people in professional, romantic, and social
contexts via social networking sites. Pictures that arechosen to represent us in these online environments—“profile images”—establish a critical link between an
individual’s online and offline personas.
Profile image choices are likely to have a significant
impact on the way people are perceived by others. We
make inferences about an individual’s character andpersonality within a split second of exposure to a photograph of their face (Willis & Todorov, 2006). These
impressions have been shown to predict important and diverse real-world outcomes—both online and offline—including the number of votes received by politicalcandidates (Olivola, Funk, & Todorov, 2014), company
profits generated during a CEO’s tenure (Rule & Ambady,© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

White et al. Cognitive Research: Principles and Implications (2017) 2:23

Page 2 of 92008), selection as a suspect from police line-ups (Flowe &Humphries, 2011), and the popularity of an Airbnb host’s
rental accommodation (Ert, Fleischer, & Magen, 2016).
Importantly, previous studies are almost exclusively
based on the premise that a single image is representative of a person’s appearance. In studies of facial first impressions, participants tend to form impressions ofcomputer-generated images or photographs captured incontrolled studio conditions (e.g., expressionless, facing
forwards; for a review see Todorov, Olivola, Dotsch, &Mende-Siedlecki, 2015). This procedure minimizes natural variation found in photos of faces captured outsideof the laboratory. However, recent studies have emphasized the important role of this natural variability informing social impressions. Critically, ratings of attractiveness (Jenkins, White, Van Montfort, & Burton, 2011)
and character traits (Hehman, Flake, & Freeman, 2015;
Todorov & Porter, 2014; cf. McCurrie et al., 2016) can vary
more across different images of the same person’s face, thanthey do across faces of different people.
In previous works, the types of images found on theInternet have been described as “ambient” photographs,
as they capture dynamic aspects of faces and the environment such as expression, pose, and lighting (see Fig. 1;
Jenkins et al., 2011; Sutherland et al., 2013; Vernon,
Sutherland, Young, & Hartley, 2014). Importantly, influential models of social trait judgments that have been

generated by ratings of studio-captured imagery (Oosterhof & Todorov, 2008) do not fully capture impressions
made from ambient facial images (Sutherland et al.,2013; Todorov & Porter, 2014).
Focus on invariant aspects of facial appearance has
also caused facial first impression research to overlook
the importance that photograph selection has in moderating the social impact of a person’s face. However,recent work has begun to address this shortfall. In one
recent study, unfamiliar viewers were able to selectstudio-controlled images of unfamiliar faces that accentuated traits associated with specific scenarios: for example, selecting images for a resume that accentuatedimpressions of competence, relative to other images ofthat individual (Todorov & Porter, 2014, Experiments 2& 3). Separately, studies of impression management inonline social networks have found that people report
selecting images to transmit desirable impressions(Siibak, 2009) and that dating profile images tend to portray people to be more attractive than images taken in alaboratory (Hancock & Toma, 2009).
Critically, however, the process of self-selecting profile
images has not been studied experimentally. Thus, while
it is clear that variation in photos of the same face canmodulate social impression formation (see also Jenkins
et al., 2011; Wu, Sheppard, & Mitchell, 2016), it is notclear how well people exploit this variation to confer

Fig. 1 Example image sets provided by two participants in the Profile Image Dataset. Each participant selected the most and least likely image tobe used in three social media contexts (see Fig. 3a), then rated the likelihood that each image would be used in each context, before rating trait
impressions. They then repeated this procedure with an unfamiliar face. Images used with permission and the full Profile Image Dataset isavailable online in Additional file 2

White et al. Cognitive Research: Principles and Implications (2017) 2:23

Page 3 of 9favorable impressions. This is important because perception of one’s own face is often less veridical than perception of other faces. For example, when asked to selectimages that represent the best likeness of themselvesfrom photo albums, participants choose images that areless representative of their current appearance than images chosen by people with no prior familiarity (White,
Burton, & Kemp, 2015). Previous studies also report systematic biases to choose images of their own face as better likenesses when they have been digitally altered to be
more typical (Allen, Brady, & Tredoux, 2009), more attractive (Epley & Whitchurch, 2008; Zell & Balcetis,2012), and more trustworthy (Verosky & Todorov,2010); perhaps reflecting a general bias to evaluate oneself more favorably than others (Epley & Whitchurch,
2008; cf. Brown, 2012).
Given that people appear to be sensitive to variation in
impressions produced by different photographs(Todorov & Porter, 2014) and are motivated to portraythemselves favorably in profile images (Hancock &Toma, 2009; Siibak, 2009), we predicted that people
would be able to select images of themselves to accentuate positive traits. In addition, we compared the benefitof selecting profile images of oneself to selection by an
unfamiliar person. This comparison is critical in order todifferentiate two, equally plausible, hypotheses: namelythat self-selection may help or hinder the process of
selecting favorable profile images.
On the one hand, the ability to select flattering profile
images may be hindered by an impaired ability to view
one’s own face accurately (e.g., White et al., 2015) and inan overly optimistic light (Epley & Whitchurch, 2008).
This evidence leads to the prediction that people would
select better photographs for strangers. On the other
hand, this ability may be enhanced by people’s expertisein selecting flattering online photographs of themselves(Hancock & Toma, 2009) and in self-presentation moregenerally (e.g., Goffman, 1959; Leary & Allen, 2011;
Schlenker, 2003). This reasoning leads to the opposite
prediction that people would select better photographs
for themselves.
We tested these predictions by examining the effect of
selecting profile images on first impressions. We asked
participants to indicate the likelihood that images of
their own face, and of an unfamiliar face, would be usedas profile images in three key social networking contexts(Facebook, dating, professional: see the “Profile Image
Dataset” section). We then recruited unfamiliar viewers
via the Internet to provide trait impressions of theseimages (see the Calibration experiment and Selection experiment sections). This approach enabled us to systematically examine the impact of photo selection on
appearance-based inferences for the first time, by comparing the effect of selecting one’s own profile image(self-selection) to selection by unfamiliar others (otherselection).

Method and results
Profile Image Dataset

The Profile Image Dataset collected in this research consists of 12 images each of 102 students (1224 total
images), downloaded from their Facebook accounts.1
Previous studies of photo selection have used studiocaptured imagery that does not capture the full diversityof facial images shared via social media (Todorov &Porter, 2014), in terms of variations in pose, expression,and image-capture conditions across images of the same
face (see Jenkins et al., 2011). Downloading photos fromFacebook ensured that these were representative of variations in portrait photographs that are posted online.
In total, 114 first year undergraduate students consented to take part in the study in exchange for course
credit. Participants provided 12 images in which theirface: (1) took up a substantial proportion of the overall
image; (2) was in clear view; (3) faced the camera; and(4) was not obscured (e.g., by sunglasses, hair, or hands).
Any images not meeting these criteria or with poor resolution were rejected and the participant was asked to replace the image with another from their Facebook
gallery. In total, 102 participants (51 women; mean age= 19.4 years, SD = 2.28 years) provided a full set of 12
usable images. Images were then cropped to frame theface at a fixed aspect ratio and resized to 200 ×
300 pixels. Examples of images provided by two participants are shown in Fig. 1.
To capture self-selection profile image preferences, theset of 12 downloaded images were then presented on acomputer monitor. Participants were asked to selectwhich of the 12 images they were most and least likely
to use as profile images for Facebook, professional (e.g.,
LinkedIn), and dating (e.g., Match.com) network sites.
Context order was counterbalanced across subjects. The
most and least likely profile images were used in the Selection experiment. After making these selections,participants then indicated their profile image preferences by rating the likelihood that they would use eachof their 12 images in these contexts.
Finally, participants rated their images for five social
impressions (attractiveness, trustworthiness, dominance,competence, confidence). These five ratings were madeconcurrently. Trustworthiness, dominance, and attractiveness were included to capture the three main dimensions of facial first impressions (Oosterhof & Todorov,
2008; Sutherland et al., 2013). Competence and confidence were included because these judgments are associated with romantic and professional success (Murphy
et al., 2015; Todorov et al., 2015). Both selection likelihood and trait judgments were rated on scales from 1

White et al. Cognitive Research: Principles and Implications (2017) 2:23

(very low) to 9 (very high), and these ratings were usedin the Calibration experiment.
To capture other-selection profile image preferences,
participants completed an identical procedure with a setof 12 images of a randomly selected subject of the samegender that had participated in the study previously. The
experimenter confirmed that the participant was
unfamiliar with the person pictured in the photographs
before recording their selections and instructed them toevaluate the likelihood that they would select each image
if they were the person depicted. Order of self/other rating procedures was counterbalanced across participants.
Online rating experiments

Next, we recruited new unfamiliar viewers via the Internet to rate the trait impressions produced by the Profile
Image Dataset. Online ratings were collected in two experiments. First, in the Calibration experiment, we collected ratings of trait impressions to the entire image
database and calculated the extent to which these first
impressions were predicted by profile image preferences,provided during collection of the Profile Image Dataset.
Second, in the Selection experiment, we collected ratingsof trait impressions to only those images that had been
explicitly selected as most/least likely to be selected as
profile images. In both experiments, we examine themoderating effect of profile image preferences on first
impressions; comparing the impact of participants’
preferences for images of their own face (self-selection)
to preferences for images of an unfamiliar face (otherselection).
Calibration experiment
MethodA total of 178 unfamiliar viewers were recruited online
via the online crowdsourcing platform Amazon Mechanical Turk (M-Turk; see Buhrmester, Kwang, & Gosling,2011) and were paid US$1. Eighteen were excluded before analysis as they reported engaging in a distracting
activity during the experiment, leaving a final sample of
160 (80 women, mean age = 36.4 years; SD = 12.2 years).
Each unfamiliar viewer rated 12 different images of 12
different people (144 images presented individually in arandom order). This method resulted in a predetermined sample size of 20 raters per image that wasconsidered sufficient to provide a stable estimate oftrait impressions (see Oosterhof & Todorov, 2008).
Viewers were instructed to rate how attractive, trustworthy, dominant, confident, and competent the person
appeared in each image on a scale from 1 (very low) to
9 (very high). These five ratings were made on separate
rating scales and scales were presented concurrently on
the same screen as the photos.

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Results

We calculated the extent to which both self-photographand other-photograph selection likelihood ratings were
calibrated with: (1) participants’ own ratings of trait impressions collected in the image collection phase (Owncalibration); and (2) ratings of unfamiliar viewers trait
impressions, collected via the Internet (Internet calibration).2 Calibration scores indexed participants’ ability tochoose images that accentuated positive impressions andwere calculated separately by face identity using Spearman’s rank correlation. We calculated calibration foreach of the three social network contexts, to reveal
which traits were most accentuated by profile image
selection in each context, and analyzed these data separately for own and Internet ratings. Results of this analysis are shown in Fig. 2.
Own and Internet calibration scores were analyzed bymixed ANOVA with between-subject factor of Selection
Type (self, other) and within-subject factors Context(Facebook, dating, professional) and Trait (attractiveness, trustworthiness, dominance, competence, confidence). For own calibration, the main effect of Selection
Type was non-significant, F (1,202) = 1.48, p = 0.25, η2p =0.007, with high average calibration between image
selection and positive social impressions for both selfselected (M = 0.509; SD = 0.319) and other-selected
photographs (M = 0.543; SD = 0.317). For Internet calibration, the main effect of Selection Type was significant, F (1,202) = 4.12, p = 0.044, η2p = 0.020. Critically,
there was greater calibration between image selection
and positive social impressions for other-selected (M =0.227; SD = 0.340) compared to self-selected photographs (M = 0.165; SD = 0.344).
In both own and Internet calibration analysis, theinteraction between Context and Selection Type was significant (Own: F [2, 404] = 4.16, p = 0.016, η2p = 0.020;
Internet: F [2, 404] = 4.26, p = 0.015, η2p = 0.021), reflective of higher calibration for other-selections compared
to self-selections in professional (Own: F [1, 202] = 5.73,
p = 0.018, η2p = 0.028; Internet: F [1, 202] = 11.16, p <0.000, η2p = 0.052) but not Facebook or dating contexts(all Fs < 1). In general, interactions revealed that traits
were aligned to network contexts, such that attractiveness tended to calibrate most with social and dating
networks and competence and trustworthiness to professional networks (see Additional file 1 for full details of
this analysis).
Discussion

Consistent with predictions based on studies of selfpresentation (e.g., Hancock & Toma, 2009; Siibak, 2009),
the pattern of results observed in the Calibration
experiment lends broad support to the notion thatpeople select images of themselves to accentuate positive

White et al. Cognitive Research: Principles and Implications (2017) 2:23

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Fig. 2 Results from the Calibration experiment. Calibration was computed separately for self-selection and other-selection as the correlation between
likelihood of profile image choice and: (1) participants’ own trait impressions (top panels); (2) impressions of unfamiliar viewers recruited via the Internet(bottom panels). Higher calibration indexes participants’ ability to choose profile images that increase positive impressions. Participants’ likelihood of
selecting a photograph of their own face (self-selection: top left) and an unfamiliar face (other-selection: top right) was strongly calibrated to their own
impressions. However, in general, self-selections were less well calibrated to the impressions of unfamiliar viewers (bottom left) than were other-selections(bottom right). Error bars represent ±1 standard error

trait impressions and that these selections are fitted to specific social networking contexts (cf. Leary & Allen, 2011).
Strikingly, however, the profile image preferences indicatedin other-selections were more calibrated to impressions
formed by unfamiliar viewers than self-selections. This result is contrary to the prediction based on self-presentation
literature, that participants would select more flattering images of themselves than of other people.
Notably, the cost of self-selection applied only to professional profile image selections, raising the possibility
that costs of self-selection were specific to this network
context. Therefore, in a second experiment, we again examined effects of self-selection on first impressions, butusing a more direct test: comparing trait judgments toimages that had been explicitly chosen as most and least
likely to be used as profile images for different network
contexts (see “Profile Image Dataset” method).
In the Calibration experiment, unfamiliar viewers also
rated 12 images of a single individual, making it likelythat this diluted their first impressions. Further, theseviewers made multiple trait judgments to a single photo,which may increase overlap in these judgments (Rhodes,

2006). We addressed these potential concerns in the Selection experiment, by now presenting unfamiliar
viewers with only two images of each participant (most/least likely profile image choice) and asking viewers torate these images for a single trait impression.
Selection experiment
MethodA total of 482 new unfamiliar viewers were recruited online via M-Turk and were paid US$1. Data from 50
viewers were excluded from the analysis because they
did not pass the quality criteria used in the previous experiment, leaving a final sample of 432 (273 women),with an average age of 36.4 years (SD = 11.6 years).
In this experiment, we focused on impressions of attractiveness, trustworthiness, and competence. Viewers
rated images that had been selected by participants in
the Profile Image Dataset as being most and least likely
to be used in each social network context. This procedure resulted in 12 images of each pictured identity (3contexts × self/other selected × least/most likely; Fig. 3a).
To balance the design of the Selection experiment weWhite et al. Cognitive Research: Principles and Implications (2017) 2:23

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Fig. 3 a Examples of most and least likely image selections used in the Selection experiment. Images are used with permission and the full set ofexperimental materials are available online in Additional file 5. b Mean difference between trait impression ratings to photographs chosen as most andleast likely profile pictures for each of three contexts. Positive values signify higher trait ratings for images selected as “most likely” profile images, again
revealing more positive first impressions for images that were selected by an unfamiliar other (light gray) when compared to self-selections (dark gray).
c Significant two-way interactions (see text for details of analysis). All error bars denote ±1 standard error

randomly selected a subset of 96 pictured identities fromthe Profile Image Dataset. A total of 1152 images weredivided into 12 counterbalanced versions of the experiment. This method resulted in a sample size of 36
viewers per counterbalanced version. Each viewer rated
192 images on a single trait (attractiveness, trustworthiness, competence), with each pictured identity appearing
twice (most and least likely images from one combination of Context/Selection Type). The experimental design ensured that assignment of pictured identities toconditions was counterbalanced across viewers.
Results

Difference scores were calculated separately for eachviewer in the Selection experiment by subtracting theirmean trait ratings to “least likely” images from ratings to“most likely” images. This provided a measure of the effect of image selection on facial first impressions at thelevel of the viewer. These data were analyzed by using amixed-factor ANOVA with between-subject factor of
Trait (attractiveness/trustworthiness/competence) andwithin-subject factors of Selection Type (self/other) andContext (Facebook/dating/professional). Mean difference
scores for each condition are shown in Fig. 3b.
This analysis revealed a significant main effect of Selection Type, F (2, 429) = 77.2; p < 0.001, η2p = 0.152, withother-selections again enhancing trait impressions morethan self-selections. The main effect of Context was alsosignificant, F (2, 858) = 78.7, p < 0.001, η2p = 0.155, withimage selection having the greatest effect on trait judgments in professional network (M = 0.621; SD = 0.787)
compared with Facebook (M = 0.370; SD = 0.657) anddating contexts (M = 0.255; SD = 0.587).
Main effects were qualified by three two-way interactions. First, the interaction between Context and Trait
was significant (see Fig. 3c [left]: F [4, 858] = 73.8; p <White et al. Cognitive Research: Principles and Implications (2017) 2:23

Page 7 of 90.001 η2p = 0.256), indicating that different traits wereaccentuated in different online contexts. Specifically, selections for Facebook (M = 0.619; SD = 0.355) and dating (M= 0.475; SD = 0.366) accentuated ratings of attractivenessmore than professional networking selections (M = 0.246;
SD = 0.380). Selections for professional networking contexts
conferred significantly more benefit to trustworthiness (M= 0.590; SD = 0.648) and competence (M = 1.029; SD =0.638) relative to selections for Facebook (Trustworthiness:
M = 0.137; SD = 0.470, Competence: M = 0.353; SD = 0.503)
and Dating (Trustworthiness: M = 0.058; SD = 0.372,
Competence: M = 0.232; SD = 0.391).
Second, the interaction between Selection Type andTrait was significant (see Fig. 3c [middle]: F [4, 858] =9.18; p < 0.001; η2p = 0.041). The benefit of other-selection
over self-selection was carried by other-selections conferring more positive impressions for trustworthiness, F(1, 429) = 46.2; p < 0.001; η2p = 0.103, and competence, F(1, 429) = 46.8; p < 0.001; η2p = 0.104. Interestingly, otherselections did not confer a significant benefit for attractiveness impressions, F (1, 429) = 2.47; p > 0.05; η2p =0.012. Third, the interaction between Selection Type andContext was significant (see Fig. 3c [right]: F [4, 858] =9.18; p < 0.001; η2p = 0.041). Other-selections produced
more positive effects on trait impressions in comparison
to self-selection across all contexts, but to differing degrees (Facebook: F [1, 429] = 27.6; p < 0.000; η2p = 0.063;
dating: F [1, 429] = 53.1; p < 0.001; η2p = 0.112; professional: F [1, 429] = 10.5; p = 0.001; η2p = 0.024).

of social contexts (e.g., Goffman, 1959; Schlenker, 2003),
including social networking sites (see Hancock & Toma,
2009; Siibak, 2009). Interestingly, pioneering work by
Erving Goffman conceptualized self-presentation as aprocess of projecting deliberately choreographed “face”
to others (Goffman, 1955) and a large literature showsthat people manage their appearance to improve likelihood of desirable outcomes.
Given this apparent expertise in showing face, it might
be expected that people would also be experts in choosing
face: they would be more adept at selecting favorable facial
images of themselves than they would be at selecting favorable facial images of unfamiliar people. However, ourresults clearly argue against any such self-expertise.
Although our results are surprising in the context of
self-enhancement research, they may be related to thefinding that people tend to perceive themselves morepositively than other people. For example, it has beenshown that people evaluate images of one’s own face as
more trustworthy than unfamiliar faces (Verosky &Todorov, 2010). Importantly, the task faced when selecting profile images is to discriminate between images ofyour own face. The existence of positivity biases istherefore unlikely to improve a person’s ability to makethese selections, if such biases are independent of discrimination (cf. Macmillan & Creelman, 2004). One apparently plausible account of our findings is that,somewhat paradoxically, these self-enhancing biases inperception may in fact interfere with a person’s ability todiscriminate between images when selecting one to portray a positive impression.
Although plausible, this account of self-selection costs
is inconsistent with the fact that costs were specific tocertain trait impressions. In the “Selection experiment,”
although we observed overall costs within each social
network context, costs were nevertheless specific to impressions of trustworthiness and competence and were
not observed for attractiveness. Previous studies haveshown that people perceive their own face to be bothmore trustworthy (Verosky & Todorov, 2010) and more
attractive than other people’s faces (Epley & Whitchurch,
2008; Zell & Balcetis, 2012). Explanations of self-selection
costs in terms of self-enhancing biases are not able to account for the fact that we observed costs in one trait
evaluation but not the other. This in turn suggests thatthe mechanisms responsible for self-enhancing biases, andthe cost of self-selection reported here, are relatively
independent.
Given that this is the first report of self-selection costs
in profile image choice, future research is necessary to
elucidate the precise mechanisms underlying these costs.
In particular, it will be important to examine the contribution of familiarity more closely. Recent work shows
similar self-selection costs when choosing images that

Discussion

Results of the Selection experiment replicated the main
findings of the previous experiment. First, profile image
selection accentuated positive first impressions and these
impressions were matched to specific network contexts.
This confirms that people are aware of the different impressions that different images confer and adjust theirchoices to fit the particular context. Second, and more
surprisingly, self-selected profile images conferred lessfavorable impressions when compared to other-selected
images. Whereas this effect was limited to professional
networking contexts in the Calibration experiment,using a more sensitive test in the Selection experiment,
we observed the effect across all networking contexts.

General discussion
This paper reports the first systematic test of people’s
profile image selection behavior. Strikingly, we foundthat people selected images of themselves that cast lessfavorable first impressions than images selected bystrangers. At face value, this result appears to run contrary to a vast literature showing that people portraythemselves more positively than other people. Selfenhancement is a pervasive human tendency in a variety

White et al. Cognitive Research: Principles and Implications (2017) 2:23

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are representative of our current appearance: peoplechoose images of themselves that are less representative
than images chosen by unfamiliar viewers after brief
familiarization (White et al., 2015). This shows that difficulties in selecting images of our own face are not specific to socially motivated tasks. Interestingly, very
recent evidence suggests that memory for specificimages of familiar faces may be impaired relative to unfamiliar faces (Armann, Jenkins, & Burton, 2016); raising
the possibility that familiarity for any face—not only ourown face—causes difficulty in discriminating between
different images of that face. Future studies designed totest this possibility can help to separate contributions ofvisual familiarity from the broader cognitive system ofself-representation (see Devue & Brédart, 2011).
Notwithstanding a large cost of self-selection, we
found that first impressions were substantially enhanced
by profile image selection and these selections were tailored to social networking contexts. Overall, participants
were aware of the impressions made by different imagesof their face and made profile image choices accordingly,
fitting facial first impressions to the social context of theaudience. This extends recent work showing that people
can detect subtle differences in impressions made by different photos of the same unfamiliar face, both whenphotos are captured in controlled studio conditions(Todorov & Porter, 2014) and in ambient environments(Jenkins et al., 2011). In parallel, computer scientists
have made impressive progress in developing automatedmethods for predicting human’s first impressions fromambient facial imagery. Using deep neural networks
trained on human’s ratings of first impressions,
McCurrie et al. (2016) were able to predict facial first
impressions from face images relatively accurately (cf.
Vernon et al., 2014). In future work, it may be useful tocompare human profile selection choices to these computational benchmarks.
More broadly, our results have implications for selfpresentation in modern society. Recent data show that
1.8 billion images are uploaded every day to popularsocial networking sites (KPCB, 2014), leading to a multitude of new opportunities for self-monitoring behavior(see also Hancock & Toma, 2009; Siibak, 2009; Van
Dijck, 2008; Walther, 1996). Self-selection of images is amulti-staged process: taking “selfies” (see Re, Wang, He,& Rule, 2016); deleting images from digital cameras;
selecting images to upload to social media; “untagging”
images on Facebook (see Lang & Barton, 2015). In thiscontext, an important limitation of the present study isthat images were initially downloaded from Facebook.
Therefore, selection behavior reported in this paper may
represent the final stage in a hierarchy of selection filtersthat combine to determine a person’s online appearance.
Nevertheless, given the robust cost of self-selection we

observe here, it is likely that this effect serves to limit
positive facial impressions at multiple levels in this hierarchy, thereby curtailing people’s ability to “put theirbest face forward.”

Conclusions
Given the diverse opportunities for self-monitoring via
digital media, understanding the dynamics of selection
behavior will be important in developing models of facial
first impressions that are relevant to real-world social
networking contexts. We propose that image selection
tasks can provide a lens through which to understandprocesses that modulate the signaling and receiving ofthese impressions in daily life—from current impression
management goals to inherent perceptual abilities. For
now, it is clear that the facial first impressions we transmit to unfamiliar people—via online social networks—are constrained by how we perceive ourselves. Our
results also impart practical wisdom: when it comes tochoosing the best version of ourselves, it may be wise tolet other people choose for us.
Endnotes
1
All images and accompanying rating data are available
in Additional files. Participants have consented to the useof their images in future research. To protect participants’privacy, the mapping between images and rating data has
been withheld. Should researchers require this information, the full Profile Image Dataset is available from theauthors on request.
2
Because average ratings of M-Turk raters were used tocompute calibration, we checked the stability of theseratings across subjects using Cronbach’s Alpha. This analysis confirmed high levels of reliability for all impressions(Attractiveness = 0.893, Trustworthiness = 0.821, Dominance = 0.721, Competence = 0.756, Confidence = 0.785).
Additional files
Additional file 1: Full description of analysis in the Calibration
experiment. (PDF 166 KB)
Additional file 2: Images used in the Calibration experiment. (PDF 16.7 MB)
Additional file 3: Raw rating data from Calibration experiment.
(XLSX 107 KB)
Additional file 4: Spearman's rho scores from Calibration experiment.
(XLSX 125 KB)
Additional file 5: Images used in the Selection experiment. (PDF 17.0 MB)
Additional file 6: Rating data from the Selection experiment (by viewer).
(XLSX 87.4 KB)
Additional file 7: Rating data from the Selection experiment (by image).
(XLSX 528 KB)
Acknowledgements
This research was supported by Australian Research Council grants to DW(LP130100702) and CS (DP170104602), postdoctoral research support from theAustralian Research Council Centre of Excellence in Cognition and its Disorders,White et al. Cognitive Research: Principles and Implications (2017) 2:23

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University of Western Australia (CE110001021) and an ESRC Overseas Institutional
Visit award (ES/1900748/1) to CS. The authors thank Manuela Tan andundergraduate volunteers at the UNSW School of Psychology for assisting withthe pilot work that led to this research.

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Authors’ contributions
DW developed the study concept. All authors contributed to the study
design. AB performed experimentation and data collection. All authors
contributed to the data analysis and prepared Additional files 1, 2, 3, 4, 5, 6
and 7. DW drafted the manuscript and CS provided critical revisions. All
authors approved the final version of the manuscript for submission.
Competing interests
The authors declare that they have no competing interests.
Ethics approval and consent to participate
This study was approved by the Human Research Ethics Committee at the
University of New South Wales. All participants provided written informed
consent and appropriate photographic release.
Author details
1
School of Psychology, University of New South Wales Sydney, Sydney,
Australia. 2School of Psychology, University of Western Australia, Crawley,
Australia. 3ARC Centre of Excellence in Cognition and its Disorders,
Macquarie University, Sydney, NSW, Australia. 4School of Psychology,
University of Sydney, Sydney, Australia.
Received: 14 December 2016 Accepted: 21 February 2017

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