photo editing research paper

• Open access
• Published: 06 April 2023

How photo editing in social media shapes self-perceived attractiveness and self-esteem via self-objectification and physical appearance comparisons

• Phillip Ozimek 1 ,
• Semina Lainas 2 ,
• Hans-Werner Bierhoff 2 &
• Elke Rohmann 2  

BMC Psychology volume  11 , Article number:  99 ( 2023 ) Cite this article

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As photo editing behavior to enhance one?s appearance in photos becomes more and more prevalent on social network sites (SNSs), potential risks are increasingly discussed as well. The purpose of this study is to examine the relationship between photo editing behavior, self-objectification, physical appearance comparisons, self-perceived attractiveness, and self-esteem.

403 participants completed self-report questionnaires measuring the aformentioned constructs. A parallel-sequential multiple mediation model was conducted to examine the relationship between photo editing behavior and self-esteem considering multiple mediators.

The results indicate that photo editing behavior is negatively related to self-perceived attractiveness and self-esteem mediated via self-objectification and physical appearance comparisons.

Conclusions

The postulated mediation model was justified by our data. Thus, SNS users should be aware of potential negative consequences when using photo editing applications or filters.

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Introduction

Sometimes I forget that I am human with a body, not a playdough that can be pressed and squeezed until it fits the predetermined mould this society has deemed “beautiful”. - Anonymous

Social media represent digital platforms based on new communication technologies fostering new possibilities for carrying out social interaction and communication [ 1 ]. They are based on Web 2.0 technology which allows the sharing of information among a large number of persons. Social media provide computer-mediated communication channels enabling users to communicate with each other.

An example of social media are social network sites which are defined by the use of profiles, the embeddedness in networks, and by the use of streams [ 2 ]. Profiles contain personal attributes related to the users which enable them to present themselves positively. Therefore, users are not necessarily obliged to include only true information in their profiles. Instead, deceptive self-presentation is a viable alternative. An example is research on dating platforms showing that the information provided is not always sincere [ 3 ]. With respect to height, weight, and age, 81% of profiles were not accurate because underreporting (especially with respect to weight) and overreporting (especially with respect to height) occurred. In addition, profile photographs also were inaccurate to some extent. This was less the case the more friends and family members were aware if the online dating profile.

Obviously, temptations to whitewash the profile are weighted against reality anchors and issues of credibility. Another feature of social network sites is their embeddedness in smaller or larger networks of users. Furthermore, social network sites comprise user-generated messages which are encoded in streams.

Social media including social media sites constitute a new context of social interaction which is contrasted with face-to-face interaction and digital communication media like email and video conferencing [ 1 ]. Social media differ from face-to-face interaction and digital communication media with respect to a plethora of communication variables including accessibility, latency, physicality, interdependence, synchronicity, permanence, verifiability and anonymity resulting in the reduction of time and distance barriers. For example, physicality contrasts face-to-face interactions taking place in a material context, which is tangible and perceptible, with an artificial environment based on digitalization. In general, the artificial environment facilitates communication. In addition, latency is reduced on the internet because it takes less time to share content within the social network site in comparison to face-to-face communication. Furthermore, the truth of messages is easier to verify on social media sites (e.g., by background checks). In general, social media facilitate communication processes considerably by offering the sender more channels to share information with many recipients.

An important domain of profile information refers to the use of profile photographs, which are more or less accurate [ 3 ]. Photo editing behavior tends to improve the impression conveyed by the profile photo at the cost of deception. In this context, social media communication reduces the verifiability of the accuracy of photographs with the consequence that the accuracy of the photograph is hard to verify. This contradicts the general trend [ 1 ], that social media enhance the likelihood of genuine communication and that the truth of messages is relatively easy to verify.

Photo editing behavior increases the options available for self-presentation on social network sites and constitutes a significant restriction on verifiability of the accuracy of profile photos. In addition, it is likely to be negatively correlated with self-perceived attractiveness. Therefore, the importance of photo editing behavior in the context of social network sites is high.

Photo editing behavior represents an emerging trend. According to a survey of the Renfrew Center Foundation [ 4 ], 50% of SNS users edit their photos before posting them to Social Network Sites (SNSs). Still, the effects of these new photo editing applications on the individual are largely unknown.

Compared to past decades, people are nowadays constantly confronted with highly edited beauty pictures on SNSs, which could significantly change the perception of beauty by raising beauty standards. Accordingly, individuals of average attractiveness may perceive themselves as less attractive when evaluated in comparison with photos of more attractive individuals which were edited by photo editing behavior. Analogous contrast effects have been found in a field study in which a moderately attractive woman was evaluated less positively following exposure to highly attractive actresses [ 5 , 6 ]. In addition, social comparisons on social media are likely to impair self-esteem [ 7 ].

What happens when the comparison is made with a more beautiful and optimized version of oneself? Numerous studies indicate a negative association between photo editing behavior on SNSs and body satisfaction [ 8 - 13 ]. Furthermore, users who retouched their pictures reported feeling less attractive, poorer self-esteem [ 12 ], and increased negative mood ([ 13 ]. Photo editing behavior may also encourage individuals to view their body as an object [ 14 ] reinforcing associated risks such as body shame, depression, and eating disorder [ 15 ].

The aim of this research is to reveal risks of the engagement in photo editing behavior. For this purpose, various factors identified in previous studies were incorporated into a parallel-sequential mediation model with multiple mediators. From a theoretical point of view, we integrate selfie editing, social comparisons, self-objectification, and well-being which is captured by self-esteem.

Whereas almost all previous studies have investigated the impact of photo editing behavior solely on body image, this study refers to self-perceived attractiveness in general by including the body and face as part of self-perceived attractiveness. One reason is that, so far, filters focus on the face and not on the body. Also, users post more pictures of their face on SNSs than full body pictures [ 16 ]. The face is usually more salient in pictures of oneself [ 13 ]. Thus, photo editing may lead individuals to pay closer attention to their facial attractiveness. Accordingly, photo editing behavior can have a significant effect on facial dissatisfaction, but not on body dissatisfaction [ 13 ].

Additionally, both men and women are included in this study. To date, almost all studies on photo editing have only included women, as they are more likely to engage in photo editing behavior [ 17 ] and experience higher pressure to conform to the cultural beauty ideal [ 18 ]. Yet, [ 10 ] found that photo editing behavior was positively associated with body dissatisfaction for both genders. The effects of photo editing behavior for men are nevertheless nearly unexplored.

Theoretical background

Photo editing behavior.

Photo editing behavior refers to the use of filters as well as various photo editing applications. While filtering options within Instagram change the face using a template with features, such as makeup, enlarged eyes, fuller lips, and narrower noses, photo editing applications provide more specific options. Thus, users can specifically select which parts of their face and body they want to edit. The functions range from changing skin tones, removing blemishes, slimming faces, making body parts slimmer, making body parts appear bigger, changing the shapes of noses, lips, cheeks, chins and eyes, and various makeup options.

Moreover, there are photo editing applications that use artificial intelligence (AI) to fully reconfigure the face [ 19 ]. While the use of photo editing options is mostly self-determined, as the user consciously decides which physical features should be changed, the use of filters or AI provides less self-determination. In this case, it is not the user but the technology that determines which of the photo’s physical features require modification. This could cause users to discover flaws in themselves that they would not have noticed without using the photo editing application.

As the physical appearance of users plays an important role in impression management on SNSs, photo editing behavior serves as an impression management strategy of online self-presentation [ 20 ] besides, for example, selecting one’s best photo. Regarding self-presentation, users can manage the impressions they have on others by minimizing perceived flaws or imperfections to get more favorable attention from others [ 3 ]. However, the use of photo editing applications can create an unrealistic expectation of one’s own attractiveness [ 21 ].

Self-perceived attractiveness

Self-perceived attractiveness refers to people’s beliefs about the quality of their physical appearance [ 22 ]. In contrast to body image, self-perceived attractiveness involves not only the perception of one’s own body but also of one’s face.

Several studies revealed a positive correlation between photo editing behavior and body dissatisfaction [ 8 - 13 ], whereas others found no significant association [ 8 , 11 ]. Overall, research has suggested that photo editing behavior may represent a risky behavior in terms of its potential to negatively impact body image [ 12 ] and facial satisfaction [ 13 ]. In addition, higher involvement in photo editing behavior, but not higher media exposure, is associated with higher body dissatisfaction [ 11 ]. Therefore, the importance of the general level of media exposure as a potential confounding variable is likely to by small. A plausible explanation of these results is that photo editing makes users think more about their flaws and imperfections [ 12 ]. Thus, individuals who engage in photo editing behavior are unfortunately more likely to notice a gap between their actual and ideal appearance [ 23 ]. This is likely to diminish self-perceived attractiveness in terms of appearance. Conversely, it can be argued that low self-perceived attractiveness tends to elicit photo editing behavior [ 24 ]. Accordingly, the first hypothesis states:

Photo editing behavior is negatively correlated with self-perceived attractiveness in terms of appearance.

• Self-objectification

As individuals engaging in photo editing behavior focus more on their appearance [ 16 ], it is tempting for them to anticipate the reactions of other users to the edited photo and look at themselves from an outside viewers’ perspective. Since the focus on many SNSs is on the user’s appearance, SNS users tend to expect to be evaluated based on their appearance [ 25 ]. Both conditions are risk factors for self-objectification.

Self-objectification is defined as the act of “[internalizing] an observer’s perspective on self” ([ 15 ] pp. 179 f.). The difference between self-objectification and body dissatisfaction is that self-objectification is a perspective toward the body, whereas body dissatisfaction involves negative feelings about one’s body [ 26 ]. As the objectification theory originally included only women, it was argued that women frequently experience sexual objectification by being valued for their appearance or by being regarded as objects. This regular experience of sexual objectification, such as exposure to objectifying media, socializes women to internalize an outside viewers’ perspective on their appearance [ 27 ]. Consequently, when a woman self-objectifies, she thinks about how her body might look to others [ 15 ]. The negative consequences of such an approach may include, among others, body dissatisfaction, body shame, disordered eating [ 28 ], depression [ 26 ], and lower well-being [ 29 ]. Meanwhile, it is proven that also men experience self-objectification and are therefore equally exposed to these risks [ 27 ].

In general, the nature of photo editing behavior activates feelings of self-objectification [12; 14] and physical appearance comparisons. Taking an outsider’s perspective makes users focus on their appearance rather than unobservable attributes such as abilities [ 16 , 27 , 30 ]. Additionally, photo editing behavior reinforces the evaluation of their appearance [ 31 ]. [ 32 ] argued that self-objectification can be triggered when people spend time editing their own photos because they view themselves in photos as manipulated objects. Furthermore, [ 33 ] proposed the circle of objectification, which suggests that individuals who self-objectify seek out more appearance comparisons, which in turn acerbate tendencies of self-objectification, as appearance comparisons increase the salience of one’s appearance [ 34 ]. Therefore, the positive association between photo editing behavior and self-objectivation may also be triggered by physical appearance comparisons resulting from self-objectification (cf., Sect.  2.4 ). Based on former studies, we derived as replication hypothesis:

Photo editing behavior is positively correlated with self-objectification.

• Physical appearance comparisons

In general, self-objectification is closely linked to appearance comparisons because both constructs share the perspective toward the body. According to social comparison theory, humans have an innate drive to compare themselves with others as a source for self-evaluation [ 35 ]. This happens relatively automatically. While social comparisons include abilities, affect, self-esteem, performance satisfaction, and other personal characteristics [ 36 ], physical appearance comparisons focus on physical characteristics [ 37 ]. In upward comparisons, the individual evaluates her- or himself relative to someone who is considered more attractive. [ 36 ].

During photo editing, users compare their own appearance to sociocultural beauty standards and might think about the required modification through photo editing to get closer to this ideal [ 38 ]. Therefore, photo editing behavior is likely to be positively associated with physical appearance comparisons.

Photo editing behavior is positively correlated with physical appearance comparisons.

In general, social comparisons tend to elicit contrast effects [ 36 ]. Therefore, upward comparisons are likely to reduce appearance evaluation. Individuals of average attractiveness may be perceived as less attractive when evaluated in comparison with more attractive individuals. Therefore, one’s tendency to engage in physical appearance comparisons is likely to be associated with body dissatisfaction [ 39 ], internalization of appearance ideals, low self-esteem, sexual objectification, body surveillance, and body shame [ 37 ]. Similar contrast effects have been demonstrated when a moderately attractive individual is evaluated following exposure to highly attractive media stimuli [ 40 ]. Moreover, individuals who perceive themselves as less attractive are more likely to engage in physical appearance comparisons and upward comparisons (Patrick et al., 2004). Although in theory individuals who are satisfied with their body may engage frequently in physical appearance comparisons, the empirical evidence reveals that individuals who perceive themselves as less attractive primarily engage in physical appearance comparisons, more than individuals who perceive themselves as attractive [ 41 ]. Therefore, empirical results and theoretical considerations lead to the following hypothesis:

Physical appearance comparisons are negatively correlated with self-perceived attractiveness in terms of appearance.

According to the self-discrepancy theory, individuals compare one self-state to another self-state and find that a discrepancy exists [ 42 ]. This discrepancy in turn triggers dissatisfaction. Therefore, the negative effects of photo editing behavior are likely to occur when SNS users perceive high discrepancy between their edited self (i.e., the idealized self) and real self [13; 42]. It is quite likely that the individual will fall short of the unrealistic beauty ideal promoted by filters and photo editing applications, resulting in a body-related self-discrepancy [ 43 ].

• Self-esteem

Individuals who express low self-esteem more often gravitate to physical appearance comparisons, seeking reassurance and validation compared with individuals high on self-esteem. However, they also more often engage in upward comparisons [ 44 ], which in turn is associated to a decrease in self-esteem [ 37 ]. Such upward comparisons may serve as a reminder of the beauty ideal they do not meet [ 41 ] eliciting a contrast effect.

Self-esteem represents an important part of subjective well-being. It is defined as the affective-evaluative facet of the self and includes cognitive-knowledge-based, affective-evaluative, and action guiding facets [ 45 ]. Moreover, self-perceived attractiveness is an important component of self-esteem. Numerous studies indicated a positive correlation between self-perceived attractiveness and self-esteem [ 44 , 46 - 49 ], as it is an important source of power and social status [ 50 ] Also, attractive individuals develop and internalize more positive self-views than less attractive people [ 51 ]. Several researchers have argued that it is not attractiveness itself that is associated with self-esteem, but individuals’ evaluation of their own attractiveness [ 52 ]. In conclusion, self-perceived attractiveness is likely to play an important role in determining self-esteem, possibly more important than objective attractiveness. This conclusion does not imply that individuals have only a vague idea of their objective attractiveness or no idea at all. There is definitely objectivity regarding self-perceived facial attractiveness [ 53 ]. Therefore, objective facial features affect self-perceived facial attractiveness. The fact that objective facial attractiveness is registered by individuals suggests that objective facial attractiveness may constitute a confounding factor with respect to self- esteem that could influence the results of the study (cf., the Discussion section). Nevertheless, self-perceived attractiveness is associated with self-esteem beyond objective facial attractiveness having a positive impact on self-confidence [ 54 ]. Moreover, individuals with high self-esteem are more likely to accept their physical appearance [ 52 ]. This reasoning leads to the following hypotheses:

Self-perceived attractiveness in terms of appearance is positively correlated with self-esteem.

In sum, photo editing behavior leads to self-objectification [12; 14; 20; 32] and physical appearance comparisons [ 24 ]. A possible explanation is that photo editing behavior makes users focus more on their appearance, which consequently increases physical appearance comparisons [ 16 ]. In turn, physical appearance comparisons are likely to trigger photo editing behavior to compensate for one’s optical flaws (cf., the occurrence of objective facial attractiveness; [ 53 ]). Since physical appearance comparisons [ 39 , 55 ] and self-objectification [ 28 ] are negatively correlated with body image and self-perceived attractiveness, it is reasonable to assume that the two constructs are mediators of the relationship between photo editing behavior and self-perceived attractiveness. When individuals self-objectify and compare themselves regarding their physical appearance, they may pay more attention to their physical appearance and more easily find a gap between their physical appearance and their beauty ideal. As a result, they presumably feel dissatisfied with their own appearance [ 30 ].

Finally, in H5 a parallel-sequential multiple mediation model is proposed, which is based on previous results in general and H1 to H4 in particular. The model connects photo editing behavior with self-esteem via three mediators.

Photo editing behavior is associated with higher self-objectification and more physical appearance comparisons, that result in lower self-perceived attractiveness, which, in turn, implies lower self-esteem.

An overview of the research plan including the hypotheses Footnote 1 is shown in Fig.  1 .

Overview of the research plan including hypotheses

The program G*Power (version 3.1.9.7; [ 56 ]) was used to calculate in advance how many participants constitute a sufficient sample size for a mediation model (i.e., multiple regression with 5 predictors). The significance level was set at 5%, Power (1-ß) at 95%. Furthermore, small to medium effect sizes were assumed, f 2  = 0.15 since in social psychological research these effect sizes are to be expected [ 57 ]. The appropriate sample size turned out to be N  = 138. 403 participants met the inclusion criteria and provided complete data sets. 316 (78.4%) were female, 85 (21.1%) male and 2 (0.5%) identified as neither female nor male. The average age was 27.6 years ( SD  = 8.3) ranging from 18 up to 61 years. 42.2% held an academic degree and 53.3% were at least high school graduates. 86.6% of the participants were students and most of them studied psychology (59.1%).

An online survey was conducted via Unipark ( https://www.unipark.de ). Participation was dependent on being active on SNSs in general. More specifically, the sample is based on Instagram members because Instagram is the most widely used SNS [ 44 ], primarily image-based compared to other SNSs [ 13 ], and has its own photo editing features. To confirm the inclusion criteria, participants were asked if they had an Instagram account. Therefore, the likelihood of engaging in photo editing behavior was expected to be relatively high for Instagram users. Furthermore, participants had to be at least 18 years old. They were recruited via social media posts following a snowball-sampling technique.

Questionnaires

Demographic variables, photo editing behavior, self-objectification, physical appearance comparison, self-perceived attractiveness and self-esteem, and Instagram activity were obtained. In general, higher scores indicate higher levels of the corresponding variable.

Photo editing scale

The Photo Editing Scale (PES) was newly developed to measure photo editing behavior related to the use of SNSs. It provides a brief measure and consists of five items, each of which are answered on a 5-point Likert scale with a response format ranging from 1 = “never” to 5 = “always”. To assess different types of photo editing behavior, participants were asked about their use of filters (e.g., “I use filters that beautify my facial features.”) and photo editing applications (e.g., “I edit my facial features before uploading a photo (using apps such as Facetune or Faceapp, for example”). One item was included focusing on body features (e.g., “I edit my figure before I upload a photo (e.g., with apps like Facetune or Faceapp)”). Two further items referred in general to using photo editing or not. Reliability analysis indicated a satisfactory internal consistency, α = 0.75, given the small number of items. The response scale of one item had to be inverted. Higher scores represent more photo editing behavior. On average, participants exhibited relatively low photo editing behavior-scores, M  = 1.89, SD  = 0.79. Note that the content validity of the items is high. The scale items are included in OSF (with respect to the double-blinded review process, the link will be added after acceptance of the paper).

Instagram activity questionnaire

To measure Instagram activity, the Instagram Activity Questionnaire (IAQ; [ 58 ]) was used. The questionnaire consists of 38 items rated on a 5-point Likert scale from 1 = “never” to 5 = ”very often”, based on the two factors: Active (27 items; e.g., “I post pictures.”) and Passive (11 items; e.g., “I look at the photos of other users.”). Reliability analyses indicated very good internal consistencies (α IAQ  = 0.91, α Active  = 0.88, α Passive  = 0.82). Higher scores indicate higher Instagram activity. Participants exhibited moderately high ratings of Instagram activity, i.e., M IAQTotal = 2.64, SD IAQ Total = 0.57, M Active = 2.52, SD Active = 0.64, M Passive = 2.91, SD Passive = 0.67, with highest ratings for passive Instagram use.

Self-objectification beliefs and behaviors scale

For measuring self-objectification, the Self-Objectification Beliefs and Behaviors Scale (SOBBS; [ 26 ]) was used. This is a relatively new measure that addresses the primary limitations of existing measures. Participants were asked to rate their level of agreement with each item using a 5-point Likert scale from 1 = “strongly disagree” to 5 = “strongly agree”. The 14 items are based on two factors: (1) internalizing an observer’s perspective of the body (7 items; e.g., “I try to imagine what my body looks like to others (i.e., like I am looking at myself from the outside)” and (2) equating the body to who one is as a person and valuing physical appearance above other attributes (7 items; e.g., “How I look is more important to me than how I think or feel.”). For the present German sample, the SOBBS was translated and validated by using the back-translation procedure (see Appendix A). The measure demonstrated very good internal consistencies in the original study [ 26 ] as well as in the current sample, α SOBBS_Total  = 0.89, α Factor_1  = 0.89, α Factor_2  = 0.84. Higher scores indicate more self-objectification. In general, participants exhibited moderately high ratings of self-objectification, M Factor_1 = 2.92, SD Factor_1 = 0.88, M Factor_2 = 1.70, SD Factor_2 = 0.62, M SOBBS_Total = 2.31, SD SOBBS_Total = 0.66, with highest ratings on the first factor (internalizing an observer’s perspective of the body) and comparatively low ratings on the second factor (equating the body to who one is as a person and valuing physical appearance above other attributes).

Physical appearance comparison scale

The German version [ 59 ] of the Physical Appearance Comparison Scale (PACS; [ 60 ]) was used to assess an overall tendency to compare one’s own appearance with others (e.g., ‘‘In social situations, I sometimes compare my figure to the figures of other people’’). With only 5 items, the PACS is a very economical measure. Items are rated on a 5-point Likert scale (1 = “never” to 4 = “always”). Higher mean scores indicate higher frequency of physical appearance comparisons. As the most widely used measure for physical appearance comparisons, the PACS has demonstrated good reliability and validity (Schaefer & Thompson, 2014). Accordingly, [ 59 ] have found that the internal consistency of the scale based on a German sample is within an acceptable range. The current investigation revealed moderately high levels of physical appearance comparisons, M  = 2.73, SD  = 0.79 and a satisfactory internal consistency, α = 0.73.

Body-esteem scale

Self-perceived attractiveness was measured with the Body-Esteem Scale (BES) [ 61 ] that consists of 23 items rated on a 5-point Likert scale ranging from 1 = “never” to 5 = “always”. [ 61 ] suggested that feelings about one’s weight can be differentiated from feelings about one’s general appearance. Also, one’s own opinions may be differentiated from the opinions attributed to others. Therefore, the BES contains three subscales: (a) Appearance (6 items; e.g., “I wish I looked better”), (b) Weight (4 items; e.g., “I really like what I weigh”) and (c) Attribution (4 items; e.g., “People of my own age like my looks”). These subscales measure (a) general feelings about one’s own appearance, (b) weight satisfaction, and (c) evaluations attributed to others about one’s appearance [ 46 ]. For the present research, only the subscale on Appearance was used. Hence, 6 of the 14 items of the questionnaire were included and translated into German. The translation was verified through back-translation procedure (see Appendix B). There is good evidence that the original scale is valid and reliable over a wide age range [ 61 ]. Reliability analyses in the current sample indicated that internal consistency was excellent, α Appearance  = 0.91. On average, participants ratings regarding their self-perceived attractiveness were moderately high, M Appearance = 2.72, SD Appearance = 0.74.

Rosenberg self-esteem scale

For measuring self-esteem, the German version of the Rosenberg Self-Esteem Scale (RSES) [ 62 ] was used, which showed good reliability and validity [ 63 ]. With respect to construct validity, strong positive correlations between self-esteem and both self-satisfaction and self-efficacy and a substantial negative correlation between self-esteem and self-derogation were obtained. The RSES includes six items rated on a 4-point Likert scale from 1 = “strongly disagree” to 4 = “strongly agree ” . Positive and negative feelings about the self were both assessed. The response scales of five of the ten items had to be inverted (e.g., “Every now and then I think I’m no good at all.“). Higher scores indicate higher self-esteem. The RSES is characterized by easy and quick use and high face validity [ 64 ]. [ 65 ] reported a satisfactory internal consistency for the German version of the RSES. In the current sample, reliability analyses indicated a very good internal consistency, α = 0.90. On average, participants reported relatively high self-esteem, M  = 3.21, SD  = 0.61.

Preliminary analysis

As indicated by Kolmogorov–Smirnov test and Q–Q plots, some of the scales (PES, BES, RSES) were not normally distributed. Consequently, Spearman’s rank correlation (Rho), which presupposes an ordinal association between variables, was used for all computations of their interrelations.

Validity checks

In general, the content validity of the employed scales is high. The construct validity of each of the scales included in hypothesis tests was scrutinized thoroughly except for the self-esteem scale whose construct validity was comprehensively demonstrated by [ 63 ]. For checking construct validity of the PES, it was correlated with the IAQ because Instagram use is likely to promote appearance concerns [ 12 ] and in this respect corresponds with the PES because of its visual, photo-oriented features. Therefore, a positive correlation between the IAQ and PES was expected. This expectation was confirmed because the IAQ was positively correlated with the PES, r s (401) = 0.358, p  < .001, and its subscales, both Active, r s (401) = 0.207, p  < .001, and Passive, r s (401) = 0.223, p  < .001. Note that the highest correlation was displayed between PES and the total Instagram Activity Questionnaire accounting for 12.8% of common variance.

Subsequently, the construct validity of the German version of the Appearance subscale of the Body Esteem Scale was examined by correlating it with the passive subscale of the Instagram Activity Scale. High exposure to Instagram content in a passive mode is likely to undermine positive attitudes toward own appearance because of the elicitation of social comparisons which lead to body dissatisfaction [ 18 ]. Results correspond with the assumption of construct validity of the Appearance subscale, r s (401) = − 0.135 p  < .01, indicating that higher passive Instagram consumption is negatively associated with positive attitudes toward own appearance.

Additionally, the construct validity of the Self-objectification Beliefs and Behaviors Scale (SOBBS) was examined by correlating it with the Body Esteem Scale (BES) because higher self-objectification seems to imply lower self-perceived attractiveness [ 66 ]. In correspondence with expectations significant negative correlations were found between self-perceived attractiveness and self-objectification, r s (401) = − 0.569 p  < .001, as well as on corresponding subscales, referring to internalizing an observer’s perspective of the body , r s (401) = − 0.462, p  < .001, and referring to equating the body to who one is as a person and valuing physical appearance above other attributes ), r s (401) = − 0.477, p  < .001), respectively. The highest correlation was exhibited between the total SOBBS and perceived attractiveness. indicating 23.9% of common variance.

Finally, the construct validity of the Physical Appearance Comparison Scale was examined. In accordance with results by [ 7 , 67 ] indicating that social comparison orientation is positively associated with Facebook activity it was assumed that the PACS is positively linked with Instagram activity substituting comparison orientation by the Physical Appearance Comparison Scale and Facebook activity by Instagram activity. Note that comparison orientation and PACS both represent individual-difference measures of the readiness to perform social comparisons and that the Instagram activity questionnaire was developed analogously with the Facebook activity questionnaire. Both instruments capture behavioral reports of activities on SNSs. In correspondence with expectations and corroborating the construct validity of the PACS results indicated that total Instagram activity and PACS are correlated positively, r s (401) = 0.264, p  < .01. In addition, the results for both active and passive Instagram activity measures correspond with the results for the overall activity measure.

Testing hypotheses

The hypotheses pertain to correlational relationships. The complete intercorrelation matrix is summarized in Appendix C. In H1 it was hypothesized that photo editing behavior is negatively correlated with self-perceived attractiveness. This hypothesis was confirmed because a significant negative correlation was found between photo editing behavior and self-perceived attractiveness in terms of appearance, r s (401) = − 0.146, p  < .01.

As hypothesized in H2a , photo editing behavior displayed a significantly positive correlation with the SOBBS measuring self-objectification, r s (401) = 0.221, p  < .001. This correlation represents a moderate effect. Moreover, with respect to the SOBBS-subscales, substantial negative correlations were found between photo editing behavior and internalizing an observer’s perspective of the body , r s (401) = 0.227, p  < .001, and equating the body to who one is as a person and valuing physical appearance above other attributes , r s (401) = 0.124, p  < .001. Therefore, H2 was confirmed.

Furthermore, the proposition ( H2b ) was investigated that photo editing behavior is positively associated with physical appearance comparisons. The results corresponded with H3 , r s (401) = 0.238, p  < .01. More specifically, participants who were more active in terms of photo editing behavior exhibited more physical appearance comparisons.

H3 postulated a negative association between physical appearance comparisons and self-perceived attractiveness in terms of appearance. It was corroborated by a significant negative correlation between the subscale Appearance of the BES and physical appearance comparisons, r s (401) = − 0.536, p  < .001, Therefore, H4 was confirmed.

H4 refers to the association between self-perceived attractiveness and self-esteem. Previous research demonstrated that self-perceived attractiveness and self-esteem are positively linked [44; 49]. Supporting previous results, self-perceived attractiveness and self-esteem were correlated positively. Specifically, the BES-subscale Appearance was positively associated with self-esteem, r s (401) = 0.604, p  < .001, representing a strong effect. Thus, H5 was confirmed by the results.

The statistical mediation model summarized in H5 proposed that photo editing behavior is associated with higher self-objectification and more physical appearance comparisons, and that both mediators are associated with a lower self-perceived attractiveness, which, in turn, is associated with lower self-esteem. Note that the mediators self-objectification and physical appearance comparisons display a high positive correlation which is taken into account by applying a path-analytic model. In overview, the corresponding path analysis summarized in Fig.  2 , revealed a significant parallel-sequential multiple mediation in the expected direction, total indirect effect: β = − 0.020, BC 95% CI [-0.0377; − 0.0063]; overall model: F 4,398 = 7.12, p  < .001, adj R 2  = 0.067. While the direct effect of photo editing behavior on self-esteem was not significant, β  = 0.012, BC 95% CI [-0.0300; 0.0538], the indirect effect was mediated via self-objectification, physical appearance comparisons, and self-perceived attractiveness. Therefore, H5 was confirmed. Photo editing behavior significantly predicted more self-objectification as well as more physical appearance comparisons which both predicted lower self-perceived attractiveness and lower self-esteem. In addition, self-objectification directly predicted lower self-esteem.

Mediation model. Note: m = 10,000; bootstrapping intervals in brackets; Age (ß = 0.015, p  > .05; CI [-0.0445; 0.0679]) and gender (ß = 0.029, p  > .05; CI [-0.0015; 0.0036]) as covariates show no significant effect on the statistical mediation model, all p s > 0.05. * p  < .05, ** p  < .01. *** p  < .001

Post-hoc tests of observed power and replicability

We used the test of excessive significance (TES) [ 68 ] to calculate the success rate, median observed power, the inflation rate, the replicability index, and a test of insufficient variance (TIVA) based on 6 hypotheses-oriented effects (i.e., 5 t- statistics and 1 F -statistics based on intergroup deviations and mediation models). Therefore, we used the p-checker-app (see http://shinyapps.org/apps/p-checker/ ).

The TES revealed a success rate of 1, which indicates that 100% of our predictions were confirmed, and a median observed power of 99.6. In addition, the TES revealed a minimal inflation rate 0.004, which indicates that no more hypotheses have been confirmed than possible under consideration of the power. The r-index  = 0.99 indicates that our findings can be (theoretically) replicated in X * 0.99 follow-up studies.

At last, the TIVA, X 2 (5) = 98.311, p  = 1, var = 19.66, indicated that no bias was present confirming that all entered test statistics and p-values are in the expected direction.

The hypotheses connected the construct of photo editing with social comparison, self-objectification, and self-esteem as an indicator of well-being. Photo editing includes selfie editing as a special case. Whereas H1 to H4 postulated associations between two constructs, H5 combined five constructs based on a path-analytic model. In general, our findings support the hypotheses.

Consistent with other studies [ 8 - 13 ], the results regarding H1 indicate that photo editing behavior is associated with lower self-perceived attractiveness in terms of appearance. Although the explained variance is rather small, the corresponding correlation is highly significant. One explanation is that individuals who often edit their pictures create an idealized virtual self-image which enhances the discrepancy between the real and ideal self [ 24 ]. Furthermore, even people satisfied with their appearance presumably want to look even better and edit their selfies to post perfect ones which maximize ideal online self-presentation [ 24 , 53 ].

As expected in H2a , a significant positive correlation between photo editing behavior and self-objectification was found. On the one hand, self-objectification may predispose individuals to engage in photo editing behavior. On the other hand, photo editing behavior is likely to enhance feelings of self-objectification [ 12 , 14 ], as the individual simultaneously becomes the editor and the object of photo editing in general and selfie editing in particular. Self-objectification may foster an individual’s need to constantly present and improve his or her physical appearance to please others [ 14 ]. Therefore, people with a higher degree of self-objectification may place a higher value on posting photos that reflect the societal beauty ideal. Individuals who self-objectify are more likely to experience body shame and body dissatisfaction [ 28 ], Editing specific body parts may reduce the body to its component parts rather than viewing it as a fully functioning whole. As filters and photo editing applications tend to convey beauty ideals, the internalization of these messages may guide the perception of one’s appearance, leading to a more objectified view. [ 69 ] reported that time spent on SNSs was associated with higher self-objectification. Therefore, the correlation between photo editing behavior and self-objectification may be intensified by higher length of SNS use.

H2b postulated a positive association between photo editing behavior and physical appearance comparisons. The confirmation of H2b indicates that higher sores on the photo editing scale are associated with more intense immersion in physical appearance comparisons including the construction of self-other contrasts with respect to good looks. Note that the confirmation of both H2a and H2b in combination emphasizes that self-objectification and physical appearance comparisons are closely linked with each other. The correlation between both scales is substantial, r (401) = − 0.599, p  > .001. Therefore, the confirmation of H2 taken together supports the notion that photo editing behavior is associated with change in the perspective toward the body.

The findings generally supported hypothesis H3 that physical appearance comparisons are negatively associated with self-perceived attractiveness in terms of the subscale Appearance of the BES. To explain these findings, it should be noted that such comparisons may serve as a reminder of beauty ideals that one does not meet [ 41 ]. This correlation should be particularly pronounced for SNS users, as upward physical appearance comparisons are likely to occur frequently on SNSs due to a general tendency of users to exaggerate their positive characteristics striving for positive self-presentation [ 70 ]. Intriguingly, physical appearance comparisons with peers may actually impair self-perceived attractiveness more than comparisons with fashion models, because the latter are perceived as less similar to oneself and, as a consequence, represent a less diagnostic comparison group [ 71 ]. Due to the high similarity of an optimized version of oneself to one’s real self, physical appearance comparisons with one’s artificially optimized self could have a negative effect on self-perceived attractiveness. These comparisons reveal what needs to be optimized to achieve the ideal. For example, lip injections, nose surgery, anti-wrinkle cream or weight loss could presumably make the edited selfie self-achievable, while in comparison, the appearance of a celebrity appears to be unachievable. People don’t recognize that the appearance of celebrities is usually artificially enhanced using make-up and software like Adobe Photoshop .

Individual differences in responses to physical appearance comparisons are likely. Specifically, upward comparisons could inspire some individuals, whereas others may feel discouraged. In accordance, [ 41 ] argued that reactions to physical appearance comparisons are largely a function of two individual differences: The extent to which one’s self-esteem is contingency based and one’s self-perceived attractiveness.

Several studies have already shown a positive association between self-perceived attractiveness and self-esteem [ 44 , 46 - 49 ]. The confirmation of H4 which states that self-perceived attractiveness is positively associated with self-esteem is in line with previous results. Highly attractive individuals are likely to internalize more positive self-views than less attractive people [ 51 ]. Interindividual differences should also be considered, as some people are more likely to define their self-esteem on the basis of meeting expectations such as societal beauty ideals [ 72 ]. This refers to the contingent self-esteem, which is based on the approval of others or on social comparisons [ 73 ]. Individuals who are more dependent on contingent self-esteem may be more concerned with attractiveness than others who, for example, rely more on academic success or social acceptance [ 41 ].

Self-esteem is likely to be influenced by both self-perceived attractiveness and objective attractiveness [ 53 ]. Therefore, objective attractiveness may constitute a confounding factor with respect to the link between self-perceived attractiveness and self-esteem because higher objective attractiveness could be associated both with both self-perceived attractiveness and self-esteem. Future studies, which should include a measure of objective attractiveness, could clarify this issue. Nevertheless, stereotype research indicates [ 74 ] that cultural reference systems and subjective impressions represent powerful determinants of self-esteem.

In general, the confirmation of H1 to H4 represents initial evidence for the mediation model postulated in H5 . But H5 goes beyond the other hypotheses by specifying specific paths which connect photo editing behavior with self-esteem. The employment of the sequential multiple mediator model in testing H5 allows to discover these paths. The results indicate that mediation via self-objectification and via physical appearance comparisons occupy central switching points in the model which are both associated with self-perceived attractiveness. Therefore, the link between photo editing behavior and self-esteem was sequentially mediated via self-objectification, physical appearance comparisons, and self-perceived attractiveness. Individuals who engage in photo editing behavior more often perform physical attractiveness comparisons with others sand self-objectify more frequently. Whereas self-objectification relates to self-esteem both indirectly via self-perceived attractiveness and directly, physical appearance comparisons are only indirectly connected with self-esteem via self-perceived attractiveness.

The path-model specifies links from photo editing behavior to restricted self-esteem by focusing on unintended side-effects of photo editing behavior which is performed mainly to achieve positive consequences (e.g., improved self-presentation). From an applied viewpoint it would be desirable to inform users about the danger that such side-effects may occur. Such a cautionary note might include a broader concern related to the improvement of appearance in public. For example, people don’t recognize that the appearance of celebrities is usually artificially enhanced using make-up and software like Adobe Photoshop . Therefore, photo editing of selfies on SNSs is only one instance of a general trend to edit pictures. Reality is more elusive as it appears on the surface. The depiction of reality is a constructive endeavor which is subject to concealed issues of the editors. The depiction of reality is usually not a documentary but part of a narrative which the photo editor intends to project on the public screen. By understanding the underlying narrative, the contrast between natural appearance and edited photo of it is getting transparent. Because photo editing is likely to prevail in the future, the focus of psychoeducation as part of a psychological intervention technique should be a sensibilization for the wide spread of use of corresponding techniques.

Limitations and future research

This study is subject to several limitations. Firstly, the sample is not representative. For example, the data was obtained within an online context. But research indicates that differences in results occur between offline and online contexts. Specifically, the occurrence of gender differences in personality depended on the context of measurement [ 83 , 84 ]. Therefore, in future studies the results of online and offline measurement of the assessment of photo editing variables including personality variables like self-esteem should be compared with each other in order to increase the generalizabilitv of results.

In addition, young German participants were overrepresented. But the sample comprises individuals within a large age range and from different socioeconomic and academic backgrounds. Note that age plays a major role in the perception of facial attractiveness and self-esteem[ 75 ]. The present sample includes a range of individuals between 18 and 61 years old, with 86.65% being students, and thus more likely in their twenties. To account for doubts with respect to its representativity regarding the general population we added subanalyses including age as covariate in our mediation model. However, the results confirmed our previous results.

Furthermore, 59% of the participants were psychology students and only 21% of the participants were male. the high proportion of females in the sample could mean that the results are more typical for females than for males. In fact, it was found that women are more involved in photo editing behavior than men [16; 17], are more preoccupied with appearance than men [ 76 ], and experience higher pressure to conform to the societal beauty ideal [ 18 ]. Note that we added gender as covariate in our mediation model. No significant effects were found.

Another issue that goes beyond sample characteristics is that most filters focus primarily on realizing the female beauty ideal.

The variables measured in this study are based on self-report. Therefore, they may be influences by response biases. For example, it is important to note that participants may have underreported their photo editing behavior because they may have perceived this behavior as socially undesirable. In support of this argument, previous research found that 12% of photos posted under the #nofilter tag on Instagram did in fact include filters [ 77 ]. Therefore, future research could benefit from inclusion of a measure of social desirability. In defense of the data quality of the self-report scales, we investigated the construct validity of the scales. Results indicated that each of the variables which were represented in the hypotheses exhibit substantial construct validity. In addition, the content validity of all scales is high.

Additionally, the Body Esteem [ 61 ] is validated for individuals between 12 and 25 years of age. We suggest that the scale is validated in older populations. This is in line with our results showing the same effects with respect to our hypotheses regardless of adding age as covariate.

Please note that participation was based on having an Instagram account. There are reports in the literature regarding the percentage of individuals who edit their photos before publishing them on Instagram. These range between 30 and 90%. It can by assumed that the sample also included individuals who have no experience in photo editing, although they have an Instagram account. This was also evident by the low photo editing behavior score of the participants in this study. Nonetheless, although we determined a low score on photo editing behavior in our study, we found robust results confirming our hypotheses.

Based on sample characteristics (i.e., age, gender, and participants’s photo editing behavior), some points of criticism with respect to the generalizability of our data arise. However, according to our mediation analyses including age and gender as covariates these variables had no significant confounding effect on our results. Additionally, we calculated further post-hoc analyses with respect to replicability, post-hoc power as well as insufficient variance showing that our data seem to be replicable, unbiased, and generalizable. However, future studies with a more balanced sample are necessary to confirm our findings.

In addition, the statistical analyses in this study are correlational, meaning that no causal conclusions are warranted. Given the early phase of research on photo editing, this restriction may be acceptable. Furthermore, significant mediation does not imply true mediation but only that the data fits with the proposed mediation model [ 78 ]. Future studies are needed to examine causal inferences. For example, is photo editing behavior the cause of more self-objectification or vice versa? Such questions might be tackled by experimental studies [ 79 ], with respect to the negative impact of social comparisons on self-esteem) or longitudinal research design [ 80 ], with respect to effects of social media use on mental health).

Finally, the list of potential mediators between photo editing behavior and self-esteem includes variables that were not considered in our research design. It may include appearance contingent self-esteem [ 81 ], upward and downward social comparisons [ 24 ], and narcissism [ 82 ] Time spent on SNSs should be included as a possible confounding variable in future research. Furthermore, research should be conducted to determine the extent to which reactions to edited photos in the form of likes, comments, or compliments reinforce photo editing behavior.

Future research might investigate the outcome of photo editing behavior in contexts like dating platforms. As especially adolescents are vulnerable in terms of self-esteem and appearance-based self-worth, further research should also be conducted on the impact of photo editing behavior on this vulnerable target group. Future research might also explore more systematically reasons why users of SNSs edit their selfies and what motivates them to engage in photo editing behavior.

Data and materials availability

The datasets and materials used and/or analysed during the current study are available online at: https://osf.io/kz3gb/?view_only=02591d9f59544570853fa7d394c2bfc5 .

The hypotheses were not preregistered.

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P.O., S.L. developed the research idea, P.O., S.L. conducted the research, S.L. prepared all figures and the APPENDIX, P.O.: supervision, all authors wrote and reviewed the main manuscript.

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Ozimek, P., Lainas, S., Bierhoff, HW. et al. How photo editing in social media shapes self-perceived attractiveness and self-esteem via self-objectification and physical appearance comparisons. BMC Psychol 11 , 99 (2023). https://doi.org/10.1186/s40359-023-01143-0

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materialmodifier: An R package of photo editing effects for material perception research

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• Published: 10 May 2023
• Volume 56 , pages 2657–2674, ( 2024 )

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• Hiroyuki Tsuda   ORCID: orcid.org/0000-0001-9396-5327 1 &
• Hideaki Kawabata 2  

In this paper, we introduce an R package that performs automated photo editing effects. Specifically, it is an R implementation of an image-processing algorithm proposed by Boyadzhiev et al. ( 2015 ). The software allows the user to manipulate the appearance of objects in photographs, such as emphasizing facial blemishes and wrinkles, smoothing the skin, or enhancing the gloss of fruit. It provides a reproducible method to quantitatively control specific surface properties of objects (e.g., gloss and roughness), which is useful for researchers interested in topics related to material perception, from basic mechanisms of perception to the aesthetic evaluation of faces and objects. We describe the functionality, usage, and algorithm of the method, report on the findings of a behavioral evaluation experiment, and discuss its usefulness and limitations for psychological research. The package can be installed via CRAN, and documentation and source code are available at https://github.com/tsuda16k/materialmodifier .

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Introduction

Material perception is a rapidly growing research area in vision science today (Fleming, 2017 ; Komatsu & Goda, 2018 ; Spence, 2020 ) and it is relevant to a wide range of human cognition and behaviors (as described below). To study material perception, we need a set of controlled images for stimuli, such as images with high and low roughness. However, unlike basic visual features such as color and lightness, controlling specific material properties of objects in photographs is an intricate endeavor. To alleviate this situation, we created an R package called materialmodifier that can be used to modify the surface properties of objects such as gloss and roughness (Fig. 1 ). This method was proposed by Boyadzhiev et al. ( 2015 ), and we implemented it in the R package to make it accessible to psychologists. Before going into the details of the package, we briefly describe recent research trends in material perception to provide some background on our contribution.

By using the materialmodifier package in R, the user can modify the appearance of objects in photographs. For example, they can make skin smoother or make marks or blemishes more visible; enhance the gloss of food or make it look wilted

People easily perceive and recognize materials in their daily lives and can identify categories of materials quickly and reliably (Fleming et al., 2013 ; Sharan et al., 2014 ). People can also distinguish subtle differences in certain material properties, such as the degree of surface roughness and gloss (Fleming, 2017 ). This visual ability is important for diagnosing the freshness of food or the health of a person based on the condition of their skin. Despite the subjective ease of material perception, achieving stability therein is a computationally challenging problem because retinal input for objects of the same material can vary greatly depending on illumination and the surface shape of the object (Anderson, 2020 ; Chadwick & Kentridge, 2015 ; Fleming, 2014 ). Recent theories suggest that the brain achieves material perception not through inverse-optics computation but through statistical inference based on internal image models (Fleming, 2014 ; Fleming & Storrs, 2019 ). From this perspective, systematic manipulation of image features and examining their effects on perception is an effective approach to understanding the mechanisms of material perception (Nishida, 2019 ).

Material perception is interesting because of its relevance to a wide range of human cognition and behaviors. For instance, material perception has been related to the perception of the freshness of foods (Arce-Lopera et al., 2013 ; Péneau et al., 2007 ), judgments of facial impressions from skin conditions (Fink et al., 2006 ; Fink & Matts, 2008 ; Jaeger et al., 2018 ; Nkengne et al., 2008 ; Stephen et al., 2009 ), action planning for touching objects and walking on slippery floors (Adams et al., 2016 ; Joh et al., 2006 ; Lesch et al., 2008 ), pathogen detection (Iwasa et al., 2020 ), product packaging design (Di Cicco et al., 2021 ), and aesthetic appreciation of textures (Stephens & Hoffman, 2016 ), paintings (Di Cicco et al., 2020 ), and sculptures (Schmidt, 2019 ). Furthermore, studies have explored how material perception contributes to other cognitive domains, such as memory (Tagai et al., 2016 ; Tsuda et al., 2020 ) and multisensory perception (Fujisaki, 2020 ; Spence, 2020 ).

Despite its wide importance in cognition and behavior, studies on material perception are relatively limited in size and scope. One of the reasons for this may be the difficulty of creating a set of controlled stimuli that differ in certain material properties. There are image databases of materials and textures that are useful for psychological research (Lagunas et al., 2019 ; Sawayama et al., 2019 ; Serrano et al., 2021 ; Sharan et al., 2014 ; van Zuijlen et al., 2021 ). However, we often need a new image set tailored for specific research purposes. In such cases, images are created either by manual photo editing, using software such as Photoshop and GIMP (Fink & Matts, 2008 ; Jaeger et al., 2018 ), taking photographs of objects (Motoyoshi et al., 2007 ), or using computer graphics rendering (Fleming et al., 2003 ). In any of these circumstances, a decent amount of time, effort, or technical expertise is required. Moreover, the manual editing of photographs suffers from the low reproducibility of the image production process.

In this study, we implemented the image processing algorithm proposed by Boyadzhiev et al. ( 2015 ) as an R package. It is one of the image-based material editing methods: a heuristic method that manipulates image features that are associated with human material perception. Specifically, it decomposes an image into spatial-frequency subbands (i.e., images representing specific spatial frequency information of the input image) and changes the input image’s appearance by manipulating (boosting/reducing) the energy of specific subbands therein (details are given in the Algorithm section). Although the algorithm for this method is simple and heuristic, it is effective and compelling for the following reasons. The human early visual system represents visual information in spatial frequency and orientation selective channels (Blakemore & Campbell, 1969 ), and a computational model of early vision based on spatial frequency decomposition explains human contrast detection/discrimination well (Schütt & Wichmann, 2017 ). Spatial frequency subband statistics are associated with the perception of the material properties of objects, e.g., gloss (Dror et al., 2004 ; Kiyokawa et al., 2021 ; Motoyoshi & Matoba, 2012 ). Manipulating the energy of specific spatial frequency subbands and their correlations can effectively modify the perceptual attributes of textures (Giesel & Zaidi, 2013 ; Portilla & Simoncelli, 2000 ). Therefore, image-editing methods based on the manipulation of an image’s spatial frequency characteristics can be thought of as effectively exploiting the mechanism by which the human visual system encodes information about the external world. In this regard, Boyadzhiev et al.’s (2015) method is interesting not only as an image-editing tool, but also as a model of vision.

In the following sections, we describe the functionality and usage of the package, illustrate the behavior of the algorithm with a number of image examples, and explain how the algorithm works. We also report on an experiment that examined how face and food images edited using this method affect viewers’ perception of the material properties of said faces and food, and discuss its usefulness and limitations in psychological research.

Functionality and usage of the package

This section describes the features of the package and how to use them. Note that detailed instructions and practical tips for using the package, as well as the source code, are provided on our GitHub page ( https://github.com/tsuda16k/materialmodifier ). The basic procedure to use this package is as follows.

You can load an image from the disk with the im_load() function, and apply a material editing effect with the modif() function. The effect argument of the modif function specifies the type of material editing effect applied (explained below), and the strength argument determines the strength of the effect. The plot() function can be used to display an image. To save an image on disk, use the im_save() function, specifying the name of the output image file and the path where the image will be saved. You can load/save images in jpg/png/bmp format.

Figure 2 shows example outputs of the shine and aging effects. The shine effect manipulates very bright elements in the high spatial frequency bands in the input image (e.g., highlights and gloss), and the aging effect manipulates local dark elements (e.g., stains and blemishes). The higher the value of the strength parameter, the stronger the editing effect (the features are emphasized). If the value of the strength parameter is less than 1, the opposite effect will occur, e.g., the gloss will be weakened or blemishes will be reduced.

Example outputs of the shine and aging effects. The strength parameter controls the strength of the editing effect

Using the aging effect as an example, the effect of the strength parameter is examined in more detail in Fig. 3 . If the value of the strength parameter is greater than 1, a boosting effect that increases the stains/blemishes occurs; if it is less than 1, a reducing effect that decreases the stains/blemishes occurs. To achieve a boosting effect, a strength value of 1.5 to 4 usually yields reasonable results. The strength parameter can be a negative value, but in most cases, setting a negative value will produce unrealistic results (e.g., contrast reversal; see also Fig. 5 ). Note that if the strength parameter is 1, no effect occurs, and the input image is returned unchanged. This is because this parameter is a multiplication factor for the image feature being manipulated (a detailed description of this parameter is given in the Algorithm section).

The effect of the strength parameter is examined using the aging effect. If the value of the strength parameter is greater than 1, a boosting effect occurs; if it is less than 1, a reducing effect occurs

You can also apply multiple editing effects simultaneously. For example, you can simultaneously apply the shine and the aging effects as follows.

This command simultaneously applies a shine effect of strength = 0.2 and an aging effect of strength = 3, resulting in a less shiny and more blemished image. This procedure is the same as the one used to create output example #2 in Fig. 1 . Although you can obtain almost the same result (but not identical, because the first process changes the input image for the second process) by applying each effect in turn (e.g., applying an aging effect to the output of a shine effect), we recommend doing them in a single line, as in the example above, because it saves time needed for image processing. The order of effect names specified in the effect argument does not affect the result; effect = c("shine", "aging") and effect = c("aging", "shine") produce identical results.

This package has several other effects in addition to the shine and aging effects. The available effects are shine, spots, rough, stain, blemish, shadow, and aging. A visual summary of these effects is shown in Fig. 4 . The first column of the figure shows the name of each effect, and the second column shows the perceptual features controlled by that effect. The third and subsequent columns show the input and output images.

Visual summary of image editing effects. By specifying the name of an effect (or BS feature), the algorithm detects that feature in the input image and modifies the appearance of the input image by reducing or boosting the feature. Note that the aging effect controls both HLA and HHN features. See the main text for the definition of BS features

Figure 4 contains a column labeled “BS (band-sifting) feature”; this is an important term related to the image processing algorithm (briefly, an image component to be manipulated, extracted from the input image based on a certain criterion). The algorithm achieves image editing effects by decreasing or increasing the weights of the BS features in the input image. The effect names, such as shine and spots, are aliases for these BS features. The input to the effect argument of the modif function can also be the BS feature names.

Since it is easier to know what kind of editing effect will be achieved if there is an alias, our implementation allows the user to specify image editing by alias as well as by BS feature name.

To understand the nature of each image editing effect better, it is helpful to compare the results of all editing effects on a single image. Figure 5 summarizes the results of the editing effects on face and food images (note that the image in the row with a strength value of 1 is the input image). By comparing the images in the rows with large values of the strength parameter, it is easier to see the characteristics of each effect.

Summary of image processing results with different strength values for each effect. The image inside the dotted line is the input image. Each image in the row with a strength value of −5 has an unnatural appearance (contrast reversal) and is not suitable for use as a stimulus, but it is useful to visualize what feature in the input image is manipulated by each effect

Setting a negative value for the strength parameter often results in an unnatural image (see images in the bottom rows of Fig. 5 ), but using a large negative value for the strength parameter makes it easier to compare which areas are affected by each effect. For example, the rough effect and the blemish effect produce similar results, but if you compare the images in the row with a strength value of −5, you can clearly see that they are not identical. Technically, the blemish effect is equivalent to giving both the rough and stain effects at the same time. To acquire a more formal understanding of these properties, we need to know more about the specifics of how image processing algorithms work.

By default, the modif function targets the entire image for editing. However, in some situations, you may want to edit only certain objects or areas of the image (for example, you may want to edit only the skin area of a portrait). By using a mask image, you limit editing to certain areas within an image. To use this feature, you need to prepare a mask image of the same size as the input image you wish to edit. The mask image contains the area to be edited—white in color—and the rest of the image—which is black. The mask image does not have to be a binary image; gray can be present (the intensity of the gray will be used to alpha blend the input image with the edited image). For example, the mask image representing the skin region of a face image is shown in Fig. 6 .

Editing only specific areas in an image using a mask image. The results of the HHP (shine) effect (strength = 3) with and without using a mask image are shown

The image edited without using a mask image has an increased gloss not only on the skin, but also on the hair and eyes. On the other hand, the image edited using a mask image has increased gloss only in the skin area. To use the masking feature, a mask image in the mask argument of the modif function must be specified.

In this section, we will describe the image processing algorithm in detail. First, there are two points to note. First, the user does not necessarily need to understand the details of the algorithms to use this package. In fact, as we have seen, it is possible to perform image processing by simply specifying the name of the effect and the strength parameter. However, by reading this section, users will have a better understanding of the behavior of this package and will be able to use it in an advanced way. Secondly, this paper will explain the algorithm at a conceptual level, which would be appropriate for the average psychologist. The technical and mathematical aspects are explained in the original paper (Boyadzhiev et al., 2015 ).

Boyadzhiev et al. ( 2015 ) proposed an image-based material editing method called “band-sifting decomposition.” It extracts and controls a variety of perceptual properties of images such as gloss, roughness, and blemishes based on a combination of image processing procedures. How this algorithm modifies the surface appearance of an input image is shown in Fig. 7 , using the control of blemishes as an example. Pixels corresponding to image features that are to be manipulated (e.g., blemishes) are extracted from the lightness channel of the input image based on specific criteria. By decreasing or increasing the lightness value of the pixels in the feature image (Fig. 7 , top right), the appearance of the object surface in the input image is controlled.

The central idea of the material editing algorithm. Pixels corresponding to image features that are to be manipulated (e.g., blemishes; top right) are extracted from the lightness channel of the input image based on specific criteria. By decreasing or increasing the lightness value of those pixels, the appearance of the object surface in the input image is controlled (bottom left and bottom right)

The overall flow of image processing is summarized in Fig. 8 . The input image is first converted to the CIELAB color space. We only process the lightness channel (L channel) and keep the color channels intact. The L channel is log-transformed and then decomposed into “scale subbands”; each subband image represents the lightness information at a given scale (or spatial frequency). As in Boyadzhiev et al. ( 2015 ), we employed the “guided filter” (He et al., 2010 ) to perform scale decomposition. This procedure is a type of band-pass filtering, which decomposes the image based on spatial frequency. This decomposition differentiates between small-scale elements, such as blobs and wrinkles, and large-scale gradients, such as shading and shadows. The number of subband images is determined by the resolution of the input image: if the shorter side of the input image has N pixels, then log 2 N − 1 subband images are produced (e.g., if N = 512, then 8 subband images are produced). The decomposition also produces a low-frequency residual.

The image processing flow for material editing. The L channel of an input image is decomposed into multiple component images based on three criteria: scale (spatial frequency), amplitude (low or high contrast), and sign (sign of pixel value: positive or negative). The component images are assigned to eight groups based on scale, amplitude, and sign. The images are then combined (added together) within each group, resulting in eight images that we call BS (band-sift) features. These images represent different aspects of the perceptual quality of the input image, which can be used to control the appearance of objects. See the main text for a definition of each abbreviation (e.g., HHP)

Each subband image is further decomposed into four images based on the amplitude and sign of the pixels in that subband, and this process is the core idea of the algorithm. For amplitude, the standard deviation (1 SD) of pixel values in each subband is used as a threshold between high and low amplitude pixels to separate low- and high-contrast regions of the subband. Each (low/high) amplitude image is then separated by the sign of pixel values, positive or negative; all the negative-value pixels of an amplitude image are set to zero to produce a positive image, and all the positive-value pixels of an amplitude image are set to zero to produce a negative image. Thus, each scale subband is decomposed into four images (high/low amplitude × positive/negative sign; the images of the row labeled “split by amplitude & sign” in Fig. 8 ).

Next, all the component images (8 scale subbands × 2 amplitudes × 2 signs = 32 images in total, in this example) are grouped by the combination of scale (high or low), amplitude (high or low), and sign (positive or negative). Note that the scale subbands are classified as either high or low frequency (two categories, instead of eight in the original decomposition). If we have N scale subbands, then the first floor (N/2) images are categorized as high frequency and the remaining images as low frequency. This grouping assigns the component images into eight (and always eight, regardless of the resolution of the input image) groups. The images in each group are relatively similar to each other (because they have similar spatial frequencies and belong to the same amplitude and sign group). Finally, the images (pixel values) in each group are added together, resulting in eight images that we call BS features (the images of the row labeled “BS features” in Fig. 8 ). As in Boyadzhiev et al. ( 2015 ), we call each BS feature by the acronym of its grouping criterion. For example, HHP represents the grouping criterion (and the resultant image or BS feature) of the H igh spatial frequency, H igh amplitude, and P ositive sign.

The BS features represent distinct information associated with the perceived material properties of objects in the input image. For example, HHP represents bright (because of their high amplitude and positive sign) and small (because of their high spatial frequency) spots, typically found on wet and glossy surfaces, whereas the HHN (high frequency; high amplitude; negative sign) feature represents small dark blobs that are typical of wrinkles and blemishes in the skin. To amplify gloss, for example, we will boost the HHP feature (i.e., all the pixel values of the HHP image are multiplied by a coefficient greater than 1). To reduce wrinkles, we will reduce the HHN feature (i.e., multiply the HHN image by a coefficient less than 1). Subsequently, all the BS features and the residual image are added together and inverse-log transformed to reconstruct the L channel. The L channel is then combined with color channels and converted back to the standard RGB (red, green, blue) color space to produce the final output (Fig. 8 , images in the bottom two rows).

Depending on which BS feature is being manipulated (boosted/reduced), we obtain different material editing effects. The bottom rows of Fig. 8 show the effect of boosting or reducing each BS feature. For example, for the column labeled HHP, the upper image is the result of boosting the HHP feature, while the lower image is the result of reducing the HHP feature. Manipulating BS features with a positive sign (e.g., HH P and LL P ) adjusts the bright areas in the input image, resulting in the editing of features such as glossiness, while manipulating BS features with a negative sign (e.g., HH N and HL N ) adjusts the dark areas in the input image, resulting in the editing of features such as blemishes. Note that not all BS features produce perceptually meaningful changes in the input image. Based on Boyadzhiev et al. ( 2015 ) and our observations, we have given aliases (alternative names) to some of the BS features that can be used most effectively for perceptual editing effects. For example, HHP is called the “shine” feature because it can be used to manipulate gloss, and HHN is called the “spots” feature because it can be used to manipulate small stains and wrinkles (see Fig. 4 for the list of aliases).

Advanced usage of the package

This section describes how to use the package, based on an understanding of how the algorithm works. By using the modif2() function, image manipulation can be performed by specifying a BS feature in detail. There are two ways to do this: using acronyms, or specifying scale, amplitude, and sign separately.

A list of parameters, which specifies a BS feature to be manipulated, is given to the params argument. A BS feature can be specified by the feature parameter using acronyms. A strength value must also be specified in the list. Instead of using acronyms, each criterion can be specified individually. The freq parameter specifies the spatial frequency (“H” for high spatial frequency, “L” for low spatial frequency, or “A” for all frequencies). The amp parameter specifies the amplitude (“H” for high amplitude, “L” for low amplitude, or “A” for all (both) amplitudes). The sign parameter specifies the sign (“P” for positive sign, “L” for negative sign, or “A” for both signs).

The advantage of specifying the features we want to manipulate using individual criteria is that we have more freedom to specify the scale. As mentioned in the algorithm section, the number of scale-subband images that the algorithm creates is determined by the resolution of the input image. The number of scale subband images for an image can be known using the modif_dim() function. In addition to the number of subband images, this function also outputs the indices of high- and low-scale images. An example is shown below, where the input image is 500 × 500 px in size.

The output of the modif_dim() function shows that the number of subband images to be created from this image is 7; the indices for higher-scale (spatial frequency) images are 1, 2, and 3, and the indices for lower-scale images are 4, 5, 6, and 7. This shows that, in the case of this input image, freq = “H” is equivalent to setting freq = 1:3, and freq = “L” is equivalent to setting freq = 4:7. Therefore, each pair of commands below will produce the same output (but note that which freq corresponds to H/L depends on the resolution of the input image).

What is the advantage of being able to specify the scale to be manipulated in detail? We may want to manipulate only a specific scale, rather than as a high- or low-scale group. To understand this motivation, the result of manipulating individual scales is shown in the bottom row of Fig. 9 . When freq = 1, regions such as eyebrows, which are not considered to be skin gloss, are controlled. Image features with the highest spatial frequency (i.e., freq = 1) often reflect the high spatial frequency noise. Therefore, in some cases it is useful to exclude the highest spatial frequency subband when controlling images. The image in the top middle of Fig. 9 has scale = 1, 2, 3, which is identical to specifying freq = "H" (and therefore this command specifies the HHP feature and is equivalent to the shine effect). Some areas of the eyebrows are brighter in this image because the image subband with freq = 1 has been manipulated. On the other hand, the image in the top right of Fig. 9 does not show a manipulation of the freq = 1 region, so there is no change in the brightness of the eyebrows.

Variants of the HHP (or shine) effect. The modif2 function can be used to specify only one scale subband to be controlled (bottom row), or multiple scale subbands (top middle, top right). The argument of the function was params = list(freq = x, amp = "H", sign = "P", strength = y). The x and y values are shown in the label of each image

Another example of the effect of selecting a particular scale is shown in Fig. 10 . These are variants of the HLA (blemish) effect. Instead of specifying freq = "H" (or, equivalently, freq = 1:3), we can specify freq = 1:2 (excluding the third scale), which gives a somewhat different result (Fig. 10 , top right) than the normal blemish effect (Fig. 10 , top middle).

Variants of the HLA (or blemish) effect. The argument of the modif2 function was params = list(freq = x, amp = "L", sign = "A", strength = y). The x and y values are shown in the label of each image. Excluding the third scale resulted in a somewhat different output than the normal stain effect (top right)

Recall that in the case of the modif function, it is possible to apply multiple effects simultaneously. A similar approach can be taken with the modif2 function. The following script shows how to apply the spots/rough effect simultaneously using the modif2 function. A list containing several lists of parameters is given as the params argument to the function.

In summary, the modif2 function allows the user to specify the scale subband (i.e., spatial frequency) to be manipulated in more detail than the modif function, and provides greater flexibility in controlling the appearance of images.

Evaluation experiment

In this section, we report on an online experiment that evaluated how material editing effects can produce perceptual changes in an input image. We created a series of images with different strength parameter values for each editing effect (HHP, HHN, etc.), and conducted an experiment in which we asked participants to rate the perceived material properties of objects, such as gloss and roughness, as well as the naturalness of the images. The material, data, and R scripts for this experiment are available on OSF: https://osf.io/72dqz/ .

Participants

To detect an effect size of Cohen’s d = 0.55 (a difference of 0.5 on a six-point scale; the standard deviation was based on our pilot study) with 90% power (alpha = .05, two-tailed), G*Power (Faul et al., 2007 ) suggested that we needed 37 participants. In total, 48 participants (female=24, male=24) took part in the experiment. Participants were recruited from psychology classes at Keio University and Doshisha University (M age = 21.1, SD age = 3.8), and they provided informed consent to take part in the study. The study was approved by the Ethics Committees of Keio University (#21026) and Doshisha University (#22070).

Two human face images and two food images were used in the experiment. The face images were of an Asian male and a Caucasian female, selected from the Chicago Face Database (Ma et al., 2015 ; image names: CFD-AM-215-120-N and CFD-WF-003-003-N). The food images were photographs of meat and of a sliced orange; they were selected from public domain licensed images at a stock photos website. Each image was edited with six editing effects (HHP, HHN, HLP, HLN, HLA, and HLA+HHN), and seven strength values (0, 0.25, 0.5, 1, 1.5, 2, and 2.5) were set for face images and 0.25, 0.5, 1, 2, 3, and 4 for the food images. The shadow effect was not included in the experiment because we assumed that shadows are a less important feature in the perception of material attributes tested in the experiment. The reason for varying the range of the strength parameter for the face and food images is that people are more perceptually sensitive to editing manipulations of faces than of non-face objects (Boyadzhiev et al., 2015 ). For each original image, 42 image variations (6 editing effects × 7 strength values) were created, resulting in 168 stimulus images. Note that images with a strength value of 1 are identical to the original (unedited) image. All images were 500 × 500 px in size and presented at that size on the display.

Design and procedure

For each stimulus, participants rated six attributes (five material properties and naturalness) on a six-point scale. The material attributes were matte–gloss, dry–wet, opaque–translucent, rough–smooth, and old–young/fresh. These attributes are representative dimensions in skin perception (Otaka et al., 2019 ) and food perception (Hanada, 2020 ; Spence et al., 2022 ). Participants were instructed to rate the naturalness of the images, that is, photorealism. An image was presented in the center of the display and participants responded by pressing a key (there was no time limit on the response). Each image set (42 variations of an original image) and attribute was rated in blocks and in random order. Within each block, images of varying editing effects and strength values were presented in random order. Images with a strength value of 1 were presented twice, while images with other strength values were presented once. The total number of trials was 1152 (4 image contents × 6 rating dimensions × 6 editing effects × (7+1) strength values). Participants completed practice trials prior to the experimental session (a face image and a food image were used in this trial, but not in the experimental session).

The data of five participants were not recorded (possibly owing to network errors). Thus, 43 participants (20 male and 23 female) were included in the analysis.

Figure 11 shows the averaged standardized rating values for each condition (standardization was calculated by subtracting the mean rating value for images with a strength value of 1 from each rating value) and their 95% confidence intervals. The results of the face images are shown in the top row and those of the food images are shown in the bottom row. Each column shows the results for each rating attribute. Each image editing effect is indicated by the color of the line and the shape of the symbol. As an example of how to interpret this graph, editing a face image with a strength value of 0 with the HLA+HHN (aging) effect increases the gloss rating by two points on a six-point scale compared to the unedited image (see the red line in the upper leftmost column in Fig. 11 ).

Results of the evaluation experiment. The results of each rating attribute (column) are shown for each image category (face and food). The ratings are standardized with respect to the ratings for unedited images (i.e., images with a strength parameter value of 1). Error bars indicate 95% CI

To test each material editing effect per rating attribute, the Friedman test (Myles & Douglas, 1973 ) was conducted for each experimental condition using the stats::friedman.test function in R. As responses for the unedited stimulus (strength parameter = 1) were collected twice in each condition, we averaged them before performing the statistical tests. In two conditions, the effect of image editing was not significant (χ 2 (6) = 10.1, p = 0.12 for HHP on old–young rating for face, and χ 2 (6) = 9.8, p = 0.14 for HHP on rough–smooth rating for food). In all other conditions, each editing effect influenced each rating value (all p s < 0.05, corrected for each rating attribute and editing effect with the Bonferroni procedure).

The results show that each editing effect changed the perceived material properties in the expected directions. For example, the HHP effect increased or reduced glossiness and wetness ratings, and reduced the translucency rating. The HHP effect on the smoothness rating was statistically significant only for face stimuli, and its effect on the freshness rating was statistically significant only for food stimuli. The aging (HLA+HHN) effect changed the skin youthfulness rating to old or young depending on the strength parameter value, and changed the food freshness rating to old (but did not increase freshness). The aging effect also affected the other rating dimensions in the same way as observed for the HLA and HHN effects. The HHN (spots), HLP (rough), HLN (stain), and HLA (blemish) are features related to surface roughness and uniformity, all of which affected the smoothness rating. That is, when these features were boosted, the stimulus was rated as rough, and when they were reduced, the stimulus was rated as smooth.

We also found that the roughness-related editing effects (HHN/HLP/HLN/HLA) were also associated with ratings of material properties other than roughness. That is, facial stimuli rated as rough skin were rated as matte/dry/opaque, and facial stimuli rated as smooth skin were rated as glossy/wet/translucent. These results suggest that the HHN/HLP/HLN/HLA effects manipulate image features that are commonly used as perceptual cues across these ratings. Effects such as HLP (rough) and HLN (stain) were rated very similarly (Fig. 11 ). However, this result does not imply that these editing effects create similar images. In fact, the HLP and HLN effects give different textures to the input image (Fig. 12 ). Both are similar in that they increase surface roughness, but HLP produces unevenness across the entire surface of the skin, while HLN emphasizes localized dark areas such as stains and blemishes. We believe that the rating scales used in this experiment did not reflect these subtle differences in perceptual experience.

Difference between HLP (rough) and HLN (stain) effects. The image to the right of the output image is a magnified view of the woman’s left cheek

In general, when comparing the results for faces with those for food, the effect of reduce edits (strength parameter value less than 1) on perception was small for food. This may be due to the nature of the image (food) objects used in the experiments: raw meat and oranges, which tend to have a high degree of gloss and translucency. In fact, the glossiness, wetness, translucency, smoothness, and youthfulness/freshness ratings for the unedited (strength = 1) images were all higher for food (4.1 vs. 3.7 for glossiness, 4.3 vs. 3.6 for wetness, 4.0 vs. 3.7 for translucency, 4.2 vs. 3.8 for smoothness, and 4.2 vs. 3.7 for youthfulness/freshness, two sample t -test, two sided, all p s < 0.05). Thus, perceptual effects of reduce editing on the food images used in the experiment would have been difficult to observe. These results are not considered a procedural artifact (ceiling effect). This is because ratings for stimuli with an intensity parameter of 1 were approximately 4, and there was room for higher ratings.

The results of the naturalness rating showed that the further away from 1 the strength parameter value was, the more unnatural the image was rated (although for some editing effects, such as HLP and HLN, image naturalness was preserved for reducing effects). Therefore, if the naturalness of the image is important, the strength parameter should not be set to an extreme value.

Limitations

This section describes some of the limitations of this package. First, depending on the input image, the material editing effect may not work as intended. If the feature of interest is weak or absent in the input image, the algorithm cannot control the image in that dimension. For example, if a photo of a woman’s face with smooth skin due to heavy makeup or strong lighting is used as an input image, the algorithm cannot increase skin blemishes because it cannot add features that are not present in the input image (Fig. 13 ). The average face, which is often used in face perception research, also has this limitation because average faces tend to have smooth facial textures.

Results of applying the aging effect to a photo of a face (top) and an average face of Japanese females (bottom; adapted from Nakamura et al., 2020 ). When the skin of the input image is smooth, the aging effect does not work well

Figure 14 shows another example where the editing effect is less apparent: the HHN effect is applied to make the food look wilted (middle row), but it does not look wilted enough. One way to deal with this is to increase the strength value, but the larger the value, the more unnatural the image will look. Another way is to apply multiple editing effects (right column). This will increase the spots while concurrently eliminating the gloss, giving the image a more wilted appearance.

By combining multiple editing effects, it may be possible to create a desired change in appearance. Observe the image at a larger size to see the changes in the image

Second, the names of effects such as shine and blemish do not necessarily correspond to the appearance of the output image. For example, the effect named “stain” is just an alias for the image feature selection criterion HLN, and the manipulation of the HLN feature is just an adjustment to the lightness information of high spatial frequency, low amplitude, and minus sign in the input image. Therefore, when you want to manipulate stains in an image, it could be more effective to use other effects such as the spots (HHN) effect rather than the stain (HLN) effect. The parameter settings to achieve the desired effect will likely vary depending on the scale of the object and the lighting of the scene, which will be a practical difficulty when using this technique.

Third, the psychophysical properties of the material editing effects are not clear. That is, doubling the strength parameter does not necessarily mean that the perceptual effect will be doubled. It is not a trivial problem to formalize the relationship between the strength parameters and perception, because it will be specific to each material dimension, and possibly to each image category. For example, Boyadzhiev et al. ( 2015 ) compared naturalness ratings for face and non-face images and reported that face images are more likely to look unnatural with small image changes. Material perception is subject to complex nonlinearities. As a result, in studies that require a series of controlled images that are perceptually equidistant across each condition, researchers may need to conduct psychophysics experiments to assess the psychophysical properties of the image set.

Finally, it should be noted that the processing time required for image conversion depends on the resolution of the input image. The larger the input image, the more processing time required (Fig. 15 ). If the resolution of the input image is equal to or smaller than 1024 × 1024 px, the process will be completed in a relatively short time. However, if the resolution of the input image is 2048 × 2048 px, the processing time will be more than a minute (these numbers will also depend on the machine specs). Therefore, the resolution of the input image should be as small as possible, especially when a large number of images need to be prepared. The modif and modif2 functions have an argument named max_size. If the shorter side of the input image is larger than max_size, the image will be automatically scaled down so that the shorter side of the input image matches max_size. The default value of max_size is set to 1280. Thus, if an image with a resolution of 2000 × 3000 px is used as the input image, the output image will have a resolution of 1280 × 1920 px. If you do not want to change the resolution of the image, you can enter a larger value for max_size, or set max_size = NA. This feature was provided to avoid the extremely long execution time when a high-resolution image such as 4K is accidentally used as input.

Execution time of the modif function. Ten measurements were taken for each resolution, and the mean and 95% CI of the execution time were calculated. Tested on a MacBook Air (M1, 2020, 8 GB). The type of effect (shine, blemish, etc.) has no effect on the execution time

In this paper, we presented materialmodifier, an R package for photo editing effects. The software uses image processing techniques to parametrically manipulate the surface properties of objects (e.g., gloss/roughness) in photographs, providing an automatic and reproducible method to create a set of image stimuli. We have confirmed that this software can be used effectively to control the appearance of faces, foods, and objects. We believe that this software will be useful for researchers interested in topics related to material perception, such as face perception and aesthetic evaluation of objects. The package can be installed via CRAN, and documentation and source code are available at https://github.com/tsuda16k/materialmodifier .

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This work was supported by JSPS KAKENHI, Grant Numbers JP19K23376 for HT, and JP19H05733 for HK.

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Tsuda, H., Kawabata, H. materialmodifier: An R package of photo editing effects for material perception research. Behav Res 56 , 2657–2674 (2024). https://doi.org/10.3758/s13428-023-02116-2

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Digital distortions: Study explores the consequences of photo editing on self-perception and self-esteem in social media users

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A recent study published in BMC Psychology looked into how editing photos on social media platforms affects people’s self-image, self-esteem, and comparisons with others. The findings suggest that editing photos can negatively impact how individuals perceive their attractiveness and overall self-esteem.

This connection seems to be influenced by comparing physical appearance and treating oneself as an object. These results serve as a warning to social media users to be aware of the potential negative effects of using photo-editing tools or filters.

Nowadays, social media is widely used by millions of people to connect and share their lives. However, research suggests that excessive use of social media may lead to mental health issues like depression and anxiety.

One behavior that researchers have focused on recently is photo editing, which involves altering one’s appearance in pictures before posting them on social media. While some studies have suggested that photo editing is linked to negative outcomes such as seeing oneself as an object and having low self-esteem, the relationship between these factors is not well understood.

Phillip Ozimek and his colleagues conducted a study with 403 young adults recruited through social media platforms to investigate the potential risks associated with photo editing on social media. Participants completed an online survey that included questions about their social media use, photo editing behavior, self-perception as an object, comparisons of physical appearance, self-esteem, and other relevant factors.

To measure photo editing behavior, participants reported how often they edited their selfies before posting them on social media. To assess self-perception as an object and comparisons of physical appearance, participants answered questions about how much they monitored their bodies and compared themselves to others’ looks. Finally, participants’ self-esteem was measured using a questionnaire that evaluated their overall sense of self-worth.

The data showed that higher levels of photo editing were associated with increased self-perception as an object and more comparisons of physical appearance among young adults. These factors, in turn, were related to lower self-esteem.

The researchers suggested that photo editing behavior may contribute to feelings of self-perception as an object and basing one’s worth on appearance, especially among vulnerable groups like teenagers. They emphasized the need for further research to explore the impact of photo editing on mental health outcomes and understand why people engage in this behavior.

The study had some limitations, as acknowledged by Ozimek and his colleagues. Firstly, the data was collected through self-report measures, which may be biased. Secondly, the study design was cross-sectional, which means causality cannot be determined. Lastly, the sample size was relatively small and not representative of the entire population of adolescents who use social media.

This study highlights the importance of considering overall social media use and specific behaviors like photo editing when studying the relationship between social media and mental health outcomes. Interventions aimed at reducing excessive social media use or promoting healthier engagement with social media could have positive effects on the mental well-being of young people.

“Reality is more elusive as it appears on the surface,” the researchers wrote. “The depiction of reality is a constructive endeavor which is subject to concealed issues of the editors. The depiction of reality is usually not a documentary but part of a narrative which the photo editor intends to project on the public screen.”

“By understanding the underlying narrative, the contrast between natural appearance and edited photo of it is getting transparent. Because photo editing is likely to prevail in the future, the focus of psychoeducation as part of a psychological intervention technique should be a sensibilization for the widespread of use of corresponding techniques.”

The study, titled “ How photo editing in social media shapes self-perceived attractiveness and self-esteem via self-objectification and physical appearance comparisons ,” was conducted by Phillip Ozimek, Semina Lainas, Hans-Werner Bierhoff, and Elke Rohmann.

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Avoiding Image Fraud: 7 Rules for Editing Images

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Figures represent an extremely important, yet often overlooked, aspect of a scientific paper. Learn how to use and adapt them without violating copyright.

Updated on August 11, 2014

Figures represent an extremely important, yet often overlooked, aspect of a scientific paper. Figures are often one of the first things that a reader sees when deciding whether to read a paper, and they have the power to convey much more information per square inch than text. Many of you have likely noticed that PubMed provides thumbnail images of the figures from many papers along with the abstract.

True fraud is rare, but journals may question other changes made to your figures

Because of the weight that figures carry, they are scrutinized carefully by editors and reviewers to ensure that they have not been manipulated to hide or falsify data. As a result, many journals are implementing steps to check submitted figures for evidence of tampering.

Unfortunately, while true fraud is rare, some completely harmless changes to a figure file can appear fraudulent to the journal. It is therefore important to understand what to avoid when manipulating images. Here, we offer a few suggestions, largely patterned around the Journal of Cell Biology 's industry-leading standards for defining improper image manipulation.

The Journal of Cell Biology's guidelines state : No specific feature within an image may be enhanced, obscured, moved, removed, or introduced. The grouping of images from different parts of the same gel, or from different gels, fields, or exposures must be made explicit by the arrangement of the figure (e.g., using dividing lines) and in the text of the figure legend. Adjustments of brightness, contrast, or color balance are acceptable if they are applied to the whole image and as long as they do not obscure or eliminate any information present in the original. Nonlinear adjustments (e.g., changes to gamma settings) must be disclosed in the figure legend.

Some changes are obvious fraud (deleting one portion of an image or copying an image and passing it off as multiple figures), but other manipulations are more subtle.

7 things to consider when altering figures for publication:

• Always have the original, unaltered file available in case the journal requests it. If you cannot produce the original file, the journal will likely reject your submission. You should also be able to explain exactly what alterations were made to the image (i.e., the software and particular tools used).
• If you supervise any other researchers who have the ability to alter images or figures before submission, make sure they are aware of which types of image manipulation are acceptable and which are not.
• In general, it is appropriate to adjust the brightness, balance, or contrast, but only if the entire image is adjusted equally. Each pixel should be adjusted linearly.
• All bands or features evident in the original image must still be visible; do not adjust the image to the point where some parts of it disappear.
• If you have multiple images (e.g., a control cell and a treated cell), make sure that the brightness and contrast are equal for both. Changes to one panel of a figure but not another can be misleading.
• Removing some background fuzziness is acceptable, but do not remove so much that the background becomes white. At this point, reviewers may wonder if faint bands or other features disappeared, too.
• Never combine multiple images into one field. It is acceptable to splice out one lane of a gel if the information in that lane is no longer relevant. However, a black or white line should clearly indicate where the gel has been spliced.

For more information, see Rossner and Yamada (2004) .

Knowing the rules saves time and hassle.

In the first two years of testing for image manipulation, JCB editors found that 25% of manuscripts submitted contained figures that were manipulated in ways that could be construed as misconduct . However, only 1% of cases were actually fraud; most were resolved by providing the original file.

Being aware of what is acceptable and what is not can help you avoid the hassle of defending your work, even when you didn't carry out any fraud.

Did you know that AJE offers Figure Services to help you design or format your figures for publication? To learn how AJE can reformat your figures to fit a specific journal's guidelines or create custom figures, visit our website .

Have questions about what could be construed as improper image manipulation? Send us an  e-mail .

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Softwares for Creating Scientific Images and Figures

In the process of academic research, the data obtained by researchers can only mean something when it gets published. Part of the publishing process involves being able to generate scientific images and figures that represent the findings. Academic journals have a set of standards for images and figures to be published. These style guidelines usual include certain features such as the size of the image/figure, the resolution, the spacing, the font size and style, the file type, and the layout.

The process of creating an image or figure for a manuscript is often a very time-consuming step and, for younger researchers, this can be very daunting. Every academic journal has its own requirements, so each time a researcher submits a manuscript they have to pay careful attention to these guidelines and follow them exactly. Fortunately, with advances in online publications and online submissions, the process can actually go quite smoothly. In fact, there are a number of software programs now available to researchers that make figure and image preparation something that goes hand in hand with data collection.

Using Software to Create Images and Figures

Every researcher has their own favorite software program or programs that they use when creating scientific images and figures. There are a number of programs available that aid in image/figure preparation and each has its own unique features and capabilities. Here we present a brief summary of some of the most commonly used software programs.

• ImageJ is an image-processing program that was developed at the National Institutes of Health . It is freely available and provides extensibility via Java plugins and recordable macros. ImageJ lets users edit, analyze, process, save and print 8-bit color and grayscale, 16-bit integer, and 32-bit floating point images. It is compatible with many image file formats and supports image stacks. ImageJ can be used to calculate the area and pixel value statistics of user-defined selections and intensity-thresholded objects. It measures distances and angles and can be used to make density histograms and line profile plots. It basically allows for image analysis and preparation, and when the images are saved they can be easily imported into other software programs for further preparation.

Related: Need help with scientific illustrations? Make sure you read our post on MindTheGraph . Check out this section today!

• PowerPoint is a software program that is part of the Microsoft Office. It provides users with the ability to enhance images and figures through features such as cropping, add text, aligning, resizing, and changing the brightness and contrast. Many researchers find this a standard mechanism for creating images and figures as they collect data because this format is often used for presentation purposes. Images and figures that are created with PowerPoint can be saved as other file types to be exported into other software programs for further preparation if necessary.
• Adobe Photoshop is considered as the most powerful image manipulation software that exists. It has to be used carefully when manipulating images for publication in scientific journals because it contains so many features for enhancing images that it is easy to inadvertently violate the image manipulation rules set by the publishers. Images can be sized and the resolution and color can be altered. In addition, adjustments can be made using the levels, curves, and brightness and contrast features. One of the key features is that several images can be combined to create figures for publication using layers and masking techniques. There is a bit of a learning curve with Photoshop, but once the user has the basics covered, it is a very powerful tool for scientists.
• Adobe Illustrator is another popular image editing software. It is a vector-based drawing program that allows the user to import images, create drawings, and align multiple images into one figure. The figure that is generated can be exported as a high-resolution image that is ready for publication. Illustrator allows the user to fully customize and polish their figures. It has a large toolbox with many features that can be used to create high-quality images and figures.

Tips to Prepare Figures

Although many researchers may find figure preparation a wearisome task, however, with the right tools it can actually go quite smoothly. Many scientists use a combination of some of the above-mentioned software programs to create their figures. Each program has its own special features and depending on the users’ preferences certain aspects are quite intuitive. Here are some important tips to keep in mind when preparing images and figures during the manuscript preparation process.

• Be sure to follow the journal guidelines exactly as they are written. By not following these standards the journal could automatically send our manuscript back without a review.
• Review what constitutes image manipulation fraud, as this can cause rejection and embarrassment.
• Review the figures for error before you submit your manuscript. When you focus on the details of how the image looks you might not focus on the actual content. Make sure it reflects the data you are presenting in the text.
• It is always a good idea to print out your images before you submit them. This will ensure they are presented well for the reviewers.

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Title: edibert, a generative model for image editing.

Abstract: Advances in computer vision are pushing the limits of im-age manipulation, with generative models sampling detailed images on various tasks. However, a specialized model is often developed and trained for each specific task, even though many image edition tasks share similarities. In denoising, inpainting, or image compositing, one always aims at generating a realistic image from a low-quality one. In this paper, we aim at making a step towards a unified approach for image editing. To do so, we propose EdiBERT, a bi-directional transformer trained in the discrete latent space built by a vector-quantized auto-encoder. We argue that such a bidirectional model is suited for image manipulation since any patch can be re-sampled conditionally to the whole image. Using this unique and straightforward training objective, we show that the resulting model matches state-of-the-art performances on a wide variety of tasks: image denoising, image completion, and image composition.

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Hunters and Gatherers of Pictures: Why Photography Has Become a Human Universal

Leopold kislinger.

1 Independent Researcher, Leonding, Austria

Kurt Kotrschal

2 Department of Behavioral Biology and Konrad Lorenz Forschungsstelle, University of Vienna, Vienna, Austria

3 Domestication Lab at the Konrad-Lorenz Institute of Ethology, Wolf Science Center, University of Veterinary Medicine, Ernstbrunn, Austria

Associated Data

The original contributions presented in the analysis are included in the article, further inquiries can be directed to the corresponding author.

Photography is ubiquitous worldwide. We analyzed why people take, share, and use personal photographs, independent of their specific cultural background. These behaviors are still poorly understood. Experimental research on them is scarce. Smartphone technology and social media have pushed the success of photography, but cannot explain it, as not all smartphone features are widely used just because they are available. We analyzed properties of human nature that have made taking and using photographs functional behaviors. We did this based on the four levels, which Nikolaas Tinbergen suggested for analyzing why animals behave in a particular way. Including findings from multiple disciplines, we developed a novel conceptual framework—the “Mental Utilization Hypothesis of Photography.” It suggests that people adopt photography because it matches with core human mental mechanisms mainly from the social domain, and people use photography as a cognitive, primarily social coping strategy. Our framework comprises a range of testable predictions, provides a new theoretical basis for future empirical investigations into photography, and has practical implications. We conclude that photography has become a human universal, which is based on context-sensitive mental predispositions and differentiates itself in the social and societal environment.

Introduction

Photography is ubiquitous around the world, with the number of people taking and using personal photographs steadily increasing (Lee and Stewart, 2016 ; Canon, 2018 ). More than 90 percent of all photographs (henceforth photos ) are taken with smartphones (Carrington, 2020 ), and more than half of the world's population uses smartphones or mobile phones to take, view, and share photos (Statista, 2019 ; Kemp, 2021 ). Smartphones integrate photography with many other functions, notably with access to the internet and social media (Smith, 2011 ; GSMA and NTT DOCOMO, 2014 ). This has rapidly shifted photography from an exclusive activity of socio-economically capable minorities toward engaging a majority of the world's 7.8 billion people.

We examined the question why people take, view, own, share, and use personal photos, and why photos are important to them. We consider the distribution of smartphone technology and social media a precondition for the sweeping success of photography, but insufficient to explain it, as not all smartphone features or technologies are widely used just because they are available. The technology to make audio-recordings, for example, has not been adopted by many people (Milgram, 1976 ). Although smartphones are capable of easily recording the voices of loved ones, conversations, the sounds of a birthday party, or of a strange city, people rarely use this function (GSMA and NTT DOCOMO, 2014 ; Lutter et al., 2017 ).

There is extensive research on the psychological bases of pictorial representation and art (e.g., Deacon, 2006 ; Donald, 2006 ; Dutton, 2009 ). No theory, however, has suggested an integrated psychological basis of the wide range of photography-related behaviors. Photography differs significantly from other visual representation techniques. The invention and further technical developments in photography have conveyed images with characteristics that drawings, paintings, maps, or plans do not have: (a) photos are realistic in a special way; (b) photos are produced by technical devices; (c) part of the information in photos is there by chance; (d) people tend to believe that what they see in photos really happened that way; and (e) photos can be created easily, quickly and effortlessly. We will describe these properties in more detail at the beginning of the following section.

Milgram ( 1976 ) assumed that taking and using photos conveys specific abilities, which can be best understood if cameras and photos are regarded as “evolutionary developments” (p. 7). We followed this approach and hypothesized that the urge to take, view, share, and use photos is based on human nature (Wilson, 2012 ; Kotrschal, 2019 ), i.e., on evolved context-sensitive predispositions and mechanisms, mainly rooted in the social domain. We examined this hypothesis on the basis of the four levels of Tinbergen ( 1963 ) to analyze and explain “natural” traits, i.e., those which evolved via the Darwinian processes. These levels relate to (1) the physiological mechanisms underlying a certain behavior, (2) it's ontogeny, (3) evolutionary history, and (4) adaptative value. This frame guided half a century of behavioral research and may be considered the research program of organismic biology in general (Bateson and Laland, 2013 ; Nesse, 2013 ).

We place photography in the context of the coherent theory of the evolution of life (Darwin, 1859 , 1879/2004 ; Jablonka and Lamb, 2014 ) and human nature as an outcome of this evolution. The four levels proposed by Tinbergen are the theoretical and practical formulation of this context. Since there is only a single Darwinian theory of evolution, and culture is part of human nature (Jablonka and Lamb, 2014 ), the biological context should allow us to develop a unified explanation, coherent with contemporary knowledge particularly on the proximate mechanisms (i.e., current physiological mechanisms and their ontogeny). According to the four levels of Tinbergen, our central research questions are: What are the cognitive and physiological mechanisms underlying taking and using photos? How does taking and using photos develop ontogenetically?—which is important for understanding the development of inter-individual variation. What is the phylogenetic basis for photographic behavior? What may the functions and adaptative value of taking and using photos be? In this respect, a contribution of taking or using photos for survival and individual reproductive success may not be obvious in modern humans, but to qualify as an evolutionary function, the proof of a direct effect would not be needed. Rather, it would be sufficient to find a plausible positive effect on a person's social and mental well-being, which, in turn, on a population level, would entail a positive, supportive effect on societal and biological fitness.

Our aim was to create a theoretical framework, which describes why and in what way taking, viewing, sharing, and using personal photos are functional behaviors in terms of what is presently known about human nature. The development of this framework was based on the integration of available empirical findings on photography from multiple research areas with findings from biology, psychology, and neuroscience. We consider cultural and biological traits as closely interconnected and interacting in driving evolution and individual behavior (e.g., Jablonka and Lamb, 2014 ; Kotrschal, 2019 ). To the best of our knowledge, a similarly comprehensive integration of findings into a coherent theoretical framework has not been attempted before. Our framework generates a number of predictions about the specific characteristics of personal photos and photography-related behaviors, which can be tested through empirical investigations.

Based on our framework and data on the global availability of smartphones and social media, we intended to show that photography qualifies as a human universal (Murdock, 1945 ; Brown, 1991 ; Antweiler, 2016 ; Christakis, 2019 ; Kotrschal, 2019 ). The concept human universal is traditionally associated with traits, activities, characteristics, or institutions, which are observed in all cultures and societies worldwide, like social organization, cooking, language, music, or weapons (Brown, 1991 ). According to this view, photography would not be a human universal. Historically, photography is a new development and did not exist in the traditional societies described by ethnology. For traits or behaviors, which have only recently become universal, Brown ( 1991 ) introduced the term “‘new’ universals” (p. 50). He cited dogs, tobacco, metal tools, and plastic containers as likely examples. Hence, according to Brown's classification, photography is a “new universal.” We describe photography as a human universal, which is based on context-sensitive predispositions, which differentiates itself over ontogeny in the societal environment. Our evolutionary approach does not suggest categorizing photography as a stereotypic behavior based on “innate” dispositions. In line with the present concepts of human social behavior and human universals, we emphasize context-sensitivity, inter-individual variability and individual uniqueness of photography-related behaviors within the frame of the human reaction norm (Woltereck, 1909 ), as comparable, for example, with language or music.

Materials and Methods

Specific characteristics of photographs and photography.

Our focus is on personal photography , that is, on photography-related behaviors, including taking, viewing, sharing, and using photos, which are performed for personal reasons and without commercial intent (Chalfen, 1987 ; Kindberg et al., 2005 ). In particular, we refer to photography-related behaviors, which people perform immediately and voluntarily (spontaneously), without intentional preparation or planning beforehand. We specifically referred to characteristics of photos and behaviors related to photos, which other representational pictures and behaviors associated with them do not have:

Photos Are Realistic Images

An object depicted in a photo can share a large number of visual features with the object that was seen in the environment at a specific point in time from a specific location (Bradley and Lang, 2007 ). Because of this characteristic, photos are called realistic images (DeLoache et al., 1998 ). When individuals see a photo, a retinal image can be formed, which is similar to the image that would be formed if they saw the represented event or object in the environment in real life (Perrett et al., 1991 ). When investigating the neural bases of recognizing or categorizing objects (e.g., faces, bodies, sites, or objects), neuroscientists and cognition researchers often assumed that there is an equivalence between the photographic representation and the perceptible object in the environment and presented photos of objects as stimuli instead of the real objects in question. Important psychophysiological mechanisms underlying photography-related behaviors are related to the fact that photos of objects elicit reactions in certain areas of the brain similar to events, which are effectively seen in the environmeint.

Photos Are Produced by Technical Devices

Drawings and paintings can also be realistic images. In contrast to photos, the creation of drawings and paintings involves the hands of the artists who created them, and important visual characteristics resulted from the dispositions, ideas and decisions of these artists. Photos are created by technical devices, and viewers know this fact.

Part of the Information Came Into the Photo by Chance

The people who use cameras choose a certain perspective, a certain frame and a certain moment when they press the shutter button. Photographers use this selection to control the characteristics and meanings of photos. In complex natural scenes, photographers cannot control all of the information that gets into the photos. Some information comes into the pictures by chance (Talbot, 1844/2011 ). This is hardly the case with representative drawings or paintings.

People Assume They See Reality in Photos

People tend to believe that what they see in photos really happened that way—even if photos are posed, manipulated or forged (Wade et al., 2002 ; Nightingale et al., 2017 ). This phenomenon is still poorly understood. It is possibly related to the knowledge of the viewers that they see a picture that was produced by a technical apparatus. This knowledge could be linked to the assumption that the picture is little affected by the personal attitudes and intentions of the person who made it (Miller, 1973 ; Gu and Han, 2007 ).

Photos Can Be Created and Understood Easily, Quickly, and Effortlessly

Unlike drawings, paintings, maps, or plans, photos can be created easily, quickly and effortlessly. Three-year-old children can take informative and expressive photos (Magnusson, 2018 ). Without complex knowledge or skills, people can take photos that they and other people find excellent (De Looper, 2016 ). Complex events represented by photos are quickly and easily understood. A single quick glance is enough for viewers to understand, for example, an interaction between two individuals (Hafri et al., 2013 ).

Taking, sharing, and using photos are not behaviors, which have all of a sudden appeared as something completely new and an emergent property of culture. We hypothesized that they are deeply rooted in organismic and cultural evolution. The basic cognitive and physiological factors underlying photography-related behaviors are common to all people. Some of these factors may vary relatively little between individuals, but others, for example, related to individual personality structure may show great inter-individual variability. But even such a pronounced inter-individual variability is far from random, as much of ontogeny seems to depend on context-sensitive human dispositions (e.g., Jablonka and Lamb, 2014 ; Kotrschal, 2019 ). Such dispositions are the result of non-random interactions between genes, epigenetics, and the social and societal environments during ontogeny. They frame the way people tend to take, view, share, and use photos.

Empirical Data and Findings on Photography-Related Behaviors

Empirical data and findings on taking, viewing, recognizing, sharing, and using photos come from a variety of disciplines, such as psychology, neuroscience, human-computer interaction, and anthropology. In analyzing the questions on the level of the cognitive and physiological mechanisms underlying photography-related behaviors, we referred to studies that examined the following questions: Which cognitive processes in the brain play a special role in photographing (Barasch et al., 2017 ; Blitch, 2017 )? How do people's brain responses to photos they have taken themselves differ from their responses to photos taken by others (Sellen et al., 2007 ; St. Jacques et al., 2011 ; Diefenbach and Christoforakos, 2017 )? Which brain responses do photos elicit in which viewers see a person with whom they are connected through a close emotional relationship (Bartels and Zeki, 2004 ; Gobbini et al., 2004 ; Leibenluft et al., 2004 ; Master et al., 2009 ; Eisenberger et al., 2011 )? Which brain responses do photos evoke in which viewers see themselves (Devue et al., 2007 ; Butler et al., 2012 )? Which neural processes form the basis for viewers to find a picture beautiful or ugly (Kawabata and Zeki, 2004 ; Jacobs et al., 2012 )?

To describe the ontogenesis of photography-related behaviors, we refer to studies that examined the development of the ability to recognize the representational properties of photos (DeLoache et al., 1998 ; for review, see Bovet and Vauclair, 2000 ), as well as to studies, which examined the age at which children start taking photos and for what purposes they use cameras (Mäkelä et al., 2000 ; Sharples et al., 2003 ; GSMA and NTT DOCOMO, 2014 ).

In analyzing the evolutionary roots of photography-related behaviors, we refer to studies of the ability of non-human primates and other animals to recognize objects depicted in photos (Bovet and Vauclair, 2000 ; Kano and Tomonaga, 2009 ; Aust and Huber, 2010 ). Information was also provided by investigations into the question how people develop the ability to recognize objects pictured in photographs (Deregowski et al., 1972 ; Miller, 1973 ; Bovet and Vauclair, 2000 ).

Table 1 briefly summarizes some of the research that will be used to analyze the level related to the adaptative value of photography-related behaviors. Every single referenced study provides a number of answers that are not always consistent with the answers from the other studies. The answers given are therefore rather examples of content to which we refer in the article than representative information.

Questions and studies used to analyze the adaptative value of photography-related behaviors.

Results: the Four Levels of Tinbergen ( 1963 ) As a Theory Frame

Psychophysiological mechanisms underlying photography-related behaviors.

Researchers have used photos as stimuli. Thus, quite some knowledge on the psychophysiological mechanisms involved in recognizing and viewing photos has accumulated, but experimental research on the mechanisms involved in taking photos is essentially lacking (except for Blitch, 2017 ). The success of photography, however, is primarily related to features of taking photos (Krämer and Winter, 2008 ; Hu et al., 2014 ; Lee et al., 2015 ; De Looper, 2016 ; Malik et al., 2016 ; Carrington, 2020 ). These include various activities and outcomes. These activities are, for example, associated with relating to individuals or objects as well as creating and appropriating images of them and their desirable properties. Outcomes may be associated with a sense of control and efficacy. The rapid global spread of photography was not driven by new opportunities to get, acquire, or exchange photos taken by other people, but mainly by the increased availability of inexpensive cameras, particularly smartphones, and opportunities to share one's own photos electronically. For this reason, we address in this section the specific mechanisms that form the neural basis of taking personal photos. The following description of the processing steps in taking pictures corresponds to hypothetical predictions. We mainly employ findings on processes in primates including humans from various contexts, which can be related to the psychophysiological mechanisms involved in taking photos. Figure 1 shows a hypothetical model including the major steps of taking a photo.

Hypothetical process model including the major processes and activities that occur in an individual who engages in taking a personal photo. The place within the sequence where the processes and activities are located indicates either when they occur or when they first occur. In order to keep the presentation clear, possible feedback effects of activities on the antecedent steps are not shown. Downward arrows mean “then occurs”; a horizontal arrow means “interacts with”.

Initial Steps in Taking Photos

The first steps in taking a photo do not involve conscious awareness (Custers and Aarts, 2010 ). A mother, for example, responds spontaneously to the happy expression on the face of her 6-year-old son at his birthday party, or a hiker responds to the overwhelming panorama at a mountain top. In these examples, the perceptual input activates a fast, low-level system of subcortical structures related to affective processing (Baxter and Murray, 2002 ; Pourtois et al., 2013 ), including neurons responding to the visual information and others responding to relevance and information related to primary (evolutionarily developed) or individually acquired reward value. Some of these structures project to the midbrain dopaminergic system (Dommett et al., 2005 ; Schultz, 2006 ). In turn, dopaminergic projections from the ventral tegmental area (VTA) in the midbrain reach the ventral striatum, including the nucleus accumbens (NAcc), amygdala, hippocampus and other areas of the mesolimbic system (Berridge and Robinson, 1998 ; Alcaro et al., 2007 ), which functions as the central neural basis for approach and motivation. This mesolimbic system overlaps with the social behavior network in the brain, responsible for the control of social behavior (Goodson, 2005 ; O'Connell and Hofmann, 2011 ).

The activity of the dopaminergic neurons in the brain of the mother who sees her happy son corresponds to a “wanting” reaction (Berridge and Robinson, 1998 ). It makes her son's excited face salient and attractive. The fact that the mother likes what she perceives may be related to the release and processing of endogenous opioids (Panksepp, 1998 ; Kringelbach and Berridge, 2009 ; Hsu et al., 2013 ). Whereas dopamine conveys motivational incentives, endogenous opioids convey “liking,” but also have a calming effect and reduce neural responses to pain, stress and anxiety (Carter, 1998 ; De Kloet et al., 2005 ).

In everyday life, people usually take photos of pleasant events (Chalfen, 1987 ; Sharples et al., 2003 ; Hu et al., 2014 ). We assume, however, that the motivation to take a picture is often also related to the activation of a mental representation of a negative context, which is processed non-consciously. In our example, this negative context would be that the mother knows that her son celebrates his last birthday party before entering school. As her own mental representations of school are ambivalent, she develops an anticipatory concern regarding the situation of her son, which is threatening and creates mental stress (Ulrich-Lai and Herman, 2009 ). Representations of such threats correlate particularly with activities in the amygdala (Baxter and Murray, 2002 ; Pourtois et al., 2013 ), triggering a cascade of adaptive neural and neuroendocrine reactions (De Kloet et al., 2005 ; Schiller et al., 2008 ; Ulrich-Lai and Herman, 2009 ; Hostinar et al., 2014 ). They include the activation of the stress systems leading to an increase in excitement and alertness.

Hence, we suggest that two conflicting representations are activated in the mother's brain, each associated with a different behavioral response than the other. The mother needs to mobilize cognitive and behavioral resources to be able to balance the two possible meanings and reactions, which in essence employ different parts of her brain. The anterior cingulate cortex (ACC) plays an important role in this. The ACC lies inside the frontal cortex, where it extends around the dorsal side of the corpus callosum, the nerve tract that connects the two cerebral hemispheres. It integrates and organizes emotional and cognitive information related to coping with pain, fear, anxiety, and stress, and potential motor responses, and is a major neural basis of cognitive control (Bush et al., 2000 ; Shenhav et al., 2013 ). Cognitive control is defined as regulating reactions to pieces of information that are in conflict with one another and in which automated processing may lead to errors (Miller and Cohen, 2001 ). The goal of cognitive control is to integrate conflicting information into representations that support appropriate behavioral decisions.

The mother's medial prefrontal cortex (mPFC) signals that there is something out there that offers the opportunity to collect or appropriate something valuable—mPFC is a central part of the neural basis of appropriating or collecting something (Anderson et al., 2005 ; Turk et al., 2011 ). Based on the dopaminergic processes involved, the motivation for appropriating something can be very strong: mPF and ACC have the greatest densities of dopaminergic projections from the midbrain of all areas in the cortex (Williams and Goldman-Rakic, 1993 ; Cohen et al., 2002 ).

Based on her photography-related knowledge, the mother categorizes what she perceives as “something that is photographed.” What is going on out there, could enable her to create a valuable picture. According to the assumptions of Event Cognition (Newtson, 1973 ; Zacks et al., 2001 ), “a children's birthday party” is not represented in the mother as a continuous, uniform event, but in the form of a few interconnected discrete units or steps, such as welcoming the guests, eating the birthday cake, blowing out the candles, and so on. The mother has detected that such a discrete step of the party has occurred. A photo of it could represent much of her son's birthday party. Activation patterns in prefrontal and hippocampal areas switch on photography-related memory contents that are connected to one another and retained in various locations widely spread over the cerebral cortex (Tonegawa et al., 2015 ). Context and scene are associated with possible outcomes of taking a photo with a smartphone camera, including a coarse anticipatory representation of the possible photo and its use.

Still without the involvement of conscious processes, the representation of the goal to take a photo is activated in structures of the mother's anterior prefrontal Cortex (PFC) (Soon et al., 2008 ; Custers and Aarts, 2010 ). Processes in OFC, mPFC, ACC, and ventral striatum analyze whether the goal to take a photo can be achieved in the given situation, and whether it is worth the effort. The result is the decision that the photo is worth the effort.

Steps Accompanied by Conscious Awareness

For taking the photo, representations from different explicit and implicit memory and processing systems must be integrated. Our mother is now consciously recognizing (Dehaene and Naccache, 2001 ; Damasio, 2010 ) that she is perceiving something that might be worth photographing. She takes her smartphone and points the camera at her son, who is surrounded by friends. She controls what will be seen in the picture. OFC, ACC, amygdala, and the anterior insula build the neural bases of various valuation, filtering, ordering and decision processes (Hsu et al., 2005 ). The mother's working memory (Baddeley and Hitch, 1974 ; Miller and Cohen, 2001 ) processes, maintains and integrates different pieces of information of internal and external origin.

The mother takes a photo of her son, a person with whom she is connected through a close positive emotional relationship. Seeing him activates areas in the mother's brain that have a high density of the peptide hormones oxytocin and vasopressin (Bartels and Zeki, 2004 ). Oxytocin and vasopressin are produced in the hypothalamic Nucleus preopticus (NPO), stored in pituitary, and are involved in the development and maintenance of close selective social relationships (Carter, 1998 ; Panksepp, 1998 ; Scheele et al., 2013 ; Hostinar et al., 2014 ). They also support the control and suppression of threat-related information (Nelson and Panksepp, 1998 ; Donaldson and Young, 2008 ; Scheele et al., 2013 ). Particularly oxytocin is involved in the development and maintenance of close selective social relationships or attachment and conveys the feeling of social connectedness (Carter, 1998 ; Panksepp, 1998 ; Scheele et al., 2013 ; Hostinar et al., 2014 ). Both hormones are associated with activating the mesolimbic reward system (Donaldson and Young, 2008 ). Oxytocin release correlates with opioid activities, reduces stress and thereby causes a calming effect (Nelson and Panksepp, 1998 ). In fact, there is a strong antagonism between oxytocin release and glucocorticoids synthesis, i.e., metabolic hormones that are produced and released in response to stressors (Carter, 1998 ; Hostinar et al., 2014 ; Preckel et al., 2015 ).

The mother's vmPFC assigns a positive value to the neural representations of the situation, photographing in general, and the intended photo in particular. On a non-conscious processing level, however, the anticipatory representation of the threat of her son's potentially negative experiences at school is still effective. This threat is primarily processed in the amygdala, but the mother's vmPFC projects into the amygdala and, thereby, inhibits its activity, which reduces fear and anxiety (Andolina et al., 2013 ; Hostinar et al., 2014 ). In addition, vmPFC, OFC and ACC project to the hypothalamus and reduce the activity of the mother's stress systems (Ulrich-Lai and Herman, 2009 ; Hostinar et al., 2014 ). Her implicit processing mechanisms suggest that she can now safely ignore the threat (Schiller et al., 2008 ).

When she recognizes a sufficient correspondence between the characteristics of the picture on the smartphone display and the mental representation of the desired photo, she presses the shutter button. She creates a permanent external picture of her son in a particular context, a representational digital object, which she possesses and can share with others. An important part of the value the picture has for her is related to the fact that she has created it herself. Actually, people can reliably distinguish between photos that they have taken themselves and photos taken by others (Sellen et al., 2007 ; St. Jacques et al., 2011 ).

A mountain hiker who discovers something she wants to photograph may have a different experience than a mother at her son's birthday party. She likes to hike alone and enjoys nature and silence. When looking at the mountain landscape, the anticipation of a longer period of non-self-chosen solitude has been activated. The hiker can take a picture, which will allow her to share her experience with her friends. Unlike our example mother, the hiker has more time for taking the picture, because the landscape does not change as quickly as social situations at a party. The hiker can use this time for creatively composing a photo, which will be different from ordinary photos depicting similar landscapes and which the viewers will find beautiful, useful, or important (Thagard and Stewart, 2011 ; Ellamil et al., 2012 ). She associates and integrates the incoming visual information with certain conceptual and emotional categories as well as with internal representations of existing extraordinary landscape pictures. The neural bases of these operations include structures of two cortical networks that are usually not active at the same time. One of these networks is activated when people focus their attention on external stimuli, the other network when attention is focused on thoughts, memories or imagery (De Pisapia et al., 2016 ).

Ontogeny of Recognizing, Taking, and Using Photos

Human babies recognize certain photos at an age of 3 months or even earlier (for review, see Bovet and Vauclair, 2000 ). In a cross-cultural study, DeLoache et al. ( 1998 ) showed that 9 months old babies treated pictured objects as if they were real objects, explored them with their hands, tried to touch them, or to take them out of the picture. At the age of 19 months, human children understood that pictures are both concrete real objects, but also representations of other objects. From about 1-year of age, children begin to create traces on two-dimensional surfaces with suitable materials (Thomas and Silk, 1990 ; Wright, 2010 ). At the age of two, children begin to name the meanings of their drawings or paintings. They also know that pictures are made with specific intentions to represent objects or events (Preissler and Bloom, 2008 ). Children aged 3- to 4-years know what properties of pictures are helpful if they are used to convey ideas of objects to other people, and that there are better and worse pictures for this purpose (Allen et al., 2010 ). They know that pictures containing a lot of visual details are best used to tell others what objects look like.

Many children like to draw. As much as they develop joy and zeal in drawing, they usually have little interest in owning the pictures as soon as they are done (Thomas and Silk, 1990 ; Cox, 2005 ; Cherney et al., 2006 ; Wright, 2010 ). If they have mastered a special pictorial challenge, they proudly show their picture and look at it together with others, but they do not go for drawings they made the week or the month before to look at them again. The fascination lies in the activity of drawing itself, in experiencing the ability to create a picture with a certain meaning—and to use this to relate to others (Cox, 2005 ; Wright, 2010 ). The early ontogenetic development of competences related to producing and using representational pictures happens in the social environment, usually the family. The family is also one of the most productive places of personal photography (Chalfen, 1987 ; Petrelli and Whittaker, 2010 ). The most successful photography exhibition of all time even had “family” in its title: The Family of Man ( 1955 ).

Children see the photos their parents keep in photo albums, photo books, boxes or computer folders. The photos of the ancestors—and their actions, experiences, relationships, occupations, and possessions—that a family owns can give children a sense of social belonging, societal significance, and security (Csikszentmihalyi and Rochberg-Halton, 1981 ; Chalfen, 1987 ; Petrelli and Whittaker, 2010 ). These photos are heritable assets of knowledge. They are usually linked to oral or written information, which shows and tells to whom the children belong and whom they can trust. Through mechanisms of social learning, family traditions of photography emerge (Mäkelä et al., 2000 ; Sharples et al., 2003 ; Petrelli and Whittaker, 2010 ). Children get to know certain ways of using cameras and photos early on (Mäkelä et al., 2000 ; Sharples et al., 2003 ). They experience how their mother or father reacts to certain events by taking photos—usually positive, which supports this behavior via positive reinforcement learning. Children also realize that taking, viewing, and sharing photos is repeatedly done in certain social contexts, for example at a birthday party, graduation, or wedding. They learn that photographers keep some pictures and discard others and may shape their own taste along this.

Many children start taking photos themselves at preschool age (Sharples et al., 2003 ; Magnusson, 2018 ). Seven- to 15-year-olds take and use photos in connection with the playful and explorative use of electronic devices (Mäkelä et al., 2000 ; Sharples et al., 2003 ). They use cameras and photos for joking, like making faces or adopting funny poses, for expressing feelings, or telling stories. According to an international survey, 81% of the 8- to 18-year-olds in Algeria, Egypt, Iraq, and Saudi Arabia used a mobile phone in 2013 (GSMA and NTT DOCOMO, 2014 ). Most of the children got their own mobile phone between 10- and 12-years of age and 55% had access to the internet. The features most used by children and adolescents were cameras (91%), followed by music players and video players. Many young people in their teens and early twenties take and use photos to create a sense of self and an identity (Schiano et al., 2002 ; RSPH and YHM, 2017 ). Social media provides them with a platform where they can use photos to express different characteristics of themselves and to experience other people's reactions (Krämer and Winter, 2008 ; Hu et al., 2014 ; Lee et al., 2015 ). Photos of family members and pals are especially important for people (Mäkelä et al., 2000 ; Sharples et al., 2003 ), and the value of these photos increases with the age of their owners (Csikszentmihalyi and Rochberg-Halton, 1981 ).

Evolutionary Roots of Taking and Using Photos

Why do photos have the characteristics they have? Why are they important for people all over the world? Which meanings can almost only be represented and communicated through photos Kislinger, 2021 ), and which cannot? In this section, we will refer to cognitive and social building blocks, which are part of the evolved nature of modern humans and suggest answers to these questions. Figure 2 presents an overview of the evolutionary building blocks that underlie the success of photography to which we refer.

Schematic illustration of the building blocks that are part of the evolved nature of modern humans and form the basis of the importance of photos for people. The arrows show the order in which we describe the building blocks in our model.

Vision as a Central Element of Human Cognition

People take and use photos to represent important events in the environment. Representing features of the environment with survival value is one of the core functions of central nervous systems (CNSs) since they exist. Organisms developed sensory organs, which react to relevant physical and chemical events in the environment, as well as neurons, that is, cells capable of receiving, generating, and transducing signals for internal communication and for relating to the environment (Butler and Hodos, 2005 ; Gregory, 2008 ). By means of neural activation patterns, organisms have used “images” for hundreds of millions of years (Damasio, 2010 )—as representations of the environment enhancing predictability in interaction with this environment and, thus, survival. Although it is hard to imagine how a jellyfish with its dispersed nervous system should be able to form an image-like mental representation, the fact that its body responds to stimuli in a coordinated and adaptive way at least hints at such a possibility.

Mammals evolved out of mainly visually oriented reptiles (Northcutt, 2011 ; Striedter, 2020 ). During their first 100 million years of evolution, however, the reign of dinosaurs forced them underground or into a nocturnal lifestyle. This led to a reduction in visual orientation, while olfaction and hearing were optimized. Within the modern mammals we see a full reinstatement of trichromatic vision only in the primates, while most other mammals remain bi-chromatic as an adaptation to being active at dusk and dawn and at night. Due to specific properties of their central nervous systems, including retinae, primates can extract a broad range of information from the properties of light and its reflections in the physical world (Felleman and Van Essen, 1991 ; Gollisch and Meister, 2010 ). Vision is a central component of human cognition. Visual content dominates, for example, perception, memory, imagining, and dreaming (Posner et al., 1976 ; Zimmermann, 1989 ).

Living Together in Groups and Social Attachment

People primarily take photos of other people, especially people to whom they are emotionally connected (Chalfen, 1987 ; Hu et al., 2014 ; Lee et al., 2015 ). Processing stimuli with social significance has a long evolutionary history (Wilson, 1984 , 2012 ). The tegmental and diencephalic parts of the brains of birds, bony fish, and mammals feature an evolutionary extremely conservative—hence homologous—social behavior network (Goodson, 2005 ). This regulates social recognizing, mating, parental behavior, persistent bonding, expressive behavior, aggression, and responses to social stressors. Primates inherited this network virtually unchanged in structure and function from their ancestors. The primate ancestors of humans established close relationships with other individuals in their groups who were not reproductive partners or relatives (De Waal and Brosnan, 2006 ; Wilson, 2012 ). Social cohesion improved the ability of individuals and groups to adapt to variable environments, to survive and to reproduce. Living together in groups affected both behavior and cognition. In primates, the social domain hosts a substantial part of the motivation to orient to and perceive stimuli, and to carry out certain behaviors and actions. Important and mutually linked social behavior systems are attachment and care , that is, a close selective emotional connection with another individual—the caregiving attachment figure, or the other way round, the attached dependent (Bowlby, 1974 ; Carter, 1998 ; Panksepp, 1998 ). There is a strong antagonistic interaction between the feelings of safe attachment and distress (Panksepp, 1998 ). Threatening or stressful situations elicit the desire for social closeness, and societal cohesion increases in times of crisis. Support by an attachment figure provides a sense of security and calmness. Conversely, being isolated from attachment figures or other socially supportive individuals is perceived as a potential threat. This antagonistic interaction is relevant in terms of photography-related behaviors. Photos of attachment figures or of the attached dependents convey important potentials. In experiments, for example, merely seeing the photo of an attachment figure reduced physical pain as effectively as the actual closeness to that person (Master et al., 2009 ; Eisenberger et al., 2011 ).

Social Learning, Cultural Evolution, and Symbol Systems

An important ability of animals living in groups is profiting from experiences or interactions with other individuals, called social learning (Richerson et al., 2010 ; Jablonka and Lamb, 2014 ). A system of characteristic behavior patterns and preferences, which are socially passed on through generations, is referred to as culture and its gradual change as cultural evolution (Jablonka and Lamb, 2014 ). Cultural phenomena have probably played a greater role in human evolution than in any of the other animals showing cultural diversification (for example wolves or orcas) ever since the common ancestors of humans and chimpanzees (Richerson et al., 2010 ; Whiten, 2011 ).

Social learning is the base for tradition forming and transferring information via culture. The ancestors of humans used gestures, vocalizations, and found objects as signs for something that was not currently present in the environment to communicate with others (Seyfarth et al., 2005 ; Deacon, 2006 ; Arbib et al., 2008 ). Over many generations, groups gradually developed a complex system of gestural and vocal signs, as well as rules specifying how these signs were to be combined into larger units of meaning (Seyfarth et al., 2005 ; Arbib et al., 2008 ). As a crucial step in human evolution, humans began to use symbols , this is, signs that represent meanings based on rules and conventions. Symbols are part of an evolved cultural system, which regulates the relationships between individual signs and indicates how they are combined to represent units of meaning (Jablonka and Lamb, 2014 ). The use of symbols for organizing and conveying information was a crucial step in human evolution. Human symbols are considered as discrete dimensions of inheritance and evolution which interact with genetic evolution. People developed systems of symbols to represent and communicate knowledge, rules and ideas (Jablonka and Lamb, 2014 ; Tomasello, 2014 ). Language became the most important symbol system, likely also pushing brain development. Cultural evolution and genetic evolution interacted and led to a positive feedback selection between cognitive mechanisms, language, and social skills (Deacon, 1997 ; Jablonka and Lamb, 2014 ).

Cooperation, Property, Status, Reputation, Courtship, and Mating

Among the evolutionary mechanisms, which favored cooperation in groups, direct and indirect reciprocity appear to be particularly relevant (Nowak, 2006 ). These mechanisms are also relevant in terms of taking and using photos. Direct reciprocity is effective when two individuals encounter each other repeatedly: one cooperates assuming that the other one will reciprocate later. Cooperation, hence, benefits both. The mechanism of indirect reciprocity explains cooperation in situations where one individual helps another individual whom the individual may not meet again or from whom no help is expected. This can still pay off, if the helpful behavior is observed by other group members. Indirect reciprocity describes the benefit of an altruistic act for the helping individual, which spreads via gossip or other information. In this detour, the helping individual acquires the reputation of being “generous,” i.e., able and willing to cooperate. This reputation supports access to resources and reproductive success (Nowak and Sigmund, 2005 ; Nowak, 2006 ). With the evolution of complex language—and later with the distribution of photos—the subset of a population that could receive information about the cooperative potential of an individual tremendously increases as compared to the number of people able to directly observe an individual's behavior. Photography and social networking services on the internet have increased the potential audience enormously.

In human societies, it is generally advantageous to regulate resource use and ownership through rules or conventions in order to avoid costly redundant conflicts (Stake, 2004 ). Depending on socio-economic background, people have developed specific rules about the appropriation of things as well as about the retention and distribution of property (Stake, 2004 ). Many animals appropriate things and retain them (Stake, 2004 ). Property-related experiences and behaviors are based on specific neural substrates, especially in the frontal cortex (Anderson et al., 2005 ; Turk et al., 2011 ). The brain structures involved are particularly rich in dopamine receptors. The acquisition of property is accordingly associated with strong motivation. When individuals acquire and possess valuable resources, it may also be beneficial to their status within their groups, or may even be to the benefit and status of these groups (Brown, 1991 ; Van Vugt and Tybur, 2016 ). Much of human social complexity is about status and prestige. This modulates, in turn, individual access to resources in a social dynamic between cooperation and competition (Nowak and Sigmund, 2005 ; Van Vugt and Tybur, 2016 ). Individuals can display their property and signal that they have a certain status within the social and cultural hierarchies of their group or society. To communicate this status, individuals may use symbols of their possessions. Individuals can also share their resources with others, be generous or even wasteful with their possessions to increase their prestige and, ultimately, their reproductive success (Buss and Schmitt, 1993 ). In women and men, the acquisition, retention, and use of resources or possession have specific characteristics (Brown, 1991 ; Buss and Schmitt, 1993 ).

Photography has provided people with effective means to signal their social status to a large audience (Krämer and Winter, 2008 ; Piazza and Bering, 2009 ). Distributing selfies with famous people or in front of famous sights, for example, is motivated by telling others about one's own potential to meet these famous people or to travel, and to communicate one's own interests and attitudes (Krämer and Winter, 2008 ; Diefenbach and Christoforakos, 2017 ). Many people also take and share status-relevant photos of themselves with their “belongings,” such as house, car, boat, their beautiful partner, or children (De Looper, 2016 ; Jain and Mavani, 2017 ). Empirical data suggest that people also use photos for enhancing their mate value in the minds of potential romantic or sexual partners (Piazza and Bering, 2009 ; Smith, 2016 ; Hobbs et al., 2017 ; Sedgewick et al., 2017 ; Gale and Lewis, 2020 ; Kemp, 2021 ; Morris, 2021 ). We will discuss this in more detail in the section on the functions of photography.

Memory and the Urge to Create Coherent Explanations for Events and Conditions

Humans improved their ability to use language to categorize behaviors, events, objects and states. They developed a special system of comprehensive memory for experiences, including social, called episodic memory (Tulving, 2005 ). Thereby, experiences of “what,” “with whom,” “when,” and “how it felt” are integrated in a way that individuals can consciously access their stored representations and have a comprehensive awareness of their own life as related to others. With the ability to represent, process, and communicate past and future, as well as possible or imagined events through language, came the urge to explain what happens in the world, to interpret the past and to predict the future (Pettitt, 2011 ). Humans developed an awareness of mortality, thinking about death, and the desire to overcome mortality. The earliest burial sites found with material traces of ritual practices are around 100,000-years old (Pettitt, 2011 ; Wilson, 2012 ). The desire for extending one's effectiveness beyond lifespan could also play a role in taking pictures (Csikszentmihalyi and Rochberg-Halton, 1981 ; Chalfen, 1987 ). Many people retain photos of ancestors in a respectful way, in the implicit understanding that their descendants will do the same. This is reminiscent of animistic cultures, where identity and existence of people are deeply rooted in cults around ancestors (Frazer, 1911 ; Bird-David, 1999 ).

When taking a photo of another person, the photographer not just appropriates a picture of the light reflections from this person, but also of the visual, behaviorally relevant signals that this person emits at that particular moment. This may be part of the reason why many people consider appropriating a picture of a person to have “magical” (Frazer, 1911 ; Kittredge, 1929 ) or “animistic” (Bird-David, 1999 ; Harvey, 2005 ) properties. The term “animistic” refers to the belief that not only humans, but also animals, plants, lakes, mountains, etc. have souls and are animated (Harvey, 2005 ). With taking a picture of a certain person her or his personality and even “soul” may be captured, and the owner of this picture can change the condition of the pictured person—with potentially negative consequences (Hetherwick, 1902 ; Frazer, 1911 ; Hocart, 1922 ). Image magic has a long tradition going back far into human prehistory (Kittredge, 1929 ). Today, there is an ongoing struggle for legal regulation of the protection of one's image as part of personal rights and property rights, indicating that personal images still retain their special private status. Even on a rational base, the power that is conveyed by taking, owning, and using photos [e.g., Regulation (EU), 2016 ], is still a delicate topic in modern Western societies.

Language enables humans to integrate a huge amount of information into meaningful contexts and to create explanations of events in which these events appear ordered and understandable toward a goal, rather than meaningless, accidental and pointless (Kahneman, 2011 ). In addition to language, an evolutionarily older cognitive system remained (Evans, 2008 ; Kahneman, 2011 ), providing quick reactions to relevant events in the environment on the basis of minimal sensory information, for example, via faces with emotional expressions or expressive body poses (Kislinger, 2021 ). Certain events depicted in photos cause activations of evolutionarily old brain structures, like superior colliculus, pulvinar, and amygdala (Morris et al., 1999 ; Van Le et al., 2013 ; Almeida et al., 2015 ). Objects and events pictured in photos are not only recognized by humans, but by many other species (Bovet and Vauclair, 2000 ; Kano and Tomonaga, 2009 ). In some cases, the last common ancestor of humans and a species in question lived long ago, e.g., 220 million years in the case of pigeons (Aust and Huber, 2010 ). This either hints at an ancient ability shared via phylogenetic inheritance (homology) or at parallel evolution (analogy).

Functions and Adaptative Value of Photography

A “function” of a behavior describes a specific contribution of the individual expression of this behavior to survival and reproductive success (Jablonka and Lamb, 2014 ). Photography-related behaviors touch the evolutionary functional domains of well-being and social connectedness, which are at the core of human nature. These behaviors will therefore, directly or indirectly, relate to potential individual societal and—ultimately—reproductive success. We suggest that taking, owning, viewing, sharing, and using photos provide a specific and effective strategy for coping with complex environments fraught with uncertainty. Photography as a coping-strategy comprises four core domains: (1) making sense, (2) appropriating an image, (3) establishing and supporting social connectedness, and (4) courtship and mating. These four domains can be involved in different photography-related behaviors to different degrees.

Making Sense

“Making sense” plays a role in many photography-related behaviors (Harrison, 2002 ; Frohlich et al., 2013 ); it is particularly evident in the taking of photos (Chalfen, 1987 ; Gillet et al., 2016 ). Thereby, people assign a certain cause to an event—that is, they create an explanation for why this event occurs—or a certain meaningful order, which is consistent with a goal. The 6-year-old birthday boy from our example above laughs because he gets along well with other children, and other people want him to be happy. The photo of the hiker shows that being alone on a mountain top is great, because it gives one a deep personal feeling for nature, which still can be shared with friends via a picture. According to our framework, people build mental representations, which make an event understandable. As a consequence, the future course of the event appears predictable and controllable. Taking photos allows making sense immediately and intuitively, without the involvement of complex reasoning.

Sharing and viewing photos can also be used for making sense of events. The photo of a family reunion can show a group of laughing people who relate to each other in a friendly and nice way, even if a heated argument broke out at this meeting, which may have led to long-term insults and resentments. Particularly, people who were at this meeting can look at this photo to reassure themselves that, despite certain controversy, things are fine and people like each other. This is supported by the propensity of viewers to assume that what they see in photos reflects reality. Understanding photos does not require the mastery of a particular language, complex cultural knowledge, or elaborate thinking. A single photo can give a fairly comprehensive idea of an event—possibly better than any verbal description: “a picture is worth a thousand words” (The Post-Standard, 1911 ).

Appropriating an Image

The domain “appropriating an image” is related to the fact that people gain permanent access to valuable information by taking, sharing, or getting a photo—most frequently related to social relationships. Photographers relate to an event or object through the camera, select certain properties of this object and the scene in which it is contained, and create a focus. From the flow of the object's appearances, which they perceive over a certain period of time, they extract a single picture and fix it. It represents only a small fraction of the sensory information that is available in that situation. By selecting and organizing the information, which they include in the picture, they interpret the object or event. If they have managed to create the picture with the intended meaning, this success conveys the experience of effectiveness and competence (Krämer and Winter, 2008 ). This experience reduces emotional arousal and physiological stress responses to potential threats and supports coping with them (Bandura, 1997 ). A man looking out the window of the plane that is taking off, for example, may take a number of photos, thereby potentially also coping with his fear of flying. Taking photos may help to maintain control in a potentially stressful situation.

Establishing and Supporting Social Connectedness

Seeing an important person in a photo allows the viewer to relate emotionally to this person, although she or he is absent or may have passed away. Photos of their own children, parents, or romantic partners are particularly important to people (Bartels and Zeki, 2004 ; Gobbini et al., 2004 ; Leibenluft et al., 2004 ; Petrelli and Whittaker, 2010 ; Hu et al., 2014 ). Photos of loved ones enable people to feel close to them, provide a sense of security and calmness and reduce the sensation of pain (Master et al., 2009 ; Eisenberger et al., 2011 ). Photos, to some extent, can substitute for physical closeness. Viewing, owning, or sharing photos of family members or ancestors support developing cultural and genealogical roots (Csikszentmihalyi and Rochberg-Halton, 1981 ; Petrelli and Whittaker, 2010 ). Hence, taking and using photos relates to establishing, maintaining and strengthening social connections (Kindberg et al., 2005 ; Barasch et al., 2014 ; Lee et al., 2015 ; Pittman and Reich, 2016 ).

Many people share their photos, and if a photo is liked and appreciated by others, the photographer experiences self-efficacy (Krämer and Winter, 2008 ) and self-esteem (Burrow and Rainone, 2017 ). This is exploited by the “like buttons,” an enormously popular feature of social media platforms (Kemp, 2021 ). Sharing photos contributes to a common understanding of the world. Sharing photos also enables people to convey others views that they enjoy, e.g., photos of hilarious events or natural sceneries. Photos of natural scenes (as opposed to human artifacts or urban environments) have a positive influence on the well-being of viewers (Berto, 2005 ; Valtchanov and Ellard, 2015 ) as “Biophilia” is a human universal (Appleton, 1975 ; Ulrich, 1983 ; Wilson, 1984 ; Kaplan and Kaplan, 1989 ). Viewing such photos relaxes, reduces emotional stress, and thereby regenerates depleted cognitive resources.

People often use photos to show others who they are and what role they play in society. Issues of identity, reputation, prestige, or status often play a role in personal photography (Chalfen, 1987 ; Barasch et al., 2014 ; RSPH and YHM, 2017 ). If one person photographs another person, this can be of value only for the photographer, or for both (Milgram, 1976 ). People can use photos to influence how other people perceive the pictured individuals, objects, or events and thus exert social control (Sharples et al., 2003 ; Diefenbach and Christoforakos, 2017 ). People being photographed, however, may also use this circumstance for their own goals, like for influencing how others perceive them (Harrison, 2002 ; Krämer and Winter, 2008 ; Jain and Mavani, 2017 ). Being photographed can immensely increase the size of the “audience.”

As humans are radically social in their nature, observing or monitoring the behavior of other people plays a central role in the motivation to use social media on the internet and to post photos (Joinson, 2008 ; Lee et al., 2015 ; Malik et al., 2016 ). People are usually aware of the presence of cameras. This may produce “audience effects,” i.e., the feeling of being watched influences behavior and makes people behave in a socially agreeable way (Bateson et al., 2006 ; Oda et al., 2015 ), by showing, for example, “photo faces.” Through this tendency, photography supports cooperative coexistence in complex societies and has an adaptative value both, on the individual and on the societal level.

We are well aware that people also distribute photos of atrocities. The impact of such photos can be used to boost the importance of the photographer or distributor, or even to hurt other people, to violate the rights of others, or to deceive (Smith et al., 2008 ; Kowalski et al., 2014 ). Manipulative to harmful photo use is facilitated by the fact that photographic forgeries are becoming increasingly difficult to detect, both in social and in journalistic media (Campbell, 2014 ; Nightingale et al., 2017 ). Various detrimental outcomes of taking and using photos have required legal regulation of photography-related behaviors [e.g., Regulation (EU), 2016 ]. The ubiquity of taking photos has massively reduced possibilities of intimacy and privacy. A vast dark side of photography exists outside of personal experience. The large social media providers use the shared personal photos as a data source. The acquisition of these data, their possession, the algorithms of their management and the extraction of information from them give the companies enormous power, which has not been put under democratic control until now (Zuboff, 2015 ).

Courtship and Mating

Courtship and mating are certainly part of the domain of establishing and strengthening social connections and attachment (Hazan and Shaver, 1987 ; Fisher et al., 2002 ). But they are directly relevant in terms of evolutionary function and as such encompass a range of distinct strategies and conflicts (Fisher et al., 2002 ; Buss and Duntley, 2011 ). The global prevalence of intimate partner homicide reflects the high value of the activities and resources that are at stake, as well as the severity of the conflicts in question (Stöckl et al., 2013 ). Sexual or reproductive behaviors shaped all living beings and played a central role in the evolution of human cognition (Miller, 2001 ; Nowak, 2006 ). Sexual themes and symbols are featured in some of the oldest preserved artifacts (Conard, 2009 ). With photos, a new type of visual cueing was developed that fulfills special functions in attracting potential partners, mate selection, and sexual behavior. The potentials of photography range from tender romantics to hardcore pornography.

“Beauty” plays a special role in this context. Many people want to take and use beautiful photos (Bakhshi et al., 2015 ; De Looper, 2016 ). Darwin ( 1879/2004 ) associated the “sense of beauty” (p. 114) with the context of sexual selection: the function of beauty is that the choosing female or male individuals are “excited” by it. Individuals considered to be beautiful manage to “excite attention” (p. 467). In this sense, beauty is a sensory signal that it could be advantageous to pay attention to, and approach, the sender of this signal. Among the hashtags (terms assigned to posted photos) that were most frequently used on Instagram in 2020, “Love” came first, “Art” fifth, and “Beautiful” sixth (Kemp, 2021 ). Instagram is the most photography-related social platform and was the fifth most visited website worldwide in 2020 (Kemp, 2021 ).

The invention of photography and its further technological developments, including digital communication, allowed people to create a new type of sensory cues relevant to courtship activities, mate selection, sexual intercourse, and (ultimately) reproduction. Photography has been used almost from the start to satisfy cravings for pictures of naked people and for erotic images. Retinal images of naked potential partners expressing interest in sexual activity has meant observers had access to reproduction for hundreds of thousands of years. Photos of sexual acts are among those images that are most emotionally arousing (Bradley and Lang, 2007 ; Wehrum et al., 2013 ) and pornography is one of the most prominent domains of internet use.

People also use photos to influence choices of potential romantic or sexual partners. The success of dating applications on the Internet has greatly increased the importance of photos in connection with courtship and mating (Piazza and Bering, 2009 ; Smith, 2016 ; Hobbs et al., 2017 ). Social media platforms and dating apps enable users to form relationships with people they have never seen before. Mobile dating applications are used by more and more people (Smith, 2016 ; Morris, 2021 ). People looking for partners create profiles on these apps that they use to present themselves. Photos of oneself play a central role in this. People show photos of themselves—often also taken by themselves—in which they are represented as they would like to be seen by potential romantic partners (Sedgewick et al., 2017 ; Gale and Lewis, 2020 ). The use of such photos enables people to reveal actual traits of themselves, but also to make themselves appear more attractive than they potentially are (Sedgewick et al., 2017 ; Gale and Lewis, 2020 ). People can also use symbolic self-made photos to create a desirable impression of themselves in potential romantic partners, for example photos of groups of nice, laughing people, pets, flowers, a beautiful garden, an elegant apartment, tourist attractions, dangerous environments, sporting events, or full bookshelves (Krämer and Winter, 2008 ; Piazza and Bering, 2009 ). Online dating is not only increasing rapidly among young adults, but also among the older population (Smith, 2016 ; Morris, 2021 ). Through dating apps, photos play an increasingly important role in mate selection, which played a central role in the evolution of human cognition (Miller, 2001 ). When photos are used in dating and courtship, there is also the characteristic connection between emotionally positive information and the processing of uncertainty (Berger and Calabrese, 1975 ; Knobloch and Solomon, 1999 ), addressed above. In this context, the positive information concerns one's own attractive properties. Uncertainty is associated with one's search, and potential negative outcomes of establishing relationships with people one does not know from face-to-face encounters.

The Mental Utilization Hypothesis of Photography

We propose that the success of smartphones as well as photography is based on core human mental mechanisms which are primarily related to the social domain. Photography exploits evolved cognitive and social predispositions. In this sense, our framework is a mental exploitation hypothesis, analogous to the Sensory Exploitation Hypothesis in evolutionary biology (e.g., Ryan, 1990 ). This hypothesis states that new preferences evolve along established pre-existing sensory biases and response tendencies, such as primates owing their social and/or sexual preference for red to their old predilection for this color, which usually indicates ripe fruits (Ghazanfar and Santos, 2004 ).

Sensory biases and preferences also play an important role in photography-related behaviors. The visual channel provides information, which is converted into, or affects, mental representations. In our framework, however, the focus is on a higher, more integrated level of processing, on which those mechanisms and functions are organized that control the mental representation of the world and flexibly adapt social behavior. In connection with photography, the term exploitation may have a negative connotation, such as photographers exploiting the people in front of the camera (e.g., Sontag, 1978 ). For this reason, we refer to our framework as the mental utilization hypothesis of photography. It suggests that photography fits the nature of human perception and mental processing like a key fits its lock.

Photography as a Coping Strategy

Along to the four levels of Tinbergen, our analysis of photography-related behaviors suggests that people take and use photos to cope with certain stressful and threatening events in specific ways. The conceptualization of photography as a coping strategy is counterintuitive against the background that people usually like to take photos and generally take, share and own photos of events associated with happiness, pleasure, love, or success (Chalfen, 1987 ; Sharples et al., 2003 ; Hu et al., 2014 ). Individuals who take or use such photos, we propose, experience a pleasant situation, but are also—non-consciously—exposed to threatening information or uncertainty. As examples, we mentioned the mother who photographs her 6-year-old son, the lonely hiker, and the man who is afraid of flying. Taking and using photos allows people to search for, and engage, in emotionally positive information. Successful coping through photography-related behaviors reduces complexity, uncertainty, and anxiety. Coping, or the exercise of cognitive control, does not have to be exclusively reactive, but can also be carried out proactively (Bandura, 1997 ). Coping through taking and using photos has features that can be described on a continuous scale, with reactive coping at one end and proactive coping at the other.

People use photography not only to cope with events with generally positive emotional value, but also in coping with negative events. For example, traffic accidents, high-rise fires or other disasters tend to lure in bystanders and onlookers taking smartphone photos of the scene or of the victims (Vollmuth, 2017 ; Newton, 2019 ). There is no research on the motives which drive such photography-related behaviors. They may be similar to the motives which make people watch horror or crime films (Bartsch and Mares, 2014 ). What people see confronts them with something extremely meaningful—threats that exist in the world, their own mortality and vulnerability (Arndt et al., 1997 ). Most of these bystanding photographers immediately share their products. Taking and sharing the photos, we suggest, enable people to make sense of threatening events to get along with them, but also use them to push their own importance and prestige within their networks.

Has Photography Become a Human Universal?

Several researchers discussed the creation and use of representational pictures as human universal (Deacon, 2006 ; Donald, 2006 ; Dutton, 2009 ). The creation of realistic visual pictures appeared more than 30,000-years ago and some of them have been preserved on cave walls (Guthrie, 2005 ; White et al., 2018 ). Photography, in connection with digital technology and smartphones, has made it possible for everyone to create, own, and share realistic pictures easily and effortlessly and to integrate such pictures in everyday life. Based on our analysis and statistical data (Statista, 2019 ; Carrington, 2020 ; Kemp, 2021 ), we conclude that taking, viewing, and sharing photos through the use of smartphones has become a human universal—a “new” universal, according to Brown's ( 1991 ) classification—that is based on context-sensitive predispositions, particularly connected with the radically social human nature, and differentiates itself in the societal domain (Kotrschal, 2019 ). Photography not only classifies as a human universal, but also as a unique human feature not shared with any other animal species—not only because other species lack the technical means of photography, but before all, they seem to lack the motivation and mental mechanisms behind the typical human urge to capture the world in images. We conclude that photography is closely matching the unique construction of the human mind and qualifies as a feature of human nature, i.e., the Conditio humana (Arendt, 1958 ; Kotrschal, 2019 ).

Figure 3 summarizes the conditions, components, and abilities that have made photography a human universal as proposed by the mental utilization hypothesis of photography. One element of Figure 3 relates to the specific social contexts and environmental features that generate photography-related behaviors, as suggested by the evolutionary building blocks of photography. They are (1) coexistence in large, complexly structured societies; (2) frequent encounters with strangers, the outcome of which is often difficult to predict; (3) strong mutual observation of behavior; (4) individuals' well-being and prosperity depend on judgments by strangers; (5) requirement to display one's own status symbolically in public; (6) continuous confrontation with the news of success or profit, as well as disaster, illness, or death; (7) large number of potential sexual or reproductive partners among strangers; (8) individuals have to make far-reaching decisions about their future lives; (9) requirement of communication with absent or distant people; and (10) requirement of quick communication with strangers across cultural or linguistic boundaries.

The Mental Utilization Hypothesis of Photography. The schematic illustration shows the proposed conditions, components, and abilities that made photography a human universal. An arrow means “provides the basis for” or “leads to”.

Limitations

The analysis of a particular behavior on the basis of the four levels of Tinbergen requires the integration of findings from a range of disciplines. Despite the referenced mechanisms and functions of taking photos, which represent the present state of knowledge, our conclusions remain necessarily speculative—because of the preliminary nature of all scientific results, because of the inherent pitfalls of attempting to integrate such diverse results into a comprehensive synthesis, and due to the space constraints of a journal article. In addition, there are very few empirical findings on taking photos, and they come only from the Western world. Thus, we may underestimate the cultural diversity in photography, although we are quite confident that the behavioral core is based on human nature, and therefore, should in principle, apply to all people. Within our conceptual frame we describe taking and using photos as functional outcomes of cognitive and social adaptations. It could certainly be argued that the success of photography is ultimately a byproduct of the accessibility, affordability and success of smartphones and social media, which results from marketing activities of powerful companies. But this is a different level argument not contradicting our utilization hypothesis. Our analysis of photography-related behaviors as coping strategies creates a picture of photography in which the benefits are generally greater than the cognitive and social costs, which also explains why photography became such a sweeping worldwide success once the smartphone technology became available.

The goal of producing an image that supports memory only plays a subordinate role in our description of photography-related behaviors. In this respect, our framework differs from explanations that describe the production of memory pictures as a central function of photography (Milgram, 1976 ; Kahneman, 2011 ; Frohlich et al., 2013 ; Henkel, 2014 ). These explanations are consistent with the fact that many people stated the retention of memories, when asked about the purpose of photographing (Chalfen, 1987 ; Kindberg et al., 2005 ; Broekhuijsen et al., 2017 ; Lee, 2018 ). Empirical findings, however, show that people lose many photos they have taken or never look at them again (Kirk et al., 2006 ; Whittaker et al., 2010 ). Furthermore, the experimental studies on the question of whether taking or seeing photos improves people's ability to remember past events produced a multitude of different and sometimes contradicting results (for review, see Foley, 2020 ). This was one of the incentives for us to attempt a new synthesis within an evolutionary theory frame.

Testable Predictions for Future Research

As shown in Table 2 , the mental utilization theory of photography allows generating a number of testable predictions. Ideally, these would be tackled by experimental and behavioral field studies in natural environments, in both everyday and lab situations. Rapidly developing mobile techniques (such as EEG headsets, eye-tracking devices, etc.) open up new possibilities for the investigation of the attention structures and specific cognitive mechanisms involved in taking and using photos.

A sample of testable predictions along the 4 levels of Tinbergen based on the mental utilization hypothesis of photography.

We position viewing, sharing, and using personal photos within the coherent theory of the evolution of life and human nature. On the basis of the four levels of Tinbergen ( 1963 ), we developed a theoretical framework that describes the characteristics of photos and photography-related behaviors, including potential adaptative values related to the evolutionary functional domains of coping, well-being, social connectedness, courtship, and mating. We hypothesized that people take or use photos in contexts in which a pleasant event is coupled with uncertainty or with the processing of threatening information, and that people generally use photography as a coping strategy. Based on our analysis, we propose the Mental Utilization Hypothesis that explains the success of photography by its match with core human mental mechanisms, which characterize human nature.

The proposed hypothesis provides a novel conceptual framework, potentially useful in devising future experimental studies of photography. Despite the global ubiquity of photos, there is still almost no research into the cognitive mechanisms underlying photo taking. Investigations into the courtship or mating functions of photography are still limited to the explicit use of photos in online dating, but these functions are more fundamental and embracing. Studies regarding evolutionary functions of photography are particularly desirable. Important findings could be gained through comparisons between cultures, subcultures and sociological strata, gender and age classes. Important questions in such comparisons could be whether social prestige and social, occupational, or reproductive success can be linked with photography. Is photography an addition to existing social and sexual behavior or is it part of a socio-sexual change which compensates for or replaces previous behaviors or customs? Does it have “emergent properties” not found in its constituent elements? Last but not least, our description of taking and using photos as a coping strategy provides a comprehensive theoretical basis for new experimental research into the application of photography in psychotherapeutic contexts. With photography, people developed a new means of representing experiences and ideas through pictures with special characteristics, the understanding of which requires a minimum of effort and cultural knowledge. We are creatures in an increasingly complex social world for whom and in which these pictures open up powerful possibilities for action, but also for feeling at home and safe.

Data Availability Statement

Author contributions.

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank the reviewers for their valuable comments and suggestions. They also thank Jenna Hicken for personal assistance in translating the manuscript.

Funding. This project was supported by the Austrian Science Fund (FWF): W1262-B29.

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