In 1967, 82.7% of 25-34 years old Americans lived with their spouse; today it is 37.5%

 

Subjective orgasm experience: Heterosexual people (vs. gay & bisexual people) had a more intense experience

Evaluating the Subjective Orgasm Experience Through Sexual Context, Gender, and Sexual Orientation. Laura Elvira Muñoz-García, Carmen Gómez-Berrocal & Juan Carlos Sierra. Archives of Sexual Behavior, Dec 12 2022. https://link.springer.com/article/10.1007/s10508-022-02493-3

Abstract: The subjective orgasm experience (SOE) is the psychological perception of orgasm sensations and closely related to sexual health. Here, SOE was studied through the context in which it is experienced (sexual relationships and solitary masturbation), gender, and sexual orientation. For this purpose, data were collected from 4255 people (1927 men and 2328 women) of different sexual orientations (heterosexual = 1545; bisexual = 1202; and gay = 1508) who completed two versions of the Orgasm Rating Scale (ORS) for both contexts (i.e., sexual relationships and solitary masturbation) along with a socio-demographic questionnaire. Results showed that the ORS in the context of solitary masturbation is an instrument invariant by gender and sexual orientation. Significant differences in SOE were found by context: it was more intense in the context of sexual relationships (vs. solitary masturbation); by gender: women (vs. men) reported greater intensity; and by sexual orientation, with heterosexual people (vs. gay and bisexual people) having a more intense experience.

Discussion

The main objective of this study was first, to test measurement invariance by gender and sexual orientation of the Spanish version of the ORS (Mah & Binik, 2011) of Cervilla et al. (2022) in the context of solitary masturbation. Second, it was to analyze the SOE across situational (i.e., context in which orgasm was experienced: solitary masturbation vs. sexual relationships) and individual characteristics (i.e., gender and sexual orientation).

The results of the measurement invariance by gender and sexual orientation of the ORS in the solitary masturbation context confirmed that it is an invariant scale (H1), both by gender and sexual orientation. Therefore, it is a valid instrument to measure and compare the SOE of different groups (men vs. women, and heterosexual vs. bisexual vs. gay) (Pineda et al., 2018).

Previous studies indicated that the SOE was more intense in the context of sexual relationships (vs. that of solitary masturbation) (Bensman, 2011; Levin, 2007; Mah & Binik, 2002; Pinkerton et al., 2003; Santtila et al., 2007; Sierra et al., 2022). Although our results in general confirmed this pattern (H2), they also allowed us to qualify the role of context on the SOE. The results showed that the dimensions related to emotions, sensations, and intimacy were more intense in sexual relationships, while that related to the rewarding effect of orgasm was more intense in solitary masturbation. Specifically, the scores for the Affective, Sensory, and Intimacy dimensions were higher in the sexual relationships context (vs. solitary masturbation), while the scores for the Rewards dimension were more intense in the solitary masturbation context (vs. sexual relationships). Moreover, these results were repeated when the variables of an individual’s gender and sexual orientation are considered. This pattern confirmed the results of previous studies, which indicated that both men and women value partnered orgasms as more intimate and solitary orgasms as more rewarding (Mah & Binik, 2002).

Based on previous research, we expected to find a significant relationship between gender and the SOE dimensions in both contexts (Arcos-Romero & Sierra, 2019; Arcos-Romero et al., 2018; Sierra et al., 2021, 2022). In accordance with these studies (H3), women got higher scores than men on three dimensions (Affective, Sensory, and Intimacy) in the sexual relationships context, which refers to emotions, physiological changes, and the intimate aspect of the orgasmic experience. This is consistent with previous evidence showing that women associated orgasms achieved through sexual relationships with more intense bodily sensations, more intimacy, and greater connection in sexual relationships (Fahs, 2014). However, we found that on the Rewards dimension, in the context of sexual relationships, men scored higher than women, which is also consistent with previous studies in which men reported having a more rewarding orgasm (Paterson et al., 2014). Finally, no differences were found between men and women on the Rewards dimension in the solitary masturbation context. Thus, in this context, gender did not influence the rewarding aspect of orgasm.

One explanation for the gender gap in orgasm could be the idea that traditional heteronormative sexual scripts seem to grant men more agency than women, encouraging sexual acts that are more likely to produce orgasms in men (such as penile–vaginal intercourse) (Blair et al., 2017). In addition, the fact that the dimensions where women score higher than men are the ones related to emotions and intimacy is consistent with traditional sexual scripts where women are typically depicted as sexual gatekeepers who prioritize emotional closeness and fidelity. On the other hand, men scored higher in the rewarding dimension, also congruent with traditional sexual scripts where they seek a more physical aspect of sex (Masters et al., 2013). Herein, a dichotomous, antagonistic paradigm of heterosexuality is produced by the confluence of the opposing discourses in which men are pursuing subjects, while women are passive objects (Tolman, 2006). According to this perspective, female sexuality does not exist unless it results from emotional closeness and commitment to a relationship (Masters et al., 2013). Also, women may have higher evaluations when tested in research settings because they may have lower aspirations for sexual satisfaction (McClelland, 2010).

Traditionally, studies on SOE have been conducted on heterosexual people and in the context of sexual relationships (Arcos-Romero & Sierra, 2018, 2019, 2020; Arcos-Romero et al., 2018, 2019; Mah & Binik, 2001). To analyze the SOE in non-majority sexual orientations, this study included bisexual and gay people, expecting that as postulated in H4, heterosexual people would present higher scores (Frederick et al., 2018; Garcia et al., 2014). Our results showed that heterosexual, bisexual, and gay people differ on two dimensions of the solitary masturbation context (Affective and Sensory) and two dimensions in the sexual relationships context (Sensory and Rewards), partially confirming H4. The intensity of the SOE was always higher for heterosexual people than gay people, which clearly shows the need to consider sexual orientation when conducting studies on SOE, and that there were more differences between heterosexual people and gay people than between heterosexual and bisexual people. The fact that gay people reported lower SOE intensity than heterosexual and bisexual people specifically in the dimensions more related to physical experiences could be because people with same-sex partners tend to place less emphasis on the consequences, instead concentrating on the process or development of the sexual relationship rather than its outcome (Mangas et al., 2022). This may be supported by findings that suggest that same-sex couples exhibit higher levels of emotional closeness than heterosexual couples (Spitalnick & McNair, 2005), which may cause them to place a higher priority on the emotional aspects of a relationship (Mangas et al., 2022). Research also shows that queer women prioritize non-genital sexual acts like kissing, snuggling, and hugging even though orgasm is less likely to occur because of them alone (Garnets & Peplau, 2006) and even do not mention orgasm at all when describing their best sexual encounters (Chatterji et al., 2017).

Many studies conducted among gay population focus on a binary conception of sexual orientation, in which same-sex and other-sex attraction are presented as the only categories (Bradford, 2004). Because of this, people who identify as bisexual experience a unique form of stigmatization and discrimination called “biphobia” (Bradford, 2004), which stems from both the heteronormative society and LGBTIQ+ community, thus experiencing double discrimination (Brewster & Moradi, 2010; Mitchell et al., 2014). Currently, there is no information regarding the SOE of this group. In our study, we observed that bisexual people present similar scores to heterosexual people, surpassing scores of gay people in the Affective and Sensory dimensions of the solitary masturbation context, and in the Sensory dimension of the sexual relationships context. However, in the Rewards dimension of orgasm, in the sexual relationships context, they presented lower scores than heterosexual people. Considering that bisexuality is a minority orientation and is more invisible than a gay orientation, this finding could be related to the minority stress theory (Meyer, 1995), in which internalized homophobia is included as one of the processes that compose it (Meyer, 2003). However, it is not currently possible to confirm this relation because of the scarcity of data on aspects of bisexual people’s sexuality. Finally, in the remaining dimensions (Affective in the sexual relationships context, Intimacy in both contexts, and Rewards in the solitary masturbation context), no significant differences were found according to sexual orientation. Thus, we conclude that the differences by orientation in SOE are not generalized but dependent on the context and dimension studied.

Limitations and Future Research Directions

One limitation of this study was that the sample was collected using a convenience non-probability sampling technique in an online format. In addition, for bisexual people when answering the ORS, the gender of the sexual partner with whom they had the orgasm they were rating was not asked, which would have been an interesting addition. Another limitation is that no information was asked about how orgasm was obtained, which would also have added an interesting nuance. Finally, the use of the Kinsey scale to measure sexual orientation is a limitation since it reduces sexual orientation to a purely behavior matter. In this regard, bisexuality was not considered as all the responses correspondent with plurisexual orientations, but only a subset limited to the responses 3 (Predominantly heterosexual, but more than incidentally homosexual), 4 (Bisexual), and 5 (Predominantly homosexual, but more than incidentally heterosexual).

Future research should consider the influence of gender roles and attitudes toward sexual gender norms to understand and explain the processes underlying the differences between men and women in the SOE and across contexts. It should also examine and identify the factors that may be causing lower scores of SOE of people with a minority sexual orientation, which will allow the implementation of more effective programs to promote the sexual health of individuals regardless of sexual orientation (Garcia et al., 2014). Likewise, it is necessary to keep in mind that addressing dysfunctions or problems related to any aspect of orgasm should be framed considering an approach focusing on both the gender and sexual orientation of the person. 

Holiday gift giving is in retreat in the US, it was demoted as an "inferior good"

Holiday gift giving in retreat. Joel Waldfogel. Economics Letters, December 12 2022, 110952. https://doi.org/10.1016/j.econlet.2022.110952

Abstract: Using US cross-section data, holiday gift giving is a normal good whose income elasticity of demand is about 0.5. As income rose 1914–2000, aggregate holiday gift expenditure grew as well. Since 2000, however, holiday giving has fallen in real terms as income has continued to rise. While gift giving remains normal in household cross sections, it behaves like an inferior good in the post-2000 national time series.

Introduction

Since Engel (1895), economists have classified goods with positive income effects as “normal” and those with negative income effects are “inferior”. These attributes are not inherent: As economies develop, the roles of particular goods can change. For example, some studies show that rice in Asia and beer in Germany have evolved from normal to inferior goods over time (Ito et al., 1989, Volland, 2012). What sort of a good is holiday gift giving in the US, and how has it changed over time?

I first document the relationship between household income and holiday gift giving implicit in cross-sectional Gallup survey data, confirming that holiday gift giving is a normal good with an income elasticity of roughly 0.5. I then examine a century’s data on per capita income and holiday gift giving (inferred from the December bump in retail sales). I show that holiday gift giving rose with income until 2000 and has since fallen in real terms even as income has continued to grow. Although gift giving is normal in cross sections of US households, it behaves like an inferior good in the national time series since 2000.1

Echo chambers and filter bubble are largely just a figment of the minds of political pundits

Echo chambers, filter bubbles, and polarisation: a literature review. Amy Ross Arguedas et al. Reuters Institute for the Study of Journalism, Jan 19 2022. https://reutersinstitute.politics.ox.ac.uk/echo-chambers-filter-bubbles-and-polarisation-literature-review

Abstract: This literature review examines social scientific evidence regarding the existence, causes, and effects of online echo chambers in the context of concerns about digital platforms contributing to polarisation in our societies generally, and in relation to scientific topics, specifically. The scholarship suggests echo chambers are much less widespread than is commonly assumed, finds no support for the filter bubble hypothesis, and offers a mixed picture on polarisation and the role of news and media use in contributing to polarisation, especially given limited research outside of the United States. Evidence about echo chambers around public discussions of science is limited; however, research on science communication points to the important role of self-selection, elite cues, and small, highly active communities in shaping these debates. These findings are important as terms like echo chambers are widely used in public and policy debates, sometimes in disparate ways, and not always aligned with the evidence.

Check also other literature with references: Politically partisan left-right online news echo chambers are real, but only a minority of approximately 5% of internet news users inhabit them; the continued popularity of mainstream outlets often preclude the formation of large partisan echo chambers

Aversion toward broken patterns of simple geometric shapes predicted greater adherence to social norms

Deviancy Aversion and Social Norms. Anton Gollwitzer et al. Personality and Social Psychology Bulletin, December 10, 2022. https://doi.org/10.1177/01461672221131378

Abstract: We propose that deviancy aversion—people’s domain-general discomfort toward the distortion of patterns (repeated forms or models)—contributes to the strength and prevalence of social norms in society. Five studies (N = 2,390) supported this hypothesis. In Study 1, individuals’ deviancy aversion, for instance, their aversion toward broken patterns of simple geometric shapes, predicted negative affect toward norm violations (affect), greater self-reported norm following (behavior), and judging norms as more valuable (belief). Supporting generalizability, deviancy aversion additionally predicted greater conformity on accuracy-orientated estimation tasks (Study 2), adherence to physical distancing norms during COVID-19 (Study 3), and increased following of fairness norms (Study 4). Finally, experimentally heightening deviancy aversion increased participants’ negative affect toward norm violations and self-reported norm behavior, but did not convincingly heighten belief-based norm judgments (Study 5). We conclude that a human sensitivity to pattern distortion functions as a low-level affective process that promotes and maintains social norms in society

Check also Aversion towards simple broken patterns predicts moral judgment Aversion towards simple broken patterns predicts moral judgment:

Anton Gollwitzer et al. Personality and Individual Differences, Volume 160, 1 July 2020, 109810. https://www.bipartisanalliance.com/2020/03/aversion-towards-simple-broken-patterns.html

And: Clone images elicited higher eeriness than individuals with different faces; related to distinguishableness of each face, the duplication of identity, avoidance reactions based on disgust sensitivity, inter alia:

Yonemitsu F, Sasaki K, Gobara A, Yamada Y (2021) The clone devaluation effect: A new uncanny phenomenon concerning facial identity. PLoS ONE 16(7): e0254396. Jul 13 2021. https://www.bipartisanalliance.com/2021/07/clone-images-elicited-higher-eeriness.html

General Discussion

We find that something as simple as deviancy aversion—people’s sensitivity to the distortion of patterns—contributes to the prevalence and strength of social norms in society. In Study 1, participants’ aversion toward nonsocial pattern distortion (e.g., broken patterns of geometric shapes) predicted negative affect toward social norm violations (Affect), self-reported social norm following (Behavior), and judging social norms as important (Belief). Supporting generalizability, deviancy aversion also predicted greater conformity on accuracy-oriented estimation tasks (Study 2), greater following of physical distancing norms during COVID-19 (Study 3), and greater following of fairness norms in terms of not repeating a survey for additional payment (Study 4). Finally, deviancy aversion causally heightened social norm indicators, including negative affect toward social norm violations and self-reported social norm following, but did not impact more cognitive, belief-based norm measures, such as judging norms as important and desiring tighter social norms in society (Study 5).

Our findings suggest that a low-level affective factor—deviancy aversion—plays a meaningful role in the power of social norms in society. People’s discomfort in response to pattern distortion appeared to lead them to experience norm violations as affectively aversive, in turn motivating norm adherence and conformity. Given the important role of social norms in cooperation and group functioning (e.g., Fehr & Fischbacher, 2004a; Sherif, 1936), deviancy aversion may be a low-level affective factor that contributes to social functioning in human societies.

Robustness

Our findings are robust. First, controlling for third variables, including need for closure, intolerance for ambiguity, disgust, political orientation, aversion toward unbroken patterns, novelty aversion, negativity aversion, and social desirability did not change our results (Studies 1–5). Moreover, none of these potential confounds predicted social norms as consistently as deviancy aversion did (and some failed to predict it at all; Tables 1–5).

Second, our findings are unlikely to have been driven by anthropomorphism or by participants imbuing our stimuli with agency or social content (e.g., Heider & Simmel, 1944). Past research has not found anthropomorphism to moderate links between deviancy aversion and social constructs, and in Study 5, anthropomorphism did not moderate our findings either. In addition, in Study S1, which like Study 5 examined the causal impact of deviancy aversion on social norms, only 10% to 20% of participants spontaneously generated social content or attributed agency to the broken patterns of geometric shapes (and such content did not moderate our findings; see Study S1).

Third, demand or response bias is unlikely to account for our results. Socially desirable responding did not moderate any of our results. In addition, our results remained when excluding participants who had predicted our hypothesis in Study 3, and when controlling for participants’ self-reported motivation to perform well on the deviancy word-task in Study 5. Moreover, regarding response bias, our findings remained when reverse-scaling numerous measures and when accounting for closely matched control measures, for instance, participants’ aversion toward unbroken patterns.

Theoretical Contribution

The present findings theoretically advance our understanding of social norms. Researchers have explicitly noted that affective or cognitive processes underpinning social norms are largely undiscovered despite being theoretically founded (e.g., Fehr & Fischbacher, 2004b). While researchers have shown that low-level affective processes play a large role in other domains (e.g., moral judgment; Gollwitzer, Martel, Bargh, & Chang, 2020; Haidt, 2001), such processes are still on the periphery when it comes to explaining social norms. Addressing this research gap, we find that a simple aversion to pattern distortion may be one simple affective pathway via which social norms and conformity are encouraged. Moreover, combined with past research indicating that deviancy aversion activates “intuitionist” (affective) pathways to moral judgment (Gollwitzer, Martel, Bargh, & Chang, 2020), deviancy aversion may qualify as an emotional response that is activated at the very start of the process of norm responding (e.g., when deciding whether to follow a norm or responding to norm violations). Deviancy aversion may thus qualify as an efficient affective heuristic that predisposes individuals to follow norms and denigrate norm violators (one that can only be overridden by self-regulation or deliberation; see Gollwitzer, Martel, Bargh, & Chang, 2020).

Deviancy aversion may also help explain why people so flexibly conform to norms around them. For example, on the 1962 TV Show, Candid Camera, individual people entered an elevator of occupants all facing backward. Many of these individual people conformed and joined the rather unusual behavior of staring at an elevator wall (Kent, n.d.). Outside of this staged example, norms differ depending on culture and context, and people often adapt to new norms unintentionally (see Chartrand & Bargh, 1999). Similarly to how social norms are situational, what is “patterned” or regular is also situational. Deviancy aversion may thus be a key ingredient of why people can so flexibly follow social norms. By experiencing aversion toward the violation of behavioral patterns in a specific context, people can quickly adapt to that specific environment. Indeed, this theorizing aligns with past work showing that deviancy aversion predicts context-dependent social responding in a different social domain—prejudice (prejudice against Black individuals when the majority is White, prejudice against White individuals when the majority is Black; Gollwitzer, Marshall, & Bargh, 2020). Future work should test whether deviancy aversion underlies humans’ suprising ability to flexibly and automatically adapt to the regularities and social norms in a given context.

Our findings may also inform social norms at the cultural level. Gelfand and colleagues (2011) identified nations as varying in the prevalence and rigidity of social norms—loose versus more tight societies. Although we did not find deviancy aversion to causally impact a desire for looseness vs. tightness (Study 5), past work has found higher levels of deviancy aversion in tighter cultures (China) than in looser ones (United States; Gollwitzer et al., 2017). Future research should seek to explain these contradictory findings, and more carefully examine whether loose vs. tight cultures overlap with lower vs. higher levels of deviancy aversion. If so, tight vs. loose cultures may extend beyond social norms to other domains as well; for instance, tight societies may have more rigid and patterned architecture than more loose societies.

Our findings also shed light on more specific questions. For instance, deviancy aversion may help explain why people engage in normative behaviors even when these behaviors are not clearly motivated; for example, cooperative norms that are harmful for one’s own personal gain (e.g., cooperating in a one-shot prisoner’s dilemma; Cooper et al., 1996), or descriptive norms that are not motivated by social or extrinsic factors (e.g., random trends; e.g., Muldoon et al., 2014; Schwartz & Howard, 1984). In addition, deviancy aversion may help explain why extremely positive norm-violations—such as donating one’s kidney to a stranger—are often denigrated by others (e.g., Herrmann et al., 2008; MacFarquhar, 2015). Indeed, past work has not only linked deviancy aversion to prejudice against stigmatized social outliers, but also “positive” social outliers (e.g., very smart individuals; Gollwitzer et al., 2017). Finally, deviancy aversion may help explain why even infants correct nonconforming others (Schmidt et al., 2019) and expect group-based social norms (Powell & Spelke, 2013). Given that such responses are unlikely to be driven by more conscious factors (e.g., punishment, reasoning), and that deviancy aversion has been found even in 3-year-olds, an affective discomfort toward pattern distortion may motivate such infant norm-based responding.

Our findings also directly extend research on deviancy aversion. For instance, we find deviancy aversion to impact a social construct aside from prejudice (Gollwitzer et al., 2017; Gollwitzer, Marshall, & Bargh, 2020) and moral judgment (Gollwitzer, Martel, Bargh, & Chang, 2020). In addition, we find deviancy aversion to not only relate to social norms but also have a causal impact on heightening social norm indicators (Study 5). Moreover, deviancy aversion predicted self-reported and objective behaviors that have substantial consequences, for instance, conforming to others’ judgments (Study 2), engaging in greater physical distancing norms during COVID-19 (Study 3), and reduced cheating when doing so violates fairness norms (Study 4). In doing so, we extend the potential outcomes of deviancy aversion to health (Study 2), conformity (Study 3), and fairness (Study 4) domains. Finally, we found that deviancy aversion links to social judgments even for participants who do not predict these links (Study 3), impacts social responding outside of awareness (Study 5), and fails to influence individuals’ more cognitive, belief-based judgments (Study 5). Taken together, these findings provide a new theoretical framework of deviancy aversion as an unintentional affective heuristic that influences social responding across domains by inducing negative affect toward social irregularities outside of people’s awareness.

Finally, past work finds that approximately 15% of people exhibit a stable preference for pattern distortion instead of an aversion (Gollwitzer, 2021). This 15% aligns fairly well with the percent of participants in conformity studies who refuse to conform (e.g., ~25% in Asch, 1951). Potentially, this minority group of deviancy preferers, also referred to as “rebels,” “rule-breakers,” or “trend-setters,” functions evolutionarily to motivate social norm change as well as promote opposition against social norms that are harmful (e.g., normative prejudice against minority groups, authoritarian rules).

Limitations and Caveats

First, and perhaps most importantly, though deviancy aversion positively correlated with judging social norms as valuable (Study 1), it did not causally heighten this type of more belief-based norm judgment (Study 5). Several explanations exist. In line with deviancy aversion impacting individuals’ affective responses toward norms and norm violations, these results may be driven by the norm espousal and tightness measures in Study 5 assessing more cognitive, belief-based attitudes toward social norms. This explanation aligns with the link between deviancy aversion and norm espousal being quite small in Study 1 (β = .176), and additionally, with past research indicating that deviancy aversion appears to impact social judgment via affective pathways (e.g., Gollwitzer, Martel, Bargh, & Chang, 2020).

Second, Study 5 did not include a no-treatment condition. It is thus unclear whether deviancy aversion or pattern ‘positivity’ heightens social norm indicators (or both). Supporting the former, participants’ responses to unbroken patterns did not consistently predict social norm indicators (Studies 1–4) and, in a supplemental study, deviancy aversion heightened social norms compared to a negativity aversion control condition (see Study S2). Third, the Flurp social norm measures are limited as participants may perceive the Flurps as “objects” rather than social agents. Fourth, deviancy aversion is not the only factor underlying social norms (e.g., avoiding punishment), and likely interacts with other factors to predict social norms. Fifth, the generalizability of our findings is limited. It remains unclear whether deviancy aversion predicts social norm indicators cross-culturally, predicts social norms in noisier field contexts, and predicts conformity if conformity opposes a known answer (akin to Asch’s line studies; Asch, 1951). Finally, deviancy aversion may also influence perceptions of simple statistical regularities that are not necessarily social norms (Bicchieri, 2005). This would not discount the observed effects, however. Instead, these results would align with the proposed mechanism—that social norms are regular, patterned behaviors.

Good-looking people report higher meaning in life

Pretty, meaningful lives: physical attractiveness and experienced and perceived meaning in life. Christopher A. Sanders, Alexis T. Jenkins & Laura A. King. The Journal of Positive Psychology, Dec 9 2022. https://doi.org/10.1080/17439760.2022.2155222

Abstract: Three studies examined the association between physical attractiveness and meaning in life. Study 1 (N = 305 college students) showed that self-reported physical attractiveness positively correlated with meaning in life. Study 2 (N = 598 noncollege adults) replicated the association between self-reported physical attractiveness and meaning in life and extended those findings, demonstrating that outside perceptions of attractiveness are linked to outside perceptions of how meaningful a person’s life is. Study 3 (N = 331 targets, 97 raters) replicated these findings and probed the nuances of the relationships between outside ratings and self-reports of attractiveness and meaning in life. Across the studies, existential significance, or the feeling that one’s life matters, was the facet of meaning that primarily explained the link between attractiveness and meaning in life. In addition, a person’s view of their own attractiveness is more indicative of their well-being than outsider ratings. Implications for our understanding of meaning in life are discussed.

Keywords: Existential meaningnatural beauty

People found false feedback about their personality more accurate and appealing than the real one, even if it consisted of vague and unspecific diagnoses that could fit just about anyone

How well do we know ourselves? Disentangling self-judgment biases in perceived accuracy and preference of personality feedback. Sabina Trif, Claudia Rus, Elena Manole, Octavian Calin Duma. Psihologia Resurselor Umane, Vol. 20 No. 2 (2022), Dec 6, 2022. https://doi.org/10.24837/pru.v20i2.518

Abstract: Despite personality measurement and feedback being pervasive practices, there are self-judgment biases that may impair their usage. We set out to analyze the differences between two kinds of false feedback and real feedback on personality regarding perceived accuracy and preference. We propose that there would be no differences between false and real feedback regarding perceived accuracy, but we expect differences regarding feedback preference. A sample of 146 students completed the IPIP-50 instrument that measured the Big 5 Factors and received three kinds of feedback - a general one (Barnum effect as false feedback), a positive one (Better-than-average effect as false feedback), and a real one. They rated each regarding accuracy and preference. Results indicate differences regarding both dependent variables. Participants perceive false feedback as more accurate than the real one. Moreover, they prefer positive feedback over the other two, and general feedback compared to the real one. We discuss both theoretical and practical implications, alongside a series of limitations and future research directions.

Keywords: personality, Barnum effect, better-than-average effect, psychometrics

Can Inflammation Predict Social Media Use? Linking a Biological Marker of Systemic Inflammation with Social Media Use Among College Students and Middle-Aged Adults

Lee, David S. and Jiang, Tao and Crocker, Jennifer and Way, Baldwin M., Can Inflammation Predict Social Media Use? Linking a Biological Marker of Systemic Inflammation with Social Media Use Among College Students and Middle-Aged Adults. SSRN, Dec 5 2022. http://dx.doi.org/10.2139/ssrn.4281080

Abstract: Although much research has examined the impact of social media use, relatively less is known about what predicts social media use. Drawing on recent evidence that inflammation may promote social affiliative motivation, the present research proposes a novel, biopsychosocial perspective that inflammation may be associated with more social media use. Using a nationally representative sample of middle-aged adults (N = 524), Study 1 found a positive association between C-reactive protein (CRP), a biomarker of systemic inflammation, and the amount of social media people used. Study 2 (N = 228) showed that among college students CRP was prospectively associated with more social media use 6 weeks later. Providing stronger evidence of the directionality of this effect, Study 3 (N = 171) showed that CRP predicted increased social media use in the subsequent week even after controlling for current week’s use. Additionally, in exploratory analyses of CRP and different types of social media use in the same week, CRP was only associated with using social media for social interaction and not for other purposes (e.g., entertainment). The present research sheds light on a biopsychosocial antecedent to social media use and highlights potential benefits of using biological measures in social media research.

Keywords: Social media use, inflammation, C-reactive protein, mental health, physical health, well-being

Political, but not cognitive sophistication was associated with an heightened propensity for motivated reasoning, the bending of the evidence to defend the preconceived world view; so, in the end, the problem is being infected with the political virus!

 On the Independent Roles of Cognitive & Political Sophistication: Variation Across Attitudinal Objects. Joseph A. Vitriol,Joseph Sandor,Robert Vidigal,Christina Farhart. Applied Cognitive Psychology, November 29 2022. https://doi.org/10.1002/acp.4022

Abstract: People are motivated to maintain consistency between importantly held identities, preferences, and judgments. In political contexts, motivated reasoning can help explain a wide range of political phenomena, including extremism, polarization, and misperceptions. However, recent findings in psychology have challenged this account. These perspectives emphasize the role of cognitive sophistication (e.g., analytical reasoning, numerical literacy) in political attitudes, but differ in terms of whether it is expected to attenuate or exacerbate politically motivated reasoning and belief in conspiracy theories. Yet prior investigations have not examined the relative and independent effects of both political and cognitive sophistication. Using data from two samples, including one sampled to approximate representativeness in the U.S., we demonstrate that both types of sophistication have independent and (at times) countervailing effects on belief in COVID-19 conspiracy theories and other political attitudes. Our results are critical for theories of cognitive sophistication, political cognition, and attitudes, and the psychology of conspiracy theories.

People appreciated pseudo-profound bullshit statements as equally "deep" even when their meaning was reversed

A framework for understanding reasoning errors: From fake news to climate change and beyond. Gordon Pennycook. Advances in Experimental Social Psychology, December 8 2022. https://doi.org/10.1016/bs.aesp.2022.11.003

Abstract: Humans have the capacity, but perhaps not always the willingness, for great intelligence. From global warming to the spread of misinformation and beyond, our species is facing several major challenges that are the result of the limits of our own reasoning and decision-making. So, why are we so prone to errors during reasoning? In this chapter, I will outline a framework for understanding reasoning errors that is based on a three-stage dual-process model of analytic engagement (intuition, metacognition, and reason). The model has two key implications: (1) That a mere lack of deliberation and analytic thinking is a primary source of errors and (2) That when deliberation is activated, it generally reduces errors (via questioning intuitions and integrating new information) and rarely increases errors (via rationalization and motivated reasoning). In support of these claims, I review research showing the extensive predictive validity of measures that index individual differences in analytic cognitive style—even beyond explicit errors per se. In particular, analytic thinking is not only predictive of skepticism about a wide range of epistemically suspect beliefs (paranormal, conspiratorial, COVID-19 misperceptions, pseudoscience and alternative medicines) as well as decreased susceptibility to bullshit, fake news, and misinformation, but also important differences in people's moral judgments and values as well as their religious beliefs (and disbeliefs). Furthermore, in some (but not all cases), there is evidence from experimental paradigms that support a causal role of analytic thinking in determining judgments, beliefs, and behaviors. The findings reviewed here provide some reason for optimism for the future: It may be possible to foster analytic thinking and therefore improve the quality of our decisions.

Keywords: Dual-process theoryIntuitionReasonMetacognitionBeliefsMoralityReligious beliefsClimate changeScience attitudesMisinformation

There is no distinct capacity for moral judgment, and , as a result, it is impossible for someone's "moral judgment faculty" to become selectively disabled

The disunity of moral judgment: Implications for the study of psychopathy. David Sackris. Philosophical Psychology, Dec 7 2022. https://doi.org/10.1080/09515089.2022.2155125

Abstract: Since the 18th century, one of the key features of diagnosed psychopaths has been “moral colorblindness” or an inability to form moral judgments. However, attempts at experimentally verifying this moral incapacity have been largely unsuccessful. After reviewing the centrality of “moral colorblindness” to the study and diagnosis of psychopathy, I argue that the reason that researchers have been unable to verify that diagnosed psychopaths have an inability to make moral judgments is because their research is premised on the assumption that there is a specific moral faculty of the brain, or specific “moral” emotions, and that this faculty or set of emotions can become “impaired”. I review recent research and argue that we have good reason to think that there is no such distinct capacity for moral judgment, and that, as a result, it is impossible for someone’s “moral judgment faculty” to become selectively disabled. I then discuss the implications of such a position on psychopathy research, the coherence of the disorder, and the moral responsibility of psychopaths.

Keywords: psychopathymoral judgmentmoral psychologymetaethicscognitive science

Ungated version: The disunity of moral judgment: Implications for the study of psychopathy. David Sackris. 2022. https://philarchive.org/rec/SACTDO-5

Check also The disunity of moral judgment: Evidence and implications. David Sackris, Rasmus Rosenberg Larsen. Philosophical Psychology, Mar 28 2022. https://philarchive.org/archive/SACTDO-4

Intelligence was positively and significantly correlated with face detection, face perception, and face memory

The association between intelligence and face processing abilities: A conceptual and meta-analytic review. Dana L. Walker et al. Intelligence, Volume 96, January–February 2023, 101718. https://doi.org/10.1016/j.intell.2022.101718

Abstract: Whether there is an association between intelligence and face processing ability (i.e., face detection, face perception and face memory) is contentious, with some suggesting a moderate, positive association and others contending there is no meaningful association. The inconsistent results may be due to sample size differences, as well as variability in the quality of intelligence measures administered. The establishment of a moderate, positive correlation between face processing and intelligence would suggest it may be integrated within the Cattell-Horn-Carroll model of intelligence. Additionally, developmental prosopagnosia, a specific impairment of the recognition of facial identity, may be assessable in a manner similar to a learning disability. Consequently, we employed a psychometric meta-analytic approach to estimate the true score correlation between intelligence and face processing ability. Intelligence was positively and significantly correlated with face detection (r’ = 0.20; k = 2, N = 407), face perception (r’ = 0.42, k = 11, N = 2528), and face memory (r’ = 0.26, k = 23, N = 9062). Additionally, intelligence measurement quality moderated positively and significantly the association between intelligence and face memory (β = 0.08). On the basis of both theoretical and empirical considerations, we interpreted the results to suggest that face processing ability may be plausibly conceptualised within the Cattell-Horn-Carroll model of intelligence, in a manner similar to other relatively narrow dimensions of cognitive ability, i.e., associated positively with intelligence, but also distinct (e.g., reading comprehension). Potential clinical implications for the assessment of developmental prosopagnosia are also discussed.

Introduction

On theoretical and empirical grounds, some researchers claim that face processing ability is essentially independent of general intelligence1 (Bowles et al., 2009; Shakeshaft & Plomin, 2015; Wilmer, Germine, & Nakayama, 2014), whereas others contend that it is associated positively and meaningfully with other well-known cognitive abilities, including general intelligence (Connolly, Young, & Lewis, 2019; Gignac, Shankaralingam, Walker, & Kilpatrick, 2016; Hildebrandt, Wilhelm, Schmiedek, Herzmann, & Sommer, 2011). Thus, there is currently no consensus on whether individual differences in face processing ability may be considered a conventional cognitive ability or not.

In order to advance the area forward, in this review, we refer to abstract and operational definitions of intelligence, alongside descriptions of some of the key theories and models of cognitive ability, and we note connections with face processing ability and its measurement. We also conduct meta-analyses on the association between intelligence and face processing ability. To foreshadow, we will suggest that several face processing abilities may be plausibly conceptualised within the broadly accepted model of cognitive abilities, the Cattell-Horn-Carroll (CHC) model (McGrew, 2009). We will also contend that there are potential benefits with such an integration, both theoretical and practical.

Several abstract definitions of intelligence have been provided. For example, echoing Pintner (1923), Sternberg (1997, p.1) defined intelligence as “…the mental abilities necessary for adaptation to, as well as shaping and selection of, any environmental context” (see also McIntosh, Dixon, & Pierson, 2012). Gignac (2017, p. 465) defined intelligence somewhat less abstractly as “…an entity's maximal capacity to achieve a novel goal successfully using perceptual-cognitive abilities.” As will be noted in more detail below, face processing ability may be conceptualised as an adaptive capacity relevant to achieving novel goals using perceptual-cognitive abilities, suggesting face processing abilities may be integrated within conventional intelligence conceptualisations. Although abstract definitions of intelligence are useful, especially in the context of theory, they are limited with respect to the generation of psychometric measures. By contrast, operational definitions, which are more concrete than abstract definitions, facilitate psychometric measurement.

In more operational terms, Gignac (2017, p. 465) defined intelligence as “…an entity's maximal capacity to complete a novel, standardised task with veridical scoring using perceptual-cognitive abilities.” Thus, intelligence tests have scoring that is objective and verifiable. For example, the word ‘ambiguous’ has an agreed upon definition and a person can be asked to define the word ambiguous as part of a vocabulary test. Another example is Digit Span Forward (Kaplan, 1991), a short-term memory test where participants are asked to repeat a series of numbers sequentially. As a final example, participants can complete the Trails-B task (Corrigan & Hinkeldey, 1987), a measure of processing speed, by connecting numbers and letters, within a limited time, in an alternating progressive sequence, 1 to A, A to 2, 2 to B, and so on. Thus, intelligence tests can be administered in a way that is objective and performance is verifiable. To foreshadow, published face processing ability tests can also be regarded as objective tasks of performance, much like typical tests of intelligence.

It is important to note that performance on intelligence tests correlate with each other positively, a phenomenon known as the positive manifold, i.e., ubiquitous positive correlations between cognitive abilities (Carroll, 1993; Spearman, 1904). Consider, for example, that the correlation between verbal comprehension and working memory is r = 0.64, based on the Wechsler Adult Intelligence Scale-IV (WAIS-IV) normative sample (Wechsler, 2008). Furthermore, Digit Span, a measure of short-term memory, is correlated at r = 0.50 with Vocabulary, a measure of crystallised intelligence (Wechsler, 2008). Additionally, Matrix Reasoning is correlated with Symbol Search, a measure of processing speed, at r = 0.39 (Wechsler, 2008). In fact, the average inter-subtest correlation across all 10 subtests of the WAIS-IV is 0.43; and none of the inter-correlations are negative or zero. The inter-correlations between cognitive ability measures have facilitated the development of models of intelligence via techniques such as factor analysis.

Over the years, several models of intelligence have been proposed. For example, Spearman's two-factor model (Spearman, 1904) emphasised the prominence of the general factor on the basis of the positive manifold; Cattell/Horn's model that emphasised the distinction between fluid and crystallised intelligence (Cattell, 1941; Horn & Cattell, 1966); and Carroll's (1993) extensive factor analytic work that has culminated into the CHC model of intelligence (McGrew, 2009).

The CHC model of intelligence is an amalgamation of Horn and Cattel's (1966) model and Carroll's model (Carroll, 1993). The first generation of the CHC model aimed to reconcile the differences between the two models (McGrew, 1997). The first CHC model was based substantially upon Carroll's hierarchical three-factor model, although it included a unique broad ability (reading and writing, Grw) and new narrow abilities, such as reading comprehension and reading speed (see Flanagan & Dixon, 2013). The CHC model has been refined based upon current factor analytic research, as well as developmental, neurocognitive, and heritability evidence (Flanagan & Dixon, 2013).

Today, the structure of the CHC model consists of a general factor (known as g), which is referred to as a Stratum III ability within the model. The model also includes 16 broad abilities, called Stratum II abilities, that appear under g (Newton & McGrew, 2010). The Stratum II abilities include: fluid reasoning (Gf),2 comprehension–knowledge (Gc),3 reading and writing (Grw), visual processing (Gv), long-term storage and retrieval (Glr), processing speed (Gs), short-term memory (Gsm), reaction and decision speed (Gt), and quantitative knowledge (Gq; see Table 1). As described by McGrew (2009), the CHC model includes additional possible Stratum II abilities that have not yet been validated fully, including Auditory processing (Ga), General (domain specific) knowledge (Gkn), Tactile abilities (Gh), Kinesthetic abilities (Gk), Olfactory abilities (Go), Psychomotor abilities (Gp), and Psychomotor speed (Gps).

Each Stratum II (broad) ability is divided further into narrower abilities (i.e., Stratum I abilities) that define the depth and breadth of a broad Stratum II ability. For example, memory span (MS) and working memory (WM) are Stratum I abilities and each measures a different aspect of Gsm (a Stratum II ability). In a comprehensive review, Newton and McGrew (2010) listed all nine broad (Stratum II) abilities and nearly 100 Stratum I abilities, with the latter being very narrow in scope. Examples of Stratum I abilities include writing ability (WA), mathematical achievement (A3), simple reaction time (R1), closure speed (CS), and reading comprehension (RC).

Although the CHC model of intelligence is a relatively comprehensive model of individual differences in cognitive abilities, several authors have contended that additional factors may be seriously considered for inclusion into the CHC model, including social and emotional intelligence (Wilhelm & Kyllonen, 2021). Additionally, it has been suggested that face processing abilities may be advantageously considered within the CHC model of intelligence (Meyer, Sommer, & Hildebrandt, 2021). As we detail below, commonly measured dimensions of face processing ability, including face detection, face perception and face memory, may be linked theoretically and empirically to several of the CHC model dimensions (see Table 1 for summary; see also Table S1 in supplementary materials).

Stratum I abilities are correlated positively with g (McGrew, 2009). For example, the correlation between general intelligence and reading comprehension, a Stratum I ability, has been reported to range between ≈ 0.40 and ≈ 0.55 (Jensen, 1998; Joshi & Hulme, 1998; Naglieri & Ronning, 2000; Tiu Jr, Thompson, & Lewis, 2003). Importantly, while reading comprehension is correlated moderately with general intelligence, it is not considered isomorphic with g. In fact, intelligence researchers recognise reading comprehension as a specific ability that can predict various outcomes, above and beyond general intelligence (Gersten, Fuchs, Williams, & Baker, 2001). Such an observation will be important for the theorised role of face processing ability within the context of cognitive abilities more broadly, as described in more detail further below.

Human face processing may be defined simply as the abilities necessary to process facial information, including the ability to detect, match, and recognise faces accurately (Fysh, 2018; Meyer et al., 2021). As mentioned previously, intelligence may be viewed as how well an individual adapts to an environment successfully using cognitive abilities (McIntosh et al., 2012; Pintner, 1923; Sternberg, 1997). Face processing ability, a construct that includes face detection, face perception and face recognition as dimensions (described in more detail below), are all abilities that may be suggested to facilitate successful adaptation. For example, individual differences in face processing ability correlate positively with cooperative interactions (r = 0.25; Corbett, Newsom, Key, Qualls, & Edmiston, 2014) and quality of social networks (r = 0.21; McLaughlin Engfors, Palermo, & Jeffery, 2019). Therefore, face processing ability could be defined as an adaptive ability, as per cognitive intelligence more generally. Furthermore, face processing tasks require perceptual-cognitive skills to solve novel problems. Finally, the tasks are scored objectively – again, as per conventional IQ tests. As one example, face perception tasks (e.g., Cambridge Face Perception Test, CFPT; Duchaine, Germine, & Nakayama, 2007) show a line-up of faces that need to be matched to a target face, based on the degree of visual similarity to the target face. The task is scored based upon the number of accurate matches (quantitative similarity). Therefore, in general terms, face processing ability could be defined operationally as an individual's capacity to use cognitive faculties to complete a novel task involving faces and for which there is a clear procedure to evaluate successful completion of the task (i.e., veridical scoring).

Like cognitive abilities more generally, there is evidence that face processing abilities yield a positive manifold. Verhallen et al. (2017) referred to the face processing general factor as f. McCaffery, Robertson, Young, and Burton (2018) and Verhallen et al. (2017) reported moderate to relatively large correlations (r ≈ 0.20 to 0.50) between measures of face detection, face perception, and face memory (defined below). Although not all of the empirical research is consistent (e.g., Fysh, 2018), the observation of positive correlations between face processing abilities is similar to the observation of positive correlations between cognitive abilities more generally (Carroll, 1993). It should be noted that although detection, matching and recognising faces may be considered positively inter-related processes, they are also considered to be, at least to some degree, distinct. That is, the relatively large correlations (by individual differences research standards; Gignac & Szodorai, 2016) are not large enough to suggest construct redundancy. We discuss each face processing dimension in further detail next.

Face detection is the ability to detect a face generally within a visual scene (Bindemann & Lewis, 2013; Verhallen et al., 2014). Studies show that humans are quicker at detecting a face than any other non-face object (Lewis & Ellis, 2003), implying that faces are an important object to detect for humans. It has been suggested that there may be a dedicated neurophysiological system that mediates the process of face detection, a system distinct from the detection of other objects (Lewis & Ellis, 2003). Individuals with prosopagnosia, the inability to recognise faces, can have impairments in their ability to detect faces (Garrido, Duchaine, & Nakayama, 2008). In fact, de Gelder and Stekelenburg (2005) proposed that some cases of developmental prosopagnosia may originate from deficits in face detection. Furthermore, they proposed that the face detection system is crucial for the normal development of more specialised face processing abilities, such as face memory. Thus, face detection may be considered a relatively more primary face processing ability.

A commonly used test of face detection is the Mooney test (Mooney, 1957), whereby a participant must view degraded images and determine whether an image contains a face or not (e.g., Fig. 1, left-side). Each Mooney face detection image has obstructions of the important local, featural and relational information (e.g., eyes, nose, mouth). Specifically, an individual would have to construct a specific, three-dimensional model of both the face and lighting in order to detect the face (Verhallen & Mollon, 2016). The underlying processes likely draws, to some degree, upon the observer's stored knowledge of faces acquired over their lifetime (Verhallen et al., 2017). Other tasks of face detection involve finding face-like images (see Robertson, Jenkins, & Burton, 2017) and actual face images (see Fysh, 2018) within a visual scene. Both of these tasks require participants to search visual scenes for concealed face images. Comparatively, the forementioned tasks involve visual searching of scenes to detect a real face, in comparison to the Mooney test which involves detection of a face from black and white ambiguous and non-ambiguous images. Some researchers argue that the Mooney test incorporates limited visual searching, a suggested essential component of face detection (Bindemann & Lewis, 2013; Fysh, 2018). Nonetheless, the Mooney test has been shown to be a reliable and valid measure of face detection (Schwiedrzik, Melloni, & Schurger, 2018; Verhallen et al., 2014; Verhallen & Mollon, 2016).

Overall, face detection is a holistic process whereby information is processed in a more general, “big picture” way, compared to local processing. Local processing involves attending to specific details, or processing information in a narrower and more detail orientated way (Navon, 1977). At a superficial level, the Mooney test has seemingly unrelated patches of white and black. An individual completing the task would need to look at the picture as a whole and decide whether the patches of white and black converge together to form the percept of a face. Thus, the Mooney test involves global judgments that are somewhat dependent upon the integration of local elements (Mooney, 1957). This process of organisation is often referred to as closure (or figure closure).

Arguably, the Mooney test may be considered a relatively narrow instantiation of more general figure closure tasks. For example, the Gestalt Figure Completion Task (Eliot & Czarnolewski, 1999; Goodwin, 2012; Street, 1931) is a commonly used measure of general figure closure ability: an ability regarded as a subdimension of intelligence (Closure Speed, CS; McGrew, 2009). Gestalt perception tasks tend to include incomplete figures of familiar objects, animals, or humans. In a manner similar to the Mooney test, an individual must first recognise the ambiguous stimuli and then label it (see Fig. 1, right-side). Arguably, with respect to both the Mooney test and Gestalt Figure Completion Task, an individual would have to create a mental image of the face/object, drawing upon their experience and knowledge of objects observed within their lifetime. Thus, drawing from the CHC model of cognitive abilities, performance on both tasks likely draws upon Gv (visual processing) and to some degree Gc (comprehension-knowledge). Thus, a positive correlation between face detection ability and general figure closure ability would be expected on theoretical grounds. Correspondingly, small-scale (N = 63) empirical research suggests that general figure closure and face detection tasks load onto the same cognitive ability factor (Wasserstein, Barr, Zappulla, & Rock, 2004). Therefore, whether figure closure tasks that include only face stimuli, as per the Mooney test, draw upon unique visual processing ability (i.e., somewhat distinct from general figure closure ability) remains to be determined, convincingly. Theoretically, the observation of some face detection specific (unique) variance would align with current research, suggesting that the ability to detect faces may be a process that is, at least to some degree, distinct from the ability to detect other objects (Lewis & Ellis, 2003).

Despite the fact that the Mooney test was published many years ago, little research has examined the association between intelligence and face detection ability. In one study, Vigen, Goebel, and Embree (1982) estimated the association between IQ (WAIS-R) and face detection ability (Mooney test) at r = 0.25, based on a diverse sample of college, vocation and community member participants (N = 300). By contrast, in another study with a primarily community sample (N = 104), McCaffery et al. (2018) reported a non-significant correlation (r = 0.06) between executive functioning (Card Sorting Task) and face detection ability (Mooney test). McCaffery et al. (2018) suggested that there was little association between face detection ability and other cognitive abilities. Thus, a meta-analysis may be required to help generate a consensus view on this issue.

Theoretically, face perception is an important ability that would be expected to occur after a face has been detected. That is, once a face has been detected, it is possible to discriminate or individualise faces from each other. Face perception ability, at a basic level, involves scanning faces within a group and identifying faces as distinct/similar. Correspondingly, in typical face perception tasks, participants must discriminate, or tell apart, one face from another. Face perception tasks usually require the face stimuli to remain visible, in order to ensure that the task is focused on the visual processing required to perceive faces, with minimal memory requirements. The Warrington Recognition Memory for Faces test (Warrington, 1984) has participants view two photos and make the judgement of whether the identity of the person portrayed is the same or different (see Fig. 2). By contrast, the Benton Face Recognition Test (Benton, Sivan, Hamsher, Varney, & Spreen, 1983) has participants look at a target photo and asks them to choose the target individual from six simultaneously displayed photos (see Fig. 3). In recent years, other face perception tasks have been designed, including the relatively popular CFPT (Duchaine, Yovel, & Nakayama, 2007), the Kent Face Matching Test (Fysh & Bindemann, 2018), the Glasgow Face Matching Test (Burton, White, & McNeill, 2010; White, Guilbert, Varela, Jenkins, & Burton, 2021) and the Faces Card-Sorting Task (Andrews, Jenkins, Cursiter, & Burton, 2015). Arguably, these face perception tasks involve visual processing of faces with minimal memory requirements, thus rendering them relatively pure face perception tasks.

Higher levels of face perception ability have been linked to positive outcomes, whereas lower levels of face perception ability haven been linked to social difficulties. For example, the ability to tell faces apart, or individualise a face, is an important social skill (Fysh, Stacchi, & Ramon, 2020). From a professional perspective, many common professions require at least adequate performance in the ability to perceive and differentiate faces. For example, police officers may have to match a photo of a suspect with video footage of a crime scene (White et al., 2015). Additionally, border control officers and airport security personnel often check identification by matching a passport photo with the face of the person who presents with the identification (White et al., 2015). Similarly, people who work in banks, post offices, and establishments that sell alcohol must often match photo identification to a face.

Not everyone can perceive faces well. For example, individuals with prosopagnosia are often impaired in their face perception ability (Behrmann & Avidan, 2005; Duchaine, Germine, & Nakayama, 2007). Duchaine, Yovel, and Nakayama (2007) found that healthy controls averaged statistically significantly fewer errors than people with developmental prosopagnosia (Cohen's d = −2.13). Correspondingly, people with developmental prosopagnosia often report socialisation difficulties due to their poor face processing abilities and become anxious in public locations (Dalrymple et al., 2014). Therefore, a greater understanding of face perception ability is not only important theoretically, but also practically.

Face perception requires the ability to accurately discern facial configurations and features (Hildebrandt, Schacht, Sommer, & Wilhelm, 2012). More specifically, individuals must detect similarities, or differences, between faces. It could be argued that many visual processing and fluid reasoning tasks require similar detection of image similarities and differences. Consider, for example, the Raven's Progressive Matrices Test (Raven, Raven, & Court, 1998), a measure of fluid reasoning. In this task, participants are presented with a 3 × 3 matrix of geometric figures. Fig. 4 includes an example progressive matrices item from the International Cognitive Ability Resource (ICAR, 2017); it can be seen that the bottom right geometric figure is missing and must be selected from eight multiple choice response options. Interestingly, McGreggor, Kunda, and Goel (2010) found that a computer program designed solely to compare the similarity of images (akin to face perception tasks) was able to accurately complete over half of the Raven's Progressive Matrices Test. Arguably, the computer program exhibited processes related primarily to visual processing (Gv), and perhaps specifically visual matching, in addition to fluid reasoning (Gf). Correspondingly, Raven's Progressive Matrices has been found to measure general intelligence, as well as Gf and Gv (Gignac, 2017). Therefore, it is plausible to suggest that there is a positive association between a person's ability to perceive and differentiate faces and an individual's Gf and Gv ability. Stated alternatively, face perception ability may be considered, in part, a cognitive ability imbued with visual processing and fluid reasoning variance, within the context of the CHC model of cognitive abilities, at least theoretically.

Empirically, the evidence also suggests the possibility of a positive association. For example, Wilhelm et al. (2010) found a significant, positive association (r = 0.56) between their measure of face perception, a custom-made task based upon the part-whole paradigm, and composite intelligence scores defined by multiple cognitive ability subtests (community sample: N = 209). They interpreted their findings as supportive of the hypothesis of an association between intelligence and face perception ability. By contrast, Slone, Brigham, and Meissner (2000) investigated the association between the Benton Face Recognition Task, a measure of face perception ability, and a digit span task, a measure of short-term memory. They reported a small, non-significant correlation (r = 0.09); however, their study was based on a relatively small and restricted sample of university students (N = 129). As per face detection, the inconsistent results in the literature suggest that a meta-analysis may be beneficial.

Face recognition is a term often used in the literature to describe different concepts. Some authors use the term face recognition for a task that involves perceiving faces (Oruc, Balas, & Landy, 2019). Additionally, the term face recognition has been used as a label for tasks and processes that are face perception or memory in nature. For example, the Benton Face Recognition Test (Benton et al., 1983) is a face perception test. Within this review, the term ‘face memory’ will be used, rather than the more ambiguous term ‘face recognition’.

Face memory is the ability to perceive a face, encode that face into memory, and then recall that face, in order to determine if it has been seen previously (Dalrymple & Palermo, 2016). Many face memory tasks have a short interval between viewing the face and recalling the face (e.g., Cambridge Face Memory Test; CFMT; Duchaine & Nakayama, 2006). The CFMT requires participants to recognise six learnt faces across three test stages (see Fig. 5). In the learning stage, the participants learn the faces of six identities in frontal and side-on views. The first test stage requires participants to select which image contains a learnt face amongst two distractors. The images in this stage are identical to the learning stage. The second test stage employs the same three-alternative force choice paradigm, however, the images shown are different to the learning stage, i.e. novel images where the faces have different viewpoint and/or lighting. The third test phase is the same as the second stage, however, participants must recognise a learnt face in novel images covered by heavy visual noise.

There are also face memory tasks that test an individual's ability to recall the identity of a face over a longer period, for example, a time-delayed CFMT (McKone et al., 2011). The standard CFMT and time-delayed CFMT (both 20 min and 24 h) are correlated at 0.84 (McKone et al., 2011). Even though the CFMT is the most popular face memory test used by researchers, there are other valid tasks developed to measure face memory (see Hildebrandt et al., 2011).

Arguably, face memory is an important skill for successful social interaction, as the successful recognition of another person would be expected to determine how we may interact with the person in an appropriate manner. For example, recognising a colleague compared to a family member, will impact the interaction and appropriate socialisation. Correspondingly, individuals with clinical developmental prosopagnosia report that they avoid social situations where face memory is important (Murray, Hills, Bennetts, & Bate, 2018; Yardley, McDermott, Pisarski, Duchaine, & Nakayama, 2008). Furthermore, they also report long-term, negative consequences, as a result, including (but not limited to) dependency on others, restricted social circle, more limited employment opportunities and low self-confidence (Murray et al., 2018; Yardley et al., 2008). The interpersonal struggles shown by people with developmental prosopagnosia, linked to their inability to recognise faces, highlights the importance of face memory for everyday situations.

Theoretically, face memory may be considered to be associated with multiple cognitive abilities. The ability to recognise and remember faces over a short period of time may be linked to an individual's short-term memory (Gsm). Consider that the Digit Span Forward task from the Wechsler scales is similar in structure and task design to the CFMT. In Digit Span Forward, participants must recall a series of numbers previously learnt, whereas the CFMT requires participants to recall the identity of six faces previously learnt. The two tasks arguably tap into a similar process, notably short-term memory (Gsm). By contrast, the ability to recognise and remember faces over a long period of time could be linked to an individual's long-term storage and retrieval (Glr).

Interestingly, the Wechsler Memory Scale –Third Edition (WMS-III) includes two face memory tasks. These tasks, labelled Faces I and Faces II, form part of the Visual Immediate or Visual Delayed indices. In Faces I, participants are shown 24 target faces, and each face is displayed one at a time for 2 s. Then, participants are shown 48 faces (24 targets and 24 distractors) and are asked to identify the target faces by responding either “yes” or “no” to each face. Participants are prompted to keep the target faces in mind. In Faces II, participants are shown 48 faces (24 targets and 24 distractors) after a 30-min delay and are asked again to identify the target faces. Faces I and Faces II correlated with other subtests within the WMS-III. For example, Faces I correlated at 0.14 with Logical Memory I, and also correlated with another Visual Memory Immediate index task (Family Pictures I) at 0.30 (Psychological Corporation, 1997). Ultimately, the correlations between the Faces tasks and the other tasks within the WMS-III were deemed too low (insufficient convergent validity), which led to the removal of these tasks from the WMS-IV (Hawkins & Tulsky, 2004).

On the one hand, the low correlations may be due to methodological considerations. For example, the measure itself differs from the free recall methodology employed by the WMS-III. Moreover, the recognition format of the faces subtest without a recall component may make the test easier than other nonverbal memory tests (Tulsky, Chiaravalloti, Palmer, & Chelune, 2003). On the other hand, it may be acknowledged that facial memory may require a special (unique) type of visual processing. For example, research into face recognition ability has found that recognition of faces activates a cortical region in the brain specialised to the perception of faces, known as the Fusiform Face Area (Kanwisher & Yovel, 2006; Tsao, Freiwald, Tootell, & Livingstone, 2006). Consequently, memory for faces would not necessarily be expected to be meaningfully correlated with intelligence, and some researchers contend that it is not (Bowles et al., 2009; Shakeshaft & Plomin, 2015; Wilmer et al., 2014).

The empirical results on the association between intelligence and face memory are inconsistent. For example, Gignac et al. (2016) reported a positive, significant association between intelligence, as measured by multiple subtests, and the CFMT, a measure of face memory (r = 0.35; N = 211). They interpreted their findings as supportive of an association between intelligence and face memory ability. By contrast, Richler, Wilmer, and Gauthier (2017) failed to find a significant association between intelligence (Matrices from the Wechsler Abbreviated Scale of Intelligence) and face memory (CFMT), based on a community sample (N = 279). It is noted that Richler et al.'s measurement of intelligence would not be considered good or excellent, based on Gignac and Bates (2017) guidelines, whereas several studies that did use good or excellent intelligence measurement (i.e., several subtests; multiple dimensions) did find a significant and positive association between intelligence and face memory ability (Gignac et al., 2016; Herlitz & Yonker, 2002; Zhu et al., 2010). Thus, a meta-analysis may be useful to help synthesise the empirical results and possibly identify intelligence measurement quality as a positive moderator of the effect between intelligence and face processing ability.

In addition to identity, people glean lots of information from faces, such as eye gaze, attractiveness, trustworthiness, speech decoding, first impression and emotion. These face processing abilities are beyond the scope of this review; however, it is important to review briefly the recent research on individual differences in face emotion recognition and intelligence. Face emotion recognition is the ability to accurately and efficiently recognise facial expressions (Palermo, Connor, Davis, Irons, & McKone, 2013). Empirically, individual differences in face emotion recognition have been found to be associated positively with intelligence (Borod et al., 2000; Connolly et al., 2019; Hildebrandt, Sommer, Schacht, & Wilhelm, 2015). Furthermore, a meta-analysis estimated the association between face emotion recognition and cognitive abilities at r ≈ 0.19 (Schlegel et al., 2019); however, the correlations were not corrected for measurement error and range restriction, nor was intelligence measurement quality taken into consideration. Thus, the reported 0.19 correlation is likely a substantial underestimate.

It is plausible to postulate that face processing abilities facilitate successful adaptation and involves goal/problem solving using cogntive-perceptual abilities. Stated alternatively, we define face processing ability as an adaptive cognitive-perceptual ability to detect, match or recognise facial identity and facial expressions. Such a definition aligns with abstract definitions of intelligence that focus upon successful environmental adaptation (McIntosh et al., 2012; Pintner, 1923; Sternberg, 1997).

Beyond theoretical similarities, face processing tests have characteristics that align with operational measures of cognitive abilities. That is, in more operational terms, face processing abilities can be defined as an individual's ability to complete a novel, standardised visual task involving faces and for which there is veridical scoring. For example, the CFMT is a standardised visual face task that includes novel problems/stimuli and is scored objectively; i.e., in line with conventional operational definitions of intelligence (Gignac, 2017).