Sunday, December 11, 2022

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, 2004aSherif, 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 15).
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, 2020Haidt, 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., 2014Schwartz & 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., 2008MacFarquhar, 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., 2017Gollwitzer, 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


Saturday, December 10, 2022

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.


Friday, December 9, 2022

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


Thursday, December 8, 2022

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).


Both higher and lower than average exposure to male sex hormones increases same sex attraction in male mice and in men

Carving the Biodevelopment of Same-Sex Sexual Orientation at Its Joints. Doug P. VanderLaan, Malvina N. Skorska, Diana E. Peragine & Lindsay A. Coome. Archives of Sexual Behavior, Aug 12 2022. https://link.springer.com/article/10.1007/s10508-022-02360-1

Abstract: Sexual orientation is a core aspect of human experience and understanding its development is fundamental to psychology as a scientific discipline. Biological perspectives have played an important role in uncovering the processes that contribute to sexual orientation development. Research in this field has relied on a variety of populations, including community, clinical, and cross-cultural samples, and has commonly focused on female gynephilia (i.e., female sexual attraction to adult females) and male androphilia (i.e., male sexual attraction to adult males). Genetic, hormonal, and immunological processes all appear to influence sexual orientation. Consistent with biological perspectives, there are sexual orientation differences in brain development and evidence indicates that similar biological influences apply across cultures. An outstanding question in the field is whether the hypothesized biological influences are all part of the same process or represent different developmental pathways leading to same-sex sexual orientation. Some studies indicate that same-sex sexually oriented people can be divided into subgroups who likely experienced different biological influences. Consideration of gender expression in addition to sexual orientation might help delineate such subgroups. Thus, future research on the possible existence of such subgroups could prove to be valuable for uncovering the biological development of sexual orientation. Recommendations for such future research are discussed.


Notes

We recognize that “gynephilia” and “androphilia” are terms that denote sexual attraction toward adults, and as such these terms do not apply in the case of those who are sexually oriented toward minors (i.e., children and/or adolescents). In studies of those sexually oriented toward minors, the terms “homosexual” and “heterosexual” have been used to denote attraction to the same- and opposite-sex, respectively (e.g., Blanchard et al., 2020). We, however, did not choose to use these terms because here we review to a greater extent the considerable cross-cultural literature on transgender, nonbinary, or third gender populations for which gynephilia and androphilia have been the terms typically employed.

By using same-sex sexual behavior as a proxy for sexual orientation, Ganna et al. (2019) were able to maximize inclusion of genetic data from 477,522 individuals. Importantly, the authors also reported supplemental analyses on genetic associations when using sexual attraction, fantasy, and identity measures available for subsets of participants, and the results generally aligned with those found when using the proxy behavioral measure in the full sample.

Digit ratio has been a particularly contentious biomarker given debate regarding the mechanisms that influence this trait, idiosyncratic findings across left- and right-hand 2D:4D, and the possibility that sex differences in 2D:4D are simply a consequence of allometry (i.e., reflect sex differences in physical size). For further discussion of these issues and data analyses indicating left and right 2D:4D are most appropriately analyzed separately from one another as well as from measures of physical size (e.g., hand length, height), we refer readers to Skorska et al. (2021a).

Gender expression has also often been regarded as a psychological marker of pre-/perinatal hormone exposure (Hines, 2020), but it has also been linked to the genetic and immunological mechanisms discussed in this section (e.g., Alanko et al., 2010; Blanchard, 2018; Coome et al., 2018). We refer readers to the section entitled “One Biodevelopmental Pathway or Many?” for in-depth discussion regarding the importance of gender expression to uncovering the bases of sexual orientation biodevelopment.

A large Dutch national probability sample reported a fraternal birth order effect among women belonging to female-female civil unions, suggesting birth order may be related to female sexual orientation as well (Ablaza et al., 2022); however, caution is warranted in interpreting this finding given prior inconsistencies, and primarily null findings, in birth order studies of female sexual orientation (Blanchard, 2022; Bogaert & Skorska, 2011; Semenyna et al., 2022).

Neuroscience research on transgender populations has often not included information regarding participants’ sexual orientations or reported that participants’ sexual orientations were heterogeneous (for a recent review, see Frigerio et al., 2021). Here, in addition to comprehensive review articles, we only cite examples of individual neuroscience studies that either reported on transgender samples of individuals described as being sexually oriented toward the same sex assigned at birth or that explicitly examined transgender participants’ brain features in relation to varying sexual orientation.

In the study by Rahman et al. (2020), prevalence rates varied depending on how sexual orientation was defined. For example, if sexual orientation was defined by heterosexual, bisexual, and homosexual identity, then prevalence rates were estimated at 90.7, 7.2, and 2.1% for women and of 90.0, 5.1, and 4.9% for men. In contrast, if defined by sexual attractions that were predominantly not toward the same sex, moderately toward the same sex, or predominantly toward the same sex, then prevalence rates were estimated at 66.2, 27.3, and 6.5% for women and 82.6, 10.2, and 7.2% for men.

To further verify the meaningfulness of the subgroups derived from their latent profile analysis, Swift-Gallant et al. (2019a) compared the subgroups on several psychological variables, including gender expression. Details regarding subgroup differences in gender expression are described in the subsection of the present article entitled “Research on Gender Expression and Sexual Orientation Biodevelopment.”.

Industrial policy in China: Little evidence that the Chinese government consistently “picks winners.” Firms’ ex-ante productivity is negatively correlated with subsidies, & subsidies appear to have a negative impact on productivity growth afterwards

Picking Winners? Government Subsidies and Firm Productivity in China. Lee G. Branstetter, Guangwei Li & Mengjia Ren. NBER Working Paper 30699, December 2022. DOI 10.3386/w30699


Abstract: Are Chinese industrial policies making the targeted Chinese firms more productive? Alternatively, are efforts to promote productivity undercut by efforts to maintain or expand employment in less productive enterprises? In this paper, we attempt to shed light on these questions through the analysis of previously underutilized microdata on direct government subsidies provided to China’s publicly traded firms. We categorize subsidies into different types. We then estimate total-factor productivity (TFP) for Chinese listed firms and investigate the relationship between these estimates of TFP and the allocation of government subsidies. We find little evidence that the Chinese government consistently “picks winners”. Firms’ ex-ante productivity is negatively correlated with subsidies received by firms, and subsidies appear to have a negative impact on firms’ ex-post productivity growth throughout our data window, 2007 to 2018. Neither subsidies given out under the name of R&D and innovation promotion nor industrial and equipment upgrading positively affect firms’ productivity growth. On the other hand, we find a positive impact of subsidy on current year employment, both for the aggregated and employment-related subsidies. These findings suggest that China’s increasingly prescriptive industrial policies may have generated limited effects in promoting productivity.


Estimating data corruption by publication selection bias: The presence of an effect in economics decreased from 99.9% to 29.7% after adjusting for the bias: in psychology, 98.9% → 55.7%; in medicine, 38.0% → 27.5%

Footprint of publication selection bias on meta-analyses in medicine, economics, and psychology. František Bartoš, Maximilian Maier, Eric-Jan Wagenmakers, Franziska Nippold, Hristos Doucouliagos, John P. A. Ioannidis, Willem M. Otte, Martina Sladekova, Daniele Fanelli, T.D. Stanley. https://arxiv.org/pdf/2208.12334.pdf

Abstract: Publication selection bias undermines the systematic accumulation of evidence. To assess the extent of this problem, we survey over 26,000 meta-analyses containing more than 800,000 effect size estimates from medicine, economics, and psychology. Our results indicate that meta-analyses in economics are the most severely contaminated by publication selection bias, closely followed by meta-analyses in psychology, whereas meta-analyses in medicine are contaminated the least. The median probability of the presence of an effect in economics decreased from 99.9% to 29.7% after adjusting for publication selection bias. This reduction was slightly lower in psychology (98.9% → 55.7%) and considerably lower in medicine (38.0% → 27.5%). The high prevalence of publication selection bias underscores the importance of adopting better research practices such as preregistration and registered reports.




Wednesday, December 7, 2022

The morning chronotype has significant links with political conservatism, most robustly in Switzerland; the morning chronotype may have links to liberalism in Russia

Linking sleep, political ideology, and religious observance: a multi-national comparison. Aleksander Ksiazkiewicz, Fatih Erol. International Journal of Public Opinion Research, Volume 34, Issue 3, Autumn 2022, edac020, https://doi.org/10.1093/ijpor/edac020

Abstract: Sleep is fundamental to life and essential to one’s health behavior, scholastic achievement, and work performance. Recent years have seen an increase in empirical investigations incorporating sleep research into political science. This study complements existing sleep-politics studies by examining the associations between chronotype (a person’s preferred time to sleep and wake up) and attitudinal and behavioral political outcomes (left–right ideology and social conservatism proxied by religious service attendance). We analyze representative samples from 10 national contexts (Finland, Greece, Ireland, Mexico, the Netherlands, New Zealand, the Philippines, Russia, South Korea, and Switzerland) to test our hypotheses. The results demonstrate that morning chronotype has significant links with political conservatism in six national contexts depending on model specification (most robustly in Switzerland). Unexpectedly, the morning chronotype may have links to liberalism in three other countries depending on model specification (most robustly in Russia). The results for religious observance are more uniform, indicating a link between morningness and greater religious observance across all cases in many specifications (excepting a reversed relationship in New Zealand in some models). Urbanization, seasonal effects, geographical characteristics, and religious denominations are explored as potential confounders.



Social media does not affect political participation directly, but rather through metacognitive processes such as overestimating one’s knowledge; social media news use has a negative effect on objective knowledge

Dreston, Jana H., and German Neubaum. 2022. “Exploring the Link Between Social Media News Use, Subjective Political Knowledge and Voting Intentions.” PsyArXiv. December 6. doi:10.31234/osf.io/nf9dx

Abstract: Citizens are expected to make informed voting decisions. However, research indicates that political knowledge gained through media use does not relate to political participation such as voting. In times of decreasing voter turnout and increasing consumption of news via social media, it is important to study how these two relate. Recent research underscores the fact that, in reality, social media does not increase objective political knowledge, but rather the metacognition of subjective knowledge. In turn, this metacognition might foster political participation. Nevertheless, we do not know which forms of social media use foster users’ perception of being politically knowledgeable. A pre-registered, cross-sectional, pre-election survey (N = 1,223) showed that active forms of social media news use relate more strongly to subjective knowledge than incidental exposure. All forms of usage showed no or even negative associations with objective political knowledge. While none of the forms of social media news use exerted any direct effect on voting intentions, both subjective and objective knowledge are related to increased voting intentions. This study corroborates that social media does not affect political participation directly, but rather through metacognitive processes such as estimating one’s knowledge. However, both objective and subjective knowledge are essential for one’s voting intentions. By showing that active and passive forms of social media use affect knowledge differently, this study provides preliminary and nuanced insights into the ultimate role these technologies can play in democratic processes.



People usually like each other more when they feel they are accurately known by their counterparts, even in their negative traits

Does accuracy matter? A review of the consequences of accurate personality impressions as a function of context and content. Lauren Gazzard Kerr, Lauren J Human. Social and Personality Psychology Compass, December 3 2022. https://doi.org/10.1111/spc3.12718

Abstract: What are the implications of seeing others as they see themselves? That is, does viewing others accurately have consequences, positive or negative, to relationship evaluations (e.g., liking)? To address this, the current article provides a comprehensive review of the personality accuracy literature and discusses related work from the self-verification and empathic accuracy domains. We specifically explore whether the consequences of accurate perceptions could be influenced by two key categories of moderators: the context of the impression (e.g., high vs. low stakes) and content of the impression (e.g., desirability of traits). Lastly, we discuss future directions to aid our understanding of this question.


Consumers ordinarily avoid ugly products, but when from a luxury brand, they choose distinctively ugly products as often as attractive ones, not despite their ugliness but due to their ugliness & resulting ability to signal luxury

Hideous but worth it: Distinctive ugliness as a signal of luxury. Ludovica Cesareo, Claudia Townsend & Eugene Pavlov. Journal of the Academy of Marketing Science, Dec 2 2022. https://link.springer.com/article/10.1007/s11747-022-00913-3

Abstract: Long-standing wisdom and academic research consistently agree that consumers choose attractive products and avoid ugly ones. And yet, multiple luxury brands successfully sell distinctively ugly products. This research provides an explanation, identifying distinctive ugliness as a signal of luxury and examining its impact on consumer choice. We explore this in seven studies, including a field study, a market pricing analysis, and five controlled laboratory experiments, three with consequential behavioral measures, incorporating a variety of fashion products, brands, aesthetic manipulations, and audiences. When products are from a non-luxury brand, consumers choose the attractive option and avoid the ugly. However, when from a luxury brand, consumers choose distinctively ugly products as often as attractive ones, not despite their ugliness but due to their ugliness and resulting ability to signal luxury. As such, brand prominence offers a boundary condition, as both a loud logo and distinctive ugliness serve to signal. Implications for both luxury and non-luxury brands are discussed.

Popular version: Ugly Fashion Is In | Lehigh University

Tuesday, December 6, 2022

Exploring gender and the Dark Tetrad as predictors of catfishing perpetration

Catching the catfish: Exploring gender and the Dark Tetrad as predictors of catfishing perpetration. Cassandra Lauder, Evita March. Computers in Human Behavior, December 5 2022, 107599. https://doi.org/10.1016/j.chb.2022.107599

Abstract: Catfishing, the act of deceiving and exploiting another person online, can have significant negative impact on the target. To date, limited research has explored individual differences in perpetration of catfishing. We address this paucity by adopting an evolutionary psychology theoretical framework (the “cheater strategy” hypothesis) and exploring the utility of gender and the Dark Tetrad personality traits of psychopathy, sadism, Machiavellianism, and narcissism to predict catfishing perpetration. A sample of 664 participants (55.8% men, 40.3% women) with an average age of 28.84 years (SD = 9.60) were recruited via social media and completed an anonymous online questionnaire which comprised measures of personality and catfishing behaviours. Combined, the variables explained 62.6% of variance in catfishing perpetration. Results partially supported the hypotheses, with only psychopathy, sadism, and narcissism emerging as positive predictors of catfishing perpetration. Findings of the current study indicate that evolutionary psychology may be a useful theoretical framework when exploring antisocial online behaviours. Further, these findings provide crucial information regarding the psychological profile of a “catfish” and may have important practical implications by informing the prevention and management of this online behaviour.