The attitudes that we believe a good person should or shouldn’t hold are tremendously diverse & reliably connected to a sense of self; also, we are very good at making peace with our own "bad" attitudes

A Good Person Shouldn’t Feel This Way: Moralized Attitudes, Identity, and Self-Esteem. Pierce Ekstrom, Calvin Lai. Collabra: Psychology (2022) 8 (1): 36344. Jun 16 2022. https://doi.org/10.1525/collabra.36344

Abstract: Moralized attitudes are the attitudes that people construe as matters of right and wrong. In this study, we examine how moralized attitudes relate to how people evaluate themselves using the Attitudes, Identities, and Individual Differences (AIID) dataset—a survey of over 200,000 individuals asked to report their attitudes in one of 95 domains. In pre-registered analyses that were based on exploratory analyses of a subset of the data, we found that the specific attitudes that people moralize differ greatly from individual to individual and that moralized attitudes are more central to one’s identity than non-moralized attitudes. We also examined whether mental conflict between identity-central attitudes and gut feelings about the corresponding attitude objects would be related to lower self-esteem, finding mixed and weak evidence supporting that claim. Together, our findings indicate that the attitudes that people moralize are tremendously diverse and are reliably connected to a sense of self. At the same time, peoples’ self-esteem may be resilient to specific instances in which their gut feelings fall short of the attitudes that are central to their identity.

Keywords:Morality, Attitudes, Self-Esteem, The Self

Discussion

Summary of Results

Our analyses yielded three key results. First, our descriptive analyses suggest that the attitudes that people perceive to be matters of right and wrong are extraordinarily diverse. Some participants saw room for debate concerning attitudes and behavior that most people would consider to be unequivocally moral. For example, a handful of participants “agreed” or “strongly agreed” that “Because of my personal values, I believe that making negative judgments about giving is acceptable” (6.3% of those who saw the question). At the same time, some participants passed stark judgment on some attitudes and behavior that most people would perceive as matters of taste. For example, some participants “strongly agreed” that “Because of my personal values, I believe that making negative judgments about Harry Potter is unacceptable” (8.5% of participants who saw the question). The descriptive statistics presented in Figure 4 illustrate this diversity. The end result is that there are probably very few (if any) attitudes or behaviors that all people would agree to be moral, immoral, or morally neutral. This diversity is in its own right an interesting characteristic of moral psychology, but it also suggests that researchers interested in how people think and feel about morally charged stimuli cannot always safely assume that their participants will construe a given stimulus in moral (or non-moral) terms.

Second, our results yield robust support for the Morality-Identity hypothesis. We found that when participants perceived their attitudes to be connected to their personal values, they were more likely to identify with those attitudes. They were also more likely to identify with the targets of those attitudes implicitly and explicitly (e.g., to see Harry Potter or Christians as part of their self-concept rather than to merely identify as someone who likes Harry Potter or Christians). This evidence is consistent with our prediction that people would perceive their idiosyncratic beliefs about right and wrong as a defining feature of who they are.

Finally, our results yield weak and inconsistent support for the Identity Rubric hypothesis. We find that our participants did not consistently evaluate themselves on the basis of whether their gut feelings were consistent with the “actual” attitudes that they cherish as defining features of their identity. Our first analysis found some evidence suggestive of this phenomenon; when participants reported negative gut feelings about targets with which they identified, they also reported slightly lower self-esteem. However, this may be because negative gut feelings about identity-central targets are, in and of themselves, negative gut feelings about the self. For example, negative gut feelings about Christians, Jews, European Americans, and African Americans could easily translate to negative self-evaluations among people who are themselves Christian, Jewish, European American, or African American—regardless of whether they think it is morally “wrong” or “acceptable” to judge people from these groups positively. Other models were more directly inconsistent with our predictions. When participants reported negative gut feelings about targets that they thought they ought to like, their reported self-esteem was almost identical to that of participants whose gut feelings and explicit attitudes were perfectly consistent. We observed a similar null effect for implicit identification with targets and for every other indicator of attitude strength that we analyzed. Participants generally did not have lower self-esteem when their gut feelings were inconsistent with moralized, important, certain, or extreme attitudes.

Based on these findings, our initial theory requires significant revision. We hypothesized that moralized attitudes inform self-evaluation because they structure individuals’ self-concepts. Although we did find that moralized attitudes were relatively central to participants’ identities (consistent with the Morality-Identity hypothesis), we found little if any connection between participants’ self-esteem and the extent to which their gut feelings were consistent with those attitudes. Participants did not report feeling meaningfully worse about themselves when they were attracted to things they believed they shouldn’t like or repulsed by things they believed they ought to accept.

Limitations

That said, our study has several limitations that complicate this test of our theory.

Mixed evidence for key measures’ validity. The evidence from our validity study was mixed and weaker than we had anticipated. The most widely used measure of attitude moralization (moral conviction) only sometimes predicted responses to the AIID items. For one item, the relation was what we predicted for both positive and negative attitudes, though those relations were weak. For the other three items, the relation was only present either for positive or negative attitudes. We encountered similar problems with the AIID measures of attitude identity centrality. Despite this limitation, we remain confident in our conclusions for three reasons.

First, despite evidence that some measures were more valid than others and more or less valid for positive versus negative attitudes, we find no evidence that our results depended on which AIID items we used to assess attitude moralization or attitude identity centrality. Second, although the measures we used to assess the validity of the AIID items are certainly more widely used and better established as measures of the target constructs, they are still just measures, not perfect reflections of the constructs of interest. Given that the AIID measures each make explicit reference to their respective target constructs (e.g., “personal values,” decisions about what is “wrong” or “acceptable,” whether reactions are “important to” and “inconsistent with” the “self-concept”), these measures may capture aspects of moralization and identity centrality that other measures do not. Finally, we found converging evidence for our conclusions with analyses that does not rely on the AIID measures. To do this, we used the validation study to conduct an additional un-pre-registered test of the Morality-Identity hypothesis. Our theory would predict that Skitka and colleagues’ moral conviction measure would be related to Luhtanen and Crocker’s (1992) measure of identity centrality for the 20 attitude targets in this study. It was (b = 0.32, 95% CI: [0.27, 0.37], p < 0.001), even controlling for the effect of importance. In addition, the analyses described in Table 9 suggest that alternative measures of moralization or identification would probably not yield any stronger support for the Identity Rubric hypothesis. Participants’ self-esteem was basically unmoved regardless of how severely their gut feelings contradicted certain, important, extreme, “identity-central,” or “moralized” attitudes. Even if we have failed to find a direct, precise measure of moralization or identity centrality, surely at least one of these indices of attitude strength would at least be correlated with such a measure. If the Identity Rubric hypothesis were true, then, it seems unlikely that all of these tests would be so uniformly null.

Still, our validity study offers an important caveat for our own and future work. We cannot safely assume that when people say that their attitude is moral or important to who they are, they will also say that the opposite attitude is immoral or anathema to their self-concept. Although we might say people “moralize” an attitude when they judge it to be desirable, acceptable, or wrong, these judgments probably do not lie along a single dimension.

Narrow threats to the self and a broad measure of self-esteem. In hindsight, our predictions may have presumed self-esteem to be too fragile. The Rosenberg Self-Esteem scale is intended to measure self-esteem as a global trait. Meanwhile, we analyzed participants’ gut feelings and attitudes toward only one or two targets. Contradictions so narrow and specific may be insufficient to impact global trait-level self-esteem in a meaningful way.

On the one hand, narrower measures of self-esteem might prove to be more malleable. People’s thoughts and behavior during a particular event or time period might affect how they feel about themselves during that specific time. On the other hand, more frequent, numerous, or chronically salient contradictions between people’s gut feelings and the attitudes they believe to be appropriate might have a stronger impact on self-esteem than the one or two attitudes we were able to assess.

Some scholars have conducted experiments to confront participants with moral failures and trace their impact on self-evaluations (see Wojciszke, 2005). These participants often end these experiments feeling just fine. These results are consistent with decades of social-psychological research have documented individuals’ capacity to rationalize their behaviors and what they might consider to be failures to practice what they preach, and scholars have often argued that the point of this rationalization is to protect people’s positive self-images (e.g., Aronson, 1969; Kunda, 1990).

Small wonder, then, that slight divergences between “gut feelings” and “actual” attitudes in a single attitude domain failed to leave a dent in our participants’ self-esteem. Our study leaves open the possibility that some attitude-inconsistent feelings or behavior may be more uncomfortable than others, and that measures of more specific or shorter-term self-evaluation might be more likely to change in the wake of these behaviors. Future work might find that when people behave in ways that are clearly at odds with multiple important, moralized, or identity-central attitudes, they briefly feel worse about themselves.

Correlational Design. We have tested a multi-step causal framework with a correlational dataset, which cannot permit strong causal inferences. For example, we cannot know whether people come to identify with certain attitudes because they see them in moral terms, moralize attitudes because they are central to their identity, or come to moralize and identify with their attitudes simultaneously as a part of some larger process. The reality is probably a combination of these possibilities. For example, someone may come to identify with their pro-choice attitude because they see abortion access as a moral issue and also come to moralize their attitude toward Britney Spears, 50 Cent, or Harry Potter because they identify as a fan. Regardless, our evidence suggests that these processes are connected. At the same time, the latter part of the model proposed in Figure 1 is now less plausible, as our correlational evidence was inconsistent with the Identity Rubric hypothesis.

Homicide offenders have less pronounced psychopathy, sadism, and criminal recidivism compared to other violent offenders and non-violent offenders

How dark is the personality of murderers? Psychopathy, Machiavellianism, and sadism in homicide offenders. Janko Međedović, Nikola Vujičić. Personality and Individual Differences, Volume 197, October 2022, 111772. https://doi.org/10.1016/j.paid.2022.111772

Abstract: Dark personality traits describe amoral and antisocial behavioral dispositions and are often described by psychopathy (i.e., interpersonal, affective, lifestyle, and antisocial characteristics), narcissism, Machiavellianism, and sadism. These traits are related to various socially detrimental behavioral outcomes, including criminal behavior and delinquency. Furthermore, psychopathy is frequently related to homicide, both in scientific and in popular literature; however, the empirical data on the link between psychopathy and other dark traits with homicide is still scarce. We examined self-reported psychopathy, Machiavellianism, sadism, and the indicators of criminal recidivism (number of offences, number of lawful sentences and penal recidivism) in a sample of male homicide offenders (N = 46), other violent offenders (N = 82), and non-violent offenders (N = 119). The results showed that homicide offenders have less pronounced psychopathy, sadism, and criminal recidivism compared to the other two groups – the differences were particularly evident in comparison to the group of non-homicide violent offenders. There were no statistically significant differences in Machiavellianism. Our data cast a doubt on the widely acknowledged link between psychopathy and murder. The findings can be explained largely by the fact that homicide is a heterogeneous criminal offence; while it is possible that psychopathy and other dark traits may be linked to some types of homicide, this link cannot be established for homicide in general.

Introduction

In recent decades, there has been a growing interest in personality dispositions toward amoral and antisocial behavior. A comprehensive model of these traits is labeled as the Dark Tetrad (Međedović & Petrović, 2015; Paulhus, 2014) and encompasses traits like narcissism, Machiavellianism, psychopathy and sadism. Narcissism depicts entitlement, superiority and an inflated view of self (Raskin & Terry, 1988); Machiavellianism represents an attitude which rationalizes and justifies the use of other people for one's self interest (Christie & Geis, 1970), while sadism is based on the aberration in emotional processes where one feels positive emotions (enjoyment) when hurting others or watching others in distress (O'Meara et al., 2011). The trait that has the longest history of scientific inquiry is psychopathy – it represents a behavioral syndrome itself that consists of several narrow traits. There are various models of psychopathy, one of the most prominent is proposed by Hare and collaborators and it defines psychopathy as a syndrome of interpersonal (egoistic and manipulative) behavior, affective characteristics (lack of emotional empathy, fear, and guilt), lifestyle characteristics (impulsiveness, lack of long-term plans, sensation seeking) and antisocial behavior (Hare & Neumann, 2008). The dark personality traits are related to various socially undesirable outcomes like cheating (Esteves et al., 2021), bullying (van Geel et al., 2017), violence (Pailing et al., 2014), and others.

Since the Dark tetrad traits are based on selfishness, lack of compassion, and the tendency to manipulate or hurt others, it is not surprising that they are important predictors of antisocial behavior and delinquency (Chabrol et al., 2009; Međedović & Kovačević, 2020). The relation between dark traits and criminal behavior is mostly researched in the context of psychopathy and it is most firmly established for this trait. Psychopathy is positively related to the number of violent and-nonviolent offences, substance abuse, contact with police and criminal courts, and others (Vaughn & DeLisi, 2008). It is negatively associated with educational levels, long-term jobs, the age of the first offence and first lawful sentence (Žukauskienė et al., 2010). Finally, there is reliable data that psychopathy can positively predict criminal recidivism (Leistico et al., 2008; Međedović et al., 2012a, Međedović et al., 2012b; Salekin, 2008). Due to the fact that recidivists commit most criminal offences (e.g., Someda, 2009), this association has both scientific and practical implications.

Comprehensive taxonomies of the dark traits, including the Dark tetrad, have rarely been examined in a criminological, penal, or forensic context; hence, the research on the links between the dark traits and the type of criminal offence is still very scarce. However, there is a common belief that psychopathy is related to homicide offences, both in popular culture and media (Lilienfeld & Arkowitz, 2007) and in scientific context (Fox & DeLisi, 2019). Indeed, there is a large amount of data showing that murders committed by psychopathic individuals have some specific characteristics. For example, homicides perpetrated by individuals with elevated psychopathy are more instrumental in nature (i.e., premeditated and planned), deliberate, and to a lower level motivated by affective reactions (Woodworth & Porter, 2002). Furthermore, the data show that psychopathic murderers are more frequently not close to their victim and deny their charges (Häkkänen-Nyholm & Hare, 2009). The existing data suggests that a positive link between psychopathy and reoffending exists in homicide perpetrators as well (Laurell & Dåderman, 2005). Finally, psychopathy is more pronounced in the group of offenders who have committed sexual homicides (Porter et al., 2003) and it is positively related to the criminal relapse in this group of offenders (Myers et al., 2010).

Therefore, it seems that homicide offences committed by psychopathic individuals have some specific qualities. However, is there a connection between psychopathy and murder in general - are individuals with elevated psychopathy traits more prone to commit homicide than other types of offences? The existing evidence suggests that the answer is once again positive. A recent meta-analysis showed large effect sizes of the link between psychopathy and homicide (Fox & DeLisi, 2019). However, the data is not unambiguous. There are studies that have found that psychopathy traits (i.e., lifestyle and interpersonal characteristics) are expressed to a lower extent in a group of homicide offenders compared to non-homicide repeated offenders (Sherretts et al., 2017); there were no differences between murderers and first-time non-homicide offenders in this study. The authors concluded that psychopathic traits are more likely to be found in persistent offenders (i.e., recidivists), characterized by criminal careers, than in homicide offenders.

Current empirical literature on the links between the dark traits (all but psychopathy) and homicide is quite scarce. This is in contrast with the high heuristic and practical importance of the topic: by establishing these links, personality psychologists can achieve a more in-depth understanding of the dark side of the human personality; on the other hand, forensic practitioners can achieve better understanding of the murderers' personality and provide more accurate models for predicting homicide offences. The goal of the present research is to provide a more detailed examination of the relation between the dark personality characteristics and homicide. We believe that there are three main contributions of the present study over the existing ones. Firstly, we analyzed not only psychopathy, but also Machiavellianism and sadism in homicide offenders (Narcissism unfortunately was not included in the list of variables collected in this study); the data on the two latter traits in this context are very scarce. Sadism may be especially significant in the context of homicide offences: murders are violent offences and sadism represents particularly volatile and destructive form of aggressiveness – the one which carries positive emotions as a reinforcement for the perpetrator (Međedović, 2017). Hence, it can be assumed that sadism may be relevant in understanding personality characteristics of homicide offenders. Secondly, we compared not only homicide to non-homicide offenders in this study. Homicide is just one of the violent offences, however, it may differ from other forms of violent offences; therefore, we analyzed homicide offenders, non-homicide violent offenders and non-violent offenders. Finally, we included the measures of criminal recidivism in the study.

Our analyses were guided by several hypotheses. Note that we base our hypotheses on previous research on psychopathy in a forensic and criminological context since there are no data regarding other traits. However, we believe that the same hypotheses can be set for all examined dark traits in the context of the present research (i.e., examining the relations between dark traits, the type of offence and criminal recidivism): psychopathy (especially multidimensionally-measured psychopathy as it was assessed in the present study) shares a substantial portion of variation with other dark traits (Chabrol et al., 2009; Međedović & Petrović, 2015), and this shared variation is based on the lack of empathy and interpersonal antagonism (Dinić et al., 2021). Therefore, we expected all the dark traits to be positively related to criminal recidivism. Regarding the relation between the dark traits and homicide, two contrasting hypotheses can be made: leaning on meta-analytic results (Fox & DeLisi, 2019) we could expect that psychopathy is more highly pronounced in homicide offenders than the other two groups. On the contrary, the results of Sherretts et al. (2017) suggest that the dark traits may be less expressed in the homicide offenders compared to other groups of offenders.

More intelligent individuals reported lower symptom counts, less chronic conditions, lower rates of depression, less frequent visits to the doctor, and fewer limitations in their daily lives

An intelligent mind in a healthy body? Predicting health by cognitive ability in a large European sample. Jonathan Fries Jakob Pietschnig. Intelligence, Volume 93, July–August 2022, 101666. https://doi.org/10.1016/j.intell.2022.101666

Highlights

• We demonstrate that cognitive ability predicts various aspects of health in adults over 55 years.

• Effect sizes are modest, but may have a considerable impact on population level.

• The most closely g-related construct (mathematical reasoning) predicted indicators of health most consistently.

• Environmental and behavioral risk factors do not play a meaningful role for the intelligence-health association.

Abstract: Intelligence has been consistently demonstrated to be a predictor of health outcomes. However, the exact mechanisms are subject of debate. Environmental and behavioral risk factors have been suggested to affect the intelligence-health association, but the available literature has mostly focused on children and young adults. Here, we aimed to investigate the intelligence-health association in older adults. We analyzed data from the Study of Health and Retirement in Europe (SHARE), a representative longitudinal survey in which participants above 50 years of age (N range = 10,000-30,000+) were interviewed in seven waves from 2004 to 2017. Indicators of physical and mental health (e.g., number of symptoms; self-reported depression) were associated with cognitive function variables (mathematical reasoning, word recall, verbal fluency) which were used as proxy measures for intelligence. Behavioral and environmental risk factors (e.g., legal drug consumption, physical inactivity, work environment) were examined as potential moderator variables for the intelligence-health association. More favorable health outcomes were modestly, but consistently associated with higher cognitive ability across variables (r range = |0.13|-|0.29|). Mixed-model Poisson regression analyses showed a reduction of 11% in self-reported symptom numbers with each unit increase in mathematical reasoning. Environmental and behavioral risk factors exhibited mostly trivial moderating effects on the intelligence-health association. Our findings reveal a positive association of intelligence and health in a representative longitudinal European sample. Environmental and behavioral risk factors offered little explanatory value for this association, suggesting a different underlying mechanism such as a general fitness factor that affects both intelligence and health.

4. Discussion

Here, we investigated the associations of cognitive ability and health in a representative longitudinal sample of EU residents above the age of 50. Specifically, we examined various indicators of physical and mental health, along with behavioral and environmental risk factors as well as cognitive functioning. In our analyses, a consistent pattern emerged: cognitive ability was associated with the rate of chronic illnesses, symptoms, limitations in daily activities, and other indicators of health. This was true both for individual cognitive ability indicators and the g-factor approximation we calculated from these indicators. Environmental and behavioral risk factors, such as smoking, alcohol consumption, BMI, or work environment showed little moderating effects. Physical inactivity was the only covariate that exhibited moderating effects on the intelligence-health association.

The strength of the associations was small to moderate in size. Such effects have practical meaning in the short term but can be considered even more meaningful on a larger scale and in the long term. Intelligence and health are relevant to every single human. Thus, even small differences cumulate over time and can have tremendous consequences not only on an individual but perhaps more importantly, a societal level (Funder & Ozer, 2019). Previous research has reported effect sizes of similar strength (for an overview, see Deary et al., 2021). The direction of effects was as expected: higher cognitive ability was positively related to more favorable health outcomes. More intelligent individuals reported lower symptom counts, less chronic conditions, lower rates of depression, less frequent visits to the doctor, and fewer limitations in their daily lives.

Notably, the largest effects were observed for self-perceived health. This variable encompasses a broad spectrum of health and may paint a more comprehensive picture than the other – more specific – indicators in the SHARE dataset. This makes sense because self-perceived or self-rated health is considered a reliable and robust predictor of health and mortality and has often been used in many studies on aging (e.g., French, Sargent-Cox, & Luszcz, 2012; Machón, Vergara, Dorronsoro, Vrotsou, & Larrañaga, 2016). This can be attributed to the multi-dimensional and dynamic properties of this variable. Interviewees may not be aware of every detrimental condition from which they suffer, because some conditions may remain undiagnosed. However, self-perceived health assesses a wide range of sensations and symptoms that may indicate countless physical and mental health conditions in clinical and pre-clinical stages (Benyamini, 2011). Because self-perceived health arguably captures a larger proportion of variance in overall health than any other available variable, this variable may yield therefore the best representation of the true effect in terms of the intelligence-health association in the SHARE dataset.

It is important to consider the meaning of different directionalities of the observed effects; specifically, whether higher cognitive ability is the consequence of better health or whether higher cognitive ability leads to better health over the course of life. Though the longitudinal design of this study encompassed only a later part in participants' lives and does not allow for causal inferences, our results indicate that cognitive ability has an age-independent effect on health. If the intelligence-health association were a mere consequence of deteriorating health, this correlation would be expected to be substantially attenuated when controlling for age, because age is a robust predictor of self-perceived as well as objectively measured health (e.g., Cullati, Rousseaux, Gabadinho, Courvoisier, & Burton-Jeangros, 2014; Rockwood, Song, & Mitnitski, 2011). However, this was not the case in our analyses, thus indicating that cognitive ability may be more likely to affect health instead of the other way round. Nonetheless, it seems plausible that good health also exerts positive effects on intelligence, thus representing a positive feedback loop in which higher cognitive ability facilitates better health which in turn helps maintaining high abilities.

Cancer on a general level did not correlate with any indicator of cognitive ability. This makes sense, because in contrast to many other health conditions (such as cardiovascular disease), a large number of cancer types does not depend on lifestyle factors and can therefore be expected not to be influenced by deliberated lifestyle choices. This observation is consistent with previous findings on this topic suggesting that lifestyle choices may not cause most types of cancer, with the notable exception of lung cancer (Calvin et al., 2017).

Across analyses, participants' numeracy scores proved to be the most robust predictor out of individual cognitive ability indicators: correlations decreased only to a minor degree when controlled for participant age, thus indicating that these associations were relatively unaffected by expectable age-related ability declines. In regression analyses, numeracy most reliably predicted health outcomes. This was expected because mathematical reasoning can be assumed to be more highly g-loaded than the other cognitive function indices in the SHARE dataset and is likely to estimate general cognitive ability more reliably (Peng et al., 2019). When controlled for age and education, the associations between cognitive ability and health were attenuated but remained meaningful and invariably maintained their respective directions which indicates the remarkable robustness of this effect. This was the case for correlations, as well as regression analyses.

Intelligence exhibited some associations with environmental and behavioral risk factors, but the directions of effects were not as clear-cut as with health. Higher cognitive ability was associated with lower rates of physical inactivity, which is consistent with previous findings (e.g., Wraw et al., 2018) that are suggestive of higher-intelligence individuals exhibiting higher ability and motivation to engage in vigorous physical activities (but see Kumpulainen et al., 2017, for contrasting results). Physical inactivity may be related to health literacy which is defined as the ability to gain access to information about health topics, as well as to interpret it and communicate about it. Health literacy is considered to be a prerequisite of informed health-related decision making (Berkman, Davis, & McCormack, 2010). Health literacy has been proposed as an important factor in the intelligence-health relationship because more intelligent individuals are assumed to obtain and process relevant health information more easily than less intelligent persons. Some research even suggests that health literacy is simply a context-specific component of general intelligence (Reeve & Basalik, 2014).

We had hypothesized BMI, smoking, and alcohol consumption to be negatively associated with cognitive ability. However, BMI was not meaningfully associated with cognitive ability, and neither was smoking. In previous accounts, smoking cessation has often been found to be linked with intelligence, but uptake of smoking has not (Taylor et al., 2003). Here, we only considered whether participants had ever smoked, but not if or when they quit which may have masked a potential association with cognitive ability.

Among risk factors, consumption of alcoholic beverages exhibited small, but meaningful positive correlations with cognitive ability, indicating that individuals with higher cognitive ability in fact consumed more alcohol than lower-ability persons. Previous studies have found similar correlations indicating more frequent overall alcohol consumption in subjects with higher childhood intelligence, but lower rates of problematic drinking behavior (Cheng & Furnham, 2013; Kanazawa & Hellberg, 2010). These findings may be attributed to several different causes. It has been suggested that more intelligent individuals might be better equipped to avoid adverse health effects of drinking (e.g., by reducing their intake when they become aware about an onset of problematic drinking behavior). Considering that our analyses indicated no correlation with unfavorable health outcomes, our results are generally in line with this interpretation. Others have speculated that the success in certain (particularly white-collar) professions may depend to some extent on the willingness to drink alcohol in social settings that are typically related to more cognitively challenging jobs (Batty et al., 2008). Another possibility is that more intelligent individuals are better able to veil their problematic consumption from others or even physicians tend to misattribute problematic behaviors in more intelligent persons to less socially undesirable causes (Just-Ostergaard et al., 2019).

Unsurprisingly, participants who worked in higher-risk work environments scored lower on the cognitive function variables. This could indicate that low-risk, white-collar jobs are selected for via intelligence, which would be in line with previous research (Strenze, 2007). However, because in the present study cognitive ability was assessed at an average age of 64 years, one could argue that lower cognitive function may reflect overall poor health as a consequence of environmental factors such as work environment. Nevertheless, work environment exhibited only trivial bivariate associations with health which contrasts this interpretation.

Among environmental and behavioral risk factors, physical inactivity was the only one that showed consistent associations with health, especially regarding the number of limitations in activities of daily life faced by participants. Importantly, physical inactivity can be considered both a risk factor and an outcome because it may be the result of prolonged illness (Watson et al., 2016). The SHARE participants that were included in our analyses were assessed six to seven times over the course of the study. If one assumes that declining health is associated with increasing physical inactivity, including the repeated measures as a random-effects variable would have attenuated the influence of deteriorating health. In fact, our mixed-effects regression analyses showed that physical inactivity meaningfully predicted various health outcomes, thus indicating that physical inactivity may be at least in part accountable for worse health outcomes. Nevertheless, the SHARE data do not allow for causal inferences on whether physical inactivity was the cause of illness or caused by deteriorating health. Gerontological literature has established that physical activity is a major protective factor in preventing or delaying chronic illness. Therefore, physicians recommend that physical activity is resumed or picked up even in the presence of chronic health conditions (Watson et al., 2016).

Smoking did not appear to meaningfully correlate with any unfavorable health outcome and neither did alcohol consumption. Other studies on the SHARE data came to similar conclusions (e.g., Abuladze, Kunder, Lang, & Vaask, 2017). Importantly, we did not examine the amount of smoking here, because we only included a binary item that assessed whether participants had ever smoked. The group that answered “Yes” also encompassed individuals that had quit smoking in the past. It is well-documented that quitting smoking has a positive impact on many aspects of physical and mental health (Critchley & Capewell, 2003; Taylor et al., 2014). Thus, the adverse health effects of smoking might have been obscured by the inclusion of these contrasting groups. Alcohol consumption, on the other hand, was measured by current consumption levels. Our findings align with studies that demonstrate no adverse or suggest even beneficial health effects of moderate alcohol consumption in elderly individuals (Balsa et al., 2008).

Participants with higher BMI's exhibited slightly elevated rates of chronic conditions and reported slightly worse self-perceived health. It is important to note that BMI has long been subject to criticism because it does not account for body fat percentage and body fat distribution which are the main drivers of morbidity and mortality due to obesity (Nuttall, 2015). The BMI's questionable reliability negatively affects its capacity to predict health outcomes which might explain the low correlations we found in the SHARE sample. The fact that BMI showed associations with health despite its methodological issues suggests that the correlations we found in the SHARE sample can be considered to represent a bottom threshold of the true association.

Contrary to our expectations, health behaviors did not moderate the relationship between cognitive ability and health. The only notable exception was physical inactivity, but as discussed above, the direction of effect is ambiguous and unfavorable health effects cannot be causally attributed to physical inactivity here because they arguably exacerbate one another. BMI, smoking, alcohol consumption, and work environment risk did not meaningfully interact with cognitive ability. These results suggest that the intelligence-health association cannot be sufficiently explained by environmental and behavioral risk factors (i.e., at least by those that were assessed in the SHARE interviews). Thus, a different mechanism is required to understand the relationship.

In the literature, education has often been found to explain a substantial proportion of the variance in the intelligence-health association (e.g., Ariansen et al., 2015). In our analyses, controlling for education attenuated the association, indicating a moderating influence. Nevertheless, effect magnitudes remained meaningful, and the directions of effects were unchanged. This suggests that education did exert influence on the relationship between cognitive ability and health outcomes but was not sufficient to explain the effect in its entirety.

The intelligence-health association did not decrease when participants' country of residence was included in exploratory regression analyses. This indicates that the relationship is not meaningfully impacted by regional disparities.

An alternative explanation for this remarkably robust association could be found in a genetic factor that influences health as well as cognitive functioning. The existence of a general fitness factor has been suggested before based on phenotypical findings (Arden, Gottfredson, & Miller, 2009; Prokosch et al., 2009), but in recent years more evidence from genome-wide association studies has emerged that directly supports this theory. These studies suggest that a substantial proportion of variance in the intelligence-health association can be explained by genetic variation (Hill et al., 2019). High intelligence and favorable health often coincide because the biological bases of these features are located on the same genes. One of the challenges in this line of research is to deal with the question of causality: do genetic variants affect intelligence which subsequently affects health, or vice versa – or are both intelligence and health affected by the same genetic locations (Deary, Harris, & Hill, 2019)? The results we present here lend some phenotypic support for the latter interpretation. However, more research is necessary to clarify the direction of causality.

4.1. Limitations and future directions

In the current study, our goal was to shed light on the association of cognitive ability and health as well as its underpinnings. The SHARE-dataset represents an invaluable source to investigate these questions goal due to its representativeness, comprehensiveness, and longitudinal nature, but only comprises a limited number of cognitive measures. Cognitive function was assessed using four subtests measuring mathematical reasoning, immediate and delayed word recall, as well as verbal fluency. Despite being considered important components of intelligence in most contemporary established models (such as the CHC-model of intelligence, McGrew, 1997), these four indices can be only considered to be proxies of general cognitive ability. Therefore, we were unable to provide results of more fine-grained domain-specific associations of intelligence with health. In future community surveys, it would be beneficial to include more cognitive ability subtests, especially in terms of highly g-loaded tests such as Raven-typed matrices or figural analogies.

So far, in many longitudinal studies investigating the effects of intelligence on health (e.g., the Lothian Birth Cohort studies, Deary et al., 2007), intelligence was assessed at a young age and subsequently used as a predictor of health outcomes in later life. Here, we are limited by the SHARE study design which does not provide a measure of childhood cognitive ability; the earliest point of assessment was at a participant age of 50 years. Therefore, our analyses potentially carry an inherent bias: reduced cognitive ability at an older age may be the result of poor health, not its cause. To reduce the risk of reverse causation, we controlled for age in correlation analyses. Moreover, if low cognitive ability was the result of certain health conditions, cognitive decline can be expected to progress over the course of the multiple SHARE interview waves. In regression analyses, the cognitive development over the course of the study was held constant which at the very least attenuates this effect.

Death or severe illness may have caused participants to drop out of the SHARE study in later waves. Thus, it is possible that health is overestimated in later waves because the participants exhibiting the worst health are likely to have exited the study. This may have had an attenuating effect on the intelligence-health association in our analyses, thus leading to a conservative effect estimation.

In our analyses, we included risk factors hypothesized to moderate the intelligence-health association. Although the SHARE interviews cover some aspects, other potentially relevant factors are missing. For instance, managing individual health needs is a crucial skill that gains relevance over the human lifespan (Gottfredson, 2004). How well one takes care of one's own health includes how diligently treatment regimens are being observed. Thus far, few studies have tracked patients' adherence to treatment plans in relation to their cognitive ability (e.g., Deary, Corley, et al., 2009; Deary, Gale, et al., 2009). These are only some examples of many conceivably influential variables on this association which future researchers may wish to consider.

The intelligence-health association is not merely a matter of psychometric interest, but highly relevant to society at large. In plain words, more intelligent individuals have a higher chance of good health and a long life compared to less intelligent individuals. Advances in healthcare equality seem to have little mitigating effects (Gottfredson, 2004). Consequently, this association has been causing disparities in health outcomes and life expectancies along the lines of the intelligence distribution. Scientific inquiry is a necessary first step in the process of addressing this issue, but large-scale public policy solutions are needed to ensure that all members of society have equal chances of a long and healthy life, regardless of their respective genetic makeup. Unfortunately, public health interventions are often limited to targeting health behaviors, such as smoking or obesity, and ignore the deeper nature of the intelligence-health connection that the evidence is pointing to.

People will put their moral reputations at risk to protect their competence reputations by engaging in unethical behavior that signals (false) competence to others

Clark, C. J., Keighley, D., & Vasiljevic, M. (2022). Being bad to look good: Competence reputational stakes can increase unethical behavior. Evolutionary Behavioral Sciences, Jun 2022. https://doi.org/10.1037/ebs0000301

Abstract: Two studies (total n = 1,245) explored the influence of (a) receiving public versus private performance feedback, (b) competing on a team versus solo, and (c) individual differences in team competition participation on cheating behavior. Participants were given opportunities to cheat in an online trivia competition and self-reported their cheating behavior. Meta-analyses of Studies 1 and 2 revealed that participants who believed their performance feedback would be public cheated more than those who believed their performance feedback would be private, and individuals who regularly participate in team competition cheated more than those who do not. We found no evidence that experimentally manipulating team competition (vs. solo competition) influenced cheating. Our findings suggest that people will put their moral reputations at risk to protect their competence reputations by engaging in unethical behavior that signals (false) competence to others.

Social media use bears no measurable relationship to adolescents' cognitive skills; negative effects of social media use may have been overstated in past research

Associations between social media use and cognitive abilities: Results from a large-scale study of adolescents. Stefan Stieger, Sabine Wunderl. Computers in Human Behavior, June 16 2022, 107358. https://doi.org/10.1016/j.chb.2022.107358

Highlights

• Social media use is not substantially associated with results from tests of cognitive abilities and skills.

• Results from random-forest models suggest low importance of all test results compared to known sex- and age-differences.

• Negative effects of social media use may have been overstated in past research.

Abstract: In adolescence, smartphone use in general and social media use in particular has often been associated with negative effects, such as higher anxiety levels and body dissatisfaction. Other outcomes – such as fundamental cognitive abilities and skills (e.g., intelligence, information processing, spatial perception) – have rarely been the focus of research. Here, we analysed data from a large sample of adolescents (12–16 years; N > 12,000) who performed a series of psychometric tests ranging from intelligence, spatial perception, and information processing, to practical numeracy, and compared their test results with their social media usage (average active and passive time per day, problematic social media use). We additionally applied a random-forest model approach, useful for designs with many predictors and expected small effect sizes. Almost all associations did not outperform known age- and sex-differences on social media use; that is, effect sizes were small-to-tiny and had low importance in the random-forest analyses compared to dominant demographic effects. Negative effects of social media use may have been overstated in past research, at least in samples with adolescents.

Keywords: Cognitive abilitySocial media useAdolescentsIntelligenceRandom-forest model

4. Discussion

The results of the present study can be summarised and discussed as follows. The PSMU scale using a Likert-type response option revealed very good reliability, whereas the reliability was very low for the binary response format (see also Andreassen et al., 2013; Wartberg et al., 2017). Therefore, Likert-type scales should be given preference for the PSMU scale, at least when used with adolescent samples.

The correlation between PSMU scores and the average time using social media per day was low (∼0.27; see also Wartberg et al., 2017, who also used adolescent samples and found r = 0.34 for pathological Internet use in general). This supports the assumption that adolescents of that age might miss an objective reference frame of which social media use behaviour is acceptable. As long as the core family (e.g., parents) does not provide negative feedback about adolescents’ possible social media overuse (or even show similar social media behaviour themselves), adolescents will probably not state problems in the PSMU scale although they may use social media for a substantial amount of time on average each day. Additionally, using a parent-form of the PSMU scale or non-parametric measures of social media use (e.g., objectively assessed social media usage behaviour by smartphone apps; e.g., Ellis et al., 2019) might be a good approach for future research.

Past research has found evidence that it makes a difference whether one uses social media actively (e.g., chatting, sharing photos) or passively (e.g., browsing, reposting messages, looking at content; Escobar-Viera et al., 2018; Thorisdottir et al., 2019). In the present study, some support for this assumption was found, but differences were of very low effect size (see correlation differences in Table 1, sixth column). Girls were significantly more likely to be actively using social media compared to boys, whereas boys were significantly more likely to be passively using social media compared to girls. Furthermore, active social media users had slightly lower verbal intelligence, whereas passive social media users had slightly higher scores, which is rather counterintuitive. If adolescents’ active use of social media by writing texts and so forth is associated with positive aspects, then we should not expect a negative association with verbal intelligence. Further, although past research found that social media use reduces working memory short-term (e.g., Aharony & Zion, 2019), it does not seem to have long-term effects because the association between active social media use and the long-term memory was, although in the expected direction, of tiny effect size (−0.021; see Table 1) and of marginal importance (third least important predictor in the RF model; see Fig. 1).

Although we cannot draw conclusions about the causal directions of the found effects, in general, the effects themselves were of very weak effect sizes and, compared with each other, the importance of effects mostly did not outperform known demographic differences, such as sex- or age-differences in social media usage (e.g., Coyne et al., 2020). For example, when it comes to the time social media is used actively per day, the association with fluid intelligence (figural) had only about a quarter of importance compared with the sex-difference between boys and girls (girls using social media more than boys). Or put differently, being a boy or a girl is by far more impactful on differences in active social media use than the effect found for figural fluid intelligence.

Furthermore, we found no evidence of any substantial association with adolescents’ intelligence, spatial perception, information processing, technical understanding, creativity, spelling skills, and vocabulary. The only exception was practical numeracy, where we at least found effects similar in effect size to demographics, such as sex-differences (see Fig. 1). Adolescents with higher social media use or higher PSMU scores had lower practical numeracy ability and vice versa. Because of the cross-sectional design, it remains unclear whether adolescents with lower practical numeracy skills more actively search for social media communication or the other (more alarming) way round; that is, more social media usage leads to reduced numeracy abilities, i.e., reduced ability to solve simple text-based mathematical problems (e.g., calculation of areas). Although we also found an association between social media passive use and information processing outperforming demographics, the overall explained variance was very low (1.6%); therefore, we did not interpret this result in detail.

However, the results are interesting from another point-of-view, namely the impact of social media use on cognitions in general, such as the ability to concentrate, hold attention, keep information in memory, and executive functioning (for a review, see Wilmer et al., 2017). Previous research has found evidence that even short-term interaction with smartphones can impact ongoing cognitions by impairing the ability to concentrate or distort attention (Wilmer et al., 2017). For example, one oft-described aspect of smartphone usage in everyday life is multitasking (Judd, 2014), which can have negative effects, such as delayed completion of primary tasks (e.g., Leiva, Böhmer, Gehring, & Krüger, 2012, September) but also positive ones, such as better task-switching abilities (Alzahabi & Becker, 2013) or better multisensory integration (Lui & Wong, 2012). Furthermore, past research has found that even the mere presence of a smartphone can reduce cognitive capacity, resulting in lower scores on intelligence tests (e.g., working memory, fluid intelligence; Ward et al., 2017) or reduced task performance, especially for tasks with high cognitive demands (Thornton et al., 2014).5 Similar studies exist about children doing a school test (Beland & Murphy, 2016; Levine et al., 2007).

Looking at the pattern of effects in Table 1, the directions of effects largely correspond to these earlier results. Negative significant associations were predominantly found on tests with high cognitive demand (intelligence test, spatial perception test, technical understanding, practical numeracy, spelling skills [although less spelling errors for highly active social media users]), which were all speeded tests with time restrictions. In contrast, significant positive associations were found on the speeded test with low cognitive demand, namely the information processing test (i.e., more correct answers, fewer errors), which uses simple reaction time tasks. Therefore, the effect pattern could also mean that adolescents do not have lower abilities on the tested concepts (e.g., intelligence, spatial perception), but instead have difficulties with the test procedures themselves because they needed to concentrate and focus their cognitions on a specific task under time constraints. This would also explain why these adolescents performed better in the information perception task. Here, multitasking is beneficial: coordinating information from different senses (seeing, hearing) to perform different hand/foot coordination tasks by pressing buttons. Although this might be a possible reason why we found detrimental effects on low vs. high cognitive demand tests, we do not have direct evidence for that based on the current data, though this would be a fruitful approach for future research.

The present study has also limitations. First, some of the measures (2.2.6 to 2.2.10) were developed and validated in-house at VIC and are not published in any peer-reviewed journal. Nevertheless, all measures were developed over several years under the premisses of being valid, reliable, practical, easy-to-administer, and short. Most of them have a clear face validity (e.g., technical understanding, practical numeracy) and a clear and objective test score calculation (e.g., sum of correct answers). Tests are frequently re-standardised as suggested by the DIN 33430 norm.6 Furthermore, in the present study we assessed the time of social media usage subjectively. Because past research found that subjectively assessed usage time does not necessarily correspond to objectively assessed usage time (e.g., through software-based accurate time assessments; Ellis et al., 2019; Sewall et al., 2020), future research should try to replicate the present findings by using an objective measure of social media usage. Because past research found lower associations between objective social media use with, for example, well-being (Sewall et al., 2020), it could be that when focusing on the present results, the uncovered low effect sizes could drop. Another limitation comes from the conceptual distinguishing between active and passive social media use, which has frequently been questioned (for a thoughtful discussion, see Meier & Krause, 2022, March 22), as well as the rather unspecific focus of the measure without explicitly differentiating between the broad range of possible behaviours from texting to watching videos. Because all measures were self-assessed by adolescents, we also cannot rule out a possible shared method-specific variance of the used psychometric tests with social media use. Using objective measures of social media use in future research would resolve that issue.

In conclusion, we did not find any substantial negative associations between social media use and the tested concepts. The effects found did not substantially outperform other known effects, such as sex- or age-differences (except a slightly higher value for practical numeracy on PSMU) if at all. To conclude, although past research found negative effects of social media use in early adolescents (<11 years of age; e.g., Charmaraman, Lynch, Richer, & Grossman, 2021) or children (e.g., 4 and 8 years; Skalická et al., 2019; for a review, see Wiederhold, 2019), cognitive abilities and skills of adolescents between 12 and 16 years of age do not seem to be overly affected by social media use.