Folklore in 1000 societies: Communities with low tolerance towards antisocial behavior, captured by the prevalence of tricksters getting punished, are more trusting and prosperous today

Folklore. Stelios Michalopoulos and Melanie Meng Xue. NBER Working Paper No. 25430. January 2019, Revised January 2021. https://www.nber.org/system/files/working_papers/w25430/w25430.pdf

Abstract: Folklore is the collection of traditional beliefs, customs, and stories of a community passed  through the generations by word of mouth. We introduce to economics a unique catalogue of oral traditions spanning approximately 1,000 societies. After validating the catalogue’s content by showing that the groups’ motifs reflect known geographic and social attributes, we present two sets of applications. First, we illustrate how to fill in the gaps and expand upon a group’s ethnographic record, focusing on political complexity, high gods, and trade. Second, we discuss how machine learning and human-classification methods can help shed light on cultural traits, using gender roles, attitudes towards risk, and trust as examples. Societies with tales portraying men as dominant and women as submissive tend to relegate their women to subordinate positions in their communities, both historically and today. More risk-averse and less entrepreneurial people grew up listening to stories where competitions and challenges are more likely to be harmful than beneficial. Communities with low tolerance towards antisocial behavior, captured by the prevalence of tricksters getting punished, are more trusting and prosperous today. These patterns hold across groups, countries, and second- generation immigrants. Overall, the results highlight the significance of folklore in cultural economics, calling for additional applications.

JEL No. N00,Z1,Z13

6 Concluding Remarks

Narratives are essential building blocks of our society. We introduce to economics a unique catalogue of oral traditions across approximately 1; 000 groups. After validating folkloreís content showing that episodes in a groupís oral tradition reáect its geographic and social attributes as articulated in the ethnographic record, we undertake a series of applications illustrating how to extract information from folklore. In the Örst set, we illustrate how to Öll in the gaps and expand upon a groupís ethnographic record. In the second set, we discuss how machine learning and human-classiÖcation methods can help shed light on ancestral norms. Our initial examination indicates a striking consistency between values derived from folklore and contemporary attitudes related to trust, risk-taking, and gender norms. Images and episodes in folklore appear to endure and, possibly, still shape how individuals perceive the world today. 

Next Steps

We view this study as a springboard for further research. The Önding that folklore-based measures of the economy and the polity correspond to what we know from ethnographers suggests that we can obtain more precise estimates of a groupís heritage by combining the two sources. Lowering the measurement error in the historical record will allow us to revisit and better understand our societiesílegacies and their consequences. One related idea is to use folklore to Öll in the EA and SCCS gaps for the universe of recorded traits along the lines described in Section 4. Moreover, one can utilize folklore to derive bilateral measures of cultural proximity, see Spolaore and Wacziarg (2009), or explore how di§erent geographical traits and historical events ináuence the content of oral traditions. For example, what do groups located in malaria-prone regions, fertile territories, or rugged terrains "talk" about? Similarly, what are the distinctive themes in the folklore of groups that have experienced disruptions from slavery, epidemics, forced migrations, and colonization? This approach would allow testing famous conjectures in anthropology including the "culture of honor" proposed by Goldschmidt and Edgerton in 1971 and "the original a­ uent society hypothesis" by Sahlins (1972).

 There is a long list of contemporary values and attitudes in regional and global surveys that we have not covered, including patience, aspirations, reciprocity, attitudes towards violence, strangers, the elderly, the community, the importance of imagination, obedience, independence, hard work, honesty, etc. We hope that the roadmap provided here can help trace these values in the respective oral traditions. Obtaining folklore-based measures of these attitudes may help us better understand the cultural traits that are stable over time.

Another avenue of future research relates to how motifs and concepts have traveled across oral traditions. Some motifs appear to be universal, whereas others are found in a handful of folklore traditions. Is there a pattern in the content of localized versus universal narratives? Moreover, the multiplicity of charactersíattributes in a given motif and oral tradition (at least as classiÖed by humans) may convey important information about the richness and the ambiguity of the charactersí personality. This within-oral tradition diversity in attitudes may provide a way to gauge the degree of áexibility in the norms transmitted intergenerationally. It would also be interesting to explore how the individual characteristics of those reading and classifying the motifs may systematically predict how a given motif is perceived. Finally, we posit that the degree of continuity in the narratives between contemporary childrenís books and the folktales and myths of the respective societies is a direct measure of the rate at which ancestral norms are intergenerationally transmitted.

 Given the versatility of folklore as a vehicle for obtaining a unique (and perhaps our only) view of our ancestral cultural heritage, we expect it to be useful to scholars interested in the historical origins of comparative development, social psychology, culture, and beyond.

Some people may frequently forget about their age and be horrified at those moments when they realise their age

What does feeling younger or older than one’s chronological age mean to men and women? Qualitative and quantitative findings from the PROTECT study. Serena Sabatini et al. Psychology & Health, Aug 5 2021. https://doi.org/10.1080/08870446.2021.1960989

Abstract

Objective: We explored which factors are associated with subjective age (SA), i.e. feeling younger, the same as, or older than one’s chronological age, and whether these factors differ between men and women and between two age sub-groups.

Design: Cross-sectional study using qualitative and quantitative data for 1457 individuals (mean age= 67.2 years).

Main outcome measures: Participants reported how old they feel they are and provided comments in relation to their SA judgments.

Results: By using content analysis participants’ comments were assigned to 13 categories, grouped into three higher-order categories (antecedents of age-related thoughts, mental processes, and issues when measuring subjective age). SA may result from the interaction between factors that increase or decrease age-related thoughts and mental processes that individuals use to interpret age-related changes. Chi-squared tests show that individuals reporting an older SA are more likely to experience significant negative changes and to engage in negative age-related thoughts than individuals reporting an age-congruent SA or a younger SA. Women experience a more negative SA and more age-salient events than men.

Conclusion: Individuals reporting an older SA may benefit from interventions promoting adaptation to negative age-related changes. There is the need to eradicate negative societal views of older women.

Keywords: Ageingsubjective agefelt ageawareness of age-related changehealth promotion

Discussion

This study identified thirteen factors related to SA judgments and tested whether the frequency with which individuals comment on these factors differs among individuals reporting a younger SA, an age-congruent SA, or an older SA; between age sub-groups; and between men and women. In line with our first hypothesis, when evaluating their SA participants considered, not only their health status, but also a variety of life events and psychosocial factors. Participants’ comments suggest that SA judgments emerge from the interaction between factors that facilitate or decrease age-related thoughts and several mental processes that people use to make sense of age-related changes or to decrease the emotional impact of negative changes. Use of these mental processes frequently results in positive evaluations of SA. In line with our second and third hypotheses, the factors that participants considered when reporting their SA differed among sub-samples. Participants reporting an older SA were more likely to be aware of changes and less likely to engage in activities, compared to participants reporting a younger SA or an age-congruent SA. In line with existing literature on SA, participants in the older age sub-group reported a younger SA compared to those in the younger age sub-group (Bordone et al., 2020). Women experienced more age-symbolic events, especially in the younger age sub-group, and reported a more negative SA than men.

Among the categories that we identified, awareness of changes (Bowling et al., 2005; Sabatini, Silarova, et al., 2020), poor physical health (Desrosiers et al., 2006), the experience of age-symbolic events, and some life circumstances were associated with participants reporting an older SA (Bordone & Arpino, 2016). Events such as retirement, menopause, birthdays, and bereavement, and life circumstances such as being a caregiver may have reminded participants of their position in their lifespan (Barrett, 2003; Bordone & Arpino, 2016; Brothers et al., 2016; Bytheway, 2009; Montepare, 1996a, 2009). The combination of levels of gains and losses experienced by older individuals may play a role in whether these changes are attributed to age. Indeed a recent study showed that individuals are more likely to attribute negative changes to ageing compared to positive changes (Rothermund et al., 2021). The interpretation of negative changes as being a consequence of older age may in turn result in an older SA. Indeed, evidence shows that those individuals that report higher levels of awareness of age-related losses (AARC losses) tend to report an older SA compared to those who experience fewer AARC losses (Brothers et al., 2019; Kaspar et al., 2019; Sabatini, Ukoumunne, Ballard, Brothers, et al., 2020).

As participants reporting an older SA were more likely to be aware of age-related changes, less likely to engage in adaptive behaviours or activities, and rated their health as being poor, an older SA may represent a legitimate reaction to significant and permanent losses (e.g. decrease functional and cognitive ability) (Sabatini, Ukoumunne, Ballard, et al., 2021). As the experience of AARC losses and of an older SA are related to poorer emotional and physical well-being (Mock & Eibach, 2011; Sabatini, Silarova, et al., 2020; Westerhof et al., 2014) and lower engagement in health-related and adaptive behaviours (Brothers & Diehl, 2017; Dutt et al., 2018; Montepare, 2020; Wilton-Harding & Windsor, 2021), the emotional well-being of individuals reporting an older SA could be enhanced through disengagement from unachievable goals (Wrosch et al., 2003) and acceptance of negative changes (Collins & Kishita, 2019). However, when individuals with an older SA experience potentially modifiable changes, more active coping strategies should be promoted in order to enable these individuals to continue engaging in enjoyable activities (Brandtstädter & Rothermund, 2002).

Some participants reported an age-congruent SA or even a younger SA despite experiencing negative age-related changes and negative life circumstances. This finding may be due to several reasons. First, these individuals may have experienced positive changes alongside negative ones (Sabatini, Ukoumunne, Ballard, Diehl, et al., 2020; Wilton-Harding & Windsor, 2021). Second, as those participants who reported a younger SA or an age-congruent SA perceived their health as good and were able to continue performing a variety of meaningful activities, the health changes they experienced may have been mild (Spuling et al., 2013) and not severe enough to prevent them from leading an active and independent life (Franke et al., 2017). Third, some participants may report a positive SA despite the experience of age-related losses due to the use of a variety of mental processes that enable them to perceive their situation in a more optimistic light (Heckhausen & Krueger, 1993). However, subjective evaluations of health can differ greatly from scores obtained with objective measures of health (Carstensen, 1992, 1993, 2006; Chan et al., 2007; Idler & Benyamini, 1997; Jylha et al., 2001). Due to the subjective nature of the concepts, SA may be more strongly associated with self-rated health compared to objective measures of health and future studies should test this. We were unable to test this in the current study as in 2019 the PROTECT study annual assessment did not include an objective measure of health. However, the assessment of comorbidity was included as part of the 2020 annual assessment of the PROTECT study; this will enable the authors to explore in future studies the associations of SA with self-rated health and comorbidity.

Among the mental processes identified in the current study, consistent with social comparison theory (Rickabaugh & Tomlinson-Keasey, 1997) and with temporal comparison theory (Ferring & Hoffmann, 2007), participants reported a younger SA when they compared themselves to people in worse health than themselves (Beaumont & Kenealy, 2004) or when they concluded that despite their increasing age they had not changed significantly. In line with resilience theory some participants reported a younger SA when they concluded that they did not match negative stereotypes of older individuals (Kotter-Grühn & Hess, 2012). Finally, some participants reported a younger SA when others attributed a younger age to them or when they spent time with younger people (Bordone & Arpino, 2016). In contrast, participants reported an older SA when they compared themselves with more healthy others, they felt they matched negative stereotypes of older individuals and/or they thought they had changed significantly compared to previous versions of themselves. This pattern of results emphasises the positive impact that eradicating negative age-related stereotypes at societal level and promoting more realistic age-related expectations and intergenerational contact, may have on individuals’ experiences of ageing (Levy, 2017). Intervention programs promoting positive and realistic age-related beliefs, in addition to healthy behaviours, are effective in promoting more positive experiences of ageing, healthier lifestyle (e.g. more engagement in physical activity), and better mental (e.g. reduction in depressive symptoms) and physical (e.g. better physical performance in terms of balance, gait speed, and chair rise) health (Beyer et al., 2019; Brothers & Diehl, 2017; Menkin et al., 2020).

When estimating their SA, both men and women reflected most frequently on the changes they had experienced in multiple domains (e.g. physical, cognitive, social) of their lives and on how such changes led to modifications in their lifestyle. However, as expected, we found some differences in the way in which men and women evaluate their own ageing (Antonucci et al., 2010; Barrett, 2005). Compared to men, women, especially in the older sub-group, were more likely to experience variability in their SA evaluations. As women also commented more frequently than men on the co-occurrence of positive and negative changes in multiple domains of their lives, the more frequent variability in SA reported by women may be due to them being more likely to experience a mix of positive (e.g. enjoyable social relationships) and negative (e.g. decreased health) age-related changes (Miche et al., 2014). Whereas women were more likely to reflect on age-symbolic events, men commented more frequently on whether their preserved strength enabled them to continue those activities they had initiated earlier in life. This pattern of results suggests that when evaluating their SA men are more likely to reflect on their daily performance whereas women are more influenced by age-salient events and social expectations rather than by their actual daily abilities.

Discrepancies in the way in which men and women experience ageing may be due to our society having different expectations for older men and women. In support of this Kornadt et al. (2013) showed that individuals aged 20 to 92 years attach different stereotypes to older men and women; older women are believed to be more religious, friendly, and engaged in leisure activities whereas men are believed to be more capable in financial and work-related tasks. The different expectations that our society has for older men and women may result in older men and women being treated differently, and this may explain why in our study women reported a more negative SA than men. Indeed, older women often become invisible in the public domain. For instance, among TV presenters, older men are distinguished whereas older women are frequently dismissed (Jermyn, 2013). In sum, our results highlight one more time how much our society -and men, in particular - need to learn to think differently about ageing women and how strategies aiming to eradicate negative age-related stereotypes (Levy, 2017) should give particular attention to negative stereotypes of older women.

Finally, although it was not a primary aim of the current study, participants’ comments outlined several sources of lack of validity and reliability when measuring SA with an unidimensional measure asking participants to specify how old they feel in general (Barrett, 2003). First, as different participants interpreted the SA question in distinct ways, answers to unidimensional measure of SA may not be comparable. Indeed, for instance, some participants reported their SA after reflecting on physical changes, whereas others on their mental abilities.

Second, as some participants reported that their SA fluctuates, assessing SA at one time point may oversimplify individuals’ experiences of ageing. Future studies could therefore adopt methodological designs that take into account the fluctuating nature of self-perceptions in older age (Armenta et al., 2018), for instance, by controlling for situational factors, such as levels of pain, that impact on SA (Sabatini, Ukoumunne, Ballard, Collins, et al., 2020), or by averaging individuals’ SA across several time points (Neupert & Bellingtier, 2017). Third, some participants experienced difficulty in reporting SA which arose from not being able to assign a specific number to SA. Asking individuals to report their SA on a scale ranging from ‘a lot younger than my age’ to ‘a lot older than my age’ may reduce difficulty in answering (Montepare, 1996b). Moreover, difficulty in reporting SA may underlie the difficulty of capturing the complexity of perceptions of ageing when using unidimensional measures. By collecting information about the coexistence of positive and negative experiences in individuals’ lives, multidimensional measures of SA may facilitate SA judgments (Kastenbaum et al., 1972; Turner et al., 2021).

The nature of our dataset places some limitations on our findings. First, all data were collected through self-report measures and descriptive analysis have not been conducted on objective indicators of health. Second, the sample included a majority of women and was predominantly white, with above average education and self-rated health. Among the 14757 participants that took part in the PROTECT study in 2019, only a small sub-group of participants answered the open-ended item (N = 1457); hence the opinions of the remaining participants are unknown. Third, some of the characteristics of study participants are slightly different from the remaining PROTECT participants. For instance, compared to participants included in the current study sample, those excluded from the study sample reported on average a younger SA. Fourth, SA was assessed with a single-item question rather than in a domain-specific format (Kastenbaum et al., 1972; Turner et al., 2021). This is a limitation of the current study as individuals can experience ageing differently in relation to different domains of their lives (e.g. physical and cognitive) which may lead to individuals reporting different subjective ages in relation to different domains of one’s life (Kaspar et al., 2019). Finally, views on ageing and age stereotypes were not taken into account when explaining SA and SA-related thoughts. However, views on ageing and age stereotypes may influence SA (Brothers et al., 2017; 2020; Mock & Eibach, 2011; Sabatini, Ukoumunne, Ballard, et al., 2021).

It should be noted that ours was a large sample for content analysis. The large sample also made it possible to include quantitative data for all the identified categories and to compare frequencies among individuals reporting a younger SA, an age-congruent SA, or older SA; between age sub-groups; and between men and women. The examination of sex difference in SA enriched the scarce literature on factors underpinning sex differences in SA. To analyze data, we generated categories directly from the data; this is a strength of our study as it made it possible to explore the additional role that mental processes play in shaping individuals’ SA, going beyond what has been reported by previous studies (e.g. Giles et al., 2010) and providing targets for future health promoting interventions. For instance, as we found that individuals’ mental processes impact on the age their feel, targeting negative mental processes such as self-attribution of negative age stereotypes may help to enhance mental health in older age. It also made it possible to identify limitations related to the SA questionnaire that had not been considered before and that may find application in the development of a multidomain tool assessing SA.

On Libet et al.: The readiness potential (RP) may only be an “artifact of averaging” and that, when intention is measured using “tone probes,” the onset of intention is found much earlier and often before the onset of the RP

Conscious intention and human action: Review of the rise and fall of the readiness potential and Libet’s clock. Edward J. Neafsey. Consciousness and Cognition, Volume 94, September 2021, 103171. https://doi.org/10.1016/j.concog.2021.103171

Abstract: Is consciousness—the subjective awareness of the sensations, perceptions, beliefs, desires, and intentions of mental life—a genuine cause of human action or a mere impotent epiphenomenon accompanying the brain’s physical activity but utterly incapable of making anything actually happen? This article will review the history and current status of experiments and commentary related to Libet’s influential paper (Brain 106:623–664, 1983) whose conclusion “that cerebral initiation even of a spontaneous voluntary act …can and usually does begin unconsciously” has had a huge effect on debate about the efficacy of conscious intentions. Early (up to 2008) and more recent (2008 on) experiments replicating and criticizing Libet’s conclusions and especially his methods will be discussed, focusing especially on recent observations that the readiness potential (RP) may only be an “artifact of averaging” and that, when intention is measured using “tone probes,” the onset of intention is found much earlier and often before the onset of the RP. Based on these findings, Libet’s methodology was flawed and his results are no longer valid reasons for rejecting Fodor’s “good old commonsense belief/desire psychology” that “my wanting is causally responsible for my reaching.”.

Keywords: Readiness potentialBereitschaftspotentialIntentionDecisionFree willHard problemConsciousnessLibetKornhuberNeuroscienceEpiphenomenon

4. Discussion

4.1. Intention Before RP: Has the Ghost Returned?

If intentions precede the RP, does that mean that the “ghost in the machine” (Ryle, 1949) has returned and intentions are present without any brain activity? No. Even if the RP is plausibly only an artifact of averaging and even if tone probes have shown the onset of intentions takes place well before the onset of any RPs, that does not mean that nothing is going on in the brain when these intentions begin. The UCLA neurosurgeon Itzhak Fried and his coworkers recorded neuronal activity from depth electrodes implanted into the medial frontal lobe (SMA, pre-SMA, and ACC (anterior cingulate cortex)) during performance of the Libet clock task in “12 subjects with pharmacologically intractable epilepsy to localize the focus of seizure onset” (Fried, Mukamel, & Kreiman, 2011). Each depth electrode included nine microwires capable of recording single and multi-unit neuronal activity, and 760 units (254 single units and 496 multiunits) were recorded in the SMA, pre-SMA, and ACC of the 12 patients. As seen in Fig. 5A, they found “progressive neuronal recruitment over $~$1500 ms before subjects report making the decision to move …[with a] progressive increase or decrease in neuronal firing rate, particularly in the supplementary motor area (SMA), as the reported time of decision was approached.” Much of this early neuronal activity took place in the 1500 ms preceding movement, but there were a number of neurons whose firing rates changed even earlier. And, as illustrated in Fig. 5B, in experiments in monkeys done in the lab of Mark Churchland by Lara, Cunningham, and Churchland (Jul. 2018) SMA neurons showed “preparatory and movement-related activity that covaried with reach direction,” in marked contrast to the human early RP’s lack of any movement specificity. So there is early, movement-specific neuronal activity during these early intentions.

Fig. 5

Fried, like Libet, found the W time was only about 0.2 s before the movement, but, as shown above in the studies from the labs of Matsuhashi and Hallett and Verbaarschot, W time utterly fails to capture when intention actually begins. Related to such early intentions, Miller and Schwarz (2014) comment that “At the start of each trial, it seems plausible that participants would already have a weak yet conscious urge to move within the next few seconds, simply because they know that their task is to make such movements. …In this scenario, the fact that brain activity appears to emerge before the conscious decision—i.e., before W—is merely an artifact of the experimenter’s requirement that the observer impose an arbitrary criterion for making a binary judgment about an inherently gradual process that underlies decision making.” In other words, the mere presence of the subjects in the experimental situation indicates that some form of intention is already and always present; the actual intention to “move now” (Searle’s (1980) “intention in action”) arises from and “is caused by this [earlier, pre-existing] prior intention” (Searle, 1980) to move sometime (but not now).8 In a sense, given the Libet-type experimental situation, it is not possible for the subject to be in an “intentionless” state from which a new, fully-formed intention arises, as when Athena was born as a fully formed adult from the head of Zeus. This makes it impossible, in principle, to even address the question of timing of mental and physical processes in a Libet-type experiment. And, as shown by tone probes, even the final intention to move now is not instantaneous or abrupt.

Lastly, as noted by Miller and Schwarz (2014), this same fact about intention being always and already present in experimental subjects also applies to the fMRI study by Soon, Brass, Heinze, and Haynes (2008) who found very early prefrontal activity as long as 10 s before the conscious decision times of their subjects. This early activity “predicted” whether the left or right index finger would be moved (57% accurate vs. 50% for chance)9 but this activity so long before movement likely had just as much to do with the intention NOT to move my left (or right) finger now that must also be present, especially in the frontal areas where brain damage leads to loss of frontal cortical inhibitory control over behavior (Malmo, 1942, Pribram et al., 1964). Or, as suggested by Koenig-Robert and Pearson (2019), the early activity could be viewed “not in terms of unconscious decision processes …but rather by a process in which a decision (which could be conscious) is informed.” Guggisberg and Guggisberg (2013) expressed a similar view that “intention consciousness does not appear instantaneously but builds up progressively …[and] early neural markers of decision outcome are not unconscious but simply reflect conscious goal evaluation stages which are not final yet and therefore not reported with the clock method.”

5. Conclusions

5.1. The RP Is Not What It Seemed To Be

The “paradigm” (Kuhn, 1970). that the early RP indicates brain activity preparing for movement was and is beset by several important “anomalies” The first and perhaps most important anomaly is the RP’s dependence on averaging EEG potentials whose noise, when averaged, can reproduce the RP’s waveform. This was the point of attack for Eccles (1985), Ringo (1985), Stamm (1985), Schurger et al. (2012), Schmidt et al. (2016), and Maoz et al. (2019). The second anomaly is that an RP is also seen before involuntary or unconscious movements (Keller & Heckhausen, 1990) and even before decisions that involve no movement at all (Alexander et al., 2016). The third anomaly is the early RP’s lack of movement specificity, since very similar RPs occur before completely different movements, such as right hand vs. left hand (Haggard and Eimer, 1999, Herrmann et al., 2008). Related to this is that the RPs do not differ before movements with completely different motives and intentions, as seen in the RPs in the Free Wally and Object Tasks (Verbaarschot et al., 2019). The fourth anomaly is the onset time of the RP, which, for the exact same movement, has an almost perfect linear relationship to the interval between movements (Verleger et al., 2016). And the last anomaly is the absence of the RP before deliberate choice movements (Maoz et al., 2019). All of these facts argue against the early RP having anything to do with preparation for a specific movement or the voluntary intention to move and make any comparison of RP onset times and W times pointless. Whether the RP starts before (Libet) or after (tone probes) intention means nothing because the RP’s relation to upcoming movement is an illusion.

5.2. Intentions Begin Much Earlier Than Libet’s W Times

The results from the labs of Matsuhashi and Hallett (2008) and Verbaarschot et al., 2016, Verbaarschot et al., 2019 using tone probes to measure intention clearly suggest that intention is not an all-or-none phenomenon but a gradual process that begins much earlier than estimated by Libet’s W time and in many cases before the onset of the RP. But is Libet’s clock time W intention the same as the intention detected by tone probes? Matsuhashi and Hallett (2008) told their subjects to make the movement “as soon as you think about the next movement,” to ignore the tone if they are “not thinking about the next movement,” and to stop the movement “if you hear the tone after you have started thinking about the next movement or making the movement.” So these instructions clearly identify intention to move with “thinking about the next movement.” In the two studies from Verbaarschot’s lab, the instructions explicitly said to “veto their act if they were intending to act at the time they heard the beep” so “intention to move” very clearly meant “intending to act at the time.” Both labs had similar results, with tone probe intentions beginning early and even sometimes before the onset of the RP, so the small differences in the language of the instructions given to the subjects (“thinking about the next movement” vs. “intending to act at the time”) do not seem significant and are both roughly equivalent to the variety of terms Libet’s study used for reporting the time of “conscious awareness of ‘wanting’ to perform a given self-initiated movement,” which was “also described as an “urge’ or ‘intention’ or ‘decision’ to move” (Libet et al., 1983). So it would seem that the subjects in the different labs had the same concept of “intention” and that tone probes were a more sensitive way to measure the presence of intention, forcing subjects to attend to even the slightest inkling or trace of intention.

They estimate the prevalence rate of psychopathy in the general adult population at 4.5pct

Prevalence of Psychopathy in the General Adult Population: A Systematic Review and Meta-Analysis. Ana Sanz-García, Clara Gesteira, Jesús Sanz and María Paz García-Vera. Front. Psychol., August 5 2021. https://doi.org/10.3389/fpsyg.2021.661044

Abstract: The main objective of this study was to systematically and meta-analytically review the scientific literature on the prevalence of psychopathy in the general adult population. A search in PsycInfo, MEDLINE, and PSICODOC identified 15 studies published as of June 2021. Altogether, 16 samples of adults totaling 11,497 people were evaluated. Joint prevalence rates were calculated using reverse variance heterogeneity models. Meta-regression analyses were conducted to examine whether the type of instrument, sex, type of sample, and country influenced prevalence. The meta-analytical results obtained allow us to estimate the prevalence rate of psychopathy in the general adult population at 4.5%. That being said, this rate varies depending on the participants' sex (higher in males), the type of sample from the general population (higher in samples from organizations than in community samples or university students), and the type of instrument used to define psychopathy. In fact, using the PCL-R, which is currently considered the “gold standard” for the assessment and definition of psychopathy, the prevalence is only 1.2%. These results are discussed in the context of the different theoretical perspectives and the existing problems when it comes to defining the construct of psychopathy.

Discussion

The main objective of this study was to obtain an estimate of the prevalence of psychopathy in the general adult population and, in this sense, to our knowledge, it is the first systematic or meta-analytic review carried out on this topic. Following a thorough search in the scientific literature, 15 empirical studies were found that had calculated the frequency of psychopathy in samples from the general adult population, including community, organization, and university student samples. These studies used properly described tools and procedures to assess and define psychopathy. After calculating the conjoint mean of their results with meta-analytic procedures, based on a total sample of 11,497 people, it can be estimated that the prevalence of psychopathy in the general adult population is 4.5%.

As could be expected, this prevalence is much lower than that found in samples obtained in forensic or prison contexts. For example, in the meta-analysis of Fox and DeLisi (2019), it was found that the average prevalence of psychopathy among homicide offenders could be estimated at 27.8 or 34.4%, depending on the criterion used to define psychopathy with the PCL-R (cut-off score of 30 vs. 25, respectively). In the second edition of the PCL-R manual (Hare, 2003a), the prevalence of psychopathy, based on a cut-off score of 30, was 15.7% for males (Nicholls et al., 2005) and 10.3% for females (Guay et al., 2018) in the North American normative samples of prisoners.

However, although the average prevalence of psychopathy in the general population is clearly lower than that found in the offender or prison population, the prevalence rates of psychopathy in the general population obtained in the studies reviewed in this work show considerable variation, ranging from a minimum of 0% to a maximum of 21%. In fact, the results obtained in terms of the I2 and Q statistics confirmed that the heterogeneity of the studies was statistically significant.

These variations depend on many factors, such as the role of the type of instrument used to define psychopathy, the participants' sex, the type of sample of the general population, and the participants' country of origin. These factors have been analyzed in this work. In this sense, the results of the present work indicate that the first three factors, but not the country of origin, seem to have a significant impact on the prevalence of psychopathy. Depending on the chosen instrument, the participants' sex or the type of sample selected, prevalence figures can double, triple, or quadruple the figures found with a different instrument or with participants of another sex or from a different subpopulation of the general population. Moreover, the results obtained in terms of the Doi chart and the LFK index indicate that this heterogeneity does not appear to reflect a significant publication bias, but could largely be attributed to these three moderator variables.

In particular, the results of this work indicate that, when using the PCL-R (or any of its versions), an instrument that is currently considered as the gold standard for the evaluation and definition of psychopathy, it can be estimated that the prevalence of psychopathy in the general adult population is only 1.2%. However, if other instruments are used, such as self-reports of psychopathic personality traits like the LSRP (Levenson et al., 1995) or the SRP in their different versions (SRP-II, SRP-III, and SRP-SF; Hare, 1990; Paulhus et al., 2016), the estimate of the prevalence of psychopathy in the general adult population quadruples, reaching 5.4%.

In fact, as virtually all the studies with offenders use the PCL-R or one of its versions, the comparison between the prevalence rates of psychopathy obtained in the general population and in the offender or prison population should primarily focus on studies conducted with the PCL-R. In this sense, the difference in the prevalence rate of psychopathy between the two types of population, general and criminal, is much greater: 1.2%, obtained in the present work for general population, compared to 15.7 and 10.3%, obtained in the normative samples of the PCL-R for male and female prisoners, respectively (Nicholls et al., 2005; Guay et al., 2018), or vs. 27.8%, obtained in Fox and DeLisi (2019) meta-analysis for homicide offenders.

Differences in the prevalence rates as a function of the type of instrument and cut-off point established to identify psychopathy go back to the problems in defining the construct of psychopathy. Those differences also point out a limitation of the present study. We will elaborate on these ideas later in the context of the limitations of this review.

The results of this study also indicate that the prevalence of psychopathy in the general adult population is significantly higher among males than among females. In particular, psychopathy in the general population doubles its prevalence in males compared to females (7.9 vs. 2.9%). This difference is consistent with the results obtained in samples of offenders or incarcerated people, among whom the prevalence of psychopathy is also higher in males than in females (Beryl et al., 2014).

In particular, Beryl et al. (2014) conducted a systematic review of the scientific literature on the prevalence of psychopathy in adult women from within secure settings, which included criminal justice settings, or secure inpatient healthcare settings. They found prevalence rates ranging from 0 to 31% using the PCL-R or one of its versions, although they did not report the average of these rates or the conjoint prevalence. However, from the data they submitted for females in criminal justice settings, it is possible to calculate, for the 13 unique studies that defined psychopathy based on a cut-off score of 30 in the PCL-R or of 18 in the PCL:SV, a weighted average prevalence of 11.9% (Table 3 of Beryl et al., 2014, p. 191). This figure dropped slightly to 11% when also taking into account the data from the 10 unique studies that had evaluated samples of females in secure/inpatient psychiatric settings or mixed samples—secure/inpatient psychiatric and criminal justice settings—(Tables 2, 4, respectively, of Beryl et al., 2014, p. 190, 192). Moreover, these figures hardly varied when only studies using the same instrument, the PCL-R, and the same cut-off score, 30 (12.3 and 11.4%, respectively) were taken into account. Interestingly, these prevalence figures are very similar to those presented by the scales of female prisoners collected in the second edition of the PCL-R manual, which, as noted above, show a prevalence of psychopathy in female prisoners of 10.3% (Guay et al., 2018). In summary, the average prevalence of psychopathy in female offenders or prisoners can be estimated at 10–12%.

In contrast, in male offenders or prisoners, using the PCL or its versions, rates of average prevalence of psychopathy of 15–35% are usually obtained, although the average rates of 15–25% are probably the most adequate (Hare, 1991, 2003a; Guay et al., 2007; Fox and DeLisi, 2019, cited by Nicholls et al., 2005). In the 1991 PCL-R manual, Hare reported that, in a global sample of 1,200 males incarcerated in Canadian prisons, 25% scored 30 or higher on the PCL-R. However, in the second edition of the PCL-R manual, published in 2003 and based on a much larger sample with a total of 5,408 males incarcerated in American prisons, Hare reported that 15.7% of the inmates scored 30 or higher on the PCL-R (Hare, 1991, 2003a; cited by Nicholls et al., 2005). Subsequently, with that same large sample, but eliminating the participants with missing information on some items of the PCL-R (n = 543), Guay et al. (2007) reported that 19% of the remaining 4,865 male inmates scored 30 or higher on the PCL-R. Finally, in the meta-analysis of Fox and DeLisi (2019), it was found that 27.8% of the homicide offenders scored 30 or higher on the PCL-R.

In any case, it seems clear that the prevalence of psychopathy is higher in male offenders or prisoners than in female offenders or prisoners (15–25% vs. 10–12%), and this difference between the sexes is maintained in the general population (7.9 vs. 2.9%), as shown in this meta-analysis.

Another interesting result of this work has to do with the finding of differences in the prevalence of psychopathy between different groups of adults in the general population. In particular, this review has found that the prevalence of psychopathy is significantly higher among workers in some organizations and companies (managers, executives, procurement and supply professionals, advertising workers) than among university students or among people from the general community (12.9 vs. 8.1% and 1.9%, respectively). In turn, the prevalence among university students is significantly higher than among people from the general community (8.1 vs. 1.9%).

The highest prevalence of psychopathy among workers in certain organizations and companies is based on data from only three studies with a total sample of 668 people and should, therefore, be taken with some caution. However, this result is consistent with the scientific literature that proposes that psychopathy is more prevalent in certain professions (e.g., entrepreneurs, managers, politicians, investors, sellers, surgeons, lawyers, telemarketing employees) in which the personality characteristics that define psychopathy could even facilitate their success in these professions (Hare, 2003b; Dutton, 2012; Babiak and Hare, 2019; Fritzon et al., 2020).

More surprising may be the result that among university students, there is a higher prevalence of psychopathy than among people in the community. Following the previous argument, it could be assumed that among university students of certain professions there could be more people with psychopathic traits (e.g., students in business administration and management, marketing), but it could also be assumed that among university students from other professions, there could be more people with less psychopathic traits and characterized, on the contrary, by high levels of empathy, altruism, candor, trust, humility, and responsibility (e.g., students from health professions, social work, and other professions closely linked to helping). In fact, in a study of Hassall et al. (2015), it was found that business university students, in comparison to university students of psychology, showed significantly higher levels in the four psychopathy factors measured by the SRP-III (Paulhus et al., 2016). Unfortunately, this work did not provide data on the prevalence of psychopathy in the two groups of university students. In addition, in the study of Dutton (2012), mentioned in the Introduction, among the 10 professions with higher levels of psychopathic traits, there were some that require a university degree (e.g., lawyer, surgeon, journalist) and, likewise, among the 10 professions with lower levels of psychopathic traits, there were also several that require a university degree (e.g., nurse, teacher, doctor).

Therefore, future research with university students should examine whether there are significant differences in psychopathy among students of different careers. This implies that, not only among university students of certain careers may there be a higher prevalence of psychopathy than in the general population, but that among university students of other careers, there may be a similar prevalence. It could even be that among university students of certain careers, there may be a lower prevalence of psychopathy than in the general population.

Research on differences in psychopathy between people of different professions or between university students of different careers departs from the traditional application of the construct of psychopathy to the forensic and prison area. That research intertwines, as discussed in the Introduction, with the most recent interest in the presence of psychopathy in everyday life (Dutton, 2012; Babiak and Hare, 2019; Fritzon et al., 2020), in the definition of psychopathy in terms of normal personality models such as the Big Five model (Lynam and Miller, 2019), and in the concept of successful or integrated psychopathy (Dutton, 2012; Lilienfeld et al., 2015). The fact that, as found in this review, most studies on the prevalence of psychopathy in the general population were published in the twenty-first century, especially in the last 10 years, is also consistent with those most recent interests far from the area of forensic and prison psychology.

Finally, no significant differences in the prevalence of psychopathy in the general population were found in this work as a function of the country of origin of the evaluated people. This absence of differences is not consistent with the results of the scientific literature on criminal and prison populations, which show the existence of differences between countries, especially between North American and European countries, in terms of the prevalence and levels of psychopathy in this type of population. For example, in the review of Beryl et al. (2014), a trend was found of lower rates of prevalence of psychopathy in European samples of women in prison or in prison hospitals than in American samples. Consistently, in the meta-analysis of Fox and DeLisi (2019), and after discarding the extreme values from samples composed exclusively of homicides with psychosis or psychopathy, significantly higher levels of PCL-measured psychopathy were found in homicide offenders from the USA and Canada than in homicide offenders from Finland, Sweden, and Germany.

Although these two reviews have reported that psychopathy prevalence is higher in North American male and female offenders and prisoners than in European male and female offenders and prisoners, the reasons for these differences are unclear. Beryl et al. (2014) suggest that the reason is “that the PCL instruments are designed to test the construct of ‘psychopathy’ as manifested in North American (male) offenders, and are less well-suited to identifying ‘psychopathy’ as manifested in European offenders” (p. 190). However, following the cultural facilitation model and Cooke et al.'s (2005) suggestions, an alternative reason is that complex social processes, such as socialization and enculturation, can suppress the development of certain aspects of psychopathy and facilitate the development of others. Therefore, it may be that socialization and enculturation in European countries suppress the development of certain psychopathic personality traits, or that those social processes in North American countries facilitate the development of certain psychopathic personality traits. There is also the possibility that both explanations are valid.

In any case, the results of the present review suggest that those differences between countries in the prevalence of psychopathy are unique to the prison or criminal population, but do not extend to the general population.

However, studies using samples from the general population of many different countries around the world have found cultural differences in the levels of different psychopathic traits. For example, in the study of Neumann et al. (2012) with 33,016 people (19,183 women) from 58 countries belonging to 11 world regions, significant differences were found between these regions in terms of the levels of different psychopathic traits (interpersonal, affective, antisocial, and lifestyle), as measured by one of the brief versions of the Hare SRP (SRP-E).

To further complicate the scenario of empirical results on the relationships between psychopathy and culture, the differences found in some studies with samples from the general population sometimes go in the opposite direction to those found in offender or prisoner populations. Thus, in the study of Lilienfeld et al. (2014), mentioned in the Introduction, in which they analyzed the responses of 3,338 people to the PPI-R-SF applied online, the Europeans showed higher levels of psychopathic traits than the Americans.

As a result, future research should address whether differences between countries in psychopathy only appear in terms of levels of certain psychopathic traits, but not in terms of the prevalence of psychopathy. When speaking about prevalence of psychopathy, we refer to it as defined by the presence of a clear set of psychopathic traits and with a certain level of intensity of such traits and/or a certain degree of impairment caused by such traits. It should also be examined whether such differences translate into a pattern of consistent differences between North American and European countries.

The results obtained in this work and the conclusions that have been reached should be assessed taking into account some of the limitations of the review itself. The most important limitations concern the high variability of the characteristics of the reviewed studies and the prevalence rates found, the small number of studies conducted to date that can help control such variability, and the methods assessing psychopathy in the reviewed studies. As already mentioned, prevalence rates vary greatly depending on factors such as the type of instrument used to define psychopathy, the participants' sex, and the type of sample from the general population. Given the small number of studies that currently constitute the scientific literature on the prevalence of psychopathy in the general population and the great heterogeneity of these studies in terms of their characteristics, it is very difficult to examine the effects of one of its factors while controlling the effect of the remaining factors. In fact, in this work, the number of subsamples/samples to examine gender prevalence was smaller than for calculating the overall prevalence. Therefore, in that smaller set, factors such as the type of instrument or sample did not reach statistical significance, thus preventing a more statistically potent analysis of the effect of gender after controlling the effects of these two factors and vice versa.

Among the factors that affected the variability of the prevalence of psychopathy, it is worth highlighting the type of instrument used to define psychopathy, since this factor points out important issue underlying this review. There is a high heterogeneity in the methods used to assess psychopathy in the reviewed studies. In addition, some of these method are more susceptible to criticisms related to their reliability and validity than others (e.g., the methods used in Hagnell et al., 1994; Gustafson and Ritzer, 1995; Pethman and Erlandsson, 2002). That heterogeneity and these criticisms go back to the problems in defining the construct of psychopathy. The different theoretical perspectives for this purpose which characterize the research of this construct are also an issue, and have already been discussed in the Introduction. In this sense, for example, an interesting exchange of views has recently been published on the debate over what components are essential to, or constitute part of psychopathy. It has also been discussed whether those components are necessary and/or sufficient (Brislin and Patrick, 2020a,b; Lynam, 2020; Marcus and Nagel, 2020). Consequently, one of the most important challenges that research in the area of psychopathy has to face is to achieve a valid and consensual definition of the construct of psychopathy and, related to this, to decide which instrument or instruments are the most valid and reliable to measure this construct. These needs are most evident when studying psychopathy in the general population because, as mentioned above, virtually all studies on psychopathy in the population of offenders or prisoners use the PCL-R or one of its versions (see the reviews of Beryl et al., 2014, and of Fox and DeLisi, 2019).

On the other hand, future research should also focus on the prevalence of the components of psychopathy, especially on the prevalence of psychopathic traits. Moreover, future research should also be conducted on the prevalence of the other personality constructs that are included under the Dark Triad label: Machiavellianism and narcissism.

Despite the above-mentioned limitations, the obtained results reflect relatively strong trends in the data that at least deserve to be the subject of future research and the formation of hypotheses to be taken into account in such research. In short, these trends allow the following conclusions to be drawn:

1) The prevalence of psychopathy in the general adult population can be estimated at 4.5%.

2) This prevalence is much lower than that found in the offender or prison population, which usually ranges between 10 and 35% (Nicholls et al., 2005; Guay et al., 2018; Fox and DeLisi, 2019).

3) The prevalence rates of psychopathy in the general population show considerable variation as a function of the type of instrument used to define psychopathy, the participants' sex, and the type of sample from the general population.

4) Using the PCL-R (or any of its versions), lower psychopathy prevalence rates are obtained than if self-reports of psychopathic personality traits are used.

5) As the PCL-R is currently considered the “gold standard” for the assessment and definition of psychopathy, the prevalence of psychopathy in the general population may be only 1.2% and, therefore, the difference with the prevalence of the offender or prison population may be even greater.

6) As is often the case in the offender and prison population, the prevalence of psychopathy in the general adult population is significantly higher among males than among females.

7) The prevalence of psychopathy is significantly higher among workers in some organizations and companies (e.g., managers, executives, procurement and supply professionals, advertising workers) than among university students or people from the general community. In turn, the prevalence of psychopathy among university students is significantly higher than among people from the general community, although the latter result could be due to the type of career that university students are pursuing (e.g., company careers vs. helping careers).