Chivalry norm has pervasive effects on the behavior of men during their disputes with women: Men make less violent threats/engage in less physical attacks when the adversary is female, even after been physically attacked by her

When men fight with women (versus other men): Limited offending during disputes. Richard B. Felson et al. Criminology, Oct 27 2022. https://doi.org/10.1111/1745-9125.12324

Abstract: What transpires in a dispute, even a violent dispute, is affected by the tendency for adversaries to engage in “limited offending.” We focus on one restraint: the tendency of men to limit their aggression in their disputes with women. Analyses are based on an incident-level survey about interpersonal disputes administered to 503 men who are incarcerated and 220 men who had never been incarcerated. Using multinomial and logistic regression models, we examined the extent to which an adversary's gender predicted dispute-related behaviors. The evidence suggests that the chivalry norm has pervasive effects on the behavior of men during their disputes with women. Men are more likely to engage in remedial actions (e.g., apologies) when their adversary is a woman, as opposed to another man. In addition, men are less likely to make violent threats and engage in physical attacks when their adversary is a woman, even after they have themselves been physically attacked. When men are violent, they are less likely to injure a woman than a man. However, the chivalry norm does not inhibit verbal aggression in these disputes: men are just as likely to engage in verbal attacks and nonviolent threats when the adversary is a woman.

The Oath Keepers' national organization is unusual among groups conducting political violence in that they seem to behave as a business

Klinenberg, Danny, Selling Violent Extremism (October 5, 2022). SSRN: http://dx.doi.org/10.2139/ssrn.4239242

Abstract: The Oath Keepers' national organization is unusual among groups conducting political violence in that they seem to behave as a business. Using leaked membership data, internal chat forums and publicly available articles posted to their website, I show that, unlike other far-right organizations, such as the Proud Boys, the Oath Keepers do not organize as a club. Rather, its behavior is better explained as a firm that adjusts the price of membership over time to maximize profit. I then estimate the Oath Keepers' price elasticity of demand for new membership using five membership sales between 2014 and 2018. I find the organization's demand is highly sensitive to changes in price. These results imply that political violence can be motivated by nonideological entrepreneurs maximizing profits under current legal institutions -- a chilling conclusion.

Keywords: Extremism, Applied microeconomics

JEL A10

In line with previous findings, especially Neuroticism, Extraversion, and Conscientiousness are genetically the most important personality traits for well-being

Unraveling the Relation Between Personality and Well-Being in a Genetically Informative Design. Dirk H. M. Pelt, Lianne P. de Vries, and Meike Bartels. European Journal of Personality, Oct 26 2022. https://doi.org/10.1177/08902070221134878

Abstract: In the current study, common and unique genetic and environmental influences on personality and a broad range of well-being measures were investigated. Data on the Big Five, life satisfaction, quality of life, self-rated health, loneliness, and depression from 14,253 twins and their siblings (age M: 31.82, SD: 14.41, range 16–97) from the Netherlands Twin Register were used in multivariate extended twin models. The best-fitting theoretical model indicated that genetic variance in personality and well-being traits can be decomposed into effects due to one general, common factor (Mdn: 60%, range 15%–89%), due to personality-specific (Mdn: 2%, range 0%–78%) and well-being-specific (Mdn: 12%, range 4%–35%) factors, and trait-specific effects (Mdn: 18%, range 0%–65%). Significant amounts of non-additive genetic influences on the traits’ (co)variances were found, while no evidence was found for quantitative or qualitative sex differences. Taken together, our study paints a fine-grained, complex picture of common and unique genetic and environmental effects on personality and well-being. Implications for the interpretation of shared variance, inflated phenotypic correlations between traits and future gene finding studies are discussed.

Discussion

Using a large population sample of twins and siblings, the current study provides detailed insights into the genetic overlap between personality and a broad range of well-being measures. Given our large sample size, the present study was well-powered. Overall, our results are in line with the previous finding that especially Neuroticism, Extraversion, and Conscientiousness are genetically the most important personality traits for well-being (Hahn et al., 2013; Røysamb et al., 2018; Weiss et al., 2008). Furthermore, the heritability of the personality traits of ∼40–55% (Vukasović & Bratko, 2015) and well-being traits of ∼30%–40% (Bartels, 2015; Nes & Røysamb, 2015) are comparable with previous meta-analyses.

Our results indicate that personality traits and well-being traits share considerable amounts of common genetic and environmental influences, yet that they are also influenced by their own domain-specific and trait-specific effects. Additive (vs. non-additive) genetic effects were more shared between personality traits and well-being traits, as no trait-specific additive effects were found after accounting for common effects. Non-additive genetic effects showed a greater variety in effects due to different sources. Below we discuss the results in relation to each of our three research questions in detail.

Genetic and Environmental Overlap Between Personality and Well-Being (RQ1)

Genetic and environmental effects shared between personality and well-being traits varied considerably across traits. Genetic effects due to the general, common factor ranged from 15% (Ag) to 89% (Ne) (Mdn: 60%). Genetic effects on the personality traits due to the personality-specific factor ranged from 0% (Ne) to 78% (Op) (Mdn: 2%). Genetic effects on the well-being traits due to the well-being-specific factor ranged from 4% (DEP) to 35% (SAT) (Mdn: 12%). Finally, trait-specific genetic effects ranged from 0% (SAT) to 65% (Co) (Mdn: 18%). Environmental effects were mostly trait-specific (Mdn: 68%, ranging from 26% for DEP to 91% for Op), and much less common (Mdn: 20%, ranging from 0% for Op to 72% for DEP) or domain-specific (Mdn: 9%, ranging from 2% for Ne, DEP, and LON to 43% for QOL). Of all personality traits, Neuroticism was most strongly related to well-being, and particularly strongly genetically related to depression and loneliness, in line with previous research (Abdellaoui et al., 2019; Fanous et al., 2002; Kendler et al., 2006; Okbay et al., 2016; Schermer & Martin, 2019). Because of its pivotal role, Neuroticism is sometimes included as a well-being trait (Baselmans et al., 2019a, 2019b). On the other hand, Openness, Agreeableness, and self-rated health appeared to mostly be genetically and environmentally distinct from the other traits.

Importantly, the percentages from the previous section are based on common genetic effects on personality and well-being once their respective shared variances have been taken into account. For example, Neuroticism showed the strongest bivariate genetic correlations with well-being traits, but also with the other personality traits. In the best-fitting theoretical model in which shared domain-specific variance was taken into account, it still showed the strongest overlap with well-being. Thus, genetic effects on Neuroticism and well-being were not due to the genetic overlap that Neuroticism shares with other personality traits, or the genetic overlap that well-being traits share with each other. The same was true for Conscientiousness and Extraversion. Earlier claims that these personality traits and well-being are influenced by cross-domain pleiotropic effects (Hahn et al., 2013; Røysamb et al., 2018; Weiss et al., 2008) thus seem to be robust.

Based on our results, it can be concluded that the genetic overlap between personality and well-being is quite large (Mdn: 60%). This is in line with a proposed (genetic) “covitality” factor (Figueredo et al., 2004; Weiss & Luciano, 2015) influencing the variation in both personality and well-being ratings: the recovering of such an overarching factor in our best-fitting model supports this claim. Based on the substantial genetic overlap, it has previously been suggested that “happiness is a personality thing” (Weiss et al., 2008). Yet, without explicit modeling of the direction of causation, personality may be a well-being thing just as well as well-being may be a personality thing (Keyes et al., 2015). At the phenotypic level, both directions of causality may indeed be simultaneously operating (e.g., Soto, 2015; Specht et al., 2013). However, the current study shows that shared genes will act as a confounder for these effects. Additional research on causality in which genetic confounding is taken into account is thus needed (Briley et al., 2018).

When these causal mechanisms become more clear, our results are informative for future intervention studies. Although both are relatively stable over the lifespan, well-being is thought to be more malleable than personality (Anusic & Schimmack, 2016) and several well-being interventions have proven to be successful (van Agteren et al., 2021). Again, genetic effects need to be taken into account, as they play a role in stability and change of both personality and well-being (Nes et al., 2006; Pedersen & Reynolds, 1998). By gaining more insights into what (genetically) separates well-being from personality, it will become easier in the future to target interventions specifically at effects unique to well-being.

Our findings on common, domain-specific, and trait-specific effects have implications for molecular genetic studies. GWASs are designed to identify the genetic variants associated with a trait. Several GWASs on personality (De Moor et al., 2015; Lo et al., 2017; van den Berg et al., 2016; Weiss et al., 2016) and well-being (Baselmans et al., 2019a; Okbay et al., 2016; Turley et al., 2018) have been published in recent years. Recently, multivariate methods have been developed to investigate the (latent) genetic structure underlying traits at a molecular genetic level and use this structure to find new genetic variants for the identified latent factors (Genomic SEM; Grotzinger et al., 2019). Our models can be used as input for such investigations. Ultimately, this should make it possible in the future to arrive at a clear picture of the variants that are uniquely associated with well-being and personality, or with both.

Based on our results, one could alternatively argue that, overall, personality and well-being are quite distinct (100%–60% = 40%). With regards to the overlap and distinction, we largely concur with Keyes and colleagues (2015) who noted that personality reflects how one functions in life, while well-being reflects how well one functions. Being both part of the process of functioning in life they have much in common, but they also differ in their role in this process. These differences and similarities are likely to be reflected in their genetic makeup.

The Influence and Interpretation of Domain-Specific Shared Variance

Although we fitted domain-specific factors mostly to control for domain-specific variance, our results can provide insights for the interpretation of these factors. In the CP models, we found that loadings of Neuroticism (∼ −.85), Extraversion (∼ .55), and Conscientiousness (∼ .46) on the common personality factor were sizeable, while loadings of Agreeableness (∼ .23) and Openness (∼ −.08) were low. We thus did not find strong support for a phenotypic common personality factor (referred to as the General Factor of Personality; van der Linden et al., 2016). At the same time, the domain-specific well-being factor was well-defined by all well-being traits in our CP models, with phenotypic loadings ranging from ∼.40 (self-rated health) to ∼ −.84 (loneliness). In addition, in the IP models, domain-specific effects were more pronounced for well-being compared to personality. These results provide evidence for a broad, general well-being factor underlying different well-being measures (e.g., Longo et al., 2016) and makes it plausible that this factor has a solid genetic basis (Bartels & Boomsma, 2009; Baselmans & Bartels, 2018).

Nevertheless, the superior fit of IP (vs. CP) models implies that these common factors must be interpreted with caution. This finding indicates that they may not be the causal factors influencing their indicators, as the common and unique effects operate at the indicator level, and not at the common factor level (Franić et al., 2013). Yet, the existence of a latent construct cannot be proven or disproven based on the relative fit of IP over CP models alone. For example, IP models tend to fit better than CP models when fitting them to the facets underlying each of the Big Five factors (Franić et al., 2014; Jang et al., 2002). Rather than dismissing the Big Five as constructs altogether, Jang et al. (2002) concluded that they “do not exist as veridical psychological entities per se, but rather they exist as useful heuristic devices that describe pleiotropic effects and the common influence of environmental factors on sets of individual facets.” (p. 99). Similarly, the common factors in the current study may be viewed as an organization of traits on which common genetic and environmental are operate, each of them also having their own unique influences. Ultimately, to answer the question what these common factors represent, multi-trait-multi-method (MTMM) studies based on ratings of personality and well-being (see Schimmack & Kim, 2020) in a genetically informative design are needed to accurately separate trait from method effects (Bartels et al., 2007; Borkenau et al., 2001).

Although not providing clear evidence on its meaning, the current study can parsimoniously explain why controlling for the shared Big Five variance reduces their correlations with well-being (Kallio Strand et al., 2021; Kim et al., 2018; Schimmack & Kim, 2020). In the suboptimal CP models, the genetic and environmental correlations between the latent general well-being and general personality factor were much higher (1.00. .96, and .81, for ADE respectively) than in the IP models (.25, 1.00, and .50, respectively). If then, in the CP models, the common genetic effects on indicators are aggregated to a higher level in an unbalanced way (as is the case for the higher-loading Neuroticism, Extraversion, and Conscientiousness, compared to Openness and Agreeableness), then this will artificially lead to higher genetic correlations between the common factors. These stronger genetic correlations translate to the phenotypic level. Thus, when we control for the shared phenotypic personality variance, then we are haphazardly controlling for the “true” underlying genetic and environmental effects at the indicator level, reducing the correlations between the Big Five and well-being. Again, this hypothesis needs to be tested in the future using genetically informative MTMM studies.

Non-additive Genetic Effects (RQ2)

In line with previous work, significant amounts of non-additive variance were found to influence both personality and well-being, and their overlap (Bartels & Boomsma, 2009; Hahn et al., 2013; Keller et al., 2005). Non-additive genetic effects accounted for between 14% (depressive symptoms) to 95% (Agreeableness) of the total genetic variance in the traits (Table 4). In the Cholesky model, absolute non-additive genetic correlations ranged from .13 to .93 (Mdn: .47). This is important, for example, for future molecular genetic studies trying to identify the genes associated with personality and well-being, since the methods used in such studies often assume additive genetic effects (Visscher et al., 2017). The amount of non-additive variance present in traits is also important for theoretical reasons, as it is assumed to be indicative of the evolutionary pressures that have caused these traits to emerge (Penke et al., 2007; Verweij et al., 2012).

With our current sample size, we had sufficient power to detect non-additive genetic effects (D), but this does not apply to all previous studies on this topic. We found that especially for D, traits differed in the amount of effects due to common, domain-specific, and trait-specific effects. This will obscure results when effects are aggregated to higher trait levels. For example, when one creates a general well-being scale from multiple scales that differ in their common and unique additive and non-additive effects, then the resulting general measure will be a cloudy mix of these different genetic effects. These findings stress the importance of modeling higher order factors (e.g., “general well-being”) as latent variables in twin designs, to uncover the nuances in their underlying genetic effects.

Sex Differences in Genetic and Environmental Effects (RQ3)

In our large sample, we found moderate to small mean sex differences on the Big Five. In line with previous studies (Costa et al., 2001; Schmitt et al., 2008; Weisberg et al., 2011), females scored higher on Neuroticism and Agreeableness, and somewhat higher on Conscientiousness. In contrast to other studies, we found no sex differences in Extraversion, which may be due to our focus on the Big Five factors rather than facets residing below the Big Five. Females tend to score higher on the facet Enthusiasm and males on Assertiveness (Costa et al., 2001; Feingold, 1994; Weisberg et al., 2011). At the aggregate factor level, these differences may have canceled each other out. Sex differences on well-being traits were generally small, with the largest effect found for depression, also replicating previous work (Batz & Tay, 2018; Batz-Barbarich et al., 2018; Eaton et al., 2012).

Given our large sample and similar results from previous studies (Bartels, 2015; Keyes et al., 2010; Røysamb et al., 2018; South et al., 2018; Vukasović & Bratko, 2015), it seems safe to assume that, at the aggregate level, the same genes influence personality and well-being for males and females, and to the same extent. This is important information for theoretical and practical reasons as it suggests that mean differences are probably due to non-shared environmental circumstances. These non-shared environmental exposures reflect idiosyncratic experiences that only a single twin within the same family experiences, making them more different from their siblings. This may include life events, differences in socialization, different opportunities, or specific gender roles (South et al., 2018). Our results further imply that in future gene finding studies, male-specific and female-specific genes for personality and well-being are unlikely to be found.

It is tempting to conclude that the mean sex differences on personality and well-being are completely unrelated to genetic differences. However, genes may still play a role through more subtle processes such as gene-environment interplay. For example, we investigated genetic and environmental influences independent of age effects by regressing them out from the traits. It may be that a sex by age interaction is present, implying that quantitative or qualitative sex differences are only apparent at specific ages (e.g., during adolescence). For instance, puberty seems to coincide with increases in mean levels of internalizing symptoms and with increases in its heritability, particularly in girls (Bergen et al., 2007; Patterson et al., 2018). Future studies investigating genetic and environmental effects as a function of both age and sex are needed to confirm such processes for personality and well-being.

It is also possible that genetic differences exist between males and females, but that these are masked by unmodeled gene by environment interaction (GxE) effects. Traditional twin models assume that GxE is not present, that is, that genetic effects are similar across different environments and/or subgroups. This may not be the case; Nes et al. (2010b), for example, showed that the environmental exposure marriage influenced the heritability estimates of SWB. Importantly, these marriage effects differed across males and females. GxE effects may also explain why gender differences tend to be larger in more prosperous societies: possible genetic differences between males and females may be more easily expressed in developed countries (Schmitt et al., 2008). In our study, we investigated a sample from the Netherlands, a highly developed country with relatively equal opportunities for males and females. Within our egalitarian sample, the smaller amount of variance in opportunities and gender roles between males and females may have attenuated the expression of genetic sex differences. Future studies that explicitly model GxE effects for males and females, preferably across countries with different developmental standards, are thus needed.

Limitations

There are limitations to this study. First, as this study was conducted in a single context, the Netherlands, results may not generalize to other contexts. The heritability estimates of personality traits have been found to differ across cultures (Jang et al., 1998, 2002, 2006). In addition, culture has been found to moderate mean well-being (Deaton, 2008) and mean personality (Schmitt et al., 2007) levels, and their associations (Kim et al., 2012, 2018). Thus, future studies with samples from different countries are needed to investigate whether our results apply to other cultural contexts.

Second, the data used were cross-sectional in nature and we therefore cannot make claims about causal effects or temporal changes in personality and well-being. Nevertheless, our results can still be useful as they indicate that genetic confounding needs to be taken into account in future studies investigating associations between personality and well-being. The growing availability of polygenic scores (i.e., individuals’ genetic risk for a given trait based on the effect sizes from GWAS; Wray et al., 2014) will increasingly allow for this. A third important limitation is that all our trait measures were based on self-reports. It could therefore be the case that the common effects on the personality and well-being traits were partly driven by common method biases (CMB), such as response styles related to item keying, social desirability, or acquiescence, which have been found to be partially heritable (Kam et al., 2013; Melchers et al., 2018). This mechanism is especially relevant for the common variance among personality traits, as it is proposed to mainly reflect CMB (Chang et al., 2012). Although this possibility cannot be completely ruled out, our findings suggest that such effects may be limited. This is because IP models fit better than CP models: if CMB would be driving the associations between variables, then it would probably have led to such strong correlations between the traits that phenotypic common factors would be more pronounced (and lead to improved fit). As mentioned previously, additional genetic research on the overlap between personality and well-being using multiple raters is needed, since such designs can control for rater-specific biases (Bartels et al., 2007; Borkenau et al., 2001).

Fourth, although the (extended) CTD has proven to be a robust method for estimating the heritability of complex traits, it comes with its limitations (Røysamb & Tambs, 2016). First, the CTD only provides an omnibus (upper-limit) test of the total amount of genetic and environmental effects on traits, without identifying specific genes (or environments). Relatedly, in addition to GxE effects, gene-environment correlations (rGE) are assumed to be non-present (Verhulst & Hatemi, 2013). These limitations notwithstanding, the results from extended CTD designs can still be informative for subsequent gene finding studies (e.g., Lo et al., 2017) or investigations of gene-environment interplay (e.g., Krueger et al., 2008). Finally, assortative mating (when people with the same phenotype or genotype tend to mate more than expected at random chance levels) is also not accounted for. However, little assortative mating for personality and well-being is found previously (Luo, 2017).

Finally, in this study, we incorporated a wide range of related traits to cover the broader well-being domain. However, the scope could be expanded by including more traits such as happiness or self-esteem (Bartels & Boomsma, 2009; Diener, 1984; Hufer-Thamm & Riemann, 2021Hufer‐Thamm & Riemann, 2021), which were not available to us. In addition, different conceptualizations and measures of well-being exist, which include (combinations of) hedonic, eudaimonic, emotional, and social aspects (e.g., Keyes et al., 2015). On the personality side, alternatives to the Five-Factor Model exist, such as the HEXACO six-factor model (Ashton & Lee, 2001). These models may cover broader or slightly different aspects of personality and well-being, which in turn may lead to finding different shared and unique effects in relation to well-being. However, because of the large overlap between different conceptualizations of well-being (also genetically; Baselmans & Bartels, 2018), and different personality models (Ludeke et al., 2019), results will likely be highly similar to ours (see Keyes et al., 2015).

The Paradox of Wealthy Nations’ Low Adolescent Life Satisfaction can largely be attributed to higher learning intensity

The Paradox of Wealthy Nations’ Low Adolescent Life Satisfaction. Robert Rudolf & Dirk Bethmann. Journal of Happiness Studies, Oct 26 2022. https://link.springer.com/article/10.1007/s10902-022-00595-2

Abstract: Using PISA 2018 data from nearly half a million 15-year-olds across 72 middle- and high-income countries, this study investigates the relationship between economic development and adolescent subjective well-being. Findings indicate a negative log-linear relationship between per-capita GDP and adolescent life satisfaction. The negative nexus stands in stark contrast to the otherwise positive relationship found between GDP per capita and adult life satisfaction for the same countries. Results are robust to various model specifications and both macro and micro approaches. Moreover, our analysis suggests that this apparent paradox can largely be attributed to higher learning intensity in advanced countries. Effects are found to be more pronounced for girls than for boys.

Notes

We define learning intensity as the product of quantity and complexity of learning tasks completed by a student within a given time period, e.g., a school year. The amount of learning that happens in school is known to be positively correlated with the level of economic development of a country. Due to differing returns to education across nations, Becker et al. (1990) concluded that “societies with limited human capital choose large families and invest little in each member; those with abundant human capital do the opposite”. Hence, parental investment in education of their offspring is highest in high-income countries, and so are the expectations that teachers and parents have in the actual cognitive efforts that children exert (Becker et al., 1990; Mincer, 1984). Given the importance of education and the overall level of development, high-income countries also provide higher school quality (World Bank, 2017; 2021). According to the World Bank (2017), “37 million African children will learn so little in school that they will not be much better off than kids who never attended school”. The secular expansion of schooling and of cognitive effort over the twentieth century economic development processes of OECD nations have further been associated with generational gains in intelligence levels and growth in the human prefrontal cortex (Blair et al., 2005; Flynn 1984, 1987).

A growing body of literature documents declining levels of adolescent SWB between the ages 10 and 15 (Casas and González-Carrasco, 2019). If it is true that schoolwork pressure and test requirements increased during early teen age, it would be advisable to control for education-related factors (Wiklund et al., 2012). Comparing PISA 2015 and 2018 data, Marquez and Long (2021) find declining levels of adolescent life satisfaction in 39 out of 46 countries over time.

We found that curiosity about the outcome of a lottery was enhanced for lotteries that were freely chosen, relative to lotteries that were equally preferred but not freely chosen

Choice Boosts Curiosity. Patricia Romero Verdugo et al. Psychological Science, October 26, 2022. https://doi.org/10.1177/09567976221082637

Abstract: In our connected era, we spend significant time and effort satisfying our curiosity. Often, we choose which information we seek, but sometimes the selection is made for us. We hypothesized that humans exhibit enhanced curiosity in the context of choice. We designed a task in which healthy participants saw two lotteries on each trial. On some trials, participants chose which lottery to play. On other trials, the lottery was selected for them. Participants then indicated their curiosity about the outcome of the to-be-played lottery via self-report ratings (Experiment 1, N = 34) or willingness-to-wait decisions (Experiment 2, N = 34). We found that participants exhibited higher curiosity ratings and greater willingness to wait for the outcome of lotteries they had chosen than for lotteries that had been selected for them (controlling for initial preference). This demonstrates that choice boosts curiosity, which may have implications for boosting learning, memory, and motivation.

Discussion

In the current set of experiments, we assessed whether freely choosing which information to sample increased curiosity. We found that curiosity about the outcome of a lottery was enhanced for lotteries that were freely chosen, relative to lotteries that were equally preferred but not freely chosen. In the absence of choice, curiosity was higher for lotteries that were preferred than for those that were not preferred. Furthermore, we investigated the effects of expected value and outcome uncertainty of lotteries on curiosity. In line with previous findings (Charpentier et al., 2018; Kobayashi et al., 2019; van Lieshout et al., 2018), our results showed that curiosity increased as a function of both of these factors. Interestingly, the effect of choice was independent of expected value and outcome uncertainty: Choice enhanced curiosity irrespective of how valuable the outcome was expected to be and of how much information could be gained from seeing it.

Choice boosted curiosity when participants reported their curiosity levels both explicitly (Experiment 1) and implicitly, by deciding to “pay” for information with their time (i.e., willingness to wait; Experiment 2). The fact that participants not only self-reported increased curiosity but were willing to give up a valuable resource (i.e., time) to see the outcome makes it unlikely that the effects were due to any inferred experimental-demand characteristics.

Our finding that choice increased curiosity generalizes the phenomenon of choice-induced preference change, observed in studies on value-based choice (Brehm, 1956; Izuma et al., 2010; Sharot et al., 2009, 2010), to the context of information seeking: Choice might boost the value of information just as it boosts the value of chosen options. Future studies could employ pre- and postchoice neuroeconomic preference tools to establish whether choice indeed alters the subjective value assigned to information.

Regarding what mechanisms underlie this choice-related curiosity boost, one possibility is that choice enhanced curiosity via an increase in (subjective) expected value. On the basis of previous evidence that perceived control inflates the subjective value of options proportionately to their objective value (Wang & Delgado, 2019), we initially predicted that an effect of choice on curiosity mediated by an increase in subjective expected value would result in an interaction between choice and expected value, whereas we observed an additive effect. Nevertheless, it is possible that choice enhanced curiosity by increasing subjective expected value equally for all levels of objective expected value. We cannot evaluate this possibility with our current data, but it is an interesting question that could be addressed in future studies using a revealed-preference procedure to assess participants’ subjective valuations of the chosen and unchosen vases.

Another possibility is that this choice-induced increase in curiosity resulted from participants’ drive to improve the quality of their decisions (i.e., learning to make better choices). Humans might learn through lifelong exposure that information and self-evaluation are especially useful under circumstances of agency (e.g., choice has been suggested to boost prediction errors; Cockburn et al., 2014). Hence, even though in our task, choice and no choice did not differ regarding learning opportunity, choice might generally boost capacity (or willingness) to learn, having enhanced curiosity in choice versus no choice in our experiment.

Alternatively, it could be that, rather than choice enhancing curiosity, no choice reduced it. In the choice condition, participants’ preferred lottery was always selected, but it was selected only half of the time in the no-choice condition. This could have generated an aversive association between no-choice and unfavored options or outcomes, in comparison with which choice appeared to boost curiosity. In this case, we would expect the effect of choice on curiosity to have increased throughout the task, as participants learned the contingencies and developed the aversive association for the no-choice condition. Follow-up analyses suggest that the effect of choice on curiosity was stable throughout the task (see the Supplemental Material), rendering this explanation unlikely. It remains possible that participants exhibited an aversion toward the no-choice condition right from the start. Further investigation is needed to assess to what extent choice increases curiosity and no-choice decreases it, for instance in a between-subjects experiment, in which one group performs the current task (choice vs. no choice) and another a no-choice task (otherwise matched).

An intriguing question for future work pertains to the neural mechanisms underlying the effect of the choice-related curiosity boost. Given the link between midbrain dopamine neuronal firing and information-prediction errors (Bromberg-Martin & Hikosaka, 2011), it is plausible that the mechanism suggested to underlie choice-induced preference change, involving feedback projections from the striatum to the midbrain (Cockburn et al., 2014), could also account for choice-induced increases in curiosity.

In addition, the noradrenaline system might play a role, given its implication in modulating arousal, which is likely enhanced in conditions of increased autonomy (Howells et al., 2010; Sara & Bouret, 2012; Varazzani et al., 2015). A role for noradrenaline in information seeking is also supported by recent evidence suggesting pharmacological blockade of noradrenergic-receptor stimulation with propranolol, decreased information gathering in a task in which participants uncovered cards until they felt confident to guess a predominant feature (Hauser et al., 2018). Hence, the increased autonomy experienced under choice in our task may have further implicated the noradrenergic system, boosting participants’ information-seeking drive.

Preference enhanced curiosity. In other words, for lotteries selected without participants having a choice, they were more curious about lotteries that they preferred than those they did not, suggesting that preference itself boosts the perceived value of information. Alternatively, it is possible that the mechanism underlying the effect of preference on curiosity was akin to that of choice, given that in our experiments, participants had to provide a preference indication (i.e., engaging in a comparison process, making a decision, and executing a response), which they might have experienced as a kind of choice.

Curiosity increased as a function of expected value. In other words, the more positive participants expected the information to be, the more curious they were. This aligns with previous findings that humans prefer information about future desirable outcomes over information about undesirable outcomes (Kobayashi et al., 2019) and are willing to pay for the positive information (Charpentier et al., 2018). This effect of expected value was more marked than in previous studies with a similar paradigm but without the valuation and choice phases (van Lieshout, de Lange, & Cools, 2021; van Lieshout et al., 2018; van Lieshout, Traast, et al., 2021). Unlike in the previous studies, participants in the current study were instructed to make a value-based comparison between pairs of lotteries to indicate their preferred option and to make a selection. These task features likely increased the salience of the expected value of the lotteries. Other characteristics of the task might have boosted participants’ attention and/or engagement further, including the longer duration of the lottery presentation (up to 11 s, vs. 3 s in previous studies) as well as the fact that participants got to continue with their preferred lotteries in the majority of trials (75%), whereas in previous studies, participants did not have a choice between lotteries. Surprisingly, we found that in lotteries with higher expected value, the effect of preference on implicit curiosity diminished. One possible explanation is that this higher expected value became more salient than other features of the trial, driving participants to focus their attention on the points they could win and less so on whether their preferred lottery was selected. Nevertheless, it is not clear to us why this would be the case when curiosity was assessed implicitly but not explicitly.

Our findings are in line with recent evidence that autonomy boosts curiosity: Participants who watched a video of their choice (from a given set) self-reported higher interest in the topic than those who watched a video without a choice (Schutte & Malouff, 2019), and participants bid larger amounts for lotteries they chose than those they had not chosen (Jiwa et al., 2021). However, in these recent studies, autonomy was confounded with preference. In the earlier study (Schutte & Malouff, 2019), the researchers did not consider participants’ preferences when assigning the options. This resulted in participants in the choice condition likely choosing their preferred option, and participants in the no-choice condition likely receiving their not-preferred option (with a probability of .66 because there were three options). In the more recent study (Jiwa et al., 2021), participants always made a choice, but half of the times, their choice was vetoed (resulting in a loss of agency). In agency trials, participants always received their chosen (presumably preferred) option, whereas in no-agency trials, they always received an unchosen (presumably not preferred) option. These designs did not enable dissociation between preference and choice; hence, the supposed effect of choice on curiosity also included a (confounded) effect of preference. Our study goes beyond these previous efforts by quantifying the effects of choice and preference separately, by demonstrating that these two factors boost curiosity independently, and by reporting these effects using both explicit (ratings) and implicit (willingness-to-wait) measures of curiosity.

Our paradigm enabled us to manipulate choice, outcome uncertainty, and expected value in a quantitative and controlled fashion, allowing us to draw inferences independently of extraneous variables. Although other paradigms relate more closely to real-world situations (e.g., trivia paradigms; Kang et al., 2009; Ligneul et al., 2018), these more naturalistic paradigms pose more difficulty in deconfounding factors of no interest, such as participants’ real-life knowledge and interests. Nevertheless, it would be important for future research to broaden the scope of application by, for instance, combining performance on this task with tracking sampling of real-life information (e.g., browsing news sites).

Furthermore, curiosity (Gruber et al., 2014; Kang et al., 2009; Kidd & Hayden, 2015) and choice (Cockburn et al., 2014; Murty et al., 2015) have both been shown to boost learning and memory, implicating couplings between reward-related areas (i.e., ventral striatum) and the hippocampus. However, the exact nature of the link between curiosity and choice in boosting memory remains unknown, an interesting question being whether enhanced memory for chosen items is mediated by increased curiosity. One step in that direction would be to introduce a curiosity assessment in paradigms that manipulate autonomy in learning—for example, memory for novel associations (Murty et al., 2015) or about stories and pseudoinformation—connecting these so far independently studied effects. Our findings may open the way for future work aimed at linking autonomy with curiosity and dopamine or noradrenaline as well as further investigating the nature of the relationship between choice, curiosity, learning, and memory.

Less than one in five people have actually ever acted on their favorite or most-recurring sexual fantasy

Sexual Fantasy Research: A Contemporary Review. Justin J. Lehmiller, Aki M.Gormezano. Current Opinion in Psychology, October 25 2022, 101496. https://doi.org/10.1016/j.copsyc.2022.101496

Abstract: Understanding sexual fantasies is central to understanding human sexuality. The current review synthesizes recent trends and findings in sexual fantasy research and points to several important conclusions. First, few sexual fantasies appear to be statistically unusual or rare. Second, while the bulk of sexual fantasy research to date has focused on young, cisgender, heterosexual adults in North America, studies that have accounted for diversity (e.g., LGBTQ+ inclusion, cross-cultural work) reveal multiple similarities in sexual fantasy content, but also several notable differences. Third, what people fantasize about is not necessarily synonymous with what they are interested in or do in-person. Limitations and directions for future research on sexual fantasy are discussed.

While making inferences about the future, most of us intuitively discount outlying values in a sample, treating the data as a Gaussian distribution

How people deal with …............................ outliers. Jennifer E. Dannals,Daniel M. Oppenheimer. Journal of Behavioral Decision Making, October 23 2022. https://doi.org/10.1002/bdm.2303

Abstract: People regularly make sense of distributions that are complicated by noise. How do individuals determine whether an outlying observation should be incorporated into one's understanding of the true distribution of the population or considered a fluke that ought to be disregarded? In a simple prediction task, we examine how individuals incorporate outliers and compare their behavior to various prescriptive models (e.g., averaging and tests of discordancy). We find that, on average, individuals do discount outlying values and that their outlier detection strategies approximate approaches that statisticians have recommended for Gaussian distributions, even when the observed distributions are not Gaussian. However, there are notable differences in treatment of outliers across individuals.

The proportion of lone shoppers was higher in a used versus a regular bookstore, lone individuals were more likely to select a used over a new product, people without a date on Valentine’s Day expressed stronger preference for used products

Feeling Lonely Increases Interest in Previously Owned Products. Feifei Huang and Ayelet Fishbach. Journal of Marketing Research, Volume 58, Issue 5, Jun 22 2021. https://doi.org/10.1177/00222437211030685

Abstract: Consumption of used products has the potential to symbolically connect present and previous users of these products, something that may appeal to lonely consumers. Accordingly, across seven studies, feeling lonely increased consumers’ preference for previously owned products. Specifically, the authors found that the proportion of lone shoppers was higher in a used versus a regular bookstore, lone individuals (vs. those sitting in pairs) were more likely to select a used over a new product, people without (vs. with) a date on Valentine’s Day expressed stronger preference for used products, and individual differences in loneliness during the COVID-19 pandemic predicted interest in used products. Other studies documented that the desire to symbolically connect underlies the effect of loneliness on consumption. At a time when loneliness is on the rise, the authors discuss implications for the marketing of used products and how loneliness might motivate consumers to reduce waste.

Overall, mental effort felt aversive in different tasks, in different populations, and on different continents; paradoxically, some also love chess or brain teasers

David, Louise, Eliana Vassena, and Erik Bijleveld. 2022. “The Aversiveness of Mental Effort: A Meta-analysis.” PsyArXiv. October 25. psyarxiv.com/m8zf6

Abstract: Influential theories in psychology, neuroscience, and economics assume that the exertion of mental effort should feel aversive. Yet, this assumption is usually untested, and it is challenged by casual observations and previous studies. Here we test (a) whether mental effort is generally experienced as aversive and (b) whether the association between mental effort and aversive feelings depends on population and task characteristics. We meta-analyzed a set of studies (358 tasks, 4670 people) that assessed perceived mental effort and negative affect. As expected, we found a strong positive association between mental effort and negative affect. Surprisingly, just one of our 15 moderators had a significant effect (effort felt somewhat less aversive in studies from Asia vs. Europe and North America). Overall, mental effort felt aversive in different tasks, in different populations, and on different continents. Supporting theories that conceptualize effort as a cost, we suggest that mental effort is inherently aversive.

Mistakenly, respondents from the online sample believed that people on parole would be much more likely to deceive than their counterparts

The public’s overestimation of immorality of formerly incarcerated people. Sarah Kuehn & Joachim Vosgerau. Journal of Experimental Criminology, Oct 22 2022. https://link.springer.com/article/10.1007/s11292-022-09534-w

Abstract

Objectives: This study tests if the public overestimates the immoral behavior of formerly incarcerated people.

Methods: In a benchmark study with people on parole and people without a criminal record, participants played a game that allowed them to deceive their counterparts in order to make more money. A subsequent prediction study asked an online US-nationally representative sample to estimate how both groups played the game. By comparing the estimated likelihoods to the observed likelihoods of deception we examine if people correctly assess the deception rates of both groups.

Results: Both groups showed an equal propensity to deceive. In contrast, respondents from the online sample believed that people on parole would be much more likely to deceive than their counterparts.

Conclusions: The results suggest that the public holds stigmatizing attitudes towards formerly incarcerated people, which can be a detriment to successful reentry into their communities.

The Temporal Doppler Effect (the subjective perception that the past is further away than the future) couldn't be replicated; in some cases, the correlations were significant in the opposite direction

Is the past farther than the future? A registered replication and test of the time-expansion hypothesis based on the filling rate of duration. Qinjing Zhang et al. Cortex, October 23 2022. https://doi.org/10.1016/j.cortex.2022.10.005

Abstract: Caruso et al. (2013) reported the Temporal Doppler Effect (TDE), in which people feel that the past is farther than the future. In this study, we made two high-power (N = 2244 in total), direct replication studies of Caruso et al., and additionally examined whether illusory temporal expansion, depending on the degree of fulfillment in durations, is related to the TDE. We predicted that the past would be felt farther than the future because the filling rate of duration of the past should be higher than that of the future. The results showed that psychological distance was significantly closer in the past than in the future and was inconsistently correlated with the filling rate of duration or the number and length of events and errands. Further, in some cases, the correlations were significant in the opposite direction of the predictions. Overall, our results did not replicate the previous findings but were reversed, and the filling rate of duration failed to explain the psychological distance. Based on these findings, we highlight the aspects that need to be clarified in future TDE studies.

Keywords: Temporal Doppler Effectfilling rate of durationpsychological distancefilled-duration illusion

6. General Discussion

This is a registered report per the Caruso et al.’s (2013) Studies 1a and 1b, which aimed to examine differences in psychological distance underlying past and future conditions (TDE) and to investigate the relationship between psychological distance and the filling rate of duration inspired by FDI studies in time perception. In our Study 1, the results showed that the past felt closer than the future, which is the opposite of H1, and suggested a failure to replicate the Caruso et al.’s (2013) Study 1a. We also examined whether the TDE could be explained by the filling rate of duration. The results indicated that the filling rate of duration was higher in the past than in the future, as predicted. The correlation between psychological distance and the length of errands and events was significantly positive, however, no significant correlation between psychological distance and the filling rate of duration, the number of errands and events were observed. In other words, our hypothesis that the filling rate of duration was higher in the past than in the future and had an effect on the TDE was not supported as a whole.

Next, in our Study 2, in which the time scale was changed from 1 month to 1 year, the results also indicated that the past felt closer than the future. It showed an opposite direction from H1 and suggested that the Caruso et al.’s (2013) Study 1b was not replicated. We then examined whether the TDE could be explained by the filling rate of duration. In H2-1, the filling rate of duration was higher in the past than in the future. In H2-2, there was a significant negative correlation between psychological distance and the filling rate of duration. In other words, our hypothesis that the filling rate of duration was higher in the past than in the future and that this had an effect on the TDE was not supported as a whole.

One of the aims of this study was to contribute to the robustness and transparency of the TDE research using the Registered Reports system. Although approximately 1000 people participated in Studies 1 and 2 to increase statistical power of the test, the TDE was not replicated (rather, our results were the opposite of the original research).

Investigating what contributed to these discrepancies in the results between the studies would be beneficial in forming a better understanding of the TDE. Indeed, there are several differences in the research methodology between Caruso et al. (2013) and our study: (i) our experiment used crowdsourcing services rather than face-to-face methods; (ii) the instructions and questionnaires were written in Japanese, and only Japanese people participated in the experiment; and (iii) the experiment was conducted during the COVID-19 pandemic. In the following section, we discuss these differences and how they influence the replication of the TDE.

First, unlike the previous study, we used crowdsourcing to recruit participants. Previous studies show that even for demanding cognitive and perceptual experiments, web experiments do not reduce data quality (Germine et al., 2012). Therefore, it is unlikely that the crowdsourced web experiment, especially with the present less demanding task compared with perceptual experiments, caused any significant deterioration in measurement accuracy or failed to detect any true effects that should have existed. In addition, we excluded data from participants who did not respond properly to the ACQ to ensure the quality of our data. These points led us to consider that the difference in the experimental platform did not play a major role in the present failure to replicate Caruso et al. (2013).

Second, several linguistic and cultural differences exist. In Japanese, the past is sometimes expressed as “mae (前)” which means “before” as an expression of time, while it also means “front” referring to a spatial direction, and the future as “ato (後)” which means both “after” and “back.” This suggests that the spatio-temporal metaphors in Japanese and English may be reversed. This reversal in the spatio-temporal metaphors may have led to the different results on the TDE between the previous and present studies. It should be noted that even if the spatio-temporal metaphors are reversed between Japanese and English speakers, it does not affect the original explanation of the TDE that the future approaches the present and the past moves away from it. This is because the mechanism proposed by Caruso et al. (2013), as an analogy to the Doppler effect in physics, focuses on temporal distance, that is, whether the past or future approaches or moves away from the present on the temporal dimension. In their explanation, the movement on the psychological temporal dimension is critical, regardless of the spatial metaphor unique to Japanese. Therefore, the TDE mechanism proposed by Caruso et al. cannot explain our results from the Japanese sample. Nevertheless, cross-cultural comparative studies focusing more on this point are warranted since the contributions of language and culture to the TDE, or possibly the mental timeline (Starr & Srinivasan, 2021), are important for clarifying its cognitive mechanism and generalizability.

In terms of conducting the experiment during the COVID-19 pandemic, the tendency to think about the past rather than the uncertain future may have strengthened, which may have led to an opposite result to that of Caruso et al. (2013). Previous findings showing that the tendency to think about the past, such as nostalgia, increases when psychological threat and loneliness are high, can suggest this possibility (Routledge et al., 2013; Wildschut et al., 2006, 2010). Indeed, the findings of this study that the filling rate of duration is higher in the past than in the future seem to be part of the tendency that the phenomenon of thinking more about the past rather than the uncertain future was strengthened during the COVID-19. However, since these results are not a direct indicator of the aforementioned time orientation, and this study was the first to report on the TDE during the pandemic, this influence cannot be concluded. It should be discussed from the integrated view of this study and subsequent studies that examine the TDE during the pandemic. It should also be noted that a comparison with previous studies examining the TDE before the pandemic is necessary in such cases.

Thus, more evidence is needed to determine whether the methodological and contextual differences between our study and Caruso et al. (2013) influence the TDE, as well as to understand the underlying mechanism. Moreover, the mechanism of the TDE needs to be discussed according to the differences mentioned above. This study attempts to explain the TDE based on the filling rate of duration. Although the filling rate of duration was higher in the past than in the future, as we predicted, the correlation with psychological distance was extremely weak in Study 1, and contrary to our prediction, a negative correlation was observed in Study 2. These results suggest that it is not appropriate to explain the TDE based on the filling rate of duration. However, this mechanism-oriented approach is crucial in itself, and rather than just examining whether the phenomenon is related to some factors, as in previous TDE studies, future TDE studies should focus more on the underlying cognitive mechanism. Importantly, this requires the TDE to be replicated robustly. Moreover, because there is a possibility that the TDE may not be replicated, as in our study, it is appropriate to conduct the study as a registered report to prevent publication bias.

In the present study, the TDE was not replicated as already known (although there are several possible influences) and the mechanism remains unclear. Given the sample size, the TDE does not appear to be a robust and culturally universal phenomenon, and there still seems to be room for reconsideration of this phenomenon and its mechanism.