Predicting Regional Variations in Nationalism with Online Expression of Disgust in China

Predicting Regional Variations in Nationalism with Online Expression of Disgust in China. Shuqing Gao,  Hao Chen,  Kaisheng Lai and  Weining Qian. Front. Psychol., provisionally accepted Apr 2021. DOI 10.3389/fpsyg.2021.564386

Abstract: Disgust is one of the basic emotions and is part of the behavioral immune system, which evolutionarily protects humans from toxic substances as well as from contamination threats by out-group members. Previous works have revealed that disgust not only activates humans’ defense against potential individual and collective threats but also leads to severe moral judgments, negative intergroup attitudes, and even conservative political orientations. As is already known, nationalism is an ideology that features both negative feelings toward out-groups and beliefs about native superiority or privileges. Evidence from previous studies suggests that disgust is related to nationalism’s several components but lacks direct research on nationalism and disgust. The current study examined the relationship between disgust and nationalism in China at both individual and regional levels. In study 1, participants temporally induced disgust (vs. control) increased the adoption of nationalism. In Study 2, we analyzed covariation in disgust expression in the Chinese micro-blog Weibo and nationalism index as part of an online large-scale political survey zuobiao.me at the province level across Mainland China. The results showed that online expression of disgust positively predicted the nationalistic orientation at the regional level. Finally, we discussed how the findings shed light on research concerning online emotion expression and potential future directions.

Are citizens who share fake news ignorant and lazy? Are they fueled by sinister motives, seeking to disrupt the social status quo? Or do they seek to attack partisan opponents in an increasingly polarized political environment?

Osmundsen, Mathias, Alexander Bor, Peter B. Vahlstrup, Anja Bechmann, and Michael Bang Petersen. 2020. “Partisan Polarization Is the Primary Psychological Motivation Behind Political Fake News Sharing on Twitter.” PsyArXiv. March 25. doi:10.31234/osf.io/v45bk

Abstract: The rise of “fake news” is a major concern in contemporary Western democracies. Yet, research on the psychological motivations behind the spread of political fake news on social media is surprisingly limited. Are citizens who share fake news ignorant and lazy? Are they fueled by sinister motives, seeking to disrupt the social status quo? Or do they seek to attack partisan opponents in an increasingly polarized political environment? This manuscript is the first to test these competing hypotheses based on a careful mapping of psychological profiles of over 2,300 American Twitter users linked to behavioral sharing data and sentiment analyses of more than 500,000 news story headlines. The findings contradict the ignorance perspective but provide some support for the disruption perspective and strong support for the partisan polarization perspective. Thus, individuals who report hating their political opponents are the most likely to share political fake news and selectively share content that is useful for derogating these opponents. Overall, our findings show that fake news sharing is fueled by the same psychological motivations that drive other forms of partisan behavior, including sharing partisan news from traditional and credible news sources.

Overestimation? The projected global mean sea-level rise is about 25% lower at the end of this century in the eddying model

Ocean eddies strongly affect global mean sea-level projections. René M. van Westen and Henk A. Dijkstra. Science Advances  Apr 9 2021, Vol. 7, no. 15, eabf1674

DOI: 10.1126/sciadv.abf1674

Abstract: Current sea-level projections are based on climate models in which the effects of ocean eddies are parameterized. Here, we investigate the effect of ocean eddies on global mean sea-level rise (GMSLR) projections, using climate model simulations. Explicitly resolving ocean eddies leads to a more realistic Southern Ocean temperature distribution and volume transport. These quantities control the rate of basal melt, which eventually results in Antarctic mass loss. In a model with resolved ocean eddies, the Southern Ocean temperature changes lead to a smaller Antarctic GMSLR contribution compared to the same model in which eddies are parameterized. As a result, the projected GMSLR is about 25% lower at the end of this century in the eddying model. Relatively small-scale ocean eddies can hence have profound large-scale effects and consequently affect GMSLR projections.

DISCUSSION

For the two different versions of the CESM (HR-CESM and LR-CESM), the overall responses to the increase in CO2 [GMST, contributions by glaciers to the GMSLR (34), thermo(steric) effects (21), and surface mass balance changes of the GrIS (35)] are quite similar and compare also well to 31 CMIP6 models analyzed. The projected temperature change and snowfall anomaly over the AIS for the HR-CESM and LR-CESM are also similar to the ones reported in Gregory and Huybrechts (29).

However, the Antarctic basal melt (7) strongly deviates between the HR-CESM and LR-CESM. The HR-CESM and LR-CESM simulations provide GMSLR projections of 5.4 ± 0.3 cm (95% confidence level) and 15 ± 0.8 cm (95% confidence level) through basal melt in 2100, respectively, which gives a factor 2.8 difference. The LR-CESM GMSLR projection of basal melt is within CMIP6 projections, but the HR-CESM projects quite lower GMSLR values with respect to CMIP6 ones. These differences in basal melt are related to the different horizontal resolutions in the ocean component of the models.

The Southern Ocean is a rather complex region where the large-scale ocean circulation, mesoscale ocean eddies, sea-ice formation, and atmospheric processes all play an important role in the response under global warming. Mesoscale ocean eddies are highly relevant for the redistribution and transport of heat and salt (20, 22, 36, 37) and are critical for the correct momentum balance for the large-scale circulation. Explicitly resolving ocean eddies in the HR-CESM does not only lead to a better representation of the present-day subsurface temperature distribution surrounding Antarctica (compared to LR-CESM) but also to a different response under global warming. For the HR-CESM, we find changes on both the large scale (e.g., in the ACC, sea-ice fields) and the regional scale (Weddell and Ross gyres and the Antarctic Coastal Current), while in the LR-CESM (and CMIP6 models), these occur only on the large scale.

Because of the extreme computational costs, there is unfortunately only one high-resolution simulation available for the analysis done here (HR-CESM control and HR-CESM). More of those simulations are required to provide a broader range of GMSLR projections, also under different climate change scenarios. However, the results here already indicate that sea-level projections based on low-resolution climate models should be interpreted with great care, in particular, regarding estimates of the effects Antarctic basal melt.

Adonization is the positive attitude toward the use of physical appearance to influence others; women perceiving themselves as physically or socially attractive reported higher adonization

The role of self-perceived attractiveness and contingencies of self-worth. Eugenia Mandal, Marcin Moroń. Personality and Individual Differences, Volume 179, September 2021, 110895. https://doi.org/10.1016/j.paid.2021.110895

Highlights

• Adonization is the positive attitude toward the use of physical appearance to influence others.

• Women perceiving themselves as physically attractive reported higher adonization.

• Women perceiving themselves as socially attractive reported higher adonization.

• Women focused on external contingencies of self-worth reported higher adonization.

• External contingencies of self-worth predicted adonization indirectly via low self-perceived attractiveness.

Abstract: In the present study, we examined the associations between self-perceptions of attractiveness, contingencies of self-worth and the attitude toward the use of physical attractiveness as an influence strategy (adonization) among young women. Five hundred and eighty-seven women aged 18–35 years assessed their self-perceptions of attractiveness, contingencies of self-worth, and the attitude toward adonization. Structural equation modeling demonstrated that self-perceptions of physical and social attractiveness predicted a positive attitude toward adonization. External contingencies of self-worth predicted less positive perceptions of attractiveness, while internal contingencies of self-esteem predicted more positive self-perceptions of physical attractiveness. Additionally, external contingencies of self-worth predicted a positive attitude toward adonization. Self-perceptions of physical and social attractiveness mediated between external contingencies of self-esteem and attitude toward adonization.

Keywords: AdonizationPhysical attractivenessSocial attractivenessContingencies of self-worth

Frequent, light-moderate alcohol consumption was associated with maintenance of normal weight; although heavy drinking was linked to obesity, effect sizes were modest

How alcohol consumption influences obesity in middle-aged men: A systematic review. Laura Sayers. The Journal of Health Design, Apr 2021. https://www.journalofhealthdesign.com/JHD/article/view/130

Abstract: This systematic review analyzes how alcohol consumption impacts the weight of middle-aged men. Frequent, light-moderate alcohol consumption was associated with maintenance of normal weight. Although heavy drinking was linked to obesity, effect sizes were modest. Spirits, not beer or wine, were associated with weight gain.

How are people making fortunes today? Roughly 3/4 by starting companies and 1/4 by investing

How People Get Rich Now. Paul Graham, April 2021. paulgraham.com/richnow.html

Every year since 1982, Forbes magazine has published a list of the richest Americans. If we compare the 100 richest people in 1982 to the 100 richest in 2020, we notice some big differences.

In 1982 the most common source of wealth was inheritance. Of the 100 richest people, 60 inherited from an ancestor. There were 10 du Pont heirs alone. By 2020 the number of heirs had been cut in half, accounting for only 27 of the biggest 100 fortunes.

Why would the percentage of heirs decrease? Not because inheritance taxes increased. In fact, they decreased significantly during this period. The reason the percentage of heirs has decreased is not that fewer people are inheriting great fortunes, but that more people are making them.

How are people making these new fortunes? Roughly 3/4 by starting companies and 1/4 by investing. Of the 73 new fortunes in 2020, 56 derive from founders' or early employees' equity (52 founders, 2 early employees, and 2 wives of founders), and 17 from managing investment funds.

There were no fund managers among the 100 richest Americans in 1982. Hedge funds and private equity firms existed in 1982, but none of their founders were rich enough yet to make it into the top 100. Two things changed: fund managers discovered new ways to generate high returns, and more investors were willing to trust them with their money. [1]

But the main source of new fortunes now is starting companies, and when you look at the data, you see big changes there too. People get richer from starting companies now than they did in 1982, because the companies do different things.

In 1982, there were two dominant sources of new wealth: oil and real estate. Of the 40 new fortunes in 1982, at least 24 were due primarily to oil or real estate. Now only a small number are: of the 73 new fortunes in 2020, 4 were due to real estate and only 2 to oil.

By 2020 the biggest source of new wealth was what are sometimes called "tech" companies. Of the 73 new fortunes, about 30 derive from such companies. These are particularly common among the richest of the rich: 8 of the top 10 fortunes in 2020 were new fortunes of this type.

Arguably it's slightly misleading to treat tech as a category. Isn't Amazon really a retailer, and Tesla a car maker? Yes and no. Maybe in 50 years, when what we call tech is taken for granted, it won't seem right to put these two businesses in the same category. But at the moment at least, there is definitely something they share in common that distinguishes them. What retailer starts AWS? What car maker is run by someone who also has a rocket company?

The tech companies behind the top 100 fortunes also form a well-differentiated group in the sense that they're all companies that venture capitalists would readily invest in, and the others mostly not. And there's a reason why: these are mostly companies that win by having better technology, rather than just a CEO who's really driven and good at making deals.

To that extent, the rise of the tech companies represents a qualitative change. The oil and real estate magnates of the 1982 Forbes 400 didn't win by making better technology. They won by being really driven and good at making deals. [2] And indeed, that way of getting rich is so old that it predates the Industrial Revolution. The courtiers who got rich in the (nominal) service of European royal houses in the 16th and 17th centuries were also, as a rule, really driven and good at making deals.

People who don't look any deeper than the Gini coefficient look back on the world of 1982 as the good old days, because those who got rich then didn't get as rich. But if you dig into how they got rich, the old days don't look so good. In 1982, 84% of the richest 100 people got rich by inheritance, extracting natural resources, or doing real estate deals. Is that really better than a world in which the richest people get rich by starting tech companies?

Why are people starting so many more new companies than they used to, and why are they getting so rich from it? The answer to the first question, curiously enough, is that it's misphrased. We shouldn't be asking why people are starting companies, but why they're starting companies again. [3]

In 1892, the New York Herald Tribune compiled a list of all the millionaires in America. They found 4047 of them. How many had inherited their wealth then? Only about 20% — less than the proportion of heirs today. And when you investigate the sources of the new fortunes, 1892 looks even more like today. Hugh Rockoff found that "many of the richest ... gained their initial edge from the new technology of mass production." [4]

So it's not 2020 that's the anomaly here, but 1982. The real question is why so few people had gotten rich from starting companies in 1982. And the answer is that even as the Herald Tribune's list was being compiled, a wave of consolidation was sweeping through the American economy. In the late 19th and early 20th centuries, financiers like J. P. Morgan combined thousands of smaller companies into a few hundred giant ones with commanding economies of scale. By the end of World War II, as Michael Lind writes, "the major sectors of the economy were either organized as government-backed cartels or dominated by a few oligopolistic corporations." [5]

In 1960, most of the people who start startups today would have gone to work for one of them. You could get rich from starting your own company in 1890 and in 2020, but in 1960 it was not really a viable option. You couldn't break through the oligopolies to get at the markets. So the prestigious route in 1960 was not to start your own company, but to work your way up the corporate ladder at an existing one. [6]

Making everyone a corporate employee decreased economic inequality (and every other kind of variation), but if your model of normal is the mid 20th century, you have a very misleading model in that respect. J. P. Morgan's economy turned out to be just a phase, and starting in the 1970s, it began to break up.

Why did it break up? Partly senescence. The big companies that seemed models of scale and efficiency in 1930 had by 1970 become slack and bloated. By 1970 the rigid structure of the economy was full of cosy nests that various groups had built to insulate themselves from market forces. During the Carter administration the federal government realized something was amiss and began, in a process they called "deregulation," to roll back the policies that propped up the oligopolies.

But it wasn't just decay from within that broke up J. P. Morgan's economy. There was also pressure from without, in the form of new technology, and particularly microelectronics. The best way to envision what happened is to imagine a pond with a crust of ice on top. Initially the only way from the bottom to the surface is around the edges. But as the ice crust weakens, you start to be able to punch right through the middle.

The edges of the pond were pure tech: companies that actually described themselves as being in the electronics or software business. When you used the word "startup" in 1990, that was what you meant. But now startups are punching right through the middle of the ice crust and displacing incumbents like retailers and TV networks and car companies. [7]

But though the breakup of J. P. Morgan's economy created a new world in the technological sense, it was a reversion to the norm in the social sense. If you only look back as far as the mid 20th century, it seems like people getting rich by starting their own companies is a recent phenomenon. But if you look back further, you realize it's actually the default. So what we should expect in the future is more of the same. Indeed, we should expect both the number and wealth of founders to grow, because every decade it gets easier to start a startup.

Part of the reason it's getting easier to start a startup is social. Society is (re)assimilating the concept. If you start one now, your parents won't freak out the way they would have a generation ago, and knowledge about how to do it is much more widespread. But the main reason it's easier to start a startup now is that it's cheaper. Technology has driven down the cost of both building products and acquiring customers.

The decreasing cost of starting a startup has in turn changed the balance of power between founders and investors. Back when starting a startup meant building a factory, you needed investors' permission to do it at all. But now investors need founders more than founders need investors, and that, combined with the increasing amount of venture capital available, has driven up valuations. [8]

So the decreasing cost of starting a startup increases the number of rich people in two ways: it means that more people start them, and that those who do can raise money on better terms.

But there's also a third factor at work: the companies themselves are more valuable, because newly founded companies grow faster than they used to. Technology hasn't just made it cheaper to build and distribute things, but faster too.

This trend has been running for a long time. IBM, founded in 1896, took 45 years to reach a billion 2020 dollars in revenue. Hewlett-Packard, founded in 1939, took 25 years. Microsoft, founded in 1975, took 13 years. Now the norm for fast-growing companies is 7 or 8 years. [9]

Fast growth has a double effect on the value of founders' stock. The value of a company is a function of its revenue and its growth rate. So if a company grows faster, you not only get to a billion dollars in revenue sooner, but the company is more valuable when it reaches that point than it would be if it were growing slower.

That's why founders sometimes get so rich so young now. The low initial cost of starting a startup means founders can start young, and the fast growth of companies today means that if they succeed they could be surprisingly rich just a few years later.

It's easier now to start and grow a company than it has ever been. That means more people start them, that those who do get better terms from investors, and that the resulting companies become more valuable. Once you understand how these mechanisms work, and that startups were suppressed for most of the 20th century, you don't have to resort to some vague right turn the country took under Reagan to explain why America's Gini coefficient is increasing. Of course the Gini coefficient is increasing. With more people starting more valuable companies, how could it not be?

Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives

Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives. Siqi Wu, Paul Resnick. arXiv Apr 12 2021. https://arxiv.org/abs/2104.05365

Abstract: We present the first large-scale measurement study of cross-partisan discussions between liberals and conservatives on YouTube, based on a dataset of 274,241 political videos from 973 channels of US partisan media and 134M comments from 9.3M users over eight months in 2020. Contrary to a simple narrative of echo chambers, we find a surprising amount of cross-talk: most users with at least 10 comments posted at least once on both left-leaning and right-leaning YouTube channels. Cross-talk, however, was not symmetric. Based on the user leaning predicted by a hierarchical attention model, we find that conservatives were much more likely to comment on left-leaning videos than liberals on right-leaning videos. Secondly, YouTube's comment sorting algorithm made cross-partisan comments modestly less visible; for example, comments from conservatives made up 26.3% of all comments on left-leaning videos but just over 20% of the comments were in the top 20 positions. Lastly, using Perspective API's toxicity score as a measure of quality, we find that conservatives were not significantly more toxic than liberals when users directly commented on the content of videos. However, when users replied to comments from other users, we find that cross-partisan replies were more toxic than co-partisan replies on both left-leaning and right-leaning videos, with cross-partisan replies being especially toxic on the replier's home turf.

Poland sample: We will rescue Italy, but we dislike the European Union... Collective narcissism and the COVID-19 threat

We will rescue Italy, but we dislike the European Union: Collective narcissism and the COVID-19 threat. Magdalena Żemojtel-Piotrowska et al. Group Processes & Intergroup Relations, April 15, 2021. https://doi.org/10.1177/13684302211002923

Abstract: Collective narcissists are hostile towards outgroup members, especially in response to threats against the ingroup. In the current study (N = 662; Polish community sample), we examined the associations between collective narcissism and intergroup relations using the agency–communion model of collective narcissism during the initial weeks of the COVID-19 threat in Poland. Assuming the COVID-19 threat is agentic (i.e., related to biological and economic danger), we expected it to be unrelated to collective narcissism’s communal aspect. We also expected that collective narcissists would enhance their ingroup image to modify the effects of the COVID-19 threat on intergroup relations. In general, collective narcissism was related to less favorable attitudes toward the European Union, more favorable attitudes toward China, and a willingness to help Italians. The agentic aspect of narcissism was unrelated to intergroup prosocialness, while the communal aspect of narcissism was unrelated to attitudes toward the European Union. The COVID threat suppressed unfavorable attitudes towards the European Union among collective narcissists. Therefore, the COVID threat has limited yet unexpected effects on attitudes toward outgroups among collective narcissists.

Keywords: agency–communion model, collective narcissism, coronavirus, intergroup relations, threat

Discussion

We examined how collective narcissists perceive two possible threatening outgroups in the context of the COVID-19 pandemic, looking into the general effects of collective narcissism and its agentic and communal aspects. We also examined how collective narcissists express their willingness to help outgroup victims of the pandemic. We based our predictions on the distinction between agentic and communal ingroup enhancement, resulting in higher sensitivity of agentic collective narcissists toward an agentic threat, and higher prosocialness and positive attitudes toward nonthreatening outgroups among communal collective narcissists (Żemojtel-Piotrowska et al., in press). Following intergroup threat theory (Stephan et al., 2009), we assumed that the pandemic was an example of a realistic threat pertaining to the agentic domain (Ybarra et al., 2008). Our study provided further evidence of the utility of splitting collective narcissism into agentic and communal forms, indicating that agentic (and not communal) collective narcissism is related to higher sensitivity toward realistic agentic threats. We did so using a real-life situation, the COVID-19 pandemic, to test our assumptions on a general Polish sample.

We found that collective narcissism (in general and in their agentic form) was related to less favorable attitudes towards the EU, confirming previous findings (Cisłak et al., 2020; Guerra et al., 2017). Higher levels of collective narcissism (i.e., in general and in its communal form) were related to more positive attitudes toward China. Only communal collective narcissism was related to more prosocialness toward Italians. Consistent with our assumption about the agentic nature of the COVID-19 threat, the communal aspect of narcissism was unrelated to higher perceived threat of the virus. People perceiving the coronavirus as a threat also perceived the EU more favorably, and tended to perceive China less favorably. Therefore, the only relationship between collective narcissism and attitudes toward the EU was mediated by perceived threat. Negative attitudes expressed by collective narcissists towards the EU were suppressed by individual differences in perceived threat of the virus. However, this suppression was unrelated to the agentic and the communal aspects of collective narcissism. Collective narcissism was positively related to both outgroup hostility and intergroup prosocialness. However, negative attitudes towards the EU were unrelated to the communal aspect of collective narcissism. In contrast, intergroup prosocialness toward Italians and favorable attitudes toward China were unrelated to collective narcissism’s agentic aspect. Therefore, collective narcissists may use communal and agentic means of ingroup enhancement, but only agentic motives underlying global collective narcissism are evoked by COVID-19.

Our study revealed paradoxical effects of collective narcissism on intergroup relations. Collective narcissism is predominantly agentic (Żemojtel-Piotrowska et al., in press) and, for this reason, agentic threats should not affect intergroup relations in the communal domain. Collective narcissism might be accompanied by ambivalent attitudes toward the EU, as the bloc may be perceived by Polish people as a threat to their national interests and independence (Guerra et al., 2017); yet, at the same time, the EU could serve as an ally in the joint battle against the COVID-19 and other global threats. It is plausible that the pandemic evoked some kind of “common enemy” effect, followed by a recategorization of the EU from outgroup to ingroup (Flade et al., 2019). Our study suggests that perceived threat from COVID-19 did not affect attitudes toward China, probably reflecting its ambivalent perception as both victim and to blame for the pandemic. Lastly, Italians were probably perceived by communal collective narcissists as members of an outgroup because national narcissism operates on national identification (Golec de Zavala et al., 2009), making all non-Poles (in our case) outgroup members. Helping outgroup members could satisfy the communal collective narcissist’s need for external validation through enacting ingroup moral virtues like globalism, caring for the ill, and helping the less fortunate.

Direct effects of collective narcissism on attitudes toward outgroups were much stronger than (mostly nonsignificant and weak) indirect ones, suggesting that the COVID-related threat had limited effect on how (Polish) collective narcissists perceive outgroups. Such a result suggests that the core of collective narcissism could be sensitive to threats, while its two aspects (i.e., agentic and communal) are manifestations of ingroup enhancement insensitive to threats. Therefore, our study points to the complex nature of collective narcissism as a blend of vulnerability and grandiosity (Golec de Zavala et al., 2019; Sedikides, 2020).

Limitations and Future Directions

Our study was conducted on a culturally homogenous sample in a relatively affluent society that had not been seriously affected by the pandemic at the time of data collection (< .02% of the Polish population [< 38 million people] as of April 15, 2020). While we collected data over 3 weeks, considering that infection rates across these weeks were relatively stable (i.e., Poland experienced no surge in infection rates over this period), it is reasonable to assume that there was cross-weekly equivalence in our measurements.⁵ Despite experimental studies suggesting that collective narcissism could increase in response to threat (Golec de Zavala et al., 2019), our study aimed to detect collective narcissism’s consequences for intergroup relations. However, in follow-up studies, the mutual effects of threat and collective narcissism should be controlled for.

In general, attitudes toward the EU were more favorable than toward China (see Table 1), confirming the study’s ecological validity. Our participants reported relatively high levels of perceived threat, yet the threat of COVID-19 appears to have had only a negligible effect on collective narcissists’ attitudes toward outgroups. Collective narcissists merely manifested less favorable attitudes toward the EU and more favorable ones toward China, even though collective narcissists experience threat due to agentic motives. While the effects detected in our study were small, they resemble previous findings (Golec de Zavala et al., 2009, 2016).

In addition, our study was limited by its correlational design. Subsequent research might adopt quasi-experimental or experimental methods to better determine the causal effects of the COVID-19 threat on attitudes towards outgroups, and the potential moderating/mediating role of agentic and communal collective narcissism. Further studies could elaborate more on other possible mediators and factors relevant to the link between collective narcissism and attitudes toward outgroups, such as ingroup enhancement (i.e., agentic/communal), social desirability, or cultural distance from outgroups.

How Civilization Broke Our Brains What can hunter-gatherer societies teach us about work, time, and happiness?

How Civilization Broke Our Brains What can hunter-gatherer societies teach us about work, time, and happiness? Derek Thomson. The Atlantic, Jan/Feb 2021. https://www.theatlantic.com/magazine/archive/2021/01/james-suzman-work/617266/

Comments of a book by James Suzman, Work: A Deep History, From the Stone Age to the Age of Robots

Several months ago, I got into a long discussion with a colleague about the origins of the “Sunday scaries,” the flood of anxiety that many of us feel as the weekend is winding down and the workweek approaches. He said that the culprit was clear, and pointed to late-stage capitalism’s corrosive blend of performance stress and job insecurity. But capitalism also exists Monday through Saturday, so why should Sunday be so uniquely anxiety-inducing?

[...]

The Ju/’hoansi spent an average of 17 hours a week finding food—2,140 calories daily—and devoted another 20 to chores, as Suzman gleaned from other ethnographies and firsthand research. This left them with considerably more downtime than the typical full-time employee in the U.S., who spends about 44 hours a week doing work—and that doesn’t include domestic labor and child care. In that downtime, the Ju/’hoansi remained strikingly free, over centuries, from the urge to cram it with activities that we would classify as “productive” (or, for that matter, destructive). By day, they did go on walks with children to teach them how to read the canvas of the desert for the footprints of animals. But they also lounged, gossiped, and flirted. During firelit evenings, they sang, danced, and told stories. One anthropologist studying another hunter-gatherer tribe, the Hadza people of northern Tanzania, described its members in the 1960s as habitual small-stakes gamblers whose days were filled with one particular pastime: winning and losing arrows in games of chance.

[...]

Suzman calls attention to the changing nature of work. He draws on the writing of the French sociologist Émile Durkheim, who pointed to a crucial difference between “primitive” and complex societies called interchangeability. For hunter-gatherers, chiefs and shamans could, and did, moonlight as foragers and hunters. Overlapping duties preserved a strong sense of community, reinforced by customs and religions that obscured individual differences in strength, skill, and ambition. Shared labor meant shared values.

But in industrial economies, lawyers don’t tag in for brain surgery, and drill sergeants don’t harvest wheat—and the different jobs people do, requiring different skill sets, command (often vastly) different pay. As specialization spread and superior performance was rewarded, a cult of competition emerged: High achievers believed they could and should always toil harder for a fatter raise, bigger house, higher honor, or more wondrous breakthrough. Where rest once beckoned, now restlessness did. The productivity mode thrived—and it just might deserve credit (along with luck) for almost all scientific progress and technological ingenuity. But it also bears the blame for what Durkheim called a “malady of infinite aspiration,” which by now we’ve discovered is chronic. When a recent Pew Research Center survey asked about the secret to happiness, most Americans, of all ages, ranked “a job or career they enjoy” above marriage, children, or any other committed relationship. Careerism, not community, is the keystone in the arch of life.

You might say that leisure mind never had a chance. But Suzman emphasizes another fundamental change to help account for that: our relationship to time—specifically, to the future. Small hunter-gatherer groups in tropical climates rarely stored food for more than a few days, Suzman writes. Trusting in the abundance of their environment, the Ju/’hoansi worked to meet their absolute needs, and then stopped to rest, rather than planning ahead.

By comparison, modern civilization is a shrine to the future. The shift goes back to the agricultural revolution, which subjected humans to farming cycles that separated planting and harvest by many months, and continued with the rise of finance. But a fixation on the future by now goes far beyond crop cycles and long-term loans. It is at the heart of our concept of education and corporate development, which presumes that young students and workers will gladly spend decades honing skills that they will be well compensated for only years later. The least controversial values in America today—the importance of grit, the hope for progress, the dream of social mobility—assume that the future is always changing and that our inclination is always to wish for better. Meanwhile, excessively negative future-oriented thinking is the most common feature of anxiety disorders, which afflict almost 20 percent of Americans.

At the aggregate level, high expectations for the future have surely made the world a better place. Despite routine complaining from the 21st century’s inhabitants, modern civilization has produced quite a lot to be thankful for. Slow cookers, Venmo, and internet kittens; vaccines and aspirin, heat lamps and mittens; Amazon, hand soap, air-conditioning—these are a few of my favorite things, at least. But at the individual level, Suzman offers the tantalizing promise that the Ju/’hoansi have something to teach those of us whose brains have been dizzied by the vertigo of civilization.

Even the present-oriented hunter-gatherers, it turns out, had to develop communal strategies to quash the drivers of overwork—status envy, inequality, deprivation. When a Ju/’hoan hunter returned with a big kill, the tribe perceived a danger that he might think his prowess elevated him above others. “We can’t accept this,” one tribesman said. “So we always speak of his meat as worthless. This way we cool his heart and make him gentle.” This practice became known among researchers as “insulting the hunter’s meat.”

It was not the only custom that aimed to discourage a destabilizing competition for status and avoid a concentration of power. The tribe also “insisted that the actual owner of the meat, the individual charged with its distribution, was not the hunter, but the person who owned the arrow that killed the animal,” Suzman writes. By rewarding the semi-random contributor of the arrow, the Ju/’hoansi kept their most talented hunters in check, in order to defend the group’s egalitarianism. A welcome result was that “the elderly, the short-sighted, the clubfooted and the lazy got a chance to be the centre of attention once in a while.”

Reading about these strategies, I felt several things at once—astonished by their ingenuity, mind-blown by the notion of ridiculing exceptional achievements, and worried that my failure to imagine taking comparable pains to protect leisurely harmony meant that my own brain had been addled by too many years in productivity mode, too many twitchy Sunday evenings. But what Suzman’s foray into humanity’s past reveals is that leisure has never been the ready default mode we may imagine, even in the chillest of cultures. The psychological cost of civilization, the scourge of the Sunday scaries, and the lesson of the Ju/’hoansi converge in an insight worth taking to heart: Safeguarding leisure is work. While progress depends on pinning our hopes on a world that doesn’t yet exist, those who cannot stop planning for the future are doomed to labor for a life they will never fully live.

Many negativity biases, such as negativity dominance, might not be "biases" after all but reflect people's correct encoding of evaluative ecologies

Explaining Negativity Dominance without Processing Bias. Christian Unkelbach, Alex Koch, Hans Alves. Trends in Cognitive Sciences, April 16 2021. https://doi.org/10.1016/j.tics.2021.04.005

Abstract: In a recent study, Shin and Niv explain both negativity and positivity biases in social evaluations as a function of the  diversity and low frequency of events. We discuss why negative information is indeed more diverse and less frequent, and highlight the  implications beyond social evaluations. 

Keywords: valence asymmetriespositivity biasnegativity biasessocial evaluationscognitive-ecological models

Check also Why Good Is More Alike Than Bad: Processing Implications. Hans Alves, AlexKoch and Christian Unkelbach. Trends in Cognitive Sciences, 2017. http://dx. doi. org/10. 1016/j. tics. 2016. 12. 006

Why PositiveInformationIsMoreAlikeThanNegativeInformation

 the  questionremains ‘why’ this asymmetry exists and whe there it is a feature of the  cognitive

system or afeature of the  ecology (see OutstandingQuestions). the first positionfollowsfrom

 the  affective or motivationalpotential of evaluativei nformation (Box 1). Accordingly, confrontation with a negative stimulus elicitsnegativeaffect, whichtriggersdeeperprocessing, resultingina more differentiated mental representation. However, wearguethatpositiveinformation'shigher similarity isatrueproperty of the information ecology, independent of affectiveandmotivational influences. Our idea follows the  tradition of researchers like Brunswik [13], Lewin [14], and Garner [40], whoemphasized the  importance of the informationecology for psychological

processes. 

Our explanationforpositiveinformation's higher similarity builds on the well-documented

assumption thatvalenceisafunction of attribute extremity. Aristotle [41] already recognized

that desirablequalitiesaremodestqualitiesthat are framed by both the excess and defect. That is, a

positive rangeislocatedtoward the middle of agivenattributedimensionandissurroundedby

two negativerangestoward the twoends of the dimension. the reby, positivityisnon-extreme. 

This isapparentat the mostbasiclevel: Humanlifeispossibleonlywithinasinglerange of 

temperature, oxygenconcentration, solarradiation, andsoon. Formostphysicalandchemical

dimensions thatarerelevanttohumanlife the reisa ‘too little’ and a ‘too much’. While humans

can survivewithina ‘good’ temperature range, the ycanbothfreezeandburn. the same

principle appliestointernalbiologicalstatesasprominentlyexpressedin the concept of 

homeostasis [42, 43]. Asdiscussedearlier, the perceptualsystemfollows the sameprinciple, 

as the desirablerange of prototypicalityissurroundedbyvariousdeviationsfrom the same. 

Importantly, the range principle is also ubiquitous in psychological domains. GrantandSchwartz

[44] showed thatfor virtuallyalldimensions of humanattributes, the positiveordesirablerangeis

non-extreme. Even on attribute dimensions that seemingly have one positive and onenegative

pole, the positive range reaches inflection points at which its effects turn negative. Agreeable-

ness turns into conformity, conscientiousness into perfectionism, and courage into recklessness. Consequently, desirable personality profiles are thosethat are non-extreme, which is why

 the  correlationbetweenitemmeans of personalitytestsanditemdesirabilitytypicallyexceeds

r = 0. 80 [45]. Recentresearchhasshownthat the rangeprinciplealsounderlies the mental

representation of socialgroups [46, 47]. Thatis, likeablesocialgroupsarethosethatarenon-

extreme regarding their agency and their beliefs, while non-likeable groups are those that are

extreme on these dimensions.

Of course, somequalitiesmightbelinearlyrelatedtovalence. For instance, the amount of poison

in one'sblood, or the amount of traumatic experiences one has had. However, those are

exceptions to the rule that attribute dimensions typically host one (non-extreme) range and two

(extreme) negativeranges, constituting an inverted u-shaped relation between attribute value

and valence. Further more, the reverse pattern seems evenmoreunlikely andmaybe even never

occurs; that is, attribute dimensionsthat host one(non-extreme)negativerangeandtwo

(extreme) positive ranges (but see Outstanding Questions). 

Assuming that attribute dimensions typically host one positive range framed by two negative

ranges, it follows that positive information must be on average morealikethannegative

information. the possible maximum distance between the two negativeranges on a given

attribute dimension always exceeds the distance within the positiverange. While twopositive

stimuli necessarily have to lay within the same range, two negative stimuli can lay in two different

ranges on a given attribute dimensionthat are highly distant and therefore differentfrom each

other. For example, while two attractive men must display a height that lays within the same

desirable range, two unattractive men can either be too short or too tall, and thereby highly

different.

Figure 1 (Key Figure) illustrates the rangeprincipleinatwo-dimensional attribute space in which

proximity equals similarityin accordance with a geometric model of similarity [9]. the single

positive space emerges in the center (white square), surrounded byfourdistinctnegativespaces

(dark graysquares) andfourambivalentspaces(lightgraysquares). If one would randomly

sample pairs of positive and negative stimuliandlocate the min the attributespace, the positive

stimuli willbeonaveragelocatedclosertoge the rthan the negativestimuli. 

Preponderance of NegativeConcepts

Beyond explaining the similarityasymmetry, the rangeprincipleimpliesalargernumber of 

negative states, evenonasingleattributedimension(cf. Figure 1). Evidenceforthisimplication

comes from research showing that language includes more negative than positive concepts. For example, the  majority of wordsthatcanbeusedtodescribeapersonarenegative, which has

been shown for the  English and Germanlanguages [48–50]. the sameistrueforemotion-

related wordsingeneral, as the  ‘working emotionvocabulary’ in EnglishandSpanishwasfound

to includemorenegative(50%)thanpositive(30%)words [51, 52]. Ananalysis of English ‘verbs’

also revealedapreponderance of negativeoverpositivewords [53]. Itisuncleartowhatextent

 the  preponderance of negativewordsappliestolanguagesingeneralaswearenotaware of 

research investigating the number of positiveandnegativewordsinlanguageso the rthan

English, Spanish, andGerman. 

 the  impliedlargernumber of negativestatesisals of oundinhumans’ emotional response

repertoire. While differentresearchershaveproposeddifferent ‘basic emotions’, almostall

describe moredistinctnegativethanpositiveemotions [54]. Forexample, earlyconceptualiza-

tions byWilliamJames [55] included fear, grief, rage, andlove. Later, EkmanandFriesen [56, 57]

prominently identified anger, disgust, fear, sadness, andjoyasbasicemotions, andPanksepp

[58] described the psychobiologicalsystems of fear, rage, andpanicandanappetitiveexpec-

tancy system. Howpreponderance of negativeemotionsfollowsfrom the rangeprinciplecanbe

illustrated usingappraisal the ories of emotions [59]. Accordingly, positiveemotionsresultfrom

goal-congruent appraisals, while negativeemotionsresultfromappraisals of goalincongruence

[60]. Again, while goalcongruenceconstitutesasinglecondition, the rearemanydifferentways

for conditionstobegoalincongruent. Fromthisperspective, the manifoldness of the negative

emotional repertoiremirrors the greatdiversity of goal-incongruentconditions. 

In sum, the differentialsimilarity of positiveinformationandnegativeinformationmayfollowfrom

 the  proposedrangeprinciple. We believe this explanation is plausible and parsimonious, without

denying that the re might be o the r factorscontributingtothisasymmetryinsimilarity(see

Outstanding Questions). Besidesbeinganintriguingphenomenonbyitself,positiveinforma-

tion's highsimilaritymayserveasanexplanatoryconstructfordifferencesin the processing of 

positive andnegativeinformation, whichweaddressin the following. 

Rolf Degen summarizing... Neurology patient developed the delusion that her daughter had been replaced by a different "replica" at each visit

Evolution of Capgras syndrome in neurodegenerative disease: the multiplication phenomenon. Aline von Siebenthal,Virginie Descloux,Christel Borgognon,Tatiana Massardi &Serge Zumbach. The Neural Basis of Cognition, Apr 15 2021. https://doi.org/10.1080/13554794.2021.1905850

Rolf Degen's take: https://twitter.com/DegenRolf/status/1383091142959456258

ABSTRACT: Capgras syndrome (CS) is a delusional misidentification syndrome that is encountered in various pathologies. Here, we report the case of an 83-year-old woman affected by dementia with Lewy bodies who presented a CS during the disease. The neuropsychological assessment showed executive and face processing deficits. In this case, CS was characterized, in the beginning, by the duplication of a relative and then by its multiplication. To our knowledge, the description of the evolution of a CS in the course of a neurodegenerative disease is rare and we discuss this multiplication phenomenon in light of existing models of delusions.

KEYWORDS: Capgras syndromedelusional misidentificationmultiplication phenomenondementia with Lewy bodiesneurodegenerative disease

Many practitioners in the US are unaccustomed to using probability in diagnosis and clinical practice; widespread overestimates of the probability of disease likely contribute to overdiagnosis and overuse

Accuracy of Practitioner Estimates of Probability of Diagnosis Before and After Testing. Daniel J. Morgan et al. JAMA Intern Med., April 5, 2021. DOI 10.1001/jamainternmed.2021.0269

Key Points

Question  Do practitioners understand the probability of common clinical diagnoses?

Findings  In this survey study of 553 practitioners performing primary care, respondents overestimated the probability of diagnosis before and after testing. This posttest overestimation was associated with consistent overestimates of pretest probability and overestimates of disease after specific diagnostic test results.

Meaning  These findings suggest that many practitioners are unaccustomed to using probability in diagnosis and clinical practice. Widespread overestimates of the probability of disease likely contribute to overdiagnosis and overuse.

Abstract

Importance  Accurate diagnosis is essential to proper patient care.

Objective  To explore practitioner understanding of diagnostic reasoning.

Design, Setting, and Participants  In this survey study, 723 practitioners at outpatient clinics in 8 US states were asked to estimate the probability of disease for 4 scenarios common in primary care (pneumonia, cardiac ischemia, breast cancer screening, and urinary tract infection) and the association of positive and negative test results with disease probability from June 1, 2018, to November 26, 2019. Of these practitioners, 585 responded to the survey, and 553 answered all of the questions. An expert panel developed the survey and determined correct responses based on literature review.

Results  A total of 553 (290 resident physicians, 202 attending physicians, and 61 nurse practitioners and physician assistants) of 723 practitioners (76.5%) fully completed the survey (median age, 32 years; interquartile range, 29-44 years; 293 female [53.0%]; 296 [53.5%] White). Pretest probability was overestimated in all scenarios. Probabilities of disease after positive results were overestimated as follows: pneumonia after positive radiology results, 95% (evidence range, 46%-65%; comparison P < .001); breast cancer after positive mammography results, 50% (evidence range, 3%-9%; P < .001); cardiac ischemia after positive stress test result, 70% (evidence range, 2%-11%; P < .001); and urinary tract infection after positive urine culture result, 80% (evidence range, 0%-8.3%; P < .001). Overestimates of probability of disease with negative results were also observed as follows: pneumonia after negative radiography results, 50% (evidence range, 10%-19%; P < .001); breast cancer after negative mammography results, 5% (evidence range, <0.05%; P < .001); cardiac ischemia after negative stress test result, 5% (evidence range, 0.43%-2.5%; P < .001); and urinary tract infection after negative urine culture result, 5% (evidence range, 0%-0.11%; P < .001). Probability adjustments in response to test results varied from accurate to overestimates of risk by type of test (imputed median positive and negative likelihood ratios [LRs] for practitioners for chest radiography for pneumonia: positive LR, 4.8; evidence, 2.6; negative LR, 0.3; evidence, 0.3; mammography for breast cancer: positive LR, 44.3; evidence range, 13.0-33.0; negative LR, 1.0; evidence range, 0.05-0.24; exercise stress test for cardiac ischemia: positive LR, 21.0; evidence range, 2.0-2.7; negative LR, 0.6; evidence range, 0.5-0.6; urine culture for urinary tract infection: positive LR, 9.0; evidence, 9.0; negative LR, 0.1; evidence, 0.1).

Conclusions and Relevance  This survey study suggests that for common diseases and tests, practitioners overestimate the probability of disease before and after testing. Pretest probability was overestimated in all scenarios, whereas adjustment in probability after a positive or negative result varied by test. Widespread overestimates of the probability of disease likely contribute to overdiagnosis and overuse.

Discussion

In this survey study, in scenarios commonly encountered in primary care practice, practitioners overestimated the probability of disease by 2 to 10 times compared with the scientific evidence, both before and after testing. This result was mostly associated with overestimates of pretest probability, which were observed across all scenarios. Adjustments to probability in response to test results varied from accurate to overestimates of risk by type of test. There was variation in accuracy between type of practitioner that was small compared with the magnitude of difference between practitioners and the scientific evidence. Many practitioners reported that they would treat patients for disease for which likelihood had been overestimated.

The most striking finding from this study was that practitioners consistently and significantly overestimate the likelihood of disease. Small studies with limited generalizability have had similar findings, often asking practitioners to perform one isolated aspect of diagnosis, such as interpreting a test result. However, past studies^8-11 have not explored a range of questions or clarified estimates at different steps in the diagnostic pathway. The reason for inaccurate estimates of probability are not clear, although anecdotes reported during the current study imply that practitioners often do not think in terms of probability. One participant stated that estimating probability of disease “isn’t how you do medicine.” This attitude is consistent with a previous study²⁰ of diagnostic strategies that describe an initial pattern recognition phase of care with only 10% of practitioners engaging in a secondary phase of probabilistic reasoning.

This study found that probability estimates were consistently biased toward overestimation, as has been seen in other contexts, such as expectations of high stock returns among investors.²¹ This overestimation is consistent with cognitive biases, including base rate neglect, anchoring bias, and confirmation bias.¹⁴ These biases drive overestimation because true base rates are usually lower than expected and anchoring tends to reflect experiences that represent improbable events or those in which a diagnosis was missed. Such cognitive biases have been associated with diagnostic errors that may occur from errors in estimating risk.^5,22,23 Notably, practitioners in this survey were often residents or academic physicians who often practice with populations with higher prevalence of disease. This experience may have also contributed to higher estimates of disease.

Pretest probabilities were consistently overestimated for all questions, but overestimates were particularly apparent for the pneumonia and UTI scenarios. Estimates of pretest probability generally reflect clinical knowledge. Reasons for overestimates for these infectious diseases may relate to the fact that antibiotics are often appropriately given even when the likelihood of infection is moderate. In the UTI scenario, estimates of high pretest probability may reflect the evolution of the definition of asymptomatic bacteriuria as a separate entity from UTI.²⁴

In contrast to past literature,^8-10,19 practitioners accurately adjusted estimates of disease based on the results of some tests, as demonstrated by the imputed likelihood ratios. This adjustment could be artifactual because of inability to adjust probability for tests that had high pretest estimates (ie, pneumonia and UTI). In other cases, practitioners markedly overestimated the probability of disease after testing, specifically after a positive or negative mammography result or a positive exercise stress test result. Practitioners are known to overestimate chance of disease when completing a theoretical estimate of likelihood of disease after a positive test result when pretest probability was 1 in 1000 tests.^9,10 The current study included the identical question with an identical response, with participants estimating the likelihood of disease at 95% when the correct answer was 2%.^5,8-10,19 The findings regarding real-life examples are consistent with evidence from limited past studies,^8-11 for example, physician interpretation of a positive mammography result in a typical woman as conveying 81% probability of breast cancer.⁸

The assessment of test results in this study was simplified to positive or negative. This dichotomization reflects the literature on the sensitivity and specificity of testing.^5,6 However, in clinical medicine, these tests often present a range of descriptions for a positive result from mild positives, such as well-circumscribed density on a mammogram, to a strongly positive result, such as inducible ischemia on a stress test or spiculated mass on a mammogram. A more strongly positive or abnormal result would be less sensitive but more specific for disease. This study did not evaluate interpretation of more complex test results.

There are important implications of the finding of a gap between practitioner estimates and scientific estimates of the probability of disease. Practitioners who overestimate the probability of disease would be expected to use that overestimation when deciding whether to initiate therapy, which could lead to overuse of medications and procedures with associated patient harms. Practitioners in the study reported that they would initiate treatment based on estimates of disease, including 78.2% who would treat cardiac ischemia and 71.0% who would treat a UTI when a positive test result would place their patient at 11% or less chance of disease. These errors would similarly corrupt shared decision-making with patients, which relies on practitioner understanding and communication of the likelihood of various outcomes.^25-27 Training in shared decision-making has focused on communication skills,²⁸ not on understanding the probability of disease,²⁹ but the findings suggest another important educational target.

More focus on diagnostic reasoning in medical education is important. The finding of a primary problem with pretest probability estimates may be more amenable to intervention than the more commonly discussed bayesian adjustment to probability from test results.³⁰ Pretest probability is commonly discussed in medical education, but a standard method for estimating pretest probability has not been described.³⁰ Ideally, such estimates incorporate knowledge of disease prevalence and the predictive value of components of the history and physical examination, but for many conditions this information is difficult to find. The fact that estimates are so far from scientific evidence identifies a pressing need for improvement. There are a limited number of well-characterized diseases with pretest probability calculators, notably cardiac ischemia.^31,32 Despite the fact that respondents in this study had no access to external aids while completing the survey, pretest estimates of cardiac ischemia were more accurate than for other clinical scenarios, implying that access to these calculators may improve knowledge and impact clinical reasoning. There is also a need to improve bayesian adjustment in probability from test results, which requires readily accessible references for clinical sensitivity and specificity. Computer visual decision aids that guide estimates of probability may also have a role.^5,33 Alternative approaches, such as natural frequencies and naturalistic decision-making or use of heuristics, may improve decisions.³⁴

Limitations

This study has limitations. One is that the small fraction of respondents who did not complete the survey were more likely to be female, nurse practitioners, or physician assistants or to have been in practice for more than 10 years. However, the overall response rate was high. The format of survey questions required participants to estimate pretest probability before giving interpretation of positive or negative test results, which may not reflect their natural practice. Finally, although validity was extensively assessed via a multidisciplinary expert panel, reliability of our novel survey was not assessed.

Strikingly, compared with younger adults, older people were more willing to put in effort for others and exerted equal force for themselves and others

Aging Increases Prosocial Motivation for Effort. Patricia L. Lockwood et al. Psychological Science, April 16, 2021. https://doi.org/10.1177/0956797620975781

Abstract: Social cohesion relies on prosociality in increasingly aging populations. Helping other people requires effort, yet how willing people are to exert effort to benefit themselves and others, and whether such behaviors shift across the life span, is poorly understood. Using computational modeling, we tested the willingness of 95 younger adults (18–36 years old) and 92 older adults (55–84 years old) to put physical effort into self- and other-benefiting acts. Participants chose whether to work and exert force (30%–70% of maximum grip strength) for rewards (2–10 credits) accrued for themselves or, prosocially, for another. Younger adults were somewhat selfish, choosing to work more at higher effort levels for themselves, and exerted less force in prosocial work. Strikingly, compared with younger adults, older people were more willing to put in effort for others and exerted equal force for themselves and others. Increased prosociality in older people has important implications for human behavior and societal structure.

Keywords: prosocial behavior, aging, effort, motivation, reward, computational modeling, open data

Discussion

Many prosocial behaviors require the motivation to exert effort. Here, we showed that older people, compared with younger people, are more prosocially motivated in two crucial aspects of behavior. First, computational modeling and mixed-effects models show that older adults discount rewards by effort less when benefiting others, and thus they are more willing to choose highly effortful prosocial acts. Second, whereas younger adults show a self-bias, pursuing highly effortful actions that benefited themselves more than others, older adults do not. Thus, greater prosociality was demonstrated not only in older adults’ decisions but also in how much energy they allocated to self- and other-benefitting acts. Finally, we observed individual differences in the relationship between discounting in the two groups and their feelings of positivity at helping themselves and others. Positive feelings toward rewarding others were correlated with the willingness to put in effort for others in both younger and older adults, consistent with a maintained sense of “warm glow” across the life span, but only in younger adults did the willingness to put in effort for themselves correlate with how positive the rewards made them feel. Overall, we found, across several indices, that older adults are more prosocial than younger adults and have a lower self-favoring bias in their effort-based decision-making. Therefore, prosocial behavior could fundamentally shift across the life span.

Studies examining life-span changes in prosocial behavior have been mixed. Here, we showed that older adults might be more prosocial in social interactions than younger adults, as suggested by some studies using economic games (Sze et al., 2012). However, our approach was able to show that this effect is not because older adults value money differently per se, as the cost was not money but effort. Moreover, this effort cost was adjusted to each person’s capacity and was manipulated independently from reward in separate self and other conditions, so we were able to identify changes in sensitivity to a cost between a self-benefiting and a prosocial act. Importantly, both in choice behavior and in the energization of actions, there were significant differences between young and older adults’ sensitivity to the effort cost that differed between the self and other conditions. These findings highlight the necessity to examine effort and self- and other-oriented motivation independently, in order to understand specific life-span changes in prosocial behaviors. In addition, these results highlight the importance of comparing people’s willingness to put effort into different types of behavior and not treat motivation as a unidimensional construct. Indeed, some studies in the cognitive domain have found that older adults are more averse to effort than younger adults when it comes to cognitive effort (Hess & Ennis, 2012; Westbrook et al., 2013) and also that cognitive and physical efforts are valued differently (Chong et al., 2017). Dissecting the different components of effort-based decision-making in various contexts will be crucial for accurately quantifying and unpacking the mechanisms underlying multiple facets of people’s motivation (Ang et al., 2017; Cameron et al., 2019; Chong et al., 2017; Inzlicht & Hutcherson, 2017; Kool & Botvinick, 2018; Lockwood, Ang, et al., 2017).

Why might older adults be more prosocial when deciding to put in effort and energize their actions? There are several possible explanations both at the biological and sociocultural level. Socioemotional-selectivity theory posits that as people grow older, their time horizon shrinks, leading to changes in motivational goals and shifts in priority driven by changes in emotional needs (Beadle et al., 2013; Carstensen, 2006). Evidence in support of this is provided by the observation that antisocial and aggressive behaviors significantly decrease across the life span. Young adults (16–24 years old) have the highest rates of homicide (Office for National Statistics, 2019), and several studies have suggested that criminal activity increases during adolescence and declines in older adulthood (Liberman, 2008). As levels of antisocial behavior and criminality lessen across the life span, it is plausible that such changes would, in parallel, be associated with increased prosociality. However, we did not find much evidence that changes between age groups are linked to higher emotional reactivity. In both groups, how willing someone was to put in effort for another person was positively correlated with how positive they felt when winning points for the other person, and there were no significant difference in the strength of correlation. This would not be entirely consistent with a socioemotional-selectivity account, which would posit that there is a stronger prioritization of this emotional response in older adults. Intriguingly, these results do show that the warm glow linked to how much a person will help others is maintained across the life span, with the caveat that ratings of positivity might be susceptible to experimenter demand effects.

Such findings, as well as the reduced difference between participants’ motivation for themselves and others in both choices and force exerted, suggest that older adults may have lost an emotionally driven self-bias that could lead to their putting in more effort for others compared with themselves, relative to younger adults. There is considerable evidence that young adults show a self-bias in many aspects of cognition and behavior; they prioritize self-relevant over other-relevant information. This includes effort, as shown here, but also other factors. Young adults show a self-bias when learning which of their actions earn rewards for themselves and which arbitrary stimuli belong to them, and they also demonstrate bias in many forms of memory and attention (Lockwood et al., 2016, 2018). Existing studies of changes in self-bias with increased age have been somewhat mixed. One study found an increased emotional-egocentricity bias in older adults (Riva et al., 2016), as measured by the incongruency of self and other emotional states. A study that employed an associative-matching task suggested a reduced self-bias in older compared with younger adults (Sui & Humphreys, 2017). Here, by independently manipulating costs and benefits on self and other trials, we found that when it comes to motivation to exert effort, older adults become less self-biased. Future work should begin to distinguish what aspects of the self-bias increase and which decline.

In this study, we specifically focused on willingness to exert physical effort that benefits others—effort that may relate to everyday real-world prosocial acts. Prosocial acts also include behaviors such as doing charitable work or donating money to charity. However, volunteer work can be affected by the amount of time people have available to sacrifice, and monetary donations depend on wealth; both are key issues in aging research on prosocial behavior (Mayr & Freund, 2020). In our task, one major strength was that putting in effort to give rewards to other people had no impact whatsoever on the participant’s own payment at the end. Nevertheless, in future studies, researchers could try to link prosocial effort to everyday prosocial acts, perhaps through measures such as experience sampling, to translate these findings outside the lab. Moreover, researchers could include a measure of perceived wealth to see whether any differences explain variance in how much participants value the monetary rewards on offer. It would also be intriguing to link willingness to exert effort to measures that may quantify social isolation in older adults, such as their social-network size, to examine whether those adults who choose to put in more effort to help others have larger or smaller social networks than younger adults.

Willingness to be prosocial can be affected by social norms such as reciprocity and acceptance (Gintis et al., 2003). We specifically designed our study to minimize these effects: Participants never met face to face, and they were told that they would leave the building at different times and that their identities would never be revealed. However, it could be that social norms are internalized differently across different ages and cultures. It would be interesting for researchers to try to manipulate different social norms in future studies to examine the effect on prosocial choice and force exerted. A strength of the task is that both people’s explicit choices and their implicit energization of action can be measured to provide complimentary insights into prosocial motivation. It would also be important for researchers to examine whether the nature of the receiver changes people’s prosociality, depending perhaps on their age, their closeness, or whether they are perceived as part of an in-group or an out-group. Researchers could also examine whether possible increases in empathy between age groups are linked to differences in willingness to help others: Previous research has suggested that older adults have greater empathic concern for people in need compared with younger adults, although they do not show a benefit from imagining helping others in the same way as younger adults (Sawczak et al., 2019). That also dovetails with research showing an important link between empathy and motivation (Cameron et al., 2019; Lockwood, Ang, et al., 2017). Finally, we note that our results are from a single, albeit well-powered, study, and researchers should seek to replicate our effects in future work.

Overall, we showed that older adults are more prosocial than younger adults in two core components of motivation. Moreover, different emotional considerations may drive decisions in younger and older adults to invest effort to help themselves and others. Understanding the trajectory of social behavior across the life span can inform theoretical accounts of the nature of human prosociality as well as theories of healthy aging—and ultimately, in the long term, help to develop strategies for scaffolding lifelong health and well-being.