The largest parks, greater than 100 acres, had the highest mean happiness benefit, possibly because that larger parks provide greater opportunities for mental restoration and separation from the taxing environment of the city

Schwartz AJ, Dodds PS, O’Neil-Dunne JPM, Ricketts TH, Danforth CM (2022) Gauging the happiness benefit of US urban parks through Twitter. PLoS ONE 17(3): e0261056. Mar 30, 2022. https://doi.org/10.1371/journal.pone.0261056

Abstract: The relationship between nature contact and mental well-being has received increasing attention in recent years. While a body of evidence has accumulated demonstrating a positive relationship between time in nature and mental well-being, there have been few studies comparing this relationship in different locations over long periods of time. In this study, we analyze over 1.5 million tweets to estimate a happiness benefit, the difference in expressed happiness between in- and out-of-park tweets, for the 25 largest cities in the US by population. People write happier words during park visits when compared with non-park user tweets collected around the same time. While the words people write are happier in parks on average and in most cities, we find considerable variation across cities. Tweets are happier in parks at all times of the day, week, and year, not just during the weekend or summer vacation. Across all cities, we find that the happiness benefit is highest in parks larger than 100 acres. Overall, our study suggests the happiness benefit associated with park visitation is on par with US holidays such as Thanksgiving and New Year’s Day.

Tweets inside of all park size categories exhibited a positive happiness benefit. The largest parks, greater than 100 acres, had the highest mean happiness benefit. One possible explanation is that larger parks provide greater opportunities for mental restoration and separation from the taxing environment of the city. This finding is consistent with results from our earlier study in San Francisco, in which tweets in the larger and greener Regional Parks had the highest happiness benefit [18]. Parks between 0 and 10 acres are often neighborhood parks that people use in their day to day lives. Local parks provide many essential functions; however, our results suggest that the experiences people have in larger parks may be more beneficial from a mental health perspective. Another possibility is that people spend more time in larger parks; one study suggested that 120 minutes of nature contact a week resulted in improved health and well-being [35].

Temporal analysis

Across all cities, we grouped park tweets and their control tweets according to the in-park tweet’s timestamp to test H4. First, we compared the happiness benefit by season. The mean happiness benefit was highest in the summer (0.12), followed by fall (0.10), spring (0.08), and winter (0.06) as shown in Fig 4B. Then we grouped park tweets and their respective control tweets according to the day of the week in which it was posted. Saturday exhibited the highest mean happiness benefit (.15) followed by Sunday (0.13). Monday through Friday were all between 0.06 and 0.09 (Fig 4). We also estimated the happiness benefit by hour of the day. The tweets posted during the 8:00 and 9:00 AM hours had a mean happiness benefit around 0.07 while the rest of the day did not show a clear pattern, ranging from 0.08 to 0.14 (S4 Fig).

We observe that the mean happiness benefit was higher in summer than other seasons; however, the happiness benefit was positive in all four seasons. Possible interpretations of seasonal differences may include that warmer or sunnier weather in the summer leads to an increased benefit from park visitation. People may engage in longer visits to parks during summer months, engage in physical activity, or connect with friends during the summer, all of which may increase the benefits of spending time in a park [36]. Alternatively, more non-residents may be tweeting from parks during the summer, leading to greater within-park sentiment scores. Similar dynamics may be driving the higher happiness benefits on the weekend compared to weekdays, though all days of the week exhibited positive values (See Fig 4). Prior work has shown that people on Twitter are happiest on the weekends and during times of year with more daylight [37]. Nevertheless, our comparisons indicate that a sentiment benefit occurs throughout the day, week, and year, indicating that the effect is not purely driven by temporal patterns. Our hourly comparison indicates that a sentiment benefit occurs during all hours of the day, indicating that the effect is not purely driven by leaving the office. This result is encouraging because some prior studies on nature contact using Twitter analyzed shorter time periods. Future studies should seek methods that can investigate the other temporal aspects of nature contact including the frequency and duration of visits [38].

We acknowledge that studying human behavior using Twitter data involves several potential sources of bias. Active users on Twitter tend to be younger and more affluent than the population at large [39]. Instead of investigating how individual users and demographic sub-groups respond to nature contact, we attempt to estimate the aggregate effect of park visitation on happiness across a city. While our happiness benefit calculation uses same-city tweets as a control, the results may not generalize beyond Twitter users. We only use English language tweets which may limit our ability to generalize to other languages and cultures. We do not control for nearby demographics when assessing the happiness benefit of specific parks. For example, larger parks may be promixal to more affluent neighborhoods or associated with adjacent neighborhood age structure. While this may introduce bias across parks within cities, it should not impact our results comparing the total happiness benefit across cities.

Future directions

Our results, along with those from previous studies, point to several important areas of future research. Future research should continue to explore the relationship between tweet happiness and other factors beyond park investment. While ParkScore® captures a variety of park-quality related metrics, vegetation and biodiversity are salient features of greenspace that significantly impact how people experience their time in nature [40–42].

More localized studies could look at the mental health impact of park-level vegetative cover and biodiversity metrics. Alternatively, similar methods could be applied to compare the mental benefits of nature contact with other experiences such as museum visits or sports games. This could provide insight into the benefit of investing in public goods such as parks for health outcomes relative to alternatives. Similarly, these analyses could isolate the importance of experiencing nature compared to the social and cultural factors that influence sentiment on Twitter.

While we investigated the seasonal variation of in-park happiness, climate and weather have been shown to influence happiness on Twitter as well [43, 44]. Tweets could be binned by some composite of temperature, humidity, and precipitation in order to investigate how weather moderates the association between nature contact and mental well-being [21].

Demographic, socioeconomic, and cultural factors also play a role in how people engage with parks [45]. While identifying such factors on Twitter is challenging and requires ethical consideration, other methodologies can continue to explore how different groups use and benefit from time in parks, to help ensure that the benefits of parks are available to everyone. As the evidence continues to mount on the many different benefits of nature contact, we must ensure park access to quality parks for all urban residents.

Those in the prosocial condition rated the role of genetics in causing the behavior as significantly greater than did those in the antisocial condition, due to the tendency to view prosocial behavior as more natural and more aligned with one’s true self

Asymmetric genetic attributions for one’s own prosocial versus antisocial behavior. Matthew S. Lebowitz, Kathryn Tabb & Paul S. Appelbaum. The Journal of Social Psychology, Mar 31 2022. https://doi.org/10.1080/00224545.2022.2058906

Abstrct: People tend to rate prosocial or positive behavior as more strongly influenced by the actor’s genes than antisocial or negative behavior. The current study tested whether people would show a similar asymmetry when rating the role of genes in their own behavior, and if so, what variables might mediate this difference. Participants were prompted to think about an example of their own behavior from the past year that was either prosocial or antisocial. Those in the prosocial condition rated the role of genetics in causing the behavior as significantly greater than did those in the antisocial condition. A mediation analysis suggested that this asymmetry could be accounted for by a tendency to view prosocial behavior as more natural and more aligned with one’s true self than antisocial behavior. These findings add to a growing body of evidence suggesting that people’s reasoning about genetics may be influenced by evaluative judgments.

Keywords: Geneticssocial cognitioncausal attributionmotivated reasoning

Their studies revealed a robust belief that “life gets better” over time (i.e., recollected past < current < anticipated future life satisfaction) in nations around the world, in relation to both objective & subjective indicators of societal-level functioning

Busseri, M. A. (2022). The global belief that “life gets better and better”: National differences in recollected past, present, and anticipated future life satisfaction around the world, across time, and in relation to societal functioning. Journal of Personality and Social Psychology, Apr 2022. https://doi.org/10.1037/pspp0000415

Abstract: National-level differences in individuals’ ratings of their recollected past, current, and anticipated future life satisfaction (LS) were examined using results from two pioneering projects comprising national-level results for 14 countries (Cantril, 1965) and 15 regions of the world (Gallup International Research Institutes & Charles F. Kettering Foundation, 1976; Study 1), as well as sequential results from the Gallup World Poll based on 137 countries representing a broad range of nations from around the world surveyed from 2005 to 2018 (Study 2). Results from both studies revealed a robust belief that “life gets better” over time (i.e., recollected past < current < anticipated future LS) in nations around the world. Such beliefs were examined in relation to objective and subjective indicators of societal-level functioning. Results replicated across studies in showing that nations with less positive societal functioning and prosperity were characterized by less recollected past improvements in LS, and yet greater anticipated future improvements in LS. Results from Study 2 also revealed that such expectations were positively biased compared to changes over time in national levels of LS; further, greater bias was related to less positive societal-level functioning. In conclusion, examining national-level differences in LS from a subjective temporal perspective provides valuable new insights concerning human development and prosperity across countries, over time, and around the world.

Factors proposed to explain impersonal cooperation across societies (institutions (rule of law), religion (belief in God as a third-party punisher), cultural beliefs (trust) & values (collectivism), and ecology (relational mobility)) are not so important

Spadaro, G., Graf, C., Jin, S., Arai, S., Inoue, Y., Lieberman, E., Rinderu, M. I., Yuan, M., Van Lissa, C. J., & Balliet, D. (2022). Cross-cultural variation in cooperation: A meta-analysis. Journal of Personality and Social Psychology, Apr 2022. https://doi.org/10.1037/pspi0000389

Abstract: Impersonal cooperation among strangers enables societies to create valuable public goods, such as infrastructure, public services, and democracy. Several factors have been proposed to explain variation in impersonal cooperation across societies, referring to institutions (e.g., rule of law), religion (e.g., belief in God as a third-party punisher), cultural beliefs (e.g., trust) and values (e.g., collectivism), and ecology (e.g., relational mobility). We tested 17 preregistered hypotheses in a meta-analysis of 1,506 studies of impersonal cooperation in social dilemmas (e.g., the Public Goods Game) conducted across 70 societies (k = 2,271), where people make costly decisions to cooperate among strangers. After controlling for 10 study characteristics that can affect the outcome of studies, we found very little cross-societal variation in impersonal cooperation. Categorizing societies into cultural groups explained no variance in cooperation. Similarly, cultural, ancestral, and linguistic distance between societies explained little variance in cooperation. None of the cross-societal factors hypothesized to relate to impersonal cooperation explained variance in cooperation across societies. We replicated these conclusions when meta-analyzing 514 studies across 41 states and nine regions in the United States (k = 783). Thus, we observed that impersonal cooperation occurred in all societies—and to a similar degree across societies—suggesting that prior research may have overemphasized the magnitude of differences between modern societies in impersonal cooperation. We discuss the discrepancy between theory, past empirical research and the meta-analysis, address a limitation of experimental research on cooperation to study culture, and raise possible directions for future research. 

We ask: "What is cultural evolution anyway?" Our answer: a phenomenon, not a theory/ approach. Understanding this helps clarify other issues, e.g. the role of human behavioural ecology

What is cultural evolution anyway? Alberto J C Micheletti, Eva Brandl, Ruth Mace. Behavioral Ecology, arac011. Apr 1 2022.  https://doi.org/10.1093/beheco/arac011

Abstract: The term cultural evolution has become popular in the evolutionary human sciences, but it is often unclear what is meant by it. This is generating confusion and misconceptions that are hindering progress in the field. These include the claim that behavioral ecology disregards culture. We argue that these misunderstandings are caused by the unhelpful use of term cultural evolution to identify both a phenomenon—culture changing through time—and a theory to explain it—the potential role of cultural transmission biases in driving this change. We illustrate this point by considering recently published influential studies and opinion pieces. If we are to avoid confusion, the term cultural evolution is best reserved to identify the phenomenon of cultural change. This helps clarify that human behavioral ecologists do not disregard culture, but instead have studied its evolution from the very beginning. Different approaches to the study of human behavior can coexist and complement each other in the framework offered by Tinbergen’s four evolutionary questions. Clarifying key terms is crucial to achieve this synthesis.

Cultural evolution is becoming a blanket term for any kind of human behavioral evolution. However, we believe that this is leading to confusion because the term “cultural evolution” is being used to indicate both a phenomenon—culture changing through time—and an approach to study it—the focus on cultural inheritance and the potential role of transmission biases in shaping culture. This confusing use of the term is widespread in the literature and in informal discussion (we may even have been guilty of this ourselves). For example, Schulz et al. (2019: 1) state that “cultural evolution often favoured some form of cousin marriage.” Are they referring to cultural evolution as opposed to genetic evolution? Cousin marriage is surely a culturally transmitted behavior, so this comparison appears irrelevant here. Or, by cultural evolution, do they mean the action of transmission biases? Or are they referring to the whole phenomenon of cultural change? If so, how can culture changing per se “favour” a particular outcome? Innovation, migration, or cultural drift may lead to this outcome, but only some form of selection, genetic, cultural or perhaps both, may “favour” a given outcome.

A second example reveals how this ambiguity can lead to confusion that is hindering progress in the field. A study by Barsbai et al. (2021) shows that human behaviors tightly fit local environmental conditions, following very similar patterns to those shown by mammals and birds living in the same area. In a commentary to the study (Hill and Boyd 2021), the wording appears to present cultural evolution and adaptation to local ecology as alternative explanations for the diversity and distribution of these traits. They state: “Hence, the study appears to validate the basic premise of the evolutionary perspective called ‘human behavioural ecology’. However, it is a mistake to conclude from this that culture is unimportant” (Hill and Boyd 2021: 236). This seems to suggest that human behavioral ecology ignores culture. Yet, Barsbai et al. (2021) do not deny that the foraging, reproductive, and social behaviors they examine are culturally transmitted, at least in humans. Neither do they assume that cultural history plays little to no role in shaping the observed patterns, as seems to be implied by Hill and Boyd (2021: 236) when they state: “ecological factors explain much variation in human behaviour, but so too does cultural history.” Cultural phylogeny may indeed play a role and, for this reason, the authors control for it in their analyses (Barsbai et al. 2021).

Barsbai et al. (2021) simply show that a variety of human behaviors—almost certainly culturally transmitted—fit local ecology in the same way as behaviors that are probably mostly genetically controlled in birds and mammals. Therefore, their analysis suggests that these cultural traits have been shaped by inclusive fitness interests. In line with a behavioral ecological approach, they are agnostic as to the mechanism leading to this fit. It is possible that it came about through one or more specific biases in cultural transmission or, more generally, because humans are flexible learners that make conscious, strategic choices about what to adopt, sensitive to pay-offs (Burton-Chellew and West 2021). Although it is tempting to contrast adaptation to local ecology and “culture” or “cultural evolution” as two competing forces shaping the change of behavior through time, such a contrast is impossible. As Boyd has acknowledged elsewhere (Boyd 2018), adaptation to local ecology is an outcome of the process of cultural evolution, whereby cultural selection has favored a set of cultural variants because they are adaptive in a specific environment. Therefore, the tools of behavioral ecology are always going to be needed to understand cultural evolution.

Evolutionary biologists, too, have sometimes used language suggesting this unhelpful dichotomy between adaptation and culture. For example, Burton-Chellew and West (2013: 1043) ask “Will culture be more important for certain classes of traits such as those less linked to fitness?” We suspect that these authors were meaning to suggest that fitness-insensitive cultural transmission mechanisms can sometimes result in non-adaptive outcomes (especially when a trait is less fitness relevant). However, the way they presented their argument can be potentially misleading. Behaviours can be culturally transmitted, and many human behaviors are, and yet they can still be shaped, at least to some extent, by the inclusive fitness interests of their bearer.

 

Consumers particular vulnerable to financial bullshit are more likely to be young, male, have a higher income, and be overconfident with regards to their own financial knowledge

Individual differences in susceptibility to financial bullshit. Mario Kienzler, Daniel Västfjäll, Gustav Tinghög. Journal of Behavioral and Experimental Finance, March 31 2022, 100655. https://doi.org/10.1016/j.jbef.2022.100655

Abstract: What is the effect of seemingly impressive verbal financial assertions that are presented as true and meaningful but are actually meaningless; that is, financial pseudo-profound bullshit? We develop and validate a novel measurement scale to assess consumers’ ability to detect and distinguish financial bullshit. We show that this financial bullshit scale captures a unique construct that is only moderately correlated with related constructs such as financial knowledge and cognitive abilities. Consumers particular vulnerable to financial bullshit are more likely to be young, male, have a higher income, and be overconfident with regards to their own financial knowledge. The ability to detect and distinguish financial bullshit also predicts financial well-being while being less predictive of consumers’ self-reported financial behavior, suggesting that susceptibility to financial bullshit is linked to affective rather than behavioral reactions. Our findings have implications for the understanding of how financial communication impacts consumer decision making and financial well-being. 

JEL: G41G51G53

Keywords: BullshitFinancial bullshitFinancial behaviorFinancial well-beingScale

4. Discussion and conclusion

The ability to detect and distinguish profound statements (and information) from plain gibberish is crucial for individual’s to effectively navigate any social system and make well informed decisions. Finance is often portrayed as a complex and difficult area of decision making, where interactions commonly are characterized by jargon, acronyms, and slogans. This provides a hotbed for bullshitting to thrive and obscure the view of consumers. We developed and validated a novel measurement scale that allows us to measure individual differences in susceptibility to financial bullshit – the financial bullshit scale. We show that this scale captures a unique construct that is only moderately correlated with related constructs such as financial literacy and numeric ability. Moreover, we show that the ability to detect financial bullshit is distinctively separate from the ability to detect general bullshit and predict financial behavior beyond the original general bullshit scale.

Our results also provide insights into ‘who is more susceptible for financial bullshit?’. Consumers particular vulnerable to financial bullshit were more likely to be young, male, have a higher income, and be overconfident with regards to their own financial knowledge. This finding is in line with prior research that found age to be positively related to people’s ability to distinguish profound and pseudo-profound communication in general (Erlandsson et al., 2018). The finding that women showed a greater ability to detect and distinguish bullshit from genuine financial statements is a little surprising given that prior research has documented a persistent gender gap in financial literacy which partly can be attributed to stereotype threat, which posits that inbuilt prejudices about gender and finance undermine performance among women in tasks involving finance (Tinghög et al., 2021). The finding that higher income was positively related to being susceptible to financial bullshit might also be surprising. However, it seems reasonable to believe that as income rise consumers become less vigilant when it comes to financial matters and therefore less alert when it comes to detecting to be affected by impressive financial language. Much in the same way that scarcity requires trade-off thinking and makes people more efficient (Mullainathan and Shafir, 2013).

We also investigated the consequences susceptibility to financial bullshit has for financial wellbeing and financial behavior. Our results show that the financial bullshit scale predicted subjective financial well-being. In particular, consumers with an increasing ability to detect bullshit felt more insecure about their finances. Put differently, consumers worse at distinguishing between bullshit and genuine communication exhibited an ignorance-is-bliss effect when it came to subjective financial wellbeing. This ignorance-is-bliss effect did however not extend to self-reported financial behavior in our study. Considering these results, being able to detect and distinguish bullshit from genuine financial statements is neither unequivocally a good nor a bad thing. On the good side, people who were less susceptible to financial bullshit displayed a greater ability on a number of financially relevant competencies (e.g., greater objective financial knowledge). On the bad side, susceptible to bullshit was also related to a decrease in perceived financial security about their own future financial situation.

Even if the financial bullshit scale was related to financial well-being, we did not find a systematic relationship to self-reported financial behaviors. The financial management behavior scale taps into everyday household finance behaviors and management strategies (e.g., keep a budget, pay bills on time). Prior research demonstrated that this scale is related to both self-control and financial well-being (Strömbäck et al., 2017, Strömbäck et al., 2020). In hindsight these general behaviors are likely less strongly related to individual differences in susceptibility to financial bullshit, than behaviors containing financial bullshit (e.g., purchase of questionable financial products or evaluating misleading claims about the financial performance of products). Our results, showing that susceptibility to financial bullshit was related to financial buzzword comprehension but not general financial behavior supports this notion. We also note that, the present research relates to research on overclaiming in the financial domain. For instance, previous research on overclaiming (e.g., Atir et al. 2015) used people’s self-assessed financial knowledge and compared it to their knowledge claims of fictional finance terms. We, on the other hand, used people’s self-assessed financial knowledge and compared it with their actual knowledge. We also showed that people’s financial sophistication can be related to their financial bullshit score.

Ideally the financial bullshit scale can be used in future research to advance understanding on how to make individuals better equipped to distill financial communication and navigate the financial landscape. As done here, the scale can be used to identify customers that are vulnerable to fall prey for seemingly impressive statements that could be misleading in negotiations and other financial situations involving human interactions (for more research on financial vulnerability, see O’Connor et al., 2019). By extending research on the psychology of bullshit into the domain of financial decision making we hope to spur future research on what we think is an overlooked topic in consumer research; the impact (bad) financial communication has on consumer financial decision making.

Finally, the present study has practical implications for financial institutions and policy makers. First, our results show that consumers vary in their susceptibility to financial bullshit and certain groups of consumers are more vulnerable to it than others. This information can be an important steppingstone for designing tailored interventions. For example, interventions aimed at helping consumers to make better decisions and feeling less anxious about their personal finances. Second, financial institutions need to consider that consumers with an increasing ability to detect bullshit felt more insecure about their finances. This suggests that financial institutions need to apply nuanced strategies to serve their customer base. For instance, help customers who can distinguish genuine and bullshit financial communication to feel more secure in their money matters rather than to merely provide them with sound financial advice. This should lead to positive consequences for actual and perceived financial well-being.

People at least somewhat agree on what a gullible person looks like; & people whose facial features resemble an expression of anger are perceived as particularly low on gullibility

Who Can Be Fooled? Modeling Facial Impressions of Gullibility. Bastian Jaeger and Erdem O. Meral. Social CognitionVol. 40, No. 2, March 2022. https://doi.org/10.1521/soco.2022.40.2.127

Abstract: The success of acts of deceit and exploitation depends on how trusting and naïve (i.e., gullible) targets are. In three preregistered studies, using both theory-driven and data-driven approaches, we examined how people form impressions of gullibility based on targets' facial appearance. We find significant consensus in gullibility impressions, suggesting that people have a somewhat shared representation of what a gullible person looks like (Study 1, n = 294). Gullibility impressions is based on different cues than trustworthiness or dominance impressions, suggesting that they constitute dissociable facial stereotypes (Study 2, n = 403). Examining a wide range of facial features, we find that gullibility impressions are primarily based on resemblance to an angry facial expression. We also find that young, female, and smiling individuals were seen as more gullible (Study 3, n = 209). These findings suggest that gullibility impressions are based on cues linked to low levels of perceived threat.

Rolf Degen summarizing... Christians value receiving a prayer in a hardship from a Christian stranger at an average of $2.34, while non-believers are willing to pay $1.56 not to be prayed for

Thunström L, Noy S (2022) What we think prayers do: Americans’ expectations and valuation of intercessory prayer. PLoS ONE 17(3): e0265836. Mar 31 2022. https://doi.org/10.1371/journal.pone.0265836

Abstract: Praying for others in the wake of a disasters is a common interpersonal and public response to tragedy in the United States. But these gestures are controversial. In a survey experiment, we elicit how people value receiving a prayer from a Christian stranger in support of a recent hardship and examine factors that affect the value of the prayer. We find that people who positively value receiving the prayer do so primarily because they believe it provides emotional support and will be answered by God. Many also value the prayer because they believe it will improve their health and wealth, although empirical support of such effects is lacking. People who negatively value receiving the prayer do so primarily because they believe praying is a waste of time. The negative value is particularly large if people are offended by religion. Finally, the hardship experienced by the prayer recipient matters to the intensity by which recipients like or dislike the gesture, suggesting the benefit of prayers varies not only across people, but also across contexts.

Reasons people positively value prayers from religious strangers.

Participants who stated a positive WTP for receiving a prayer from a stranger (Christian: N = 375/451; non-believers: N = 56/166), were asked about the factors that contributed to the value of the prayer.

Large majorities of both Christians and non-believers who value the prayer do so because it gives them emotional comfort to know that the stranger is thinking of them. The answers to the open ended question provide additional information on the comfort people experience from receiving the prayer–e.g., one non-believer noted that they valued prayers positively because “someone is acknowledging the hardships I am going through and wishes for me to get through them successfully” (R_161) while a Christian participant explained: as “a Christian, prayer is invaluable and a source of personal comfort through faith” (R_282).

Further, a large majority of Christians (82 percent) believe that the prayer will result in God intervening to ease their emotional pain. While shares are smaller, many Christians also value the prayer because they believe God will help materially (36 percent) or improve their health (55 percent). Such expectations appear to be misplaced, given previous research shows that prayers for others have no effect on the recipient’s health [23], and therefore might bias the value of prayers upwards. They might also explain why prayers may reduce material aid [8]–if God is expected to intervene materially in response to prayers, the perceived need for material aid may be lower.

We also asked Christians who positively value prayers (N = 375/451) about the probability that the prayer from the stranger would be answered by God. Their average response was 78 percent. Amongst these participants, those who were more religious (as measured by frequency of church attendance), Republicans and those with low income (compared to high income) stated a higher probability that God would answer the prayer. For details, see Supplemental Online Material.

The share of non-believers who value the prayer and believe the prayer will result in help from God (whether emotional, material, or health) is not statistically significantly different from zero, i.e., even though some non-believers positively value receiving a prayer, they do not expect the prayer to generate benefits due to divine intervention. Finally, a large majority of both Christians and non-believers positively value the prayer because they think sending the prayer is a meaningful activity for the stranger. Hence, altruism could be an important part of the prayer’s value, to both Christians and non-believers–the recipient believes the sender of the prayer will benefit from undertaking the prayer.

While the results shown in Fig 2 indicate why people positively value prayers, it does not show how intensely each factor affects the positive value. Next, we examined the extent to which these factors, and covariates, affect the positive WTP. To do so, we regressed WTP for the prayer from the Christian stranger on agreement with each statement in Fig 2, a set of common demographics—gender, age, conservatism, religious belonging, religiosity (measured as frequency of church attendance) income and college attendance–as well as the type of hardship (issue) described in the experimental survey.

[...]

Fig 3 shows that the highest positive value for a prayer is generated if the recipient expects emotional comfort from the prayer. Although Fig 2 shows that many participants value the prayer because it benefits the sender to pray (altruism), the benefit to the sender does not contribute to the average positive value of a prayer (if anything, it brings down the mean positive value of the prayer). Further, beliefs that the prayer generates material help or improved health do not affect the mean positive value of the prayer.

[...]

The type of hardship addressed by the prayer also matters to the intensity by which a person values receiving a prayer. Around 30 percent of participants reported a health issue (for self or a loved one) as the hardship, around 30 percent reported a financial issue, between 15 and 20 percent reported a relationship issue, and around 20 percent an issue that does not fall into any of those categories. Recipients value the prayer more if the hardship they experience consists of a health or relationship issue (for themselves or a loved one), compared to if they or a loved one experience a financial issues (the benchmark in the model underlying Fig 3). These results are robust to the inclusion of covariates. Note that while being conservative significantly affects whether a prayer is positively valued (see above), more conservative people who value prayers do not assign a particularly high positive value to the prayer. This result is stable across our measurements of conservatism—it does not matter whether we use the SEC scale (the conservatism measure in Fig 3), the liberal-conservatism scale or political party belonging as a measure of conservatism.

The stability of beliefs in conspiracy theories is comparable to or higher than some of the most stable psychological attributes

Williams, Matt N., Mathew Ling, John R. Kerr, Stephen R. Hill, Mathew Marques, Hollie Mawson, and Edward J. R. Clarke. 2022. “To What Extent Do Beliefs in Conspiracy Theories Change over Time?.” PsyArXiv. March 31. doi:10.31234/osf.io/5q2ky

Abstract: Recent years have seen an explosion in psychological research on beliefs in conspiracy theories. This research has produced a significant body of knowledge about the antecedents and consequences of inter-individual belief in conspiracy theories. What is less clear, however, is the extent to which individuals’ beliefs in conspiracy theories vary over time (i.e., intra-individual variation). In this descriptive and exploratory study we therefore aimed to describe intra-individual variability in belief in conspiracy theories. We collected data from 498 Australians and New Zealanders using an online longitudinal survey, with data collected at monthly intervals over six months (March to September 2021). Our measure of conspiracy theories included items describing ten unfounded conspiracy theories with responses on a 5-point Likert scale. While there was substantial variability in beliefs between different participants (i.e., inter-individual variability), there was much less intra-individual variability (intraclass r = 0.91). Indeed, it was common for participants to give exactly the same response to a given theory at every time point. Via power analyses, we demonstrate that the small quantity of intra-individual variation in beliefs in conspiracy theories has important consequences for sample size planning in longitudinal studies.

Parasites: Male psychopaths are not set against fatherhood, as long as someone else takes on the effort of raising the kids

Cads in Dads’ Clothing? Psychopathic Traits and Men’s Preferences for Mating, Parental, and Somatic Investment. Kristopher J. Brazil & Anthony A. Volk. Evolutionary Psychological Science, Mar 30 2022. https://link.springer.com/article/10.1007/s40806-022-00318-z

Abstract: Psychopathic traits are sometimes viewed as an alternative reproductive strategy that prioritizes mating over parental investment, particularly in men. Two aspects of this research receiving less attention are (1) the inclusion of somatic investment, which refers to the growth and maintenance of oneself, and (2) measuring perceptions of investment domains in addition to behavior and attitude outcomes. In this study, we used a sample of 255 young adult men from MTurk (Mage = 29.55, SD = 2.97) to examine how the three domains of investment (mating, parental, and somatic) relate to individual differences in men’s psychopathic traits, relationship/parental status, and age using outcome measures of (1) behavioral attitudes and (2) perceptions of stimuli associated with each investment domain (e.g., attractive women’s faces and cute infants). Results showed that while they were associated with being a parent, psychopathic traits were associated with higher mating and lower parental and somatic behavioral attitudes. Psychopathic traits were associated with negative perceptions of indirect somatic cues (e.g., working and forming friendships), positive perceptions of mating cues, and no relationship with perceptions of direct somatic (e.g., exercising) or parental cues. Our results agree with previous research but extend them by showing that while they engage in lower somatic behavior, men higher in psychopathic traits do not appear to have aversive reactions towards infant stimuli and are more likely to be parents themselves. We argue that these patterns are consistent with a parasitic parenting strategy that focuses on mating while depending on others to invest in their children.

Meta-analysis: Cognitive abilities are not related to the willingness to take financial risks

Cognitive abilities affect decision errors but not risk preferences: A meta-analysis. Tehilla Mechera-Ostrovsky, Steven Heinke, Sandra Andraszewicz & Jörg Rieskamp. Psychonomic Bulletin & Review, Mar 30 2022. https://link.springer.com/article/10.3758/s13423-021-02053-1

Abstract: When making risky decisions, people should evaluate the consequences and the chances of the outcome occurring. We examine the risk-preference hypothesis, which states that people’s cognitive abilities affect their evaluation of choice options and consequently their risk-taking behavior. We compared the risk-preference hypothesis against a parsimonious error hypothesis, which states that lower cognitive abilities increase decision errors. Increased decision errors can be misinterpreted as more risk-seeking behavior because in most risk-taking tasks, random choice behavior is often misclassified as risk-seeking behavior. We tested these two competing hypotheses against each other with a systematic literature review and a Bayesian meta-analysis summarizing the empirical correlations. Results based on 30 studies and 62 effect sizes revealed no credible association between cognitive abilities and risk aversion. Apparent correlations between cognitive abilities and risk aversion can be explained by biased risk-preference-elicitation tasks, where more errors are misinterpreted as specific risk preferences. In sum, the reported associations between cognitive abilities and risk preferences are spurious and mediated by a misinterpretation of erroneous choice behavior. This result also has general implications for any research area in which treatment effects, such as decreased cognitive attention or motivation, could increase decision errors and be misinterpreted as specific preference changes.

Discussion

We conducted a Bayesian meta-analysis with a total of 30 studies and examined whether a potential meta effect size is better explained by the risk-preference hypothesis, which assumes a correlation between cognitive abilities and risk aversion because cognitive abilities affect the evaluation of risky options and, consequently risk-taking behavior, or by the error hypothesis, which assumes that mixed results are the product of a relationship between cognitive abilities and decision errors resulting from a bias of the architectural properties of the risk-preference-elicitation task. Our results show that the correlation between cognitive ability and risk aversion is noncredible. Notably, we find that when studies applied unbalanced choice sets, they reported a stronger negative (or positive) correlation between cognitive abilities and risk aversion depending on the direction of this unbalance. The effect of the RCRT bias was robust across all meta-analytical model specifications and thus provides strong evidence for the error hypothesis. That is, our findings support the claim that previous mixed evidence of a correlation between cognitive abilities and risk aversion is mainly driven by the important interaction between the architecture of the risk-preference-elicitation task and errors in decision making. In addition, we found an effect of task framing, where including losses in risk-preference-elicitation tasks only weakly moderates the relation between cognitive abilities and risk aversion. Note that this effect was not robust across all meta-analytical model specifications and appears to be highly correlated with the RCRT bias of the choice set, where the latter has a higher explanatory power. We found no mediating effects of the type of cognitive ability test applied or of the number of decisions. We conclude that a potential correlation between cognitive abilities and risk aversion is moderated by the link between cognitive abilities and the probability of making unsystematic decision errors.

A recent meta-analysis by Lilleholt (2019) similarly explored the link between cognitive abilities and risk preferences. However, in contrast to our work, Lilleholt’s analysis did not directly test whether the mixed findings regarding the link between cognitive abilities and risk preferences could be explained by the error hypothesis and the bias in the architecture of most risk-preference-elicitation tasks. There are other important differences. First, Lilleholt had a broader literature search scope, leading to a larger set of examined studies. For instance, the author included experience-based risk-preference-elicitation tasks, which we excluded from our analysis. In such tasks, people have no information about the outcomes of gambles and the probabilities with which the outcomes occur but learn this from feedback. Thus, in these tasks, learning plays a major role in how people make their decisions, thereby making the interpretation of a potential link between cognitive abilities and risk preferences more complicated. In general, it has been argued that description-based and experience-based tasks differ in both architecture and interpretation (Frey et al., 2017). Therefore, in contrast to Lilleholt, we have focused on a description-based task that makes it easier to code all relevant task-architecture information precisely.

Since Lilleholt (2019) ran the meta-analysis for each domain separately, we compared Lilleholt’s results with our results by estimating our meta-analytic models on Lilleholt’s merged data set (see Appendix Table 8). In line with Lilleholt’s results, we find a credible metaeffect of −.05 with a 95% BCI ranging from −.07 to −.03 for the loss, gain, and mixed domains. Note that our restricted data set exhibits a comparable effect size of −.03, with a 95% BCI ranging from −.08 to .02. Additionally, the inclusion of losses as outcomes of the choice options had a credible effect on the correlation between cognitive abilities and risk preferences with a mean estimate of .12 and a 95% BCI ranging from .08 to .15 (see Appendix Table 8, Model Mf). The model comparison shows that the model that includes this variable is superior to a model that exclude it (see Appendix Table 8, Models Mf, M2). The effect of the RCRT bias towards risk aversion on the correlation between cognitive abilities and risk preferences was credible across all model specifications (see Appendix Table 8, Models Mf, M1, M2) and exhibited a mean estimate of −.17 and a 95% BCI ranging from −.25 to −.09 (see Appendix Table 8, Model Mf). More importantly, a regression model comparison procedure (see Appendix Table 8) shows that accounting for RCRT bias (Mf vs. M1 BF = 5.9×106) and the inclusion of losses (Mf vs. M2, BF = 2.1×1010) improve the model fit substantially for the merged data set of Lilleholt (2019), replicating our results. However, given the larger set of studies in Lilleholt compared with ours, this replication should be interpreted with caution.

Our finding of a moderating effect of an RCRT-biased task architecture on the correlation between cognitive ability and risk aversion contributes to the discussion in the decision sciences and experimental economics literature. For instance, in line with the error hypothesis, Andersson et al. (2016) experimentally demonstrated that the link between cognitive abilities and risk aversion is spurious, as it is moderated by the link between cognitive abilities and random choice behavior (Andersson et al., 2016). In keeping with this result, Olschewski et al. (2018) reported that in risk-taking tasks, cognitive abilities correlated negatively with decision errors. We followed this work and rigorously tested the error hypothesis with a meta-analysis. Our results show that the correlation between cognitive abilities and risk aversion can be explained by the characteristics of the choice set (i.e., task architecture), implying an RCRT bias, a phenomenon that leads to misclassifying random choices as a specific risk preference.

Our findings support the view of the error hypothesis that cognitive abilities are linked to the probability of making unsystematic errors (Burks et al., 2008; Dean & Ortoleva, 2015; Olschewski et al., 2018; Tymula et al., 2013). Additionally, it is plausible to assume that people with lower cognitive abilities apply simpler decision strategies (i.e., heuristics) that reduce information-processing load. However, the use of heuristics does not necessarily imply more or less risk-taking behavior; only the interaction between the applied heuristic and the task architecture leads to a specific observed risk-taking behavior. As we discussed above, some heuristics lead to higher (or lower) observed risk-seeking behavior compared with more complex decision strategies, depending on the choice set. Therefore, one would not necessarily expect a specific correlation between people’s cognitive abilities and the observed risk-taking behavior across the different tasks, but instead expect some heterogeneity in the results. However, the use of specific strategies cannot explain the relationship between the observed average risk preferences in a task and the RCRT bias in the task. Thus, the link between cognitive abilities and the selected decision strategies does not imply a link between cognitive abilities and the latent risk preferences. Crucially, when examining the potential link between cognitive abilities, decision strategies, and risk preferences, it is necessary to first identify the specific strategies people apply in specific environments or task architectures (Olschewski & Rieskamp, 2021; Rieskamp, 2008; Rieskamp & Hoffrage, 1999, 2008; Rieskamp & Otto, 2006). Future work should examine the different heuristics and decision strategies to arrive at a comprehensive understanding of whether and how those shape the correlation between cognitive abilities and risk preferences.

The results of this study also resonate with a recent empirical discourse on the validity of risk-preference-elicitation measures. For instance, Frey et al. (2017) and Pedroni et al. (2017) found behavioral risk-elicitation tasks to be less stable elicitations of risk preferences compared with self-reported measures. Importantly, the difference between behavioral and self-reported measures could disappear once measurement errors are accounted for (Andreoni & Kuhn, 2019) by applying better task architectures.

Our results also have implications for interpreting experimental results in other research domains. For example, when testing for a specific treatment effect it appears important to control for increased decision errors, so that a potential increase in errors is not misinterpreted as a specific treatment effect. Whether such misinterpretation is likely to occur depends on whether the task architecture has a bias, so that random choice behavior leads to a specific psychological interpretation. For instance, a potential effect of increased time pressure on people’s risk preferences could also simply be due to an increase in decision errors under high time pressure (e.g., Olschewski & Rieskamp, 2021). Likewise, the potential effect of cognitive load on people’s risk preferences, intertemporal time preferences, or social preferences could also simply be due to an increase in decision errors under cognitive load manipulations (e.g., Olschewski et al., 2018). Finally, the potential effect of increased monetary incentives on people’s preferences could also be due to lower decision errors with higher monetary incentives (e.g., Holt & Laury, 2002; Smith & Walker, 1993). In general, treatment effects on preferences have been observed in intertemporal discounting (e.g., Deck & Jahedi, 2015; Ebert, 2001; Hinson et al., 2003; Joireman et al., 2008) as well as social preferences (e.g., Cappelletti et al., 2011; Halali et al., 2014; Schulz et al., 2014). Across these domains, it is important to understand how changes in decision errors affect preference measurements. Failure to do so could potentially lead to misinterpretations of observed effects.

Consequently, addressing the issue of decision errors captured by the error hypothesis is of general importance to any research in behavioral economics and psychology with the objective to elicit individual preferences. There are two possible ways to address this matter. First, one can account for random errors ex ante by choosing an experimental design that controls for random errors. At the experimental design stage, researchers could apply a variety of measures to assess people’s preferences. In this way, they could cancel out systematic errors and minimize measurement errors in the associated biased classifications (Frey et al., 2017). For instance, Andersson et al. (2016) suggested choosing a symmetrical choice set when measuring risk preferences. However, this approach may not always be suitable for every preference-elicitation task. Leading to the second approach, one can account for error at the data analysis stage. For example, accounting for potential biases with an explicit structural decision-making model what includes an error theory at the data-analysis stage could be advantageous (Andersson et al., 2020). Recently, behavioral economists Gillen et al. (2015) and Andreoni and Kuhn (2019) proposed an instrumental variable approach to address this problem (see also Gillen et al., 2015).

It is important to note the task architecture determines the context in which a choice option is presented. Consequently, various theories relating to the context effect could also contribute to the fact that people with lower cognitive abilities are more prone to be influenced by the task architecture. For example, Andraszewicz and Rieskamp (2014) and Andraszewicz et al. (2015) demonstrated that pairs of gambles with the same differences in expected values and the same variances (i.e., risk) but various covariances (i.e., similarity) result in more unsystematic choices when the covariance between the two gambles is lower (Andraszewicz et al., 2015; Andraszewicz & Rieskamp, 2014). This effect called the covariance effect results from the fact that pairs of gambles with low covariances are more difficult to be compared with each other. Simonson and Tversky (1992) demonstrated that context effects can result from the available sample of choice options, such that extreme outcomes may appear as extreme in face of the available sample (Simonson & Tversky, 1992). Along the same lines, Ungemacht et al. (2011) demonstrated that people’s preferential choices depend on one’s exposure to hypothetical choice options.

To summarize, this meta-analysis highlights the importance of accounting for choice-set architecture, in particular, its interaction with random decision errors. Our applied methods and results go beyond the current research scope and suggest that neglecting the effect of random decision errors at the experimental design stage or at the data-analysis stage can lead to spurious correlations and the identification of “apparently new” phenomena (Gillen et al., 2019). The findings presented in this meta-analysis offer an important contribution to the scientific communities in judgment and decision making, psychology, experimental finance, and economics. In these fields of studies, measuring risk-taking propensity is particularly important. Therefore, findings of the current meta-analysis are very relevant to all researchers investigating risk-taking behavior using common risk-preference-elicitation methods.

A majority could well imagine undergoing psychotherapy via artificial intelligence, among other things because of the ability to comfortably talk about embarrassing experiences

Attitudes and perspectives towards the preferences for artificial intelligence in psychotherapy. Mehmet Emin Aktan, Zeynep Turhan, İlknur Dolu. Computers in Human Behavior, March 29 2022, 107273. https://doi.org/10.1016/j.chb.2022.107273

Highlights

• We explored the factors of choosing AI-based psychotherapy.

• The less stigma and remote access were found as key in preferring AI-based therapy.

• Trust of the security of data in AI-based therapy were less than human therapists.

• The beliefs about limited ability to empathize in AI-based psychotherapy was found.

Abstract: The use of artificial intelligence (AI) in psychotherapy has been increased in recent years. While these technologies in psychotherapy are growing, the circumstances of accepting artificial tools during psychotherapy need to be explored to improve effective AI tools during the sensitive therapeutic environment. In this study, the factors around the preferences for AI-based psychotherapy were investigated. This cross-sectional study was conducted with a sample of 872 individuals who are highly educated, 18 aged and above. Attitude towards AI-based Psychotherapy, Attitude towards Seeking Professional Psychological Help Scale- Short Form, and Stigma Scale for Receiving Psychological Help Scale were used to examine the factors of participants' preferences for AI-based psychotherapy. While 55% of the sample preferred AI-based psychotherapy, the majority of participants trusted more human psychotherapists than AI-based systems when asked participants’ trust about the security of personal data. However, three important benefits of AI-based psychotherapy were identified as being able to comfortably talk about the embarrassing experiences, having accessibility at any time, and accessing remote communication. Importantly, factors of preferences for AI-based psychotherapy were related to the idea of AI-based psychotherapy systems can improve themselves based on the results from previous therapeutic experiences. Gender and the types of profession related to psychology and technical/engineering were also associated with choosing AI-based psychotherapy. The results suggest that both raising awareness of the benefits and effectiveness of psychotherapy as well as the trust to the artificial intelligence tools can improve the rate of the preferences for AI-based psychotherapy.

Keywords: Artificial intelligencePsychotherapyAccessibilityHelp-seeking behaviorStigma