Parental Migration, Investment in Children, and Children’s Non-cognitive Development: Evidence from Rural China. Hanchen Jiang and Xi Yang.
http://www.econ2.jhu.edu/jobmarket/2019/JiangH/OtherThesisPapers/Chapter3JiangH.pdf
Abstract: Many children worldwide are left behind by parents who are migrating for work. While previous literature has studied the effect of parental migration on children’s educational outcomes and cognitive achievements, this study focuses on how parental migration affects children’s non-cognitive development. We use longitudinal data of children in rural China and adopt labor market conditions in destination provinces as instrumental variables for parental endogenous migration choice. We find that parental migration has a significant negative effect on children’s non-cognitive development. Differentiating inter- and intra-provincial migrations suggests that the negative effect of parental migration is mainly driven by inter-provincial migrations. We test four different mechanisms of how parental migration affects child development including parental financial inputs, parental time inputs, household bargaining, and children’s own time input. Our results provide insights into the relative importance of different mechanisms in determining the effect of parental migration on children’s non-cognitive skill formation.
Keywords: Left-behind Children, Parental Migration, Parental Input, Non-cognitive
Development, China
JEL Classification: J12, J13, J24, J61, R23
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Full text, notes, references in the link above. Excerpts follow.
6.5 Parenting Style and Parental Health
In addition to the mechanisms we have tested so far, the literature has emphasized other
important determinants of child development. One is parenting style (Dooley and Stewart,
2007; Fiorini and Keane, 2014; Doepke and Zilibotti, 2017). Although there are no consistent
measurements of parental style so far, the few papers that considered these kinds of variables
commonly find that parenting style has an impact on child developmental outcomes. For
example, Dooley and Stewart (2007) find that different aspects of parenting (positiveness,
hostility, consistency, and punitiveness) are more important than family income in terms of
determining children’s behavior and emotional outcomes. Fiorini and Keane (2014) find that
parental warmth and effective discipline lead to better non-cognitive outcomes for children.
More recently, Doepke and Zilibotti (2017) develops a theory to explain how parenting style
plays an important role in determining children’s welfare and economic success. Parenting
style can be an important channel through which parental migration affects child develop
ment. The GSCS provides limited information on parenting style. Nevertheless, we define
harsh parenting style by the mother’s and child’s answer to the question “whether you beat
your child/you are beaten when misbehaved.” Using the baseline fixed effects model with
instrumental variables, our results in Appendix Table A7 show that the effect of the father’s
migration on parenting style is insignificant. These results, however, do not imply that par
enting style is not important in terms of explaining the link between a father’s migration
and his children’s development outcomes. Future work needs to measure parenting styles in
a more comprehensive way to better test this mechanism.
The other determinant that was mentioned in the literature is parent’s health condition,
which may have an impact on the quality of time parents spend with their children. For
example, Ronda (2016) and Herbst (2017) find that maternal psychological distress and de
pression may have a negative impact on children’s outcomes. In the meantime, the migration
literature has provided some evidence that migration increases the probability of being in
poor physical and mental health (Antman, 2010; Barrett and Mosca, 2013). To test this
mechanism, we investigate whether the father’s migration has an impact on the self-assessed
health status of the father and mother.41 Appendix Table A7 provides the estimation re
sults, showing that the father’s migration does not increase the probability of parents in
poor health condition. However, we are reluctant to conclude that parent health status
is not an important mechanism behind the link between parental migration and a child’s
development, considering that our measure of health status is self-reported and does not
differentiate between mental health and physical health. This is a mechanism worth testing
in the future. 42
7 Discussion of Sub-sample Results
Table 15 reports the sub-sample estimation results and highlights some interesting patterns.43
First, columns (1) and (2) show that the effects of father’s migration are larger among male
children (-0.057 and -0.028) than those for female children (-0.065 and -0.047). We hypoth
esize that there are several explanations for this gender difference. First, the left-behind
mother communicates better with girls while having difficulty dealing with teen boys. Sec
ond, lacking a positive male role model has a negative impact on a boy’s development. Thus,
maternal input could be a poorer substitute for the lack of paternal inputs when it comes to
raising a boy. Third, as proposed in Bertrand and Pan (2013), the non-cognitive development
of boys, unlike that of girls, appears extremely responsive to the quality of parental inputs,
which is negatively associated with parental migration.44 Nonetheless, we are not intending
to decompose these different channels, and we leave it for future studies. This kind of gender
difference has also been documented in recent child development literature, which finds that
father absence, usually caused by divorce, has a greater influence on boys than girls. For
example, Bertrand and Pan (2013) find that boys do especially poorly and are much more
likely to develop behavior problems in broken families, which are usually associated with
worse parental inputs. Figlio et al. (2019) find that family disadvantage disproportionately
impedes the development of boys by using birth certificates matched to schooling records in
Florida.
Columns (3)-(6) divide the sample children by their parents’ education levels. We find
that the negative effect of the father’s migration is more evident if parents, especially mothers,
are less educated. For example, if the mother has not graduated from elementary school,
then the father’s migration reduces internalizing and externalizing scores by 0.036 and 0.071
points. But if the mother has graduated from elementary school, the two coefficients are
reduced to 0.025 and 0.05. These results suggest that mothers with better education are
more likely to engage in high-quality parenting and can better mitigate the adverse effect
caused by the father’s absence (Carneiro et al., 2013).
Moreover, as previously discussed, lack of time inputs seems to be the major mechanism
through which the father’s migration affects his children’s development. In that sense, it is
possible that better infrastructure, such as having access to a telephone service, may help
left-behind children connect and communicate with their fathers. In columns (7) and (8)
of Table 15, we divide our sample children by whether their resident village has telephone
services, but the negative effect of father’s migration does not vary much across the two
groups. This may be because telephone communication is not a good substitute for face-to
face communication in terms of parenting or because the telephone is not widely used among
migrant families to maintain effective communication between the migrant father and his
children left behind.
8 Conclusion
This paper sets out to identify the effects of a father’s migration on his children’s non
cognitive development. By exploiting a longitudinal data set and using instrumental variables
based on the destination provinces, we identify a negative effect of a father’s migration.
We explore several possible mechanisms behind this negative effect. First, we find that a
fathers’ migration is associated with an increase in family income, which, however, does
not lead to an increase in child-related spending. In the meanwhile, our results show that
a fathers migration increases his decision power on financial allocation within the family,
which provides a potential explanation of the observed divergence in family income and
spending. More important, a father’s migration reduces the time both parents spend talking
and playing with their children. Overall, the father’s migration reduces both parent financial
and time inputs, which might be the two major driving forces behind the negative effect of
a father’s migration on left-behind children.
These results expand the scope of current literature by uncovering negative consequences
of parental migration that is rarely studied in the literature. More important, our discussion
on potential mechanisms suggest that relevant policies are urgently needed to help migrant
parents improve their financial and time inputs on children. Considering that non-cognitive
skill is a vital dimension of human capital, these policies would have an important impact
on increasing inter-generational mobility and on reducing rural-urban inequality.
Sunday, November 10, 2019
Saturday, November 9, 2019
Oct 2019: India is trying to build the world's biggest facial recognition system
India is trying to build the world's biggest facial recognition system. Julie Zaugg. CNN Business, October 18, 2019. https://edition.cnn.com/2019/10/17/tech/india-facial-recognition-intl-hnk/index.html
Excerpts:
In July, Bhuwan Ribhu received some very good news.
The child labor activist, who works for Indian NGO Bachpan Bachao Andolan, had launched a pilot program 15 months prior to match a police database containing photos of all of India's missing children with another one comprising shots of all the minors living in the country's child care institutions.
He had just found out the results. "We were able to match 10,561 missing children with those living in institutions," he told CNN. "They are currently in the process of being reunited with their families." Most of them were victims of trafficking, forced to work in the fields, in garment factories or in brothels, according to Ribhu.
This momentous undertaking was made possible by facial recognition technology provided by New Delhi's police. "There are over 300,000 missing children in India and over 100,000 living in institutions," he explained. "We couldn't possibly have matched them all manually."
Locating thousands of missing children is just one of the challenges faced by India's overstretched police force in a nation of 1.37 billion people.
[...]
[India's federal] government now has a much more ambitious plan. It wants to construct one of the world's largest facial recognition systems. The project envisions a future in which police from across the country's 29 states and seven union territories would have access to a single, centralized database.
Excerpts:
In July, Bhuwan Ribhu received some very good news.
The child labor activist, who works for Indian NGO Bachpan Bachao Andolan, had launched a pilot program 15 months prior to match a police database containing photos of all of India's missing children with another one comprising shots of all the minors living in the country's child care institutions.
He had just found out the results. "We were able to match 10,561 missing children with those living in institutions," he told CNN. "They are currently in the process of being reunited with their families." Most of them were victims of trafficking, forced to work in the fields, in garment factories or in brothels, according to Ribhu.
This momentous undertaking was made possible by facial recognition technology provided by New Delhi's police. "There are over 300,000 missing children in India and over 100,000 living in institutions," he explained. "We couldn't possibly have matched them all manually."
Locating thousands of missing children is just one of the challenges faced by India's overstretched police force in a nation of 1.37 billion people.
[...]
[India's federal] government now has a much more ambitious plan. It wants to construct one of the world's largest facial recognition systems. The project envisions a future in which police from across the country's 29 states and seven union territories would have access to a single, centralized database.
The potential role of illness expectations in the progression of medical diseases
The potential role of illness expectations in the progression of medical diseases. Francesco Pagnini. BMC Psychology, volume 7, Article number: 70 (2019). Nov 8 2019. https://bmcpsychology.biomedcentral.com/articles/10.1186/s40359-019-0346-4
Abstract
To what extent can one’s mind promote direct changes to the body? Can one’s beliefs about the body become a physical reality, without mediating effects from behaviors? Specifically, can medical symptoms and the course of a disease be directly affected by a person’s mindset about the illness?
There is a vast literature about placebo and nocebo effects, that promote physical changes by creating the expectation of a change through a primer (for example, a fake pill). Placebos, however, often imply deception, or at least ambiguity, to be effective. The concept of Illness Expectation describes the expectations, both implicit and explicit, that a person who has received a diagnosis makes about the course of the disease. It can be characterized by different degrees of rigidity, and it is argued here that these expectations can ultimately lead to changes in the disease progression. These changes may happen through behavior modifications, or through a non-behavioral pathway, which may deserve exploration efforts from the scientific literature.
---
Effects of expectations on the body
One of the main operational mechanisms of placebos is represented by cognitive expectations, which in turn are expected to promote the occurrence of physiological changes in the body [11]. In general, placebo and nocebo effects have been studied with a primer, such as a sugar pill, that influences or conditions the person to anticipate an effect. The expectation of a medical effect promotes both subjective and objective (physiologic) changes, with clinical improvements or worsening [12]. However, expectations are not only prompted by drugs or interventions. In fact, every individual with a medical condition develops a certain mindset toward the illness [13], with expectations that spontaneously emerge. These expectations, which represent the result of the elaboration process of the information collected about the disease [14], can promote different physiological effects [15]. For example, blood glucose levels in people with type II diabetes are influence by perceived time and expected values, rather than being a mere physiological process [16]. Furthermore, expectations can influence the ageing process: older adults who think about ageing as associated with negative characteristics tend to experience a greater loss of physical function and a reduced survival, compared to those who held positive expectations [17].
Illness perceptions and health beliefs
Expectations about the disease are a central component of illness perceptions and health beliefs, which are well-established concepts in health psychology [18]. Illness Perception is often explored within the theoretical framework of the Common Sense Model (CSM) of Illness Representation [19]. In the CSM theory, patient’s illness perceptions include beliefs about what precipitated the illness (causes), how long it will last (timeline), the impact on the patient’s life (consequences), which symptoms are attributed to the illness (identity), and how the condition can be controlled or cured by the patient’s behavior (personal control) or by the treatment (treatment control). In the CSM, expectations are considered as an underlying component of the different beliefs [20, 21]. Emotional components are another key aspect of the CSM, which may interfere with cognitive processing, and it could be a source of confusion during the assessment process. For example, one of the most utilized instruments for the assessment of illness perception, the Brief Illness Perception Questionnaire [22] includes items like “How much does your illness affect you emotionally?”, which are somehow related to the expectations, but refer directly to the emotional domain. The same concern deals with questions about consequences in everyday life (e.g., “My illness has serious economic and financial consequences”, from the Illness Perception Questionnaire Revised [23]).
Thus far, most published research referencing the Illness Perception construct focuses on the role of disease representations in explaining both coping and outcomes in patients with a wide range of health conditions [24, 25]. Specifically, health psychologists have explored how disease representations can lead to lifestyle modifications, eventually leading to changes in the medical outcomes [26]. For example, adherence to the medical treatment, or lifestyle choices like eating, exercising, or smoking, can be influenced by illness representations. A person who perceives that nothing can change the course of the disease, for example, may be more prone to avoid exercising or taking prescribed medicine [27]. In other words, the effects of Illness Perceptions on the body (namely, on the course of the disease or its symptoms) have been mainly explored as mediated by behavior changes [28]. The main difference between the construct of Illness Perception and Illness Expectation is their specificity: while the former is a multifaceted concept that includes several aspects of the illness experience, the latter is a specific element, the anticipation of the future illness-related scenarios, which is merely cognitive.
Emotions and somatic changes
[...] Briefly, we know that negative emotions (e.g., depression, stress) have, among other effects, a strong impact on human physiology [29], often reflecting on poorer medical outcomes, in the case of chronic diseases. For example, depressive states and stress have been associated with reduced survival rate in patients with cancer [30]. The mechanisms underlying these associations are still under investigation.
[Full free text in the link above]
Abstract
To what extent can one’s mind promote direct changes to the body? Can one’s beliefs about the body become a physical reality, without mediating effects from behaviors? Specifically, can medical symptoms and the course of a disease be directly affected by a person’s mindset about the illness?
There is a vast literature about placebo and nocebo effects, that promote physical changes by creating the expectation of a change through a primer (for example, a fake pill). Placebos, however, often imply deception, or at least ambiguity, to be effective. The concept of Illness Expectation describes the expectations, both implicit and explicit, that a person who has received a diagnosis makes about the course of the disease. It can be characterized by different degrees of rigidity, and it is argued here that these expectations can ultimately lead to changes in the disease progression. These changes may happen through behavior modifications, or through a non-behavioral pathway, which may deserve exploration efforts from the scientific literature.
---
Effects of expectations on the body
One of the main operational mechanisms of placebos is represented by cognitive expectations, which in turn are expected to promote the occurrence of physiological changes in the body [11]. In general, placebo and nocebo effects have been studied with a primer, such as a sugar pill, that influences or conditions the person to anticipate an effect. The expectation of a medical effect promotes both subjective and objective (physiologic) changes, with clinical improvements or worsening [12]. However, expectations are not only prompted by drugs or interventions. In fact, every individual with a medical condition develops a certain mindset toward the illness [13], with expectations that spontaneously emerge. These expectations, which represent the result of the elaboration process of the information collected about the disease [14], can promote different physiological effects [15]. For example, blood glucose levels in people with type II diabetes are influence by perceived time and expected values, rather than being a mere physiological process [16]. Furthermore, expectations can influence the ageing process: older adults who think about ageing as associated with negative characteristics tend to experience a greater loss of physical function and a reduced survival, compared to those who held positive expectations [17].
Illness perceptions and health beliefs
Expectations about the disease are a central component of illness perceptions and health beliefs, which are well-established concepts in health psychology [18]. Illness Perception is often explored within the theoretical framework of the Common Sense Model (CSM) of Illness Representation [19]. In the CSM theory, patient’s illness perceptions include beliefs about what precipitated the illness (causes), how long it will last (timeline), the impact on the patient’s life (consequences), which symptoms are attributed to the illness (identity), and how the condition can be controlled or cured by the patient’s behavior (personal control) or by the treatment (treatment control). In the CSM, expectations are considered as an underlying component of the different beliefs [20, 21]. Emotional components are another key aspect of the CSM, which may interfere with cognitive processing, and it could be a source of confusion during the assessment process. For example, one of the most utilized instruments for the assessment of illness perception, the Brief Illness Perception Questionnaire [22] includes items like “How much does your illness affect you emotionally?”, which are somehow related to the expectations, but refer directly to the emotional domain. The same concern deals with questions about consequences in everyday life (e.g., “My illness has serious economic and financial consequences”, from the Illness Perception Questionnaire Revised [23]).
Thus far, most published research referencing the Illness Perception construct focuses on the role of disease representations in explaining both coping and outcomes in patients with a wide range of health conditions [24, 25]. Specifically, health psychologists have explored how disease representations can lead to lifestyle modifications, eventually leading to changes in the medical outcomes [26]. For example, adherence to the medical treatment, or lifestyle choices like eating, exercising, or smoking, can be influenced by illness representations. A person who perceives that nothing can change the course of the disease, for example, may be more prone to avoid exercising or taking prescribed medicine [27]. In other words, the effects of Illness Perceptions on the body (namely, on the course of the disease or its symptoms) have been mainly explored as mediated by behavior changes [28]. The main difference between the construct of Illness Perception and Illness Expectation is their specificity: while the former is a multifaceted concept that includes several aspects of the illness experience, the latter is a specific element, the anticipation of the future illness-related scenarios, which is merely cognitive.
Emotions and somatic changes
[...] Briefly, we know that negative emotions (e.g., depression, stress) have, among other effects, a strong impact on human physiology [29], often reflecting on poorer medical outcomes, in the case of chronic diseases. For example, depressive states and stress have been associated with reduced survival rate in patients with cancer [30]. The mechanisms underlying these associations are still under investigation.
[Full free text in the link above]
The study found auto-written news stories were rated as more objective, credible (both message and medium credibility), and less biased
Is Automated Journalistic Writing Less Biased? An Experimental Test of Auto-Written and Human-Written News Stories. Yanfang Wu. Journalism Practice, Oct 29 2019. https://doi.org/10.1080/17512786.2019.1682940
ABSTRACT: By administering an online experiment, this study examined how source and journalistic domains affect the perceived objectivity, message credibility, medium credibility, bias, and overall journalistic quality of news stories among an adult sample (N = 370) recruited using Amazon’s Mechanical Turk (MTurk) service. Within the framework of the cognitive authority theory, the study found auto-written news stories were rated as more objective, credible (both message and medium credibility), and less biased. However, significant difference was found between a combined assessment condition (news stories with source and author information) and a message only assessment condition (news stories without source and author information) in the ratings of objectivity and credibility, but not bias. Moreover, significant differences were found in the objectivity and credibility ratings of auto-written and human-written news stories in the journalistic domains of politics, finance and sports news stories. In auto-written news stories, sports news stories were rated more objective and credible, while financial news stories were rated as more biased. In human-written stories, financial news stories were rated as more objective and credible. However, political news stories were rated as more biased among human-written news stories, and in cases where auto-written and human-written stories were combined.
KEYWORDS: Cognitive authority, auto-written, human-written, automated journalistic writing, experimental test, objectivity, credibility, bias
Discussion
The study found that auto-written news stories were rated as significantly more objective than human-written news stories. This finding is in line with previous researchers’ assumptions about the inherent objectivity of algorithms, the limits of humans’ subjective “gut feeling” in the evaluation of newsworthiness and news inclusion, and the advantages of algorithms in overcoming inherent human biases and limitations (Carlson 2018; Toraman and Can 2015). Also, the results corroborated Cleary and coauthor’s(2011) conclusion that Natural Language Generation software could augment accuracy, Clerwall’s (2014) result that text generated by algorithms is perceived as more informative, accurate, and objective, and Melin and coauthor’s(2018) finding that auto-written content tend to be rated more accurate, trustworthy and objective. Additionally, these findings echoed Thurman and coauthors’ (2017) conclusion on automaton and the increase of objectivity in news stories. The reason why automatic journalistic writing was rated as more objective could be that readers favor stories distinguishing facts and opinions clearly, which NGL, the generation method used for the texts chosen in this study, was recognized as being able to generate stories of this nature in Melin and coauthor’s(2018) conclusion. Further, Graefe and coauthor’s(2018) recent studies concluded that algorithms, such as NLG, are more accurate on factual information. Moreover, choosing the right vocabulary that represents the information in numbers is major task for journalists. Word choice is always influenced by the journalist’s personal interpretation, which may reduce a news story’s objectivity. For example, Carlson (2018, 1764) believed journalists have inherent human subjectivity because they apply learned knowledge to professionally valid interpretations. In contrast, algorithms have the unthinking objectivity of computer programs, and are the apotheosis of journalistic knowledge production – objectivity. Furthermore, specialized algorithms have a narrow domain focus reducing the options for word choice, thereby increasing objectivity (McDonald 2010). Gillespie (2014) used the term “algorithmic objectivity” to describe the power of algorithms in strengthening objectivity. Additionally, the integration of automation and datafication in news reporting may increase objectivity. For example, web analytics are found to be useful tools in increasing the precision of journalists’ news judgement (Wu, Tandoc, and Salmon2018). Data driven journalism not only empowers journalists to report stories in new and creative ways (Anderson 2018), it is also believed to increase objectivity (Parasie and Dagiral 2013). Another interesting finding is that auto-written stories were even perceived as more credible (both message and medium credibility) than human-written news stories. This finding aligns with Haim and Graefe’s(2017), Van der Kaa and Krahmer’s (2014), Clerwall’s (2014), and Melin’s(2018) conclusions that readers tend to perceive auto-written news as more credible than human-written stories. The finding also aligns with Wolker and Powell’s (2018) claim that there are equal perceptions of credibility between auto-written and auto and-human-combined-written content. Although auto-written algorithms lack the skills in using nuances of languages, human reporters may produce less credible news stories due to failing to express views, or distinguish facts from opinions clearly (Meyer, Marchionni, and Thorson 2010). However, an algorithm is viewed as a “credible knowledge logic” (Gillespie 2014, 191) because it is considered a force that could eliminate human biases or errors (Linden 2017a). Algorithms also create many more possibilities for detecting falsehood (such as bias, inaccuracy) automatically and verifying truth more effectively (Kaczmarek 2008). Stories developed by programmers from multi-sourced data can fulfill functions of professional journalism and may even align with more traditional journalistic standards (Parasie and Dagiral 2013; Dörr 2016). Furthermore, algorithms may perform better than human reporters in data verification, as algorithms restrict themselves to a specialized area with a very stipulated content (Perloff 1993; Hassan and Azmi 2018).
This experimental design distinguished the effect of source and journalists’ authorship from text on readers’ ratings of quality of journalism, which is one of the major contributions of this study. This study further verified that readers consider automatic journalistic writing more objective when source and journalists’ affiliation information were not disclosed. Moreover, the message and medium credibility of automated journalistic writing were rated significantly higher without source and authorship. These findings align with Graefe et al.’s(2018) results on the confounding effect of source on readers’ ratings of credibility – the declaration of an article written by a journalist substantially increase the ratings of readability. The findings also showed a decline of trust in traditional media and human writers in producing good journalism, which was reflected in a recent survey by Knight Foundation and Gallup poll – a majority of surveyed Americans reported they had lost trust in American media due to inaccuracy, bias, fake news, alternative facts, a general lack of credibility, and reporters are “based on opinions or emotions” (Ingram 2018). Automated journalistic writing, however, showed strength in objectivity and credibility.
The confounding effect of source and authorship was also identified in the ratings of bias. In particular, whether journalists’ political belief affects the presentation of the subject is one of the important indexes of message credibility in this study. Human written stories were rated more biased than auto-written news stories. However, when source and authorship information were included, human-written stories were rated less biased. Although impartiality is recognized as one of the important journalistic ideologies, human journalists were tagged as partisan actors whose political beliefs affected their news decisions, although they define themselves as news professionals committed to a form of journalism marked by objectivity and neutrality (Patterson and Donsbagh 1996; DellaVigna and Kaplan 2007; Oosterhoff, Shook, and Ford 2018; Linden 2017a). Gaziano and McGrath (1986) identified that political bias was important factor affecting credibility in news reporting, particularly accuracy, fairness, responsibility, and role in criticism of government. With the proliferation of data, human-written news stories may contextualize the automated-generated content by using it as a multi-source from different perspectives (Carlson 2015; Thurman, Dörr, and Kunert 2017). This was described by Carlson (2018) as “a visible incursion of algorithmic judgment in the space of human editorial judgment” and Wolker and Powell (2018) as the well-rounded future of journalism. These applications are feasible to reduce human bias in journalism.
Participants in the message only assessment condition rated auto-written news stories as both more objective and more credible (both message credibility and medium credibility) than human-written news stories compared to participants in the combined assessment condition. According to the hypothesized news assessment model based on the cognitive authority theory, readers rely on textual authority (intrinsic plausibility) – whether the content is “accurate, authentic and believable” (Appelman and Sundar 2016, 73) – to execute evaluation when the affiliation of the news organization and the journalist’s name were removed from the stories. However, when institutional authority and personal authority were combined with textual authority, readers combine textual authority with whether the source of message is “authoritative, reliable, reputable and trustworthy” (Appelman and Sundar 2016, 74) to make judgment. The results showed that this combined assessment process reduced news stories’ perceived objectivity and credibility. In the internet age, readers may assess news stories with greater emphasis on textual authority due to the insufficient bandwidth for storing information (Sundar 2008). The findings from this study corroborates Wilson’s (1983) cognitive authority theory. When source – personal authority and institutional authority – are not revealed, readers have to use textual type authority (document type), and intrinsic plausibility authority (content of text) to evaluate the credibility of a news story. This may change how news quality is assessed in digital journalism. The findings further verified the result that auto-written news content is perceived as more objective and credible than human-written news stories.
Although automated journalistic writing received higher credibility ratings, it is also more likely to distribute fake news due to its dependence on data for source, processing and output. Ethical issues may arise when data is used without proper verification, transparency about the source and content in generation algorithms (Graefe 2016). In addition, whether the programmer, reporter, or editor will be responsible for the collection, analysis and presentation of data, and who should be held accountable for automated journalistic writing in which human reporters contributed to contextualize generated content, and algorithms acted as an intelligence augmenter, remain controversial topics in the field of automated journalistic writing.
Journalistic domains were found to affect readers’ evaluations of objectivity, message credibility, and medium credibility, but not bias. Sports news stories were rated more objective and credible (both message and medium credibility) than finance and political newsstories in auto-written news tories. Financial news stories were rated more objective and credible than sports and political newsstories among human-written stories. Financial news stories were rated more biased than sports and political news stories among autowritten news stories. However, political news stories were rated as more biased than financial and sports news stories in human-written news stories. Political news stories were rated as more biased than sports and finance news stories when auto-written and human-written stories were combined. Multiple business motivations may result in journalistic domains havinganeffect on readers’ assessment of news stories. First, thepublishers’ or new organizations’ political stance was found to greatly affect that of its reporters’. or example, news stories of ABC, CBS, NBC and Fox were believed to exhibit political leanings. Subsequently, political bias may be more salient in story constructions or patterns of bias in news stories (Groeling 2008). Similarly, participants’ political ideology may affect their views towards The Associated Press, The New York Times, and The Washington Post. Secondly, social identity – the sense of whether one belongs to a perceived ingroup or out-group – may affect bias in sports news. Previous studies found that American sportscasters tended to report favorably on athletes from the United States and highlight them more frequently. In-group favoritism is more pronounced when their participants’ team won (Bryant and Oliver 2009; Wann 2006). However, financial news stories, which are mostly free from political standing and social identity, were the least biased when compared to politics and sports stories in the human-written group. On the contrary, when neither political stance nor social identity plays a role, financial news stories were rated the most biased in the auto-written group.
After controlling for some of the main confounding factors identified in the literature, we find that individuals with higher cognitive abilities are more financially literate
The Role of Cognitive Abilities on Financial Literacy: New Experimental Evidence. Melisa Muñoz-Murillo et al. Journal of Behavioral and Experimental Economics, November 8 2019, 101482. https://doi.org/10.1016/j.socec.2019.101482
Highlights
• Financial literacy research focuses on why, how, and when people acquire financial knowledge, shape their financial attitudes, and adapt their financial behaviors.
• The literature demonstrates that some demographic characteristics highly correlate with financial literacy.
• Demographic factors mask the ultimate determinants of financial literacy acquisition.
• We offer experimental evidence supporting the key role of cognitive ability in financial literacy acquisition.
• After controlling for some of the main confounding factors identified in the literature, we find that individuals with higher cognitive abilities are more financially literate
• Financial literacy research focuses on why, how, and when people acquire financial knowledge, shape their financial attitudes, and adapt their financial behaviors.
• The literature demonstrates that some demographic characteristics highly correlate with financial literacy.
• Demographic factors mask the ultimate determinants of financial literacy acquisition.
• We offer experimental evidence supporting the key role of cognitive ability in financial literacy acquisition.
• After controlling for some of the main confounding factors identified in the literature, we find that individuals with higher cognitive abilities are more financially literate
Abstract: Financial literacy research focuses on why, how, and when people acquire financial knowledge, shape their financial attitudes, and adapt their financial behaviors. The literature demonstrates that some demographic characteristics highly correlate with financial literacy. However, demographic factors often mask the ultimate determinants of financial literacy acquisition such as risk aversion, time preferences, cognitive and behavioral biases, personality traits, cognitive and non-cognitive abilities, among others. Theory suggests that cognitive ability is one of the fundamental factors in explaining financial literacy. We offer experimental evidence supporting the key role of cognitive ability in financial literacy acquisition. Our experimental setting allows us to (a) overcome particular limitations of the traditional multiple-choice questions survey designs, (b) provide compatible incentives to make participants exert an appropriate level of effort to solve the assigned tasks, and (c) use a well-known measure of cognitive abilities. We find that individuals with higher cognitive abilities are more financially literate. Our main result holds even after controlling for some of the main confounding factors identified in the literature. In contrast to previous studies, we find no role for gender in explaining financial literacy once we control for cognitive abilities.
Keywords: Cognitive abilityfinancial literacyexperiment
6 Discussions
We are cautious in interpreting our results. We acknowledge that the omission of a third variable that affects cognitive ability as well as financial literacy in the same direction might generate the positive correlation we find between the two variables. In our case, the omission of relevant variables yield inconsistent estimators (Cameron & Trivedi, 2005), i.e., the estimated coefficient for CRT scores might not converge to the population value of the parameter. According to Kautz, Heckman, Diris, Ter Weel, and Borghans (2014), other non-cognitive abilities–that we do not consider in this study–as the Big Five of Personality (Openness, Consciousness, Extraversion, Agreeability, and Neuroticism–OCEAN) also matter for the acquisition of new abilities and knowledge. That said, it is worth noting that the theoretical literature supports a causal effect of cognitive abilities on financial literacy (see Delavande et al. (2008)) and that we control for some of the main confounding factors (i.e., educational achievement, non-cognitive abilities, and parental characteristics).9 On
the other hand, potential multicollinearity problems may arise with the inclusion of many independent variables. In our case, multicollinearity may embed the acceptance of the no significance null hypothesis. In this regard, we compute correlations and run variable inflation factor (VIF) tests to investigate the extent to which the presence of multicollinearity was a problem in our study. Appendix B shows the results according to which none of the included variables have high correlations with other independent variables or high VIF test values reducing concerns about multicollinearity affecting our results. The main limitations of our work are related with the main limitations of laboratory experiments in general: (a) selection bias and, therefore, external validity, and (b) budgetary constraints. 10 First, since students decided whether to register in our database for economic experiments, we do not have a random sample of our target population. In other words, our sample lacks external validity and our inferences are valid only to the sample we examine. In this regard, the external validity of the empirical findings in this research will be subject to the same criticisms of previous works. However, the literature in experimental economics increasingly recognizes that participants in economic experiments are mostly comprised by university students who voluntarily choose to participate. In this regard, student and selection bias have been discussed in Exadaktylos, Esp´ın, and Branas-Garza (2013). In a rigorous study about social preferences, these authors find that a sample of self-selected university students provide reliable results. In addition, due to time and budgeting constraints, we do not include competing measures of cognitive ability. However, we acknowledge it would be convenient to test the validity of our measure and, even more important, to build a more comprehensive measure of cognitive ability.
Despite these limitations, and given the difficulty of replicating the real conditions in which individuals make economic decisions, the results of the present study are expected to provide valuable information regarding the role played by cognitive abilities in determining financial literacy levels. If through laboratory experiments it is possible to establish that individuals with higher cognitive abilities are more financially literate, such information would be useful and difficult to ignore to support public policy decisions. Moreover, our results should be interpreted with care since we do not deal with the identification of empirical causality. However, as we claim before, the relation between cognitive ability and financial literacy has strong theoretical and empirical support. In our view, causality can go in either direction: from cognitive ability to financial literacy and vice versa. At least during childhood, cognitive ability is affected by environmental factors related to financial literacy like socioeconomic conditions, parental influences, poor sanitary conditions, low birth weight, domestic stimulation, and school attendance (Ayaz et al., 2012; Santos et al., 2008). In addition, as we claim, cognitive ability can lower the cost of financial literacy acquisition (e.g., financial knowledge and behaviors). However, establishing causality in a our laboratory setting would have implied to introduce changes to cognitive ability while controlling all other potential variables affecting financial literacy. In our view, such a task is difficult to achieve in the lab. Our results, nonetheless, point out to the need of establishing causality in other empirical settings which can measure changes in individuals’ cognitive abilities and disentangle how these variation affect financial literacy.
7 Conclusions
The literature demonstrates that some demographic characteristics explain people financial literacy levels. Demographic factors often mask the ultimate factors causing any given phenomena. Since most of the empirical evidence on the determinants of financial literacy focuses on demographic factors, we provide new experimental evidence to understand the fundamental or ultimate factors that influence financial literacy. In particular, we examine the empirical link between cognitive ability and financial literacy. Our results show that individuals with higher cognitive abilities are more financially literate. Other ultimate factors explaining people’s financial literacy acquisition could be such things as risk aversion, time preferences, cognitive and behavioral biases, personality traits, cognitive and non-cognitive abilities, etc. In this paper, we offer experimental evidence supporting the key role of cognitive ability in financial literacy acquisition. Our experimental setting allows us to overcome particular limitations of the traditional multiple-choice questions survey designs, provide compatible incentives to make participants exert an appropriate level of effort to solve the assigned tasks, and use a well-known measure of cognitive abilities. Our results indicate that individuals with higher cognitive ability are more financially literate. These results hold even after controlling for some of the main confounding factors identified in the literature. In contrast to previous studies, once we control for cognitive abilities we find no evidence of a gender gap on financial literacy levels. This result suggests that the gender gap documented in the empirical literature may be driven by differences in cognitive abilities not accounting for in previous studies. Further research is needed to understand better the fundamental factors underlying the relationship between gender and financial literacy.
Our society celebrates failure as a teachable moment. Yet in five studies (total N = 1,674), failure did the opposite: It undermined learning
Not Learning From Failure—the Greatest Failure of All. Lauren Eskreis-Winkler, Ayelet Fishbach. Psychological Science, November 8, 2019. https://doi.org/10.1177/0956797619881133
Abstract: Our society celebrates failure as a teachable moment. Yet in five studies (total N = 1,674), failure did the opposite: It undermined learning. Across studies, participants answered binary-choice questions, following which they were told they answered correctly (success feedback) or incorrectly (failure feedback). Both types of feedback conveyed the correct answer, because there were only two answer choices. However, on a follow-up test, participants learned less from failure feedback than from success feedback. This effect was replicated across professional, linguistic, and social domains—even when learning from failure was less cognitively taxing than learning from success and even when learning was incentivized. Participants who received failure feedback also remembered fewer of their answer choices. Why does failure undermine learning? Failure is ego threatening, which causes people to tune out. Participants learned less from personal failure than from personal success, yet they learned just as much from other people’s failure as from others’ success. Thus, when ego concerns are muted, people tune in and learn from failure.
Keywords: learning, feedback, failure, ego threat, motivation, open data, open materials, preregistered
The true self is not easily expressed face-to-face: On the Internet, people can express what they intrinsically think and believe with fewer concerns about others’ disapproval & judgments
The expression of the true self in the online world: a literature review. Chuan Hu, Sameer Kumar, Jiao Huang & Kurunathan Ratnavelu. Behaviour & Information Technology, Nov 4 2019. ttps://doi.org/10.1080/0144929X.2019.1685596
ABSTRACT: The true self is one of the essential parts of people’s self-concept and identity, but it is not easily expressed in face-to-face communications. On the Internet, people can express what they intrinsically think and believe with fewer concerns about others’ disapproval and judgments. Increasingly more researchers have started to investigate people’s expression of the true self in the online context. However, the existing research is quite diverse and fragmented. A rigorous and comprehensive review of the emerging literature is called for. The present study conducted a systematic literature review to examine what is already known about the expression of the true self online. This paper analysed the selected studies on the basis of research contexts, research methods, and research themes. Our review offers readers an easy access to the current status of research in this field; it also provides some insightful suggestions for future studies.
KEYWORDS: Literature review, the true self, self-expression, online world
ABSTRACT: The true self is one of the essential parts of people’s self-concept and identity, but it is not easily expressed in face-to-face communications. On the Internet, people can express what they intrinsically think and believe with fewer concerns about others’ disapproval and judgments. Increasingly more researchers have started to investigate people’s expression of the true self in the online context. However, the existing research is quite diverse and fragmented. A rigorous and comprehensive review of the emerging literature is called for. The present study conducted a systematic literature review to examine what is already known about the expression of the true self online. This paper analysed the selected studies on the basis of research contexts, research methods, and research themes. Our review offers readers an easy access to the current status of research in this field; it also provides some insightful suggestions for future studies.
KEYWORDS: Literature review, the true self, self-expression, online world
Cross-cultural consistency & relativity in the enjoyment of thinking vs doing: Participants much preferred solitary everyday activities, such as reading or watching TV, to thinking for pleasure
Cross-cultural consistency and relativity in the enjoyment of thinking versus doing. Buttrick, Nicholas et al. Journal of Personality and Social Psychology, Nov 2019. https://psycnet.apa.org/record/2018-35570-001
Abstract: Which is more enjoyable: trying to think enjoyable thoughts or doing everyday solitary activities? Wilson et al. (2014) found that American participants much preferred solitary everyday activities, such as reading or watching TV, to thinking for pleasure. To see whether this preference generalized outside of the United States, we replicated the study with 2,557 participants from 12 sites in 11 countries. The results were consistent in every country: Participants randomly assigned to do something reported significantly greater enjoyment than did participants randomly assigned to think for pleasure. Although we found systematic differences by country in how much participants enjoyed thinking for pleasure, we used a series of nested structural equation models to show that these differences were fully accounted for by country-level variation in 5 individual differences, 4 of which were positively correlated with thinking for pleasure (need for cognition, openness to experience, meditation experience, and initial positive affect) and 1 of which was negatively correlated (reported phone usage).
Discussion
As we predicted, Wilson et al.’s (2014) finding that participants enjoyed doing an external activity more than they enjoyed thinking for pleasure proved to be quite robust, replicating in all 11 of the countries studied. The average effect size was quite large, though smaller than in the original study (d ! .98 vs. 1.83). The uniformity of this finding among the participants and countries sampled here suggests that, across a wide variety of cultures, turning one’s attention inward to focus on enjoyable topics in the absence of any external cues is far less enjoyable than engaging in everyday activities such as reading or watching a video.
One reason for this is that thinking for pleasure is difficult. As noted by Westgate et al. (2017), to think for pleasure, one must choose topics to think about, maintain attention to those topics,and keep competing thoughts outside of awareness, all of which may tax mental resources (Wegner, 1994). Consistent with this view, participants in the thinking condition of the present study reported that it was somewhat difficult to concentrate on their thoughts (M ! 5.18 on a 9-point scale), and the more difficulty they reported, the less they enjoyed thinking, r(1271) = -.36, p < .001. Notably, this correlation did not differ between countries, Q(10) = 3.20, p = .98. One implication of these findings is that people might enjoy thinking for pleasure more if it were made easier, and indeed, as noted earlier, Westgate et al. found that giving people a simple thinking aid—are minder of topics they had said they would enjoy thinking about—significantly increased their enjoyment of thinking.
An additional purpose of the present study was to explore cultural differences in the extent to which people enjoy thinking for pleasure, and some country-level differences emerged. These differences, however, were fully explained by international variations in five individual differences, and once country-level differences in those variables were taken into account, the country-level differences themselves were no longer significant. Participants were more likely to enjoy their thoughts to the extent that they practiced meditation, were high in the need for cognition, high in openness to experience, reported a low level of phone usage, and were in a positive mood. What might explain these relationships?
The correlation of the enjoyment of thinking with meditation is consistent with the idea that cultural practices and norms influence the amount of experience people have spending time alone with their thoughts, and that those with greater experience enjoy thinking more (e.g., H. Smith, 1991; Tsai et al., 2006; Tsai, Knutson, et al., 2007; Tsai, Miao, et al., 2007; Yoshioka et al., 2002). The correlation of the enjoyment of thinking with need for cognition is consistent with the idea that thinking for pleasure is effortful and thus is more enjoyable for those who typically find thinking to be an attractive activity (e.g., Westgate et al., 2017; Wilson et al., 2018a). The correlation of the enjoyment of thinking with openness to experience suggests that those who value creativity and new experiences are more motivated to think for pleasure (or more skilled at it). Alahmadi et al. (2017) found that motivating people to think for pleasure increases their enjoyment considerably, and it is possible that such motivation is associated with openness to experience. The fact that people who were in positive moods enjoyed thinking more is consistent with research that those in a positive mood are likely to find it easier to recruit and think about positive topics (Matt, Vázquez, & Campbell, 1992).
We also found that the five key individual-difference variables varied by culture, which fully explained why residents of some countries enjoyed thinking more than others. For example, Japanese participants enjoyed thinking the least, perhaps because they were the lowest in openness to experience and need for cognition, among the lowest in initial positive affect and in experience with meditation (surprisingly), and among the highest in reported phone use. In contrast, American participants were in the middle of the pack in the enjoyment of thinking, probably because they were also in the middle of the pack on most of the important predictor variables (e.g., openness to experience, experience with meditation, initial positive affect). These findings suggest that to understand cultural variations in the enjoyment of thinking for pleasure, it is best to examine cultural differences in the individual practices and personality variables that are associated with it.
We additionally found evidence that three country-level variables—population density, GDP, and “masculinity”(aka cultural levels of interpersonal competitiveness)—weakly predicted individuals’ enjoyment of thinking. One possible (speculative) explanation for these findings is that people who grew up in a more rural area or in a poorer country may have had less opportunity to distractthemselves with external entertainments and more practice thinking for pleasure. Alternately, the experience of living in densely populated cities may lead to residents feeling that their lives are less meaningful and more overloaded (Buttrick, Heintzelman, Weser, & Oishi, 2018; Milgram, 1970), potentially demotivating them from making the effort to turn inward. In addition, cultures that stress masculinity and competitiveness may be more likely to view thinking for pleasure as a waste of time. It should be noted, though, that even in the countries with the lowest populationdensities (e.g., Brazil and the United States) an dthe lowest GDPs (e.g., Serbia, Costa Rica), participants enjoyed thinking less than doing.
The present study naturally has some limitations. First, as in Wilson et al. (2014) Study 8, all participants were college students, thus limiting the generalizability of the results. However, whereas college students may be an unusual population in some regards (e.g., Henrich et al., 2010), studies show that nonstudents also have difficulty thinking for pleasure (Westgate et al., 2017; Wilson et al., 2014, Study 9). Second, althoughoursample of countries represents a wide variety of cultures, we did not sampletheentirety of the world’s population, and it is possible that enjoyment of thinking for pleasure differs in some of the cultures that we did not sample.
Third, for practical reasons, we used shortened versions of most of the individual-difference measures, which resulted in reduced reliability. For example, we used Gosling et al.’s (2003) 10-item measure of the Big Five personality traits, which had low alphas, particularly for agreeableness. In this regard, it is interesting to compare the cultural differences in Big Five traits that we obtained with those obtained by Schmitt et al. (2007), who used Benet Martinez and John’s (1998) 44-item measure. The correlations between mean levels of openness to experience, conscientiousness, emotional stability, extraversion, and agreeableness, in the nine countries included in both our study and theirs, were, respectively, r(8) = .92, .90, .62, .49, and .30. This increases our confidence in the reliability of our results for some traits (particularly openness to experience and conscientiousness) but decreases it for others (e.g., agreeableness).
In sum, the preference for doing external activities such as reading, watching TV, or surfing the Internet rather than “just thinking” appears to be strong throughout the world. The magnitude of this preference is systematically related to several individual differences that characterize the residents of some countries more than others. These findings raise the question of whether there are conditions under which people throughout the world might enjoy thinking more and whether there would be value in doing so. Progress is being made on these fronts; as mentioned, Westgateetal.(2017) found that people enjoy thinking more when cognitive load is reduced by giving them a simple thinking aid, and studies have found other benefits to thinking for pleasure, namely a sense of personal meaningfulness (Alahmadi et al., 2017; Raza et al., 2018).
The fact that thinking for pleasure can be made easier is interesting in light of the present finding that reported cell phone usage was negatively associated enjoying one’s thoughts. Although much has been written about the increasing reliance on electronic devices and the possible negative consequences of “device obsession” (e.g., Carr, 2011; Kushlev & Dunn, 2015; Powers, 2010), our studyisthe firstto link device usage to a decrease in the ability to sit alone and enjoy one’s thoughts. The present findings are correlational, of course, so we do not knowwhetherusingcellphones makes it more difficult for people to enjoy thinking or whether people who do not enjoy thinking are especially likely to use cell phones, or whether some third variable causes both. It is a provocative possibility, though, that the allure of electronic devices is preventing people from making an effort to find pleasure in their thoughts.
If so, efforts to encourage people to put away their phones and “just think” may be of some benefit. For example, in a field study by Wilson, Westgate, Buttrick, and Gilbert (2018b), participants who were randomly assigned to spend spare moments during their day thinking for pleasure (with thinking aids) found this experience to be morepersonally meaningful, and as enjoyable, as did participantswho were randomly assigned to spend their spare moments as they normally did (which often involved using electronic devices). Much more work needs to be done to determine who values thinking for pleasure and when, but this initial evidence suggests that people may find it to be worth the effort if they gave it a try.
Abstract: Which is more enjoyable: trying to think enjoyable thoughts or doing everyday solitary activities? Wilson et al. (2014) found that American participants much preferred solitary everyday activities, such as reading or watching TV, to thinking for pleasure. To see whether this preference generalized outside of the United States, we replicated the study with 2,557 participants from 12 sites in 11 countries. The results were consistent in every country: Participants randomly assigned to do something reported significantly greater enjoyment than did participants randomly assigned to think for pleasure. Although we found systematic differences by country in how much participants enjoyed thinking for pleasure, we used a series of nested structural equation models to show that these differences were fully accounted for by country-level variation in 5 individual differences, 4 of which were positively correlated with thinking for pleasure (need for cognition, openness to experience, meditation experience, and initial positive affect) and 1 of which was negatively correlated (reported phone usage).
Discussion
As we predicted, Wilson et al.’s (2014) finding that participants enjoyed doing an external activity more than they enjoyed thinking for pleasure proved to be quite robust, replicating in all 11 of the countries studied. The average effect size was quite large, though smaller than in the original study (d ! .98 vs. 1.83). The uniformity of this finding among the participants and countries sampled here suggests that, across a wide variety of cultures, turning one’s attention inward to focus on enjoyable topics in the absence of any external cues is far less enjoyable than engaging in everyday activities such as reading or watching a video.
One reason for this is that thinking for pleasure is difficult. As noted by Westgate et al. (2017), to think for pleasure, one must choose topics to think about, maintain attention to those topics,and keep competing thoughts outside of awareness, all of which may tax mental resources (Wegner, 1994). Consistent with this view, participants in the thinking condition of the present study reported that it was somewhat difficult to concentrate on their thoughts (M ! 5.18 on a 9-point scale), and the more difficulty they reported, the less they enjoyed thinking, r(1271) = -.36, p < .001. Notably, this correlation did not differ between countries, Q(10) = 3.20, p = .98. One implication of these findings is that people might enjoy thinking for pleasure more if it were made easier, and indeed, as noted earlier, Westgate et al. found that giving people a simple thinking aid—are minder of topics they had said they would enjoy thinking about—significantly increased their enjoyment of thinking.
An additional purpose of the present study was to explore cultural differences in the extent to which people enjoy thinking for pleasure, and some country-level differences emerged. These differences, however, were fully explained by international variations in five individual differences, and once country-level differences in those variables were taken into account, the country-level differences themselves were no longer significant. Participants were more likely to enjoy their thoughts to the extent that they practiced meditation, were high in the need for cognition, high in openness to experience, reported a low level of phone usage, and were in a positive mood. What might explain these relationships?
The correlation of the enjoyment of thinking with meditation is consistent with the idea that cultural practices and norms influence the amount of experience people have spending time alone with their thoughts, and that those with greater experience enjoy thinking more (e.g., H. Smith, 1991; Tsai et al., 2006; Tsai, Knutson, et al., 2007; Tsai, Miao, et al., 2007; Yoshioka et al., 2002). The correlation of the enjoyment of thinking with need for cognition is consistent with the idea that thinking for pleasure is effortful and thus is more enjoyable for those who typically find thinking to be an attractive activity (e.g., Westgate et al., 2017; Wilson et al., 2018a). The correlation of the enjoyment of thinking with openness to experience suggests that those who value creativity and new experiences are more motivated to think for pleasure (or more skilled at it). Alahmadi et al. (2017) found that motivating people to think for pleasure increases their enjoyment considerably, and it is possible that such motivation is associated with openness to experience. The fact that people who were in positive moods enjoyed thinking more is consistent with research that those in a positive mood are likely to find it easier to recruit and think about positive topics (Matt, Vázquez, & Campbell, 1992).
We also found that the five key individual-difference variables varied by culture, which fully explained why residents of some countries enjoyed thinking more than others. For example, Japanese participants enjoyed thinking the least, perhaps because they were the lowest in openness to experience and need for cognition, among the lowest in initial positive affect and in experience with meditation (surprisingly), and among the highest in reported phone use. In contrast, American participants were in the middle of the pack in the enjoyment of thinking, probably because they were also in the middle of the pack on most of the important predictor variables (e.g., openness to experience, experience with meditation, initial positive affect). These findings suggest that to understand cultural variations in the enjoyment of thinking for pleasure, it is best to examine cultural differences in the individual practices and personality variables that are associated with it.
We additionally found evidence that three country-level variables—population density, GDP, and “masculinity”(aka cultural levels of interpersonal competitiveness)—weakly predicted individuals’ enjoyment of thinking. One possible (speculative) explanation for these findings is that people who grew up in a more rural area or in a poorer country may have had less opportunity to distractthemselves with external entertainments and more practice thinking for pleasure. Alternately, the experience of living in densely populated cities may lead to residents feeling that their lives are less meaningful and more overloaded (Buttrick, Heintzelman, Weser, & Oishi, 2018; Milgram, 1970), potentially demotivating them from making the effort to turn inward. In addition, cultures that stress masculinity and competitiveness may be more likely to view thinking for pleasure as a waste of time. It should be noted, though, that even in the countries with the lowest populationdensities (e.g., Brazil and the United States) an dthe lowest GDPs (e.g., Serbia, Costa Rica), participants enjoyed thinking less than doing.
The present study naturally has some limitations. First, as in Wilson et al. (2014) Study 8, all participants were college students, thus limiting the generalizability of the results. However, whereas college students may be an unusual population in some regards (e.g., Henrich et al., 2010), studies show that nonstudents also have difficulty thinking for pleasure (Westgate et al., 2017; Wilson et al., 2014, Study 9). Second, althoughoursample of countries represents a wide variety of cultures, we did not sampletheentirety of the world’s population, and it is possible that enjoyment of thinking for pleasure differs in some of the cultures that we did not sample.
Third, for practical reasons, we used shortened versions of most of the individual-difference measures, which resulted in reduced reliability. For example, we used Gosling et al.’s (2003) 10-item measure of the Big Five personality traits, which had low alphas, particularly for agreeableness. In this regard, it is interesting to compare the cultural differences in Big Five traits that we obtained with those obtained by Schmitt et al. (2007), who used Benet Martinez and John’s (1998) 44-item measure. The correlations between mean levels of openness to experience, conscientiousness, emotional stability, extraversion, and agreeableness, in the nine countries included in both our study and theirs, were, respectively, r(8) = .92, .90, .62, .49, and .30. This increases our confidence in the reliability of our results for some traits (particularly openness to experience and conscientiousness) but decreases it for others (e.g., agreeableness).
In sum, the preference for doing external activities such as reading, watching TV, or surfing the Internet rather than “just thinking” appears to be strong throughout the world. The magnitude of this preference is systematically related to several individual differences that characterize the residents of some countries more than others. These findings raise the question of whether there are conditions under which people throughout the world might enjoy thinking more and whether there would be value in doing so. Progress is being made on these fronts; as mentioned, Westgateetal.(2017) found that people enjoy thinking more when cognitive load is reduced by giving them a simple thinking aid, and studies have found other benefits to thinking for pleasure, namely a sense of personal meaningfulness (Alahmadi et al., 2017; Raza et al., 2018).
The fact that thinking for pleasure can be made easier is interesting in light of the present finding that reported cell phone usage was negatively associated enjoying one’s thoughts. Although much has been written about the increasing reliance on electronic devices and the possible negative consequences of “device obsession” (e.g., Carr, 2011; Kushlev & Dunn, 2015; Powers, 2010), our studyisthe firstto link device usage to a decrease in the ability to sit alone and enjoy one’s thoughts. The present findings are correlational, of course, so we do not knowwhetherusingcellphones makes it more difficult for people to enjoy thinking or whether people who do not enjoy thinking are especially likely to use cell phones, or whether some third variable causes both. It is a provocative possibility, though, that the allure of electronic devices is preventing people from making an effort to find pleasure in their thoughts.
If so, efforts to encourage people to put away their phones and “just think” may be of some benefit. For example, in a field study by Wilson, Westgate, Buttrick, and Gilbert (2018b), participants who were randomly assigned to spend spare moments during their day thinking for pleasure (with thinking aids) found this experience to be morepersonally meaningful, and as enjoyable, as did participantswho were randomly assigned to spend their spare moments as they normally did (which often involved using electronic devices). Much more work needs to be done to determine who values thinking for pleasure and when, but this initial evidence suggests that people may find it to be worth the effort if they gave it a try.
Friday, November 8, 2019
Older participants reported smaller social networks, largely because of reporting fewer peripheral others; yet older age was associated with better well-being
Age differences in reported social networks and well-being. Bruine de Bruin, Wändi, Parker, Andrew M., Strough, JoNell. Psychology and Aging, Nov 07, 2019. https://psycnet.apa.org/buy/2019-64493-001
Abstract: Social networks can consist of close friends, family members, and neighbors as well as peripheral others. Studies of social networks and associations with well-being have mostly focused on age-restricted samples of older adults or specific geographic areas, thus limiting their generalizability. We analyzed 2 online surveys conducted with RAND’s American Life Panel, a national adult life span sample recruited through multiple probability-based approaches. In Survey 1, 496 participants assessed the sizes of their social networks, including the number of close friends, family members, neighbors, and peripheral others. Of those, 287 rated their social satisfaction and well-being on Survey 2. Older participants reported smaller social networks, largely because of reporting fewer peripheral others. Yet older age was associated with better well-being. Although the reported number of close friends was unrelated to age, it was the main driver of well-being across the life span—even after accounting for the number of family members, neighbors, and peripheral others. However, well-being was more strongly related to social satisfaction than to the reported number of close friends—suggesting that it is the perception of relationship quality rather than the perception of relationship quantity that is relevant to reporting better well-being. We discuss implications for social network interventions that aim to promote well-being.
Check also Older age was correlated to better scores on each of the four financial decision‐making measures, and has more experience‐based knowledge, & less negative emotions about financial decisions (both of which are particularly helpful for better financial decision-making):
Age differences in financial decision making: The benefits of more experience and less negative emotions. Wiebke Eberhardt, Wändi Bruine de Bruin, JoNell Strough. Journal of Behavioral Decision Making, https://www.bipartisanalliance.com/2018/08/older-age-was-correlated-to-better.html
And Age differences in moral judgment: Older adults are more deontological than younger adults. Simon McNair, Yasmina Okan, Constantinos Hadjichristidis, Wändi Bruine de Bruin. Journal of Behavioral Decision Making, https://www.bipartisanalliance.com/2018/06/age-differences-in-moral-judgment-older.html
DISCUSSION
In a national adult life-span sample, we found support for four predictions from the
conceptual framework provided by the Convoy Model (Antonucci et al., 2013) and Socio
emotional Selectivity Theory (Carstensen, 2006), pertaining to age differences in social
networks, as well as associations with social satisfaction and well-being across the life span.
First, we found that older adults had smaller social networks than younger adults, but that the
number of close friends was unrelated to adult age. Younger adults had especially large
social networks consisting of mostly peripheral others, perhaps because online social
networking sites have facilitated the maintenance of increasingly large and mostly impersonal
social networks (Chang et al., 2015; Ellison, Steinfeld, & Lampe, 2007; Manago, Taylor, &
Greenfield, 2012; Valenzuela, Park, & Kee, 2009; Yu et al., 2018). Yet, our findings from
this national adult life span sample are consistent with previous observations in the offline
social networks of San Francisco Bay area residents (English & Carstensen, 2014; Fung et
al., 2001) and US residents before the widespread use of the internet (Morgan, 1988), as well
as more recent observations in the online social networks of Facebook users (Chang et al.,
2015; Yu et al., 2018). Additionally, older age in our national adult life-span sample was
associated with reporting social networks that included fewer family members and more
neighbors. A review of studies with older adults suggested that friends and neighbors may be
more important than family members kp"qnfgt"cfwnvuÓ"uqekcn"pgvyqtmu for promoting well
being (Pinquart & Sörensen, 2000). In older West Berlin residents, close friends and
neighbors were found to take over social and instrumental support functions to replace
unavailable family members (Lang & Carstensen, 1994).
Second, qnfgt"cfwnvuÓ"smaller networks did not appear to undermine their social
satisfaction or well-being. Although the two measures were highly correlated, reports of
social satisfaction were unrelated to age while reports of well-being increased with age.
Age differences in social networks 16
Other studies that have also suggested that life satisfaction and well-being tend to be
preserved or improve with older age (Carstensen et al., 2000, 2011; Charles et al., 2001;
Kessler & Staudinger, 2009).
Third, the reported number of close friends was associated with reported social
satisfaction and reported well-being across the adult life span. The relationship between the
number of close friends and well-being held even after accounting for the number of family
members, neighbors, and peripheral others Î which were not additionally associated with
well-being. The relationship of the reported number of close friends with greater social
satisfaction and well-being did not vary with age, suggesting the importance of close
friendships across the life span. This finding is consistent with observed patterns among
Facebook users, who reported greater well-being if they perceived more Òactual friendsÓ in
their online social networks (Chang et al., 2015). However, in the off-line social networks of
San Francisco Bay area residents (Fung et al., 2001), there was some evidence that reporting
more close friendships was related to lower happiness among younger adults, in line with the
idea that close relationships can also be emotionally taxing (Birditt et al., in press; Hartup &
Stevens, 1999). Indeed, younger adults report more problems and negative interactions in
their close social relationships as compared to older adults (Akiyama, Antonucci, Takahashi,
& Langfahl, 2003; Birditt et al., in press; Schlosnagle & Strough, 2017), which may partially
explain why we found that younger adults reported lower well-being despite having similar
numbers of close friends as older adults.
Our fourth main finding is that the reported number of close friends no longer
predicted well-being after taking into account the significant relationship between social
satisfaction and well-being. Thus, the quality of close friendships seems more important than
their quantity, for promoting well-being. Our analyses of a national adult life-span sample
confirmed patterns that had been observed in studies with age-restricted samples of older dults (Cornwell & Waite, 2009; Pinquart & Sörensen, 2000), and with a geographically
restricted adult life span sample recruited from the San Francisco Bay area (Fung et al.,
2001).
Our combined findings suggest support for a conceptual framework consisting of the
Convoy Model (Antonucci et al., 2013) and Socio-emotional Selectivity Theory, which
predicts smaller social networks of emotionally close relationships in older age, with benefits
to well-being. The Convoy Model posits that these age differences in social network size and
composition reflect age differences in personal and situational factors (Antonucci et al.,
2013). However, all findings held despite taking into account potential age differences in
self-reported health, income, and demographics. Possibly, age differences in other
unmeasured factors may have played a role. Socio-emotional Selectivity Theory suggests
that older adults may make intentional choices about their social networks, so as to optimize
emotional experiences (English & Carstensen, 2014). Although our secondary analyses can
not provide direct insight into the deliberate nature of age-related changes in centering social
networks more on emotionally gratifying close relationships, findings from the Berlin Aging
Study have shown that the main reason for discontinuing relationships in older adulthood
may be a lack of interest rather than lack of opportunity (Lang, 2000). Moreover, a survey of
a national adult life span sample revealed that younger, not older, people reported wishing
they had more friends (Lansford, Sherman, & Antonucci, 1998). Yet, our findings also
suggest that, as compared to younger adults, older adults count more neighbors among their
social contacts, which was unrelated to their social satisfaction and well-being. Thus, not all
of older adults' social contacts may be deliberately selected (or avoided) to promote better
well-being.
One limitation of our research is its cross-sectional correlational nature, which
precludes conclusions about causality or developmental changes with age. Additionally, did not have access to participants' actual social networks. It is possible that younger adults
exaggerated their reported social networks, or that older adults underestimated theirs.
However, our findings suggest that these perceptions of social networks are relevant to later
reports of social satisfaction and well-being as provided on a separate survey. Another
potential limitation is that, despite relatively good response rates, our national life span
sample may have had limited representativeness due to selection effects. Although our
demographic control variables were in line with those in the literature on age differences in
social networks (e.g., Chang et al., 2015; Lang & Carstensen, 1994; Morgan, 1988), it is
possible that unmeasured variables such as personality characteristics may have contributed
to our findings.
Furthermore, the surveys we analyzed did not ask participants to distinguish between
social contacts who were maintained online or face to face. There may have been age
differences in the number of contacts maintained online or face-to-face with younger adults
maintaining especially large online social networks with many peripheral others (Chang et
al., 2015; Ellison, Steinfeld, & Lampe, 2007; Manago et al., 2012; Valenzuela, Park, & Kee,
2009; Yu et al., 2018). However, distinguishing between online and face-to-face contacts
may not actually be possible, because online communications are typically used to
supplement face-to-face and telephone communications with existing social contacts (Bargh
& McKenna, 2004; Wellman, Haase, Witte, & Hampton, 2001). Moreover, the importance
of friendships for well-being has been reported in studies of off-line social networks and
online social networks (e.g., Fung et al., 2001; Chang et al., 2015). While the nature of
friendships and time spent face to face may change over the life span, their social meaning
and importance to well-being does not (Hartup & Stevens, 1999).
Our findings suggest that interventions that aim to improve well-being may benefit
from helping recipients to foster close social relationships. Such interventions may require different approaches among older adults, as compared to younger adults. Indeed, developing
effective interventions requires a deeper understanding of those issues that audience members
need and want to have addressed (Bruine de Bruin & Bostrom, 2013). For example, older
adults may be most interested in interventions that help them to maintain their existing close
friendships. As noted by Fung et al. (2001), older people may actively resist encouragements
to increase their social networks through senior centers or visitation programs, because
meeting new people may no longer be as important to them (see also Carstensen & Erickson,
1986; Korte & Gupta, 1991). Rather, older adults may be better able to reduce feelings of
loneliness when being provided with internet and computer training (Choi, Kong, & Jung,
2012), perhaps because it helps them to stay in touch with those social contacts they care
most about (McAndrew & Jeong, 2012; Thayer & Ray, 2006).
Younger adults, on the other hand, may be most interested in growing their social
networks, but may benefit from learning how to do so while avoiding problems with their
friendships and draining their emotional resources (Birditt et al., in press; Hartup & Stevens,
1999; Schlosnagle & Strough, 2017). Pro-social interventions may be able to help younger
adults to grow their social networks in a positive manner: Pre-adolescents who were asked to
engage in three acts of kindness (vs. to visit three places) increased their popularity among
peers as well as their well-being (Layous, Nelson, Oberle, Schonert-Reichl, Lyubomirsky,
2012).
Moreover, a review of interventions that targeted lonely adults of all ages suggested
that providing cognitive behavioral therapy that aimed to improve maladaptive social
cognitions (or heightened negative attention to social threats, which exacerbate feelings of
sadness and loneliness) may be more effective than social activity interventions (Masi, Chen,
Hawkley, & Cacioppo, 2011). A review of interventions that promote the self-expression of
gratitude has suggested a beneficial effect on feelings of social connectedness and well-being (Armenta, Fritz, & Lyubomirsky, 2016). Indeed, our findings suggest that, across the life
span, satisfaction with social relationships may be more important than the quantity of close
friends, for promoting well-being.
conceptual framework provided by the Convoy Model (Antonucci et al., 2013) and Socio
emotional Selectivity Theory (Carstensen, 2006), pertaining to age differences in social
networks, as well as associations with social satisfaction and well-being across the life span.
First, we found that older adults had smaller social networks than younger adults, but that the
number of close friends was unrelated to adult age. Younger adults had especially large
social networks consisting of mostly peripheral others, perhaps because online social
networking sites have facilitated the maintenance of increasingly large and mostly impersonal
social networks (Chang et al., 2015; Ellison, Steinfeld, & Lampe, 2007; Manago, Taylor, &
Greenfield, 2012; Valenzuela, Park, & Kee, 2009; Yu et al., 2018). Yet, our findings from
this national adult life span sample are consistent with previous observations in the offline
social networks of San Francisco Bay area residents (English & Carstensen, 2014; Fung et
al., 2001) and US residents before the widespread use of the internet (Morgan, 1988), as well
as more recent observations in the online social networks of Facebook users (Chang et al.,
2015; Yu et al., 2018). Additionally, older age in our national adult life-span sample was
associated with reporting social networks that included fewer family members and more
neighbors. A review of studies with older adults suggested that friends and neighbors may be
more important than family members kp"qnfgt"cfwnvuÓ"uqekcn"pgvyqtmu for promoting well
being (Pinquart & Sörensen, 2000). In older West Berlin residents, close friends and
neighbors were found to take over social and instrumental support functions to replace
unavailable family members (Lang & Carstensen, 1994).
Second, qnfgt"cfwnvuÓ"smaller networks did not appear to undermine their social
satisfaction or well-being. Although the two measures were highly correlated, reports of
social satisfaction were unrelated to age while reports of well-being increased with age.
Age differences in social networks 16
Other studies that have also suggested that life satisfaction and well-being tend to be
preserved or improve with older age (Carstensen et al., 2000, 2011; Charles et al., 2001;
Kessler & Staudinger, 2009).
Third, the reported number of close friends was associated with reported social
satisfaction and reported well-being across the adult life span. The relationship between the
number of close friends and well-being held even after accounting for the number of family
members, neighbors, and peripheral others Î which were not additionally associated with
well-being. The relationship of the reported number of close friends with greater social
satisfaction and well-being did not vary with age, suggesting the importance of close
friendships across the life span. This finding is consistent with observed patterns among
Facebook users, who reported greater well-being if they perceived more Òactual friendsÓ in
their online social networks (Chang et al., 2015). However, in the off-line social networks of
San Francisco Bay area residents (Fung et al., 2001), there was some evidence that reporting
more close friendships was related to lower happiness among younger adults, in line with the
idea that close relationships can also be emotionally taxing (Birditt et al., in press; Hartup &
Stevens, 1999). Indeed, younger adults report more problems and negative interactions in
their close social relationships as compared to older adults (Akiyama, Antonucci, Takahashi,
& Langfahl, 2003; Birditt et al., in press; Schlosnagle & Strough, 2017), which may partially
explain why we found that younger adults reported lower well-being despite having similar
numbers of close friends as older adults.
Our fourth main finding is that the reported number of close friends no longer
predicted well-being after taking into account the significant relationship between social
satisfaction and well-being. Thus, the quality of close friendships seems more important than
their quantity, for promoting well-being. Our analyses of a national adult life-span sample
confirmed patterns that had been observed in studies with age-restricted samples of older dults (Cornwell & Waite, 2009; Pinquart & Sörensen, 2000), and with a geographically
restricted adult life span sample recruited from the San Francisco Bay area (Fung et al.,
2001).
Our combined findings suggest support for a conceptual framework consisting of the
Convoy Model (Antonucci et al., 2013) and Socio-emotional Selectivity Theory, which
predicts smaller social networks of emotionally close relationships in older age, with benefits
to well-being. The Convoy Model posits that these age differences in social network size and
composition reflect age differences in personal and situational factors (Antonucci et al.,
2013). However, all findings held despite taking into account potential age differences in
self-reported health, income, and demographics. Possibly, age differences in other
unmeasured factors may have played a role. Socio-emotional Selectivity Theory suggests
that older adults may make intentional choices about their social networks, so as to optimize
emotional experiences (English & Carstensen, 2014). Although our secondary analyses can
not provide direct insight into the deliberate nature of age-related changes in centering social
networks more on emotionally gratifying close relationships, findings from the Berlin Aging
Study have shown that the main reason for discontinuing relationships in older adulthood
may be a lack of interest rather than lack of opportunity (Lang, 2000). Moreover, a survey of
a national adult life span sample revealed that younger, not older, people reported wishing
they had more friends (Lansford, Sherman, & Antonucci, 1998). Yet, our findings also
suggest that, as compared to younger adults, older adults count more neighbors among their
social contacts, which was unrelated to their social satisfaction and well-being. Thus, not all
of older adults' social contacts may be deliberately selected (or avoided) to promote better
well-being.
One limitation of our research is its cross-sectional correlational nature, which
precludes conclusions about causality or developmental changes with age. Additionally, did not have access to participants' actual social networks. It is possible that younger adults
exaggerated their reported social networks, or that older adults underestimated theirs.
However, our findings suggest that these perceptions of social networks are relevant to later
reports of social satisfaction and well-being as provided on a separate survey. Another
potential limitation is that, despite relatively good response rates, our national life span
sample may have had limited representativeness due to selection effects. Although our
demographic control variables were in line with those in the literature on age differences in
social networks (e.g., Chang et al., 2015; Lang & Carstensen, 1994; Morgan, 1988), it is
possible that unmeasured variables such as personality characteristics may have contributed
to our findings.
Furthermore, the surveys we analyzed did not ask participants to distinguish between
social contacts who were maintained online or face to face. There may have been age
differences in the number of contacts maintained online or face-to-face with younger adults
maintaining especially large online social networks with many peripheral others (Chang et
al., 2015; Ellison, Steinfeld, & Lampe, 2007; Manago et al., 2012; Valenzuela, Park, & Kee,
2009; Yu et al., 2018). However, distinguishing between online and face-to-face contacts
may not actually be possible, because online communications are typically used to
supplement face-to-face and telephone communications with existing social contacts (Bargh
& McKenna, 2004; Wellman, Haase, Witte, & Hampton, 2001). Moreover, the importance
of friendships for well-being has been reported in studies of off-line social networks and
online social networks (e.g., Fung et al., 2001; Chang et al., 2015). While the nature of
friendships and time spent face to face may change over the life span, their social meaning
and importance to well-being does not (Hartup & Stevens, 1999).
Our findings suggest that interventions that aim to improve well-being may benefit
from helping recipients to foster close social relationships. Such interventions may require different approaches among older adults, as compared to younger adults. Indeed, developing
effective interventions requires a deeper understanding of those issues that audience members
need and want to have addressed (Bruine de Bruin & Bostrom, 2013). For example, older
adults may be most interested in interventions that help them to maintain their existing close
friendships. As noted by Fung et al. (2001), older people may actively resist encouragements
to increase their social networks through senior centers or visitation programs, because
meeting new people may no longer be as important to them (see also Carstensen & Erickson,
1986; Korte & Gupta, 1991). Rather, older adults may be better able to reduce feelings of
loneliness when being provided with internet and computer training (Choi, Kong, & Jung,
2012), perhaps because it helps them to stay in touch with those social contacts they care
most about (McAndrew & Jeong, 2012; Thayer & Ray, 2006).
Younger adults, on the other hand, may be most interested in growing their social
networks, but may benefit from learning how to do so while avoiding problems with their
friendships and draining their emotional resources (Birditt et al., in press; Hartup & Stevens,
1999; Schlosnagle & Strough, 2017). Pro-social interventions may be able to help younger
adults to grow their social networks in a positive manner: Pre-adolescents who were asked to
engage in three acts of kindness (vs. to visit three places) increased their popularity among
peers as well as their well-being (Layous, Nelson, Oberle, Schonert-Reichl, Lyubomirsky,
2012).
Moreover, a review of interventions that targeted lonely adults of all ages suggested
that providing cognitive behavioral therapy that aimed to improve maladaptive social
cognitions (or heightened negative attention to social threats, which exacerbate feelings of
sadness and loneliness) may be more effective than social activity interventions (Masi, Chen,
Hawkley, & Cacioppo, 2011). A review of interventions that promote the self-expression of
gratitude has suggested a beneficial effect on feelings of social connectedness and well-being (Armenta, Fritz, & Lyubomirsky, 2016). Indeed, our findings suggest that, across the life
span, satisfaction with social relationships may be more important than the quantity of close
friends, for promoting well-being.
Why Boredom Is Interesting
Why Boredom Is Interesting. Erin C. Westgate. Current Directions in Psychological Science, November 8, 2019. https://doi.org/10.1177/0963721419884309
Abstract: Is boredom bad? It is certainly common: Most everybody gets bored. There is a sense that boredom sometimes causes bad things to happen (e.g., substance use, self-harm) and sometimes causes good things to happen (e.g., daydreaming, creativity), but it is hard to understand what boredom does without first understanding what it is. According to the meaning-and-attentional-components (MAC) model of boredom and cognitive engagement, the emotion of boredom signals deficits in attention and meaning. Much like pain, it may not be pleasant, but boredom critically alerts us that we are unable or unwilling to successfully engage attention in meaningful activities. Whether that is good or bad rests ultimately on how we respond.
Keywords: boredom, meaning, attention, motivation, emotion
When a Russian man stole an army tank and drove it into a local supermarket (Kiryukhinia & Coleman, 2018), you would have been forgiven for thinking he had good reason. Nope, reported journalists: He was just bored.
Tales of bored troublemakers abound. From the odd— bored shopworkers cremating a mouse (“‘Bored’ Workers ‘Cremated Mouse,’” 2019)—to the disturbing—an Irishman caught aiming his pellet gun at drivers (Ferguson & McLean, 2019)—these news stories appear regularly, and the explanation “I was bored” resonates and perplexes. What is it about boredom that drives people to steal military equipment, watch movies on the job, and lay mice to rest? Is boredom really that nefarious?
It is certainly common: Most everybody gets bored (e.g., Chin, Markey, Bhargava, Kassam, & Loewenstein, 2017). Boredom is especially common at work, where it is linked to productivity loss and burnout (Fisher, 1993). It is also common in schools: Students get bored, and bored students do not do very well (Pekrun, Goetz, Daniels, Stupnisky, & Perry, 2010). Indeed, there is growing suspicion that boredom lies behind many socially destructive behaviors, including self-harm, compulsive gambling, and substance use (Mercer & Eastwood, 2010; Weybright, Caldwell, Ram, Smith, & Wegner, 2015). Yet, at the same time, there are calls from public intellectuals for people to experience more boredom in the belief that it leads to greater well-being (Paul, 2019). Who is right? To understand when boredom is good (and when it is bad), we first need to understand what boredom is.
Attention and Meaning: Boredom’s Key Ingredients
If you are reading this, you have almost certainly had the lamentable experience of reading a boring article. We all know the feeling: Dread and irritation build, your mind wanders, you check the clock and remaining page count, or even surrender and sneak a glimpse at your phone. In short, you are bored. But why? There could be something amiss with the environment—too much constraint or too little stimulation or arousal (Berlyne, 1960). According to attentional theories, such environmental features foster understimulation that makes it difficult to focus (Csikszentmihalyi, 2000; Eastwood, Frischen, Fenske, & Smilek, 2012). There is excellent evidence that difficulty paying attention translates into feelings of boredom and that understimulation can cause inattention. But such theories do not account for times when inattention is the result of overstimulation—too much going on rather than too little—and overlook a greater problem: Sometimes attention is not the issue.
Many functional approaches to boredom set attention aside to consider its underlying purpose; their proponents argue that boredom is a signal meant to alert people to underlying problems, most often concerning goals, meaning, or opportunity costs (e.g., van Tilburg & Igou, 2012). If inattention results in boredom, such individuals argue, it is because inattention is an indirect signal that what you are doing lacks value or meaning. But that does not explain instances when people are bored during otherwise meaningful activities.
Which is it then? Is boredom caused by inattention resulting from understimulation? Or is boredom caused by a lack of meaning? Both are (partially) right.1 The meaning-and-attentional-components (MAC) model of boredom and cognitive engagement unifies past work that has examined attention, meaning, and their environmental correlates in isolation and brings these ideas together to explain what boredom is and why we experience it.
Abstract: Is boredom bad? It is certainly common: Most everybody gets bored. There is a sense that boredom sometimes causes bad things to happen (e.g., substance use, self-harm) and sometimes causes good things to happen (e.g., daydreaming, creativity), but it is hard to understand what boredom does without first understanding what it is. According to the meaning-and-attentional-components (MAC) model of boredom and cognitive engagement, the emotion of boredom signals deficits in attention and meaning. Much like pain, it may not be pleasant, but boredom critically alerts us that we are unable or unwilling to successfully engage attention in meaningful activities. Whether that is good or bad rests ultimately on how we respond.
Keywords: boredom, meaning, attention, motivation, emotion
When a Russian man stole an army tank and drove it into a local supermarket (Kiryukhinia & Coleman, 2018), you would have been forgiven for thinking he had good reason. Nope, reported journalists: He was just bored.
Tales of bored troublemakers abound. From the odd— bored shopworkers cremating a mouse (“‘Bored’ Workers ‘Cremated Mouse,’” 2019)—to the disturbing—an Irishman caught aiming his pellet gun at drivers (Ferguson & McLean, 2019)—these news stories appear regularly, and the explanation “I was bored” resonates and perplexes. What is it about boredom that drives people to steal military equipment, watch movies on the job, and lay mice to rest? Is boredom really that nefarious?
It is certainly common: Most everybody gets bored (e.g., Chin, Markey, Bhargava, Kassam, & Loewenstein, 2017). Boredom is especially common at work, where it is linked to productivity loss and burnout (Fisher, 1993). It is also common in schools: Students get bored, and bored students do not do very well (Pekrun, Goetz, Daniels, Stupnisky, & Perry, 2010). Indeed, there is growing suspicion that boredom lies behind many socially destructive behaviors, including self-harm, compulsive gambling, and substance use (Mercer & Eastwood, 2010; Weybright, Caldwell, Ram, Smith, & Wegner, 2015). Yet, at the same time, there are calls from public intellectuals for people to experience more boredom in the belief that it leads to greater well-being (Paul, 2019). Who is right? To understand when boredom is good (and when it is bad), we first need to understand what boredom is.
Attention and Meaning: Boredom’s Key Ingredients
If you are reading this, you have almost certainly had the lamentable experience of reading a boring article. We all know the feeling: Dread and irritation build, your mind wanders, you check the clock and remaining page count, or even surrender and sneak a glimpse at your phone. In short, you are bored. But why? There could be something amiss with the environment—too much constraint or too little stimulation or arousal (Berlyne, 1960). According to attentional theories, such environmental features foster understimulation that makes it difficult to focus (Csikszentmihalyi, 2000; Eastwood, Frischen, Fenske, & Smilek, 2012). There is excellent evidence that difficulty paying attention translates into feelings of boredom and that understimulation can cause inattention. But such theories do not account for times when inattention is the result of overstimulation—too much going on rather than too little—and overlook a greater problem: Sometimes attention is not the issue.
Many functional approaches to boredom set attention aside to consider its underlying purpose; their proponents argue that boredom is a signal meant to alert people to underlying problems, most often concerning goals, meaning, or opportunity costs (e.g., van Tilburg & Igou, 2012). If inattention results in boredom, such individuals argue, it is because inattention is an indirect signal that what you are doing lacks value or meaning. But that does not explain instances when people are bored during otherwise meaningful activities.
Which is it then? Is boredom caused by inattention resulting from understimulation? Or is boredom caused by a lack of meaning? Both are (partially) right.1 The meaning-and-attentional-components (MAC) model of boredom and cognitive engagement unifies past work that has examined attention, meaning, and their environmental correlates in isolation and brings these ideas together to explain what boredom is and why we experience it.
From 2014...The Price of Envy—An Experimental Investigation of Spiteful Behavior
From 2014...The Price of Envy—An Experimental Investigation of
Spiteful Behavior. Inga Wobker. Managerial and Decision Economics, April
21 2014. https://doi.org/10.1002/mde.2672
Abstract: When receiving less resources than a competitor, envy may be evoked that may result in spiteful behavior. This paper applies evolutionary theory to understand envy and its outcomes. A theoretical framework is developed that is based on the cause–effect relationships of unequal outcomes, envy, defection of cooperation, and welfare loss. To test this framework, an experiment with 136 participants is run. The results confirm that receiving less than another can indeed lead to experiences of envy and defection of future cooperation, producing a welfare loss of one‐sixth.
4.1. Discussion
The overall objective of this research was to study envyand its influence on spiteful behavior in an experimentalsetting with economic relevance. Evolutionary theoryprovided a supportive framework for studying the issue.
An unequal distribution of resources led to feelingsof envy in those who were worse off, which is in linewith prior literature (Hill and Buss, 2008b; Leach,2008). One-third of the losers chose to act spitefullyand reduce the other players’ balances. In a similar experiment by Celse (2009), participants with different levels of endowment could reduce the other players’ payoff at own cost. Of the participants with a lowerendowment than their opponents (equal to the losers inthis experiment), 31.9% reduced the other player’s balance at a personal cost. This may indicate that the rateof approximately one-third of the agents who are willing to behave spitefully is stably distributed in the population. However, one has to bear in mind that different expectations about the outcomes of the resource division probably will moderate the satisfaction with the outcome. For example, if an actor only expects a fraction of the outcome of what the others obtain, this outcome of the resource division is, however, still profitable for her or him, when actually receiving less than others, in this case, this does not trigger envy.
Although spiteful behavior as a reaction to resourcedeficiencies may be the best individual strategy (Hilland Buss, 2008b), it is certainly not the best strategy for a group or organization as a whole, as it produces welfare losses (Garay and Móri, 2011). Recognizing the impact of envy for agents may lead to new understandings of inefficient organizations and welfare losses and may help to develop approaches that better manage the destructive influence of emotions (more precisely,social emotions—those triggered by social comparisons), on behavior (Manner and Gowdy, 2008).
Agents who acted spitefully stated several motivations for their actions. Their attitude—that if they could not have the money, then no one should have it—is a common feature of envy (Feather and Nairn, 2005) and supports the evolutionary perspective that when itis not possible for a person to obtain the resource, nocompetitor should have it either, in order to preserve relative fitness (Hill and Buss, 2008a, 2008b). Some agents expressed the opinion that the distribution wasunjust as insofar that the opposing agents won and that they themselves did not. This attitude corresponds tothe traditional scholarly view that subjective assump-tions of undeserved advantage trigger envy (Featherand Sherman, 2002; Feather and Nairn, 2005; Smithand Kim, 2007), which, in turn, triggers ill will. If an agent is observed to have something that he or sheshould not have—even though this state of not deserving may be very subjective—it is understandable that the envious agent would feel hostile toward that enviedagent (Smithet al., 1994). Motivation that arises fromreasons of distributive justice is a very subjective response. Both agents had exactly the same chance of winning, so no objective criteria of fairness have been corrupted. Retaliation of the winner could be interpretedas a punishment for a defection of equity (Xiao andHouser, 2005; Axelrod and Hamilton, 2006). When an agent reduces the other agent’s prize and explains the decision on the simple basis that it is an option of the game, the stated motivation can be interpreted as apossible expression of disguised envy. A logical hypothesis for this behavior is that the agents neededto justify the reduction to themselves and to the experimenter and wanted to blame their desire to reduce on features of the game rather than on their envy.
5. CONCLUSION
The overall objective of this research was to studythe influence of envy on spiteful behavior and to understand the negative effects of envy on inter-firm and intra-firm relations. The evolutionary theory provided a supportive framework for studying the issue. In the study designed, agents played a lottery that provided them with unequal distribution and could subsequently create financial harm for each other at their own cost. One-third of thelosers in the lottery acted spitefully and reducedthe winners’balances by half. The observed welfare losses accumulated to one-seventh of thetotal income value.Addressing the broader aspects of envy, in orderto fully understand the nature of envy and its implications for relationships, more research is needed (Smith and Kim, 2007). Systematically integrating envy and other other-regarding preferences into economic modeling can provide a refreshing viewpoint in the investigation of human behavior (Horstet al., 2006; Kirman and Teschl, 2010). It will be interesting to see whether, and in what ways, this study will help to motivate researcher sto focus research on this fascinating emotion in this field—where, despite the relevance of inter-firm and intra-firm relations, the concept of envy is still neglected. It may be hoped for that adopting an evolutionary perspective on these questions will lead to more effective management strategies fo rdealing with envy
Abstract: When receiving less resources than a competitor, envy may be evoked that may result in spiteful behavior. This paper applies evolutionary theory to understand envy and its outcomes. A theoretical framework is developed that is based on the cause–effect relationships of unequal outcomes, envy, defection of cooperation, and welfare loss. To test this framework, an experiment with 136 participants is run. The results confirm that receiving less than another can indeed lead to experiences of envy and defection of future cooperation, producing a welfare loss of one‐sixth.
4.1. Discussion
The overall objective of this research was to study envyand its influence on spiteful behavior in an experimentalsetting with economic relevance. Evolutionary theoryprovided a supportive framework for studying the issue.
An unequal distribution of resources led to feelingsof envy in those who were worse off, which is in linewith prior literature (Hill and Buss, 2008b; Leach,2008). One-third of the losers chose to act spitefullyand reduce the other players’ balances. In a similar experiment by Celse (2009), participants with different levels of endowment could reduce the other players’ payoff at own cost. Of the participants with a lowerendowment than their opponents (equal to the losers inthis experiment), 31.9% reduced the other player’s balance at a personal cost. This may indicate that the rateof approximately one-third of the agents who are willing to behave spitefully is stably distributed in the population. However, one has to bear in mind that different expectations about the outcomes of the resource division probably will moderate the satisfaction with the outcome. For example, if an actor only expects a fraction of the outcome of what the others obtain, this outcome of the resource division is, however, still profitable for her or him, when actually receiving less than others, in this case, this does not trigger envy.
Although spiteful behavior as a reaction to resourcedeficiencies may be the best individual strategy (Hilland Buss, 2008b), it is certainly not the best strategy for a group or organization as a whole, as it produces welfare losses (Garay and Móri, 2011). Recognizing the impact of envy for agents may lead to new understandings of inefficient organizations and welfare losses and may help to develop approaches that better manage the destructive influence of emotions (more precisely,social emotions—those triggered by social comparisons), on behavior (Manner and Gowdy, 2008).
Agents who acted spitefully stated several motivations for their actions. Their attitude—that if they could not have the money, then no one should have it—is a common feature of envy (Feather and Nairn, 2005) and supports the evolutionary perspective that when itis not possible for a person to obtain the resource, nocompetitor should have it either, in order to preserve relative fitness (Hill and Buss, 2008a, 2008b). Some agents expressed the opinion that the distribution wasunjust as insofar that the opposing agents won and that they themselves did not. This attitude corresponds tothe traditional scholarly view that subjective assump-tions of undeserved advantage trigger envy (Featherand Sherman, 2002; Feather and Nairn, 2005; Smithand Kim, 2007), which, in turn, triggers ill will. If an agent is observed to have something that he or sheshould not have—even though this state of not deserving may be very subjective—it is understandable that the envious agent would feel hostile toward that enviedagent (Smithet al., 1994). Motivation that arises fromreasons of distributive justice is a very subjective response. Both agents had exactly the same chance of winning, so no objective criteria of fairness have been corrupted. Retaliation of the winner could be interpretedas a punishment for a defection of equity (Xiao andHouser, 2005; Axelrod and Hamilton, 2006). When an agent reduces the other agent’s prize and explains the decision on the simple basis that it is an option of the game, the stated motivation can be interpreted as apossible expression of disguised envy. A logical hypothesis for this behavior is that the agents neededto justify the reduction to themselves and to the experimenter and wanted to blame their desire to reduce on features of the game rather than on their envy.
5. CONCLUSION
The overall objective of this research was to studythe influence of envy on spiteful behavior and to understand the negative effects of envy on inter-firm and intra-firm relations. The evolutionary theory provided a supportive framework for studying the issue. In the study designed, agents played a lottery that provided them with unequal distribution and could subsequently create financial harm for each other at their own cost. One-third of thelosers in the lottery acted spitefully and reducedthe winners’balances by half. The observed welfare losses accumulated to one-seventh of thetotal income value.Addressing the broader aspects of envy, in orderto fully understand the nature of envy and its implications for relationships, more research is needed (Smith and Kim, 2007). Systematically integrating envy and other other-regarding preferences into economic modeling can provide a refreshing viewpoint in the investigation of human behavior (Horstet al., 2006; Kirman and Teschl, 2010). It will be interesting to see whether, and in what ways, this study will help to motivate researcher sto focus research on this fascinating emotion in this field—where, despite the relevance of inter-firm and intra-firm relations, the concept of envy is still neglected. It may be hoped for that adopting an evolutionary perspective on these questions will lead to more effective management strategies fo rdealing with envy
Are Aspects of Twitter Use Associated with Reduced Depressive Symptoms? It seems positive for lonely guys.
Are Aspects of Twitter Use Associated with Reduced Depressive Symptoms? The Moderating Role of In-Person Social Support. David A. Cole et al. Cyberpsychology, Behavior, and Social Networking, Vol. 22, No. 11, , Nov 7 2019. https://doi.org/10.1089/cyber.2019.0035
Abstract: In a two-wave, 4-month longitudinal study of 308 adults, two hypotheses were tested regarding the relation of Twitter-based measures of online social media use and in-person social support with depressive thoughts and symptoms. For four of five measures, Twitter use by in-person social support interactions predicted residualized change in depression-related outcomes over time; these results supported a corollary of the social compensation hypothesis that social media use is associated with greater benefits for people with lower in-person social support. In particular, having a larger Twitter social network (i.e., following and being followed by more people) and being more active in that network (i.e., sending and receiving more tweets) are especially helpful to people who have lower levels of in-person social support. For the fifth measure (the sentiment of Tweets), no interaction emerged; however, a beneficial main effect offset the adverse main effect of low in-person social support.
Discussion
This study examined the longitudinal effects of TU as a
means for offsetting the adverse effects of low social support
on depressive thoughts and symptoms. Two key results
emerged: first, support emerged for our corollary to the social
compensation (or poor-get-richer) hypothesis but not for the
rich-get-richer hypothesis. Four aspects of TU were related
to reductions in depressive thoughts and symptoms, but only
for people with low initial levels of in-person social support.
Second, conveying positive sentiment through Twitter predicted
a reduction in depressive thoughts and feelings, irrespective
of people’s level of in-person social support. Below,
we elaborate on these findings and their implications.
Our first set of findings was consistent with our corollary
to the social compensation hypothesis. People with low social
support showed improvements in depressive thoughts
and feelings over time if they reported four markers of TU:
following more people on Twitter, having more people follow
them on Twitter, posting more Tweets, and having more
of their posts retweeted by others. These markers were unrelated
to depressive thoughts and feelings of people who
already had high levels of in-person social support. These
results support Baker and Algorta’s observation that the effect
of social media on depression-related outcomes is
complicated by social, psychological, and behavioral moderators.
8 The current research provides longitudinal evidence
that in-person social support may be one such moderator.
These results also suggest that social media might be a way
to combat the adverse effects of low social support on mental
health. This possibility is commensurate with the conventional
wisdom that having one or two good friends in one
social niche can offset social adversity in other social niches.
48,49 Perhaps social media platforms represent a modern
version of such niches. We urge caution along these lines as
previous research has also shown that the relation of Twitter
with depression-related outcomes varies as a function of how
(and when) Twitter is used6,7,50–54 (also refer literature reviews
by Guntuku et al.55 and Hur and Gupta56).
Three reasons for this finding are possible.9,57 One is that
meeting people with similar interests or characteristics may
be easier online than in person, especially when such people
are not available within one’s in-person social networks.57–61
For people who lack these affiliations, connecting with others
online may have an especially strong impact. A second
explanation is that the online channels of communication are
simpler, such that people who find it challenging to develop
supportive in-person social networks may be more effective
in the more restricted online world of social media. One’s
ability to interpret nonverbal cues, one’s physical characteristics,
one’s proper use of vocal tone, and one’s timing of
social responses may be less important online than in person.
Nesi et al. referred to this as cue absence in their transformation
theory.62–65 In a related vein, online interactions tend
to be asynchronous. Delays between online communications
might allow people the time to compose more effective responses.
62 Understanding the mechanisms that underlie these
results represents an important avenue for future research.
A third explanation for these results is that the value added
by having online followers may not be as beneficial to people
who already have strong in-person social support, at least
insofar as reducing depressive thoughts and symptoms are
concerned. Some evidence even suggests that having a very
large number of online friends may actually be associated
with negative outcomes.66,67 The current interaction plots
in Figure 1 somewhat reflect this possibility, in that some of
our TU variables appeared to have adverse effects for people
who had strong in-person social supports. We caution
against overinterpreting this result, however, as the slopes for
participants with low social support were not statistically
significant.
Our second set of findings was the significant main effect of
Twitter sentiment, which offset the adverse effect of low inperson
social support. Two aspects of this result deserve
emphasis. First, this finding cannot be explained as consequences
of depression, as it derives from longitudinal analyses
in which prior levels of depression were statistically controlled.
Second, these results were not moderated by level of
social support. The effects of positive sentiment applied to
people at all levels of social support. Indeed, positive Twitter
sentiment offset much of the depressive effects of low inperson
social support. People with problematic social networks
but highly positive Twitter sentiment had similar levels
of depressive symptoms as did people with strong social
networks but more negative Twitter sentiment, reminiscent of
Granovetter’s early work on the strength of weak ties.68
Shapiro and Margolin’s extensive literature review describes
at least four reasons why effective use of online social
media platforms could offset the adverse effects of problematic
face-to-face relationships, especially with respect to
cognitive and emotional outcomes.57 First, people can engage
in selective self-presentation more easily online than in person.
By crafting carefully their online communications and
constructing their online persona, some people can accrue
more positive feedback online than they can in person, which
may in turn result in improvement on psychological outcomes.
64,65 Second, connecting with similar people or with
people who share similar interests may be easier for some
people online than in person, especially when such affiliations
are not available within in-person social networks.58–61 Third,
through the Internet, communicating with others from more
diverse intellectual, political, and social backgrounds can
expand one’s self-identity while enhancing feelings of belongingness
and affiliation.69 Trepte et al. hypothesized that
large, diverse groups may feel more connected with each
other online and are thus more likely to support each other.70
Fourth, self-disclosure may be easier online than in person,
potentially facilitating online social relationships or enabling
people to practice for in-person relationships.71,72
Taken together, these results begin to suggest interesting
supplemental strategies in the prevention of depression in
people who are at risk because of low social support. The
current findings, derived from one of very few longitudinal
studies in this area, increase our understanding about prospective
(not just correlational) relations and could have
implications for the use of social media in prevention research.
12 A powerful next step will be true experimental
research designs in which positive use of social media is
actively manipulated, so that its causal effect on mental
health outcomes can be assessed. If successful, online social
skills training could become a valuable component of comprehensive
depression prevention efforts.
Several shortcomings of this study suggest important avenues
for future research. The first focuses on our sentiment
analysis. Although examining the actual sentiment conveyed
by Twitter communications is a powerful step, in-depth
content analysis of people’s Tweets could reveal more about
more specific aspects of people’s communications that might
be responsible for the relation of sentiment with depressionrelated
outcomes. Furthermore, in short textual passages
(such as Tweets), it is extremely difficult to reliably measure
issues such as sarcasm and irony. Also, some kinds of negatively
toned messages (e.g., expressing distress) could serve
as triggers for positive responses (e.g., emotional support).
Second, depressive thoughts and symptoms are extremely
important mental health outcomes, emblematic of one of the
most common and debilitating classes of mental illnesses;
however, many other important clinical outcomes should
be explored, including Internet addiction, social anxiety,
and obsessive-compulsive disorder.73–75 Third, our study
focused only on Twitter. Other social media platforms exist
generate very different kinds of risks and benefits, which
should be explored. Fourth, we used an observational/
correlation research design, which leaves various ‘‘third
variables’’ uncontrolled. Random assignment to high versus
low Twitter conditions could control for self-selection factors
such as extraversion or level of depression. Fifth, although
use of MTurk for participant recruitment has certain
strengths, weaknesses have also been documented. These
include crosstalk among participants, misrepresentation of
personal characteristics to qualify for studies, and provision
of unreliable results.76–78 Although these issues do seem to
be characteristic of some MTurk participants, research shows
that these problems actually occur at similar rates in samples
obtained from more conventional methods.79 Future studies
should examine the generalizability of the current results
across a wider variety of populations.
Abstract: In a two-wave, 4-month longitudinal study of 308 adults, two hypotheses were tested regarding the relation of Twitter-based measures of online social media use and in-person social support with depressive thoughts and symptoms. For four of five measures, Twitter use by in-person social support interactions predicted residualized change in depression-related outcomes over time; these results supported a corollary of the social compensation hypothesis that social media use is associated with greater benefits for people with lower in-person social support. In particular, having a larger Twitter social network (i.e., following and being followed by more people) and being more active in that network (i.e., sending and receiving more tweets) are especially helpful to people who have lower levels of in-person social support. For the fifth measure (the sentiment of Tweets), no interaction emerged; however, a beneficial main effect offset the adverse main effect of low in-person social support.
Discussion
This study examined the longitudinal effects of TU as a
means for offsetting the adverse effects of low social support
on depressive thoughts and symptoms. Two key results
emerged: first, support emerged for our corollary to the social
compensation (or poor-get-richer) hypothesis but not for the
rich-get-richer hypothesis. Four aspects of TU were related
to reductions in depressive thoughts and symptoms, but only
for people with low initial levels of in-person social support.
Second, conveying positive sentiment through Twitter predicted
a reduction in depressive thoughts and feelings, irrespective
of people’s level of in-person social support. Below,
we elaborate on these findings and their implications.
Our first set of findings was consistent with our corollary
to the social compensation hypothesis. People with low social
support showed improvements in depressive thoughts
and feelings over time if they reported four markers of TU:
following more people on Twitter, having more people follow
them on Twitter, posting more Tweets, and having more
of their posts retweeted by others. These markers were unrelated
to depressive thoughts and feelings of people who
already had high levels of in-person social support. These
results support Baker and Algorta’s observation that the effect
of social media on depression-related outcomes is
complicated by social, psychological, and behavioral moderators.
8 The current research provides longitudinal evidence
that in-person social support may be one such moderator.
These results also suggest that social media might be a way
to combat the adverse effects of low social support on mental
health. This possibility is commensurate with the conventional
wisdom that having one or two good friends in one
social niche can offset social adversity in other social niches.
48,49 Perhaps social media platforms represent a modern
version of such niches. We urge caution along these lines as
previous research has also shown that the relation of Twitter
with depression-related outcomes varies as a function of how
(and when) Twitter is used6,7,50–54 (also refer literature reviews
by Guntuku et al.55 and Hur and Gupta56).
Three reasons for this finding are possible.9,57 One is that
meeting people with similar interests or characteristics may
be easier online than in person, especially when such people
are not available within one’s in-person social networks.57–61
For people who lack these affiliations, connecting with others
online may have an especially strong impact. A second
explanation is that the online channels of communication are
simpler, such that people who find it challenging to develop
supportive in-person social networks may be more effective
in the more restricted online world of social media. One’s
ability to interpret nonverbal cues, one’s physical characteristics,
one’s proper use of vocal tone, and one’s timing of
social responses may be less important online than in person.
Nesi et al. referred to this as cue absence in their transformation
theory.62–65 In a related vein, online interactions tend
to be asynchronous. Delays between online communications
might allow people the time to compose more effective responses.
62 Understanding the mechanisms that underlie these
results represents an important avenue for future research.
A third explanation for these results is that the value added
by having online followers may not be as beneficial to people
who already have strong in-person social support, at least
insofar as reducing depressive thoughts and symptoms are
concerned. Some evidence even suggests that having a very
large number of online friends may actually be associated
with negative outcomes.66,67 The current interaction plots
in Figure 1 somewhat reflect this possibility, in that some of
our TU variables appeared to have adverse effects for people
who had strong in-person social supports. We caution
against overinterpreting this result, however, as the slopes for
participants with low social support were not statistically
significant.
Our second set of findings was the significant main effect of
Twitter sentiment, which offset the adverse effect of low inperson
social support. Two aspects of this result deserve
emphasis. First, this finding cannot be explained as consequences
of depression, as it derives from longitudinal analyses
in which prior levels of depression were statistically controlled.
Second, these results were not moderated by level of
social support. The effects of positive sentiment applied to
people at all levels of social support. Indeed, positive Twitter
sentiment offset much of the depressive effects of low inperson
social support. People with problematic social networks
but highly positive Twitter sentiment had similar levels
of depressive symptoms as did people with strong social
networks but more negative Twitter sentiment, reminiscent of
Granovetter’s early work on the strength of weak ties.68
Shapiro and Margolin’s extensive literature review describes
at least four reasons why effective use of online social
media platforms could offset the adverse effects of problematic
face-to-face relationships, especially with respect to
cognitive and emotional outcomes.57 First, people can engage
in selective self-presentation more easily online than in person.
By crafting carefully their online communications and
constructing their online persona, some people can accrue
more positive feedback online than they can in person, which
may in turn result in improvement on psychological outcomes.
64,65 Second, connecting with similar people or with
people who share similar interests may be easier for some
people online than in person, especially when such affiliations
are not available within in-person social networks.58–61 Third,
through the Internet, communicating with others from more
diverse intellectual, political, and social backgrounds can
expand one’s self-identity while enhancing feelings of belongingness
and affiliation.69 Trepte et al. hypothesized that
large, diverse groups may feel more connected with each
other online and are thus more likely to support each other.70
Fourth, self-disclosure may be easier online than in person,
potentially facilitating online social relationships or enabling
people to practice for in-person relationships.71,72
Taken together, these results begin to suggest interesting
supplemental strategies in the prevention of depression in
people who are at risk because of low social support. The
current findings, derived from one of very few longitudinal
studies in this area, increase our understanding about prospective
(not just correlational) relations and could have
implications for the use of social media in prevention research.
12 A powerful next step will be true experimental
research designs in which positive use of social media is
actively manipulated, so that its causal effect on mental
health outcomes can be assessed. If successful, online social
skills training could become a valuable component of comprehensive
depression prevention efforts.
Several shortcomings of this study suggest important avenues
for future research. The first focuses on our sentiment
analysis. Although examining the actual sentiment conveyed
by Twitter communications is a powerful step, in-depth
content analysis of people’s Tweets could reveal more about
more specific aspects of people’s communications that might
be responsible for the relation of sentiment with depressionrelated
outcomes. Furthermore, in short textual passages
(such as Tweets), it is extremely difficult to reliably measure
issues such as sarcasm and irony. Also, some kinds of negatively
toned messages (e.g., expressing distress) could serve
as triggers for positive responses (e.g., emotional support).
Second, depressive thoughts and symptoms are extremely
important mental health outcomes, emblematic of one of the
most common and debilitating classes of mental illnesses;
however, many other important clinical outcomes should
be explored, including Internet addiction, social anxiety,
and obsessive-compulsive disorder.73–75 Third, our study
focused only on Twitter. Other social media platforms exist
generate very different kinds of risks and benefits, which
should be explored. Fourth, we used an observational/
correlation research design, which leaves various ‘‘third
variables’’ uncontrolled. Random assignment to high versus
low Twitter conditions could control for self-selection factors
such as extraversion or level of depression. Fifth, although
use of MTurk for participant recruitment has certain
strengths, weaknesses have also been documented. These
include crosstalk among participants, misrepresentation of
personal characteristics to qualify for studies, and provision
of unreliable results.76–78 Although these issues do seem to
be characteristic of some MTurk participants, research shows
that these problems actually occur at similar rates in samples
obtained from more conventional methods.79 Future studies
should examine the generalizability of the current results
across a wider variety of populations.
About the Implicit Association Tests (IATs)... Predicting Behavior With Implicit Measures: Disillusioning Findings
Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions. Franziska Meissner, Laura Anne Grigutsch, Nicolas Koranyi, Florian Müller and Klaus Rothermund. Front. Psychol., November 8 2019. https://doi.org/10.3389/fpsyg.2019.02483
Two decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and several meta-analyses later, however, we have to conclude that neither the IAT nor its derivatives have fulfilled these expectations. Their predictive value for behavioral criteria is weak and their incremental validity over and above self-report measures is negligible. In our review, we present an overview of explanations for these unsatisfactory findings and delineate promising ways forward. Over the years, several reasons for the IAT’s weak predictive validity have been proposed. They point to four potentially problematic features: First, the IAT is by no means a pure measure of individual differences in associations but suffers from extraneous influences like recoding. Hence, the predictive validity of IAT-scores should not be confused with the predictive validity of associations. Second, with the IAT, we usually aim to measure evaluation (“liking”) instead of motivation (“wanting”). Yet, behavior might be determined much more often by the latter than the former. Third, the IAT focuses on measuring associations instead of propositional beliefs and thus taps into a construct that might be too unspecific to account for behavior. Finally, studies on predictive validity are often characterized by a mismatch between predictor and criterion (e.g., while behavior is highly context-specific, the IAT usually takes into account neither the situation nor the domain). Recent research, however, also revealed advances addressing each of these problems, namely (1) procedural and analytical advances to control for recoding in the IAT, (2) measurement procedures to assess implicit wanting, (3) measurement procedures to assess implicit beliefs, and (4) approaches to increase the fit between implicit measures and behavioral criteria (e.g., by incorporating contextual information). Implicit measures like the IAT hold an enormous potential. In order to allow them to fulfill this potential, however, we have to refine our understanding of these measures, and we should incorporate recent conceptual and methodological advancements. This review provides specific recommendations on how to do so.
Why does he act like this? Why does she not do what she intended to do? In our everyday life, we often try to find explanations for the behavior of others, and of ourselves, respectively. Explaining and predicting behavior is also of key interest across all fields of scientific psychology, especially when it comes to deviations between individuals’ actual behavior and the attitudes, goals, or values held by these very individuals. Why do people discriminate although they report to hold egalitarian values? Why do they not quit smoking although they know that smoking is bad? Why is there a gap between people’s self-reported attitudes and actual behavior?
Dual-process or dual-system models attribute seemingly inconsistent behavior to the triumph of an impulsive system over a reflective system of behavior control (e.g., Strack and Deutsch, 2004; Hofmann et al., 2009; Kahneman, 2011). The notion that the prediction of behavior could be improved considerably if one succeeds in measuring the processes of the impulsive system (Hofmann et al., 2007; Friese et al., 2008; Hofmann and Friese, 2008) fueled research applying so-called implicit measures of attitudes. The most popular of these measures, the Implicit Association Test (IAT, Greenwald et al., 1998) evoked enthusiastic hopes regarding its predictive value. Unfortunately, however, the IAT and its derivatives have not met these expectations.
In this article, we review findings illustrating reasons for the IAT’s unsatisfying predictive value, as well as promising ways forward. We will outline that in order to improve the predictive power of implicit measures, differentiation is key. We will argue that future research should put more emphasis on the underlying processes and concepts behind these measures. We begin with sketching the discrepancy between individuals’ behaviors and their self-expressed attitudes. We then summarize the (mostly unsatisfying) attempts to close this attitude-behavior gap with the help of implicit measures. In the main part of this article, we identify features of implicit measures that are responsible for their weak predictive validity. We review findings illustrating each of these problematic aspects along with specific, sophisticated solutions providing promising directions for future research.
Two decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and several meta-analyses later, however, we have to conclude that neither the IAT nor its derivatives have fulfilled these expectations. Their predictive value for behavioral criteria is weak and their incremental validity over and above self-report measures is negligible. In our review, we present an overview of explanations for these unsatisfactory findings and delineate promising ways forward. Over the years, several reasons for the IAT’s weak predictive validity have been proposed. They point to four potentially problematic features: First, the IAT is by no means a pure measure of individual differences in associations but suffers from extraneous influences like recoding. Hence, the predictive validity of IAT-scores should not be confused with the predictive validity of associations. Second, with the IAT, we usually aim to measure evaluation (“liking”) instead of motivation (“wanting”). Yet, behavior might be determined much more often by the latter than the former. Third, the IAT focuses on measuring associations instead of propositional beliefs and thus taps into a construct that might be too unspecific to account for behavior. Finally, studies on predictive validity are often characterized by a mismatch between predictor and criterion (e.g., while behavior is highly context-specific, the IAT usually takes into account neither the situation nor the domain). Recent research, however, also revealed advances addressing each of these problems, namely (1) procedural and analytical advances to control for recoding in the IAT, (2) measurement procedures to assess implicit wanting, (3) measurement procedures to assess implicit beliefs, and (4) approaches to increase the fit between implicit measures and behavioral criteria (e.g., by incorporating contextual information). Implicit measures like the IAT hold an enormous potential. In order to allow them to fulfill this potential, however, we have to refine our understanding of these measures, and we should incorporate recent conceptual and methodological advancements. This review provides specific recommendations on how to do so.
Why does he act like this? Why does she not do what she intended to do? In our everyday life, we often try to find explanations for the behavior of others, and of ourselves, respectively. Explaining and predicting behavior is also of key interest across all fields of scientific psychology, especially when it comes to deviations between individuals’ actual behavior and the attitudes, goals, or values held by these very individuals. Why do people discriminate although they report to hold egalitarian values? Why do they not quit smoking although they know that smoking is bad? Why is there a gap between people’s self-reported attitudes and actual behavior?
Dual-process or dual-system models attribute seemingly inconsistent behavior to the triumph of an impulsive system over a reflective system of behavior control (e.g., Strack and Deutsch, 2004; Hofmann et al., 2009; Kahneman, 2011). The notion that the prediction of behavior could be improved considerably if one succeeds in measuring the processes of the impulsive system (Hofmann et al., 2007; Friese et al., 2008; Hofmann and Friese, 2008) fueled research applying so-called implicit measures of attitudes. The most popular of these measures, the Implicit Association Test (IAT, Greenwald et al., 1998) evoked enthusiastic hopes regarding its predictive value. Unfortunately, however, the IAT and its derivatives have not met these expectations.
In this article, we review findings illustrating reasons for the IAT’s unsatisfying predictive value, as well as promising ways forward. We will outline that in order to improve the predictive power of implicit measures, differentiation is key. We will argue that future research should put more emphasis on the underlying processes and concepts behind these measures. We begin with sketching the discrepancy between individuals’ behaviors and their self-expressed attitudes. We then summarize the (mostly unsatisfying) attempts to close this attitude-behavior gap with the help of implicit measures. In the main part of this article, we identify features of implicit measures that are responsible for their weak predictive validity. We review findings illustrating each of these problematic aspects along with specific, sophisticated solutions providing promising directions for future research.
Closing Thoughts
In this article, we presented an overview of possible
reasons for the weak relationship between implicit measures like the IAT
and behavioral criteria. We outlined that the unsatisfying predictive
value of the IAT is due to (1) extraneous influences like recoding, (2)
the measurement of liking instead of wanting, (3) the measurement of
associations instead of complex beliefs, and/or (4) a conceptual
mismatch of predictor and criterion. We presented precise solutions for
each of these problems. More precisely, we suggested to switch to
procedural variations that minimize extraneous influences (i.e., the
SB-IAT, Teige-Mocigemba et al., 2008; and the IAT-RF; Rothermund et al., 2009), and to apply sophisticated analysis tools (i.e., the ReAL model, Meissner and Rothermund, 2013)
that separate relevant processes from those extraneous influences.
Second, we presented an overview of different implicit measures that go
beyond the measurement of evaluative associations, and instead quantify
actual implicit wanting (e.g., the W-IAT, Koranyi et al., 2017). Third, we pointed to implicit measures of beliefs (e.g., the PEP, Müller and Rothermund, 2019)
that allow a more nuanced view on individual attitudes and values than
measures that tap into associations. Finally, we emphasized the
importance of measuring behavior proper and outlined that implicit
measures incorporating contextual information might be more adequate in
assessing the structure of implicit attitudes or beliefs and their
implications for behavior (Casper et al., 2011; Kornadt et al., 2016).
Each of the recent developments presented in the current paper has the
potential to increase the predictive power of implicit measures. Future
research will also have to clarify whether a combination of these
approaches may lead to further improvement. Inspired by the fruitful
research on dual-process or dual-systems models, we further suggest to
invest in theoretical considerations: Which forms or aspects of behavior
should be related to which processes involved in which implicit
measures? Differentiation is key, with regard to both the predictor and
the criterion.
We strongly argue not to take the validity of implicit
measures like the IAT for granted. Instead, we should take into account
the complexity of these measures, especially when it comes to the
predictive value for real-life behavior. As outlined in the current
review, the past 20 years of research have provided us with a number of
good reasons for why the IAT and its derivatives did not succeed in
closing the attitude-behavior gap, and enriched our toolbox with
promising, sophisticated improvements. Future research will benefit from
harnessing the power of such a more differentiated view on implicit
measures.
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