Tuesday, November 10, 2020

No mechanism known to explain positive observed effect of 2.45 GHz Wi‐Fi exposure on sleep‐dependent memory consolidation

Effects of 2.45 GHz Wi‐Fi exposure on sleep‐dependent memory consolidation. Ana Bueno‐Lopez  Torsten Eggert  Hans Dorn  Gernot Schmid  Rene Hirtl  Heidi Danker‐Hopfe. Journal of Sleep Research, November 9 2020. https://doi.org/10.1111/jsr.13224

Rolf Degen's take: https://twitter.com/DegenRolf/status/1326395159362342913

Abstract: Studies have reported that exposure to radiofrequency electromagnetic fields (RF‐EMF) emitted by mobile telephony might affect specific sleep features. Possible effects of RF‐EMF emitted by Wi‐Fi networks on sleep‐dependent memory consolidation processes have not been investigated so far. The present study explored the impact of an all‐night Wi‐Fi (2.45 GHz) exposure on sleep‐dependent memory consolidation and its associated physiological correlates. Thirty young males (mean ± standard deviation [SD]: 24.1 ± 2.9 years) participated in this double‐blind, randomized, sham‐controlled crossover study. Participants spent five nights in the laboratory. The first night was an adaptation/screening night. The second and fourth nights were baseline nights, each followed consecutively by an experimental night with either Wi‐Fi (maximum: psSAR10g = <25 mW/kg; 6 min average: <6.4 mW/kg) or sham exposure. Declarative, emotional and procedural memory performances were measured using a word pair, a sequential finger tapping and a face recognition task, respectively. Furthermore, learning‐associated brain activity parameters (power spectra for slow oscillations and in the spindle frequency range) were analysed. Although emotional and procedural memory were not affected by RF‐EMF exposure, overnight improvement in the declarative task was significantly better in the Wi‐Fi condition. However, none of the post‐learning sleep‐specific parameters was affected by exposure. Thus, the significant effect of Wi‐Fi exposure on declarative memory observed at the behavioural level was not supported by results at the physiological level. Due to these inconsistencies, this result could also be a random finding.


The present provocation study, which can only address acute effects, analysed whether a Wi‐Fi exposure during TIB (8 h) might affect sleep‐dependent memory consolidation processes (declarative, procedural and emotional memory) and their learning‐associated brain activity during sleep in young healthy male volunteers.

4.1 Sleep‐dependent memory consolidation: Behavioural level

Results show that although Wi‐Fi did not affect retention in the procedural and emotional memory tasks, the data reveal that retention in the declarative memory was increased after Wi‐Fi as compared to sham exposure.

In the WPT, overnight performance gain was higher after Wi‐Fi exposure compared to sham (see Figure 3a.i), with an effect size of 0.40. According to Cohen (1988) this is a small effect, which, however, has a large uncertainty (95% CI [0.11; 0.70]). This observed difference in overnight retention of correctly recalled word pairs between sham and Wi‐Fi exposure conditions represented moderate evidence for the alternative hypothesis when evaluated based on the corresponding Bayes factor (BF01 = 0.254) (see Table 2). However, the interaction of several factors needs to be taken into account in order to interpret this result accurately. Small differences in the number of correctly recalled word pairs during immediate recall might have affected performance gains in the WPT. That is, the number of correctly recalled word pairs in the evening was slightly, but not significantly, higher in the sham nights as compared to Wi‐Fi, whereas the opposite was observed in the morning (see Table 2). The lower “reference level” in the evening preceding the Wi‐Fi condition might explain why overnight change was significantly higher under Wi‐Fi compared to sham. On the other hand, as both versions of the WPT had the same level of difficulty, it is unlikely that encoding difficulties could explain this finding. Regardless of the exposure condition, the performance on the evening of the two experimental nights did not differ, which supports the absence of a learning effect between experimental nights (see Table S5). Moreover, the data did not reflect the presence of floor or ceiling effects.

Wi‐Fi exposure did not affect performance in the FRT. Overnight retention was similar between Wi‐Fi and sham exposure. Bayes factors showed that overnight retention in all categories presented moderate evidence for the absence of a decline or improvement after exposure (all faces: BF01 = 3.931; neutral faces: BF01 = 4.538; positive faces: BF01 = 3.155; negative faces: BF01 = 5.527) with effect sizes (Cohen's d) that vary from no (negative faces) to small effects (all, neutral and positive faces; see Table 2). Thus, recognition memory in the emotional task did not differ between exposure conditions.

Performance improvements in the SFTT after sleep were not affected by Wi‐Fi exposure. The results for the overnight retention in this memory task did not differ between exposure conditions. Moreover, retention in this task showed moderate evidence for the null hypothesis (BF01 = 4.539), which is supported by a small effect size (see Table 2). In contrast, Lustenberger et al. (2013) reported a reduction of the performance improvement, measured as the variance of the reaction time, in a similar SFTT under RF‐EMF exposure compared to sham (with an effect size of |d| = 0.57 representing a medium effect; effect size calculated from data presented in Lustenberger et al., 2013). This effect could not be confirmed by our results. The variance in reaction time performance in the present study did not differ significantly between the exposure conditions, the effect size indicates no effect (|d| = 0.13) and the Bayes factor indicates moderate evidence for the null hypotheses (BF01 = 6.355) (see Table S4, and Figure S2). However, beside different signal characteristics, Lustenberger et al. (2013) used substantially higher intensities of RF‐EMF exposure, whereas in the present study the applied RF‐EMF intensities represent realistic worst‐case exposure from real Wi‐Fi installations.

Irrespective of exposure, the present results confirmed the beneficial role of sleep for memory consolidation. Performance in the three memory tasks improved after a night of sleep, reflecting small (FRT, 0.014) to medium effect sizes (WPT, 0.069; SFTT, 0.116) as indicated by generalized η2 values. Sleep‐dependent improvements in memory consolidation have been extensively discussed using different declarative and non‐declarative memory tasks showing that post‐sleep memory retention is better than retention after a wake period (Rasch & Born, 2013). This sleep‐specific beneficial effect is assumed to be reflected in the present results. In particular, in the WPT, declarative memory enhancements after a night of sleep under both experimental conditions are in line with multiple other studies (for reviews, see Diekelmann et al., 2009; Rasch & Born, 2013). Regarding the FRT, recognition memory performance for all faces, regardless of their emotional valence, improved after a night of sleep, which is in agreement with previous findings (Solomonova et al., 2017; Wagner et al., 2007). Additionally, memory performance was better after sleep for neutral and positive facial expressions. These findings are consistent with the results of a recent meta‐analysis (Schäfer et al., 2020), which revealed an enhancement of recognition memory for both emotional and neutral stimuli. In contrast, recognition for negative stimuli did not improve after sleep in the present study. In this respect, only the neutral faces were recognized during the evening recall phase more effectively on the second experimental night when compared with the first night, regardless of the exposure condition (see Table S5). Finally, results of the SFTT are in line with the evidence of the contribution of sleep to procedural memory consolidation (for review, see King et al., 2017).

4.2 Sleep‐specific features related to memory consolidation: Physiological level

There is compelling evidence that depending on the type of memory, certain sleep stages and sleep EEG characteristics are related to the previously mentioned memory consolidation processes. With regard to the macrostructure of sleep, overnight improvements in declarative memory have been related to slow‐wave sleep (N3) (e.g., Diekelmann et al., 2012), whereas overnight improvements in procedural memory have been proposed to be related to time spent in stage N2 sleep (e.g., Walker et al., 2002). Additionally, REM sleep has been associated with both procedural and declarative memory consolidation (Fogel et al., 2007). Finally, the consolidation of emotional memory has been proposed to be dependent on both REM sleep and NREM sleep (Tempesta et al., 2018).

The present analysis revealed that Wi‐Fi exposure had no effect on time spent in sleep stages N2, N3 (slow‐wave sleep), NREM or REM sleep. Bayes factors for N2 and N3 sleep supported this interpretation by providing moderate evidence for the absence of an exposure effect on these two sleep stages (N2, BF01 = 6.672; N3, BF01 = 5.379). The corresponding Cohens' d values indicated also no effect. However, Bayes factors for NREM and REM sleep indicated only anecdotal evidence for the H0 (NREM, BF01 = 2.414; REM, BF01 = 2.266), with Cohens' d values representing small effects (see Table 3). In other words, these results pointed out that N2 and N3 sleep were rather unlikely to be affected by Wi‐Fi exposure, but that an exposure effect on NREM and REM sleep cannot be excluded. It could be speculated that the evaluation of these two effects, whether they are supportive of the null or alternative hypothesis, would have been more convincing if the sample size had been larger. Then, if this supported the tendency observed in NREM sleep at the descriptive level under Wi‐Fi exposure compared to sham (see Table 3), this possible change in NREM could explain at least partially the improvement of declarative memory consolidation.

The literature shows that RF‐EMF effects on sleep architecture are quite heterogeneous. Although some studies found effects in the discussed sleep parameters, others did not (for detailed overview, see Danker‐Hopfe et al., 2016). Therefore, the present results can be assigned to the group of studies that reported null findings with regard to effects of exposure on sleep macrostructure. The same applies to the study by Danker‐Hopfe et al. (2020), which examined the impact of Wi‐Fi exposure on a large number of objective sleep parameters in addition to some subjective sleep variables. This previous study, however, considered sleep data from all 34 recruited participants and disregarded deliberately some of the sleep‐specific variables that are thought to be associated with memory consolidation processes. Thus, the present study fills this gap and complements this previous publication, but with results restricted to a subsample of 30 subjects for whom behavioural data were available.

With regard to sleep microstructure, sleep spindle frequency ranges, as well as slow‐wave activity (0.1–3.5 Hz), have been associated with both declarative and procedural memory improvements (Fogel et al., 2007; Holz et al., 2012). However, other studies did not find a clear association between performance improvements and related sleep stages or EEG power in declarative (Gais et al., 2002) or procedural memory (Rångtell et al., 2017). Sleep spindle density has been proposed to be involved in declarative (e.g., Gais et al., 2002) and in procedural (e.g., Barakat et al., 2011) memory consolidation. Additionally, emotional memory has been positively correlated with fast spindle densities (13–16 Hz) and negatively with slow spindle (10–13 Hz) densities (Solomonova et al., 2017).

The present results did not reveal any Wi‐Fi exposure effect on the EEG power in the ranges of slow oscillations (0.5–0.1 Hz) and narrow (12–14 Hz) and wide (12–16 Hz) sleep spindles. Nor was the sleep spindle density in stages N2 and N3 sleep affected by exposure (see Table 4). This is supported by Cohen's d values, which indicate small or no effects (see Table S2). Bayes factors revealed moderate evidence for the absence of a Wi‐Fi effect on the narrow sleep spindle frequency range at all regions in N2 and N3. Similarly, Bayes factors indicated moderate evidence for the absence of a Wi‐Fi effect on the EEG power in the wide spindle frequency range and in the range of slow oscillations in all cortical regions in both sleep stages, except for the occipital region in N2 and N3. In these cases, Bayes factors revealed only anecdotal evidence for the absence of Wi‐Fi effects. As mentioned above, a larger sample size could have provided stronger evidence for the presence or absence of the reduced EEG power under Wi‐Fi exposure that can be observed at the descriptive level (see Table S2). Furthermore, Bayes factors revealed moderate evidence for an absence of an exposure effect on sleep spindle densities in both sleep stages, with Cohen's d values indicating no effects (see Table S3).

In this respect, Lustenberger et al. (2013) reported that pulsed RF‐EMF induced an increase of slow‐wave activity at the end of the sleep period, whereas spindle activity remained unchanged and sleep‐dependent procedural memory gains were downscaled. Similarly, other RF‐EMF studies did not report effects on the EEG in the spindle frequency range (Fritzer et al., 2007; Hinrichs et al., 2005; Nakatani‐Enomoto et al., 2013; Wagner et al., 19982000) or for spindle density (Lustenberger et al., 2015), in line with the present results. However, as pointed out previously, RF‐EMF effects on the sleep EEG power show mixed results.

In summary, the results at the physiological level did not reveal an impact of Wi‐Fi exposure on any of the sleep parameters that are generally associated with sleep‐dependent memory consolidation processing, such as NREM sleep, specifically slow‐wave sleep, as well as EEG power values in the SO and spindle frequency ranges, and sleep spindle densities. Accordingly, the positive effects that Wi‐Fi exposure had on memory retention in the declarative task were not supported by physiological changes associated with memory consolidation processes during sleep. Thus, the present behavioural and neurophysiological findings did not provide evidence that night‐time Wi‐Fi exposure affects sleep‐dependent memory consolidation, so the positive exposure effect on declarative memory should be classified as inconclusive.

Self‐directed sexist humor was seen as more affiliative (only women), less aggressive, & more self‐defeating than other‐directed sexist humor; women romantically preferred men who used self‐ rather than other‐directed sexist humor

Is it sexy to be sexist? How stereotyped humor affects romantic attraction. Diana E. Betz  Theresa E. DiDonato. Personal Relationships, November 9 2020. https://doi.org/10.1111/pere.12346

Abstract: Sexist humor is a common form of disparagement humor that is nonetheless understudied in romantic attraction contexts. Three experiments investigated how sexist humor is perceived and received during relationship initiation. In Study 1 (n = 262) participants rated self‐directed sexist humor as more affiliative (only women), less aggressive, and more self‐defeating than other‐directed sexist humor. Study 2 (n = 209) replicated these findings and found that women romantically preferred men who used self‐ rather than other‐directed sexist humor, an effect mediated by perceived warmth. Self‐directed sexist humor's attractiveness advantage persisted in Study 3 (n = 667), which also included manipulations of self‐disparaging, group‐disparaging, and benign humor. Results suggest a romantic cost for men telling sexist jokes that disparage women.

Social Media and Well-Being: Small negative effects on average, with both positive & negative sides... Alarm seems exaggerated by the media due to our focus on the negative

Social Media and Well-Being: Pitfalls, Progress, and Next Steps. Ethan Kross et al. Trends in Cognitive Sciences, November 10 2020. https://doi.org/10.1016/j.tics.2020.10.005

Rolf Degen's take: https://twitter.com/DegenRolf/status/1326180998074273797


-  Social media has revolutionized how humans interact, providing them with unprecedented opportunities to satisfy their social needs.

-  An explosion of research has examined whether social media impacts well-being. First- and second-generation studies examining this issue yielded inconsistent results.

-  An emerging set of third-generation experiments has begun to reveal small but significant negative effects of overall social media use on well-being.

-  The results of these experiments mask the complexities characterizing the relationship between social media and well-being. Whether it enhances or diminishes well-being depends on how and why people use it, as well as who uses it.

-  People use social media for different reasons (e.g., to manage impressions, to share emotions), which influence how it impacts their own and other people’s well-being.

Abstract: Within a relatively short time span, social media have transformed the way humans interact, leading many to wonder what, if any, implications this interactive revolution has had for people’s emotional lives. Over the past 15 years, an explosion of research has examined this issue, generating countless studies and heated debate. Although early research generated inconclusive findings, several experiments have revealed small negative effects of social media use on well-being. These results mask, however, a deeper set of complexities. Accumulating evidence indicates that social media can enhance or diminish well-being depending on how people use them. Future research is needed to model these complexities using stronger methods to advance knowledge in this domain.

Keywords: social mediaFacebookwell-beingonline social networksemotionlife satisfaction

Moving Forward

We have drawn multiple parallels between the printing press and social media in this review, but there is one notable difference. Whereas the printing press took decades to revolutionize the way society functioned, social media have had a transformational impact in a tiny window of time. Nevertheless, scientists have been remarkably nimble in their ability to reroute their research programs to respond to the challenge of making sense of how this technology impacts people’s emotional lives. Indeed, we view the past 15 years of research on social media and well-being as a testament to scientists doing what they do best: focusing on important phenomena, critically evaluating current knowledge in light of new results, and bringing to bear increasingly sophisticated methods and conceptual frameworks to generate novel solutions that have important basic science and practical implications. But where does all of this work leave us in terms of the question on so many people’s minds: how do social media influence well-being?

Converging reviews of the literature suggest that a small but significant negative relationship characterizes the effect of social media on well-being (Box 3). If this is all that one cares about, that is the bird’s eye view. It would be a mistake, however, to conclude from these findings that social media have little potential to influence people’s emotional lives. Our survey suggests that the situation concerning social media’s impact on well-being is considerably more nuanced than aggregate usage studies suggest. The effects of social media on well-being are not uniform. Social media present people with a new ecosystem for engaging in social interactions, and converging evidence indicates that how this ecosystem affects our well-being, and the well-being of others, depends on how we navigate it.

Box 3

Beyond ‘Active’ versus ‘Passive’ Usage

In an attempt to integrate research showing that different ways of using social media differentially impact well-being, several groups have distinguished between two general categories of social media usage: ‘active’ and ‘passive’ social media usage. According to this framework, the passive consumption of information on social media undermines well-being by increasing upward social comparisons. Conversely, the active use of social media to exchange information and to connect with others enhances well-being by enhancing social capital and support.

This framework has proved useful in pushing the field to think more mechanistically and has revealed differential negative effects of passive (vs active) use (Box 2). Nevertheless, further refinement of this framework is necessary; current research suggests that it is too coarse. As we discuss in the main text, although passively viewing other people’s social media profiles reliably undermines well-being, passively viewing one’s own profile has the opposite effect. Likewise, although actively using social media to garner support improves well-being, actively using it to cyberbully or spread moral outrage undermines well-being for others. Thus, a key challenge is to move beyond this nominal distinction to examine subtypes of active and passive social media use. In particular, two questions are pressing.

First, we need to understand how different motivations for using social media interact to influence well-being. Extant research has primarily focused on how different social media motivations operate in isolation. However, human behavior is multiply determined; multiple goals drive people’s behavior, which are activated to various degrees depending on individual differences and the circumstances people find themselves in [113,114]. And in some cases, motivations conflict. For example, a person may be driven to abstain from viewing others’ profiles to avoid feeling envy, but simultaneously motivated to share their emotions with others. Which of these motivations is stronger may influence whether and how people interact with social media and the implications that doing so has for their well-being.

Second, research is needed to examine whether people are aware of the implications that their social media behavior has for themselves and others. Our review suggests that an asymmetry characterizes how several social media behaviors impact the self versus others. For example, curating one’s profile improves how one feels, but promotes envy among others; cyberbullying disproportionately impacts the targets (vs perpetrators) of such behavior. Whether people are aware of these asymmetries is unknown, as are the consequences of informing them about them for regulating their social media behavior.

If social media have both positive and negative implications for well-being, one question concerns why the dominant narrative in the media has disproportionately focused on its dire consequences [6]. The newsworthiness of such headlines is likely to play some role in explaining this phenomenon, but we suspect it is not the only factor. In this vein, it is worth highlighting the fact that one of psychology’s most foundational findings concerns our tendency to overweight negative (vs positive) information [72,73]. Thus, it is possible that people form generalizations about social media’s overall well-being impact based on the negative effects they have in some situations (e.g., upward social comparisons, cyberbullying). A key challenge moving forward is to identify how to disseminate information about social media’s positive and negative implications without having the latter obscure the former.

From a basic science perspective, future research is needed to move beyond asking broad questions about the overall effects of social media on well-being (see Outstanding Questions). Rather, the strategy now should be to study the different psychological processes that explain how and why social media impact well-being differently, whether different social media behaviors have downstream effects that extend beyond well-being (e.g., to impact family and school life), and why these effects may vary for different people in different cultures guided by distinct social norms. Although we focused on two candidate processes in this review that have been the focus of extensive research, many other processes are waiting to be examined. Work should continue to profile how target processes operate in isolation but also explore how they interact (Box 3).

Studies that seek to address the latter issue should also consider the unique information-processing dynamics that may underlie different types of social media behaviors. Managing one’s online persona would seem, for example, to be a reflective act that requires time and deliberation to implement. Sharing emotions with others, by contrast, may be a more reflexively driven behavior. Understanding the degree to which different social media behaviors are reflexively versus reflectively driven has the potential to both illuminate the processes that underlie them and inform the development of interventions designed to enhance social media’s impact on well-being [102].

Focusing more on psychological processes also has the potential to provide insight into the question of how different social media platforms uniquely impact well-being. By focusing on the processes that different platforms activate, as opposed to simply comparing Platform A (e.g., Facebook) versus Platform B (e.g., Instagram), we can move beyond the nominal distinctions that distinguish platforms, to the more meaningful psychological variables that influence users’ experience (Figure 1).

This issue is also relevant to the emerging experimental literature examining the impact of manipulating aggregate social media use on well-being. Extant research manipulates social media usage in a variety of ways. Some work contrasts experimentally induced abstention against regular usage (e.g., [33]) while others contrast induced usage against an active or non-active control (e.g., [32]), and there is further heterogeneity within these broad approaches (e.g., in the length of abstention/usage, simple abstention vs deactivation of accounts). Each of these different manipulations may activate a different set of underlying processes that have implications for people’s well-being.

Studying psychological processes requires, however, that we utilize strong methods. The field’s overreliance on cross-sectional designs is a major weakness [35,36], yet cross-sectional research continues to proliferate. We urge researchers interested in exploring the social media–well-being relationship to incorporate experimental and longitudinal designs into their work to strengthen their ability to draw inferences about causality.

More work is also needed to validate the methodologies we use to study the impact of social media on well-being. We have already discussed the validity concerns associated with commonly used self-report Facebook usage variables. However, similar issues apply to other measures used in this area. For example, one prominent study counted the number of emotion words contained in people’s Facebook posts to draw inferences about how they felt although no validation data supported the use of such methods to track people’s emotions on social media [103]. As later research pointed out, counting emotion words does not track how people feel on Facebook [104]. The take-home point is simple: psychometrically sound measures are not a luxury: they are instrumental for valid inferences.

From a translational standpoint, there is a need to identify science-based interventions that enhance the positive and minimize the negative consequences of social media. There are at least three paths to studying these interventions (Figure 2). One involves directing people to use social media in particular ways, and then gauging the implications of such person-focused interventions. Much of the existing experimental work in this area takes this form. A second path involves examining how modifying the social media platforms that people use (with their informed consent) impacts the way they use them and how they affect well-being. For example, a platform could be augmented to promote the sharing of information that research suggests should enhance well-being. Finally, a third method involves a combination of the previous two approaches; that is, simultaneously educating people about how to navigate social media optimally and tweaking social media platforms to maximize their positive impact.

[Figure 2. Social Media Intervention Research.]

At least three pathways exist for process-focused social media intervention research. Person-centered interventions focus on changing how people use social media to enhance well-being. Potential ways of communicating this information include instructing individuals directly, relaying information through parents, teachers, or supervisors, and the creation of institutional policies. Platform-centered interventions involve changing the way that social media platforms function (with user consent) to enhance their likelihood of promoting well-being. Finally, the person + platform intervention pathway involves the examination of the effects of both kinds of intervention simultaneously.

Concluding Remarks

Social media, like the printing press, represent a kind of disruptive technology that appears once in a generation. Over the past 15 years science has done an admirable job advancing our understanding of the impact these media have on our well-being, but the work is by no means complete. Numerous questions remain. Given the energy and enthusiasm characterizing work in this area, and the enormous level of talent working on solving these questions, we suspect that the next 15 years will be ripe with discoveries that advance our understanding of how this ubiquitous technology influences our emotional lives.

Outstanding Questions

Can we find a common lexicon to conceptualize the social media landscape? Addressing this issue is vital to solving social media’s jingle-jangle problem (Box 1).

Can we develop theory-driven frameworks to identify candidate processes that explain how social media impacts well-being and generate predictions about how they operate in isolation and interactively? Can such frameworks be used to distinguish between different social media platforms?

Can we make further distinctions within active and passive social media usage? Do different active and passive behaviors relate to different psychological processes? Are some behaviors more impulsive versus deliberate? How might these different behaviors impact well-being?

Do asymmetries in the way certain social media behaviors impact the self versus others help to explain why some harmful practices persist? If so, how can such information be utilized to inform interventions?

Can we systematize the way we perform experiments on social media? Some experiments direct people to abstain from using social media while others direct them to use it more compared with baseline. Heterogeneity also characterizes the time course of different manipulations, the measures used to document their effects, and the frequency of their administration. All of these factors could differentially impact study results depending on the nature of the process being manipulated.

How can we balance the need to perform studies quickly on an evolving technology without compromising the need to use valid measures and methods?

Can we design person- and platform-centered interventions that amplify the positive and diminish the negative implications of social media use on well-being?

How can we disseminate information about social media’s positive and negative impacts without having the latter obscure the former, given the documented tendency for people to overweight negative (vs positive) information?

Women’s Hunting in Two Contemporary Forager-Horticulturalist Societies

“Hunting Otherwise.” Women’s Hunting in Two Contemporary Forager-Horticulturalist Societies. Victoria Reyes-García, Isabel Díaz-Reviriego, Romain Duda, Álvaro Fernández-Llamazares & Sandrine Gallois. Human Nature volume 31, pages203–221. Sep 11 2020. https://link.springer.com/article/10.1007/s12110-020-09375-4

Abstract: Although subsistence hunting is cross-culturally an activity led and practiced mostly by men, a rich body of literature shows that in many small-scale societies women also engage in hunting in varied and often inconspicuous ways. Using data collected among two contemporary forager-horticulturalist societies facing rapid change (the Tsimane’ of Bolivia and the Baka of Cameroon), we compare the technological and social characteristics of hunting trips led by women and men and analyze the specific socioeconomic characteristics that facilitate or constrain women’s engagement in hunting. Results from interviews on daily activities with 121 Tsimane’ (63 women and 58 men) and 159 Baka (83 women and 76 men) show that Tsimane’ and Baka women participate in subsistence hunting, albeit using different techniques and in different social contexts than men. We also found differences in the individual and household socioeconomic profiles of Tsimane’ and Baka women who hunt and those who do not hunt. Moreover, the characteristics that differentiate hunter and non-hunter women vary from one society to the other, suggesting that gender roles in relation to hunting are fluid and likely to change, not only across societies, but also as societies change.

Los Angeles’ Zones of Choice: The ZOC program boosted test scores & college enrollment markedly, closing achievement & college enrollment gaps between ZOC neighborhoods & the rest of the district

The Impact of Neighborhood School Choice: Evidence from Los Angeles’ Zones of Choice. Christopher Campos and Caitlin Kearns. Job Market Paper, November 8, 2020. https://www.cqcampos.com/research

Abstract: This paper evaluates the Zones of Choice (ZOC) program in Los Angeles, a school choice initiative that created small high school markets in some neighborhoods but left traditional attendance zone boundaries in place throughout the rest of the district. We leverage the design of the program to study the impact of neighborhood school choice on student achievement, college enrollment, and other outcomes using a matched difference-in-differences design. Our findings reveal that the ZOC program boosted test scores and college enrollment markedly, closing achievement and college enrollment gaps between ZOC neighborhoods and the rest of the district. These gains are explained by general improvements in school effectiveness rather than changes in student match quality, and school-specific gains are concentrated among the lowest-performing schools. We interpret these findings through the lens of a model of school demand in which schools exert costly effort to improve quality. The model allows us to measure the increase in competition facing each ZOC school based on household preferences and the spatial distribution of schools. We demonstrate that the effects of ZOC were larger for schools exposed to more competition, supporting the notion that competition is a key channel driving the impacts of ZOC. In addition, demand estimates suggest families place a larger weight on school quality compared to peer quality, providing schools the right competitive incentives. An analysis using randomized admission lotteries shows that the treatment effects of admission to preferred schools declined after the introduction of ZOC, a pattern that is explained by the relative improvements of less-preferred schools. Our findings demonstrate the potential for public school choice to improve student outcomes while also underscoring the importance of studying market-level impacts when evaluating school choice programs.

It seems we can use smart-phones to increase physical affection, intimacy and security in couples

Can we use smart-phones to increase physical affection, intimacy and security in couples? Preliminary support from an attachment perspective. Kerem Besim Durbin et al. Journal of Social and Personal Relationships, November 3, 2020. https://doi.org/10.1177/0265407520970278

Abstract: This study investigated whether physical affection is causally associated with momentary intimacy and security by manipulating physical affection. We used a GPS-based smart-phone application as ecological momentary intervention that prompted participants to show physical affection to their partner when they were in the same location. We also investigated whether attachment style and attachment functioning moderated the effects of the manipulation. Thirty-nine couples were assigned to experimental (N = 20) and control (N = 19) groups for 2 weeks. Multilevel dyadic data analysis revealed significantly higher momentary intimacy in the experimental group, even when spontaneous physical affection was controlled; there was no significant change for momentary security. While attachment style did not moderate the effect of manipulation for either outcome, attachment functioning significantly moderated the effect on security. This is the first study to show evidence that physical affection, when instructed by a device, is causally linked to increased momentary intimacy in daily life.

Keywords: Attachment, ecological momentary intervention, intimacy, physical affection, romantic relationships, security, touch

Letters to Our Future Selves? Failed High-Powered Replication Attempts Question Effects on Future Orientation, Delinquent Decisions, and Risky Investments

Letters to Our Future Selves? High-Powered Replication Attempts Question Effects on Future Orientation, Delinquent Decisions, and Risky Investments. Laura Quinten, Anja Murmann, Hanna Genau, Rafaela Warkentin, Rainer Banse. Social Cognition, July 2020. https://www.researchgate.net/publication/343188765

Abstract: Enhancing people’s future orientation, in particular continuity with their future selves, has been proposed as promising to mitigate self-control-related problem behavior. In two pre-registered, direct replication studies, we tested a subtle manipulation, i.e., writing a letter to one’s future self, in order to reduce delinquent decisions (van Gelder et al., 2013, Study 1) and risky investments (Monroe et al., 2017, Study 1). With samples of N = 314 and N = 463, i.e., 2.5 times the original studies’ sample sizes, the results suggested that the expected effects are either non-existent or smaller than originally reported, and/or dependent on factors not examined. Vividness of the future self was successfully manipulated in Study 2, but manipulation checks overall indicated that the letter task is rather not reliable to alter future orientation. We discuss ideas to integrate self-affirmation approaches, and to test less subtle manipulations in samples with substantial, myopia-related self-control deficits.

Rearming after Jim Crow: We find that lynchings decrease with greater Black firearm access; also, there was a frequent misclassification of homicides as accidents, which diminish after Blacks rearm

Firearms and Violence Under Jim Crow. Michael D. Makowsky, Patrick L. Warren. November 9, 2020. https://static1.squarespace.com/static/5329e895e4b09fd4786211a3/t/5fa9630d4e5ce030c7d36d79/1604936463367/Guns_under_Jim_Crow_Nov2020_circ.pdf

Abstract: We assess firearm access in the U.S. South by measuring the fraction of suicides committed with firearms. Black residents of the Jim Crow South were disarmed, before re-arming themselves during the Civil-Rights Era. We find that lynchings decrease with greater Black firearm access. During the Civil-Rights Movement, both the relative Black homicide and Black “accidental death by firearm” rates decrease with Black firearm access, indicating frequent misclassification of homicides as accidents. In the contemporary era, greater firearm access correlates with higher Black death rates. We find that firearms offered an effective means of Black self-defense in the Jim Crow South.

Counterfactual thinking and facial expressions among Olympic medalists: A conceptual replication of Medvec, Madey, and Gilovich’s (1995) findings

Hedgcock, W. M., Luangrath, A. W., & Webster, R. (2020). Counterfactual thinking and facial expressions among Olympic medalists: A conceptual replication of Medvec, Madey, and Gilovich’s (1995) findings. Journal of Experimental Psychology: General, Nov 2020. https://doi.org/10.1037/xge0000992

Rolf Degen's take: https://twitter.com/DegenRolf/status/1326175281221165056

Abstract: Counterfactual thinking, or contemplation of “what could have been,” influences facial expressions of Olympic medalists. Medvec, Madey, and Gilovich (1995) revealed that bronze medalists appeared happier than silver medalists after competition in Olympic events. Two prominent explanations for this phenomenon exist: the formation of (a) category-based counterfactuals and (b) expectation-based counterfactuals. First, Medvec et al. (1995) demonstrated that silver medalists formed an upward comparison to the gold medalist with thoughts of “I almost won Gold” while bronze medalists formed a downward comparison to a fourth place finisher with thoughts of “at least I won a medal.” A second explanation suggests that medalists form expectation-based counterfactuals in which silver medalists are more disappointed since their prior expectations for performance were higher than bronze medalists (McGraw, Mellers, & Tetlock, 2005). To test these 2 explanations, we compiled a large dataset of medal stand photographs from the Olympic Multimedia Library and Getty Images for the 2000–2016 Olympic games as well as Sports Illustrated’s predictions. Using automated facial expression encoding, we conducted a conceptual replication of prior work and found evidence supporting both category-based and expectation-based counterfactual accounts of Olympic medalists’ expressions.

Pupillometry and Hindsight Bias: Physiological Arousal Predicts Compensatory Behavior

Pupillometry and Hindsight Bias: Physiological Arousal Predicts Compensatory Behavior. Willem W. A. Sleegers, Travis Proulx, Ilja van Beest. Social Psychological and Personality Science, November 10, 2020. https://doi.org/10.1177/1948550620966153

Rolf Degen's take: https://twitter.com/DegenRolf/status/1326117586048790529

Abstract: According to violation–compensation models of cognitive conflict, experiences that violate expected associations evoke a common, biologically based syndrome of aversive arousal, which in turn motivates compensation efforts to relieve this arousal. However, while substantial research shows that people indeed respond with increased arousal to expectancy violating events, evidence for the motivating role of arousal is rarely found. In two within-subjects studies (N = 44 and N = 50), we demonstrate evidence for the motivating role of arousal in this violation–compensation process among university students. Using pupillometry and the hindsight bias phenomenon, we show that people respond with greater arousal when presented with expectancy violating information. In turn, we show that the pupillary response is positively related to the amount of hindsight bias being displayed. These findings provide further insights into the process underlying the hindsight bias and, crucially, support key predictions following from threat–compensation models.

Keywords: threat–compensation, arousal–behavior link, pupillometry, hindsight bias

We aimed to demonstrate the first direct link between physiological arousal and compensatory behavior. While the results of each study separately were not conclusive, the results from both studies combined did provide evidence for this link. Greater pupil dilation in response to an unexpected correct answer was associated with more hindsight bias. That is, participants shifted their second answer more toward the factual question’s correct answer, relative to their first answer, when they showed a larger physiological response to the correct answer to the question. This compensatory response following increased arousal is consistent with violation–compensation theories (Jonas et al., 2014McGregor et al., 2012), specifically with the shared assumption that inconsistencies evoke arousal that causes compensation reactions.

That expectancy violations induce a syndrome of aversive arousal is an important tenet of violation–compensation theories. There is abundant evidence for this first link between expectancy violations and arousal, whether the expectancy violation involves perceptual anomalies (Sleegers et al., 2015), cognitive dissonance (Gerard, 1967), self-view inconsistencies (Ayduk et al., 2012), worldview violations (Townsend et al., 2010), or category-based violations (Mendes et al., 2007). Evidence for the second link, between arousal and the subsequent compensatory behavior, is rarely observed and limited to indirect assessments of arousal such as self-report measures (Laurin et al., 2008McGregor et al., 2013, Experiment 4; Plaks et al., 2005) and the misattribution of arousal paradigm (Kay et al., 2010Losch & Cacioppo, 1990Proulx & Heine, 2008Zanna & Cooper, 1974). Our findings provide more direct evidence for the often postulated relationship between arousal and compensatory behaviors following expectancy violations.

Two reasons might explain why we were able to demonstrate a link between arousal and compensatory behavior. First, recent developments in eye tracker technology have made this technology exceptionally noninvasive. Consequently, an eye tracker is less likely to evoke arousal that interferes with the arousal process underlying violation–compensation reactions. Second, we repeatedly presented participants with an expectancy violation and an opportunity to compensate—a requirement for physiological measures to improve reliability.

Limitations and Future Research

In our studies, we relied on pupillometry to assess an aversive state of arousal following negative belief feedback because threat–compensation theories strictly postulate a state of aversive arousal to motivate subsequent compensatory behaviors. However, while pupillometry is a valid measure of physiological arousal, it is not a direct measure of aversive arousal (e.g., Bradley et al., 2008). We believe our findings nevertheless plausibly indicate a state of aversive arousal. Studies have shown that negative belief feedback and states of surprise are (at least initially) experienced as aversive (Hajcak & Foti, 2008Noordewier & Breugelmans, 2013Noordewier et al., 2016). In addition, alternative explanations such as curiosity-driven responses were ruled out by the data (see Online Appendix C). We therefore believe our findings present a strong contribution to models of threat–compensation.

It should be noted that we relied mostly on epistemic threats rather than more severe existential threats such as those relating to one’s identity or freedom. Epistemic threats were chosen in order to be able to repeatedly present participants with threats and compensation opportunities. This would not be feasible when more impactful threats are used because the physiological response would likely carry over between trials and affect the relationship between arousal and compensation. Moreover, the theoretical perspectives that guide this research share the explicit premise that the response to epistemic threats generalize to other types of threats (Heine et al., 2006Jonas et al., 2014Proulx & Inzlicht, 2012). In fact, it has been demonstrated that the experience of inconsistency, such as those experienced by our participants, can evoke the same compensation behaviors as existential threats (e.g., nonsense word pairs and identity violations; Randles et al, 2011). Nevertheless, the threat–compensation literature would benefit from more empirical demonstrations of the kind presented here.

Aside from expectancy violations inducing physiological arousal, and physiological arousal motivating compensatory behavior, compensatory behavior should also reduce the physiological arousal. We did not assess this third link. Using the present studies’ design, it might be possible to demonstrate the entire causal link by having participants again see the correct answers. We predict that instead of the positive relationship between pupil size and hindsight bias found in the present study, a negative relationship between hindsight bias and pupil size should be found.

Finally, in the present studies, we used the hindsight bias as a way to repeatedly assess compensatory behaviors following belief violations. It may be argued that due to the many trials, participants may not have always remembered their initial answer and that this ultimately shaped their hindsight bias responses. However, research on the hindsight bias largely supports a biased reconstruction view rather than a memory impairment process (Stahlberg & Maass, 1997). Our findings also contribute to the research on the hindsight bias. Several processes have been proposed to explain the hindsight bias (Hawkins & Hastie, 1990), including motivational accounts (Campbell & Tesser, 1983Fischhoff, 1975Musch, 2003). Our results are consistent with a motivational interpretation of the hindsight bias, thereby also contributing to research on the hindsight bias phenomenon.

We did employ a memory design to measure hindsight bias. Importantly, this memory-based design, although effective in demonstrating a hindsight bias, might be less effective in evoking a hindsight bias than other designs such as the hypothetical design (Pohl, 2007), in which participants are asked to respond as if they had not been told the correct answer. After all, a memory task is about recalling a previously reported answer; and when the time lag is not substantial, people can with relative ease recall their answer. For this reason, the memory design can be potentially improved in future studies by extending the retention interval between the first and second responses.