Somatosensory Cortex Efficiently Processes Touch Located Beyond the Body. Luke E. Miller et al. Current Biology, December 5 2019. https://doi.org/10.1016/j.cub.2019.10.043
Highlights
• Human sensorimotor system rapidly localizes touch on a hand-held tool
• Brain responses in a deafferented patient suggest vibrations encode touch location
• Somatosensory cortex efficiently extracts touch location from the tool’s vibrations
• Somatosensory cortex reuses neural processes devoted to mapping touch on the body
Summary: The extent to which a tool is an extension of its user is a question that has fascinated writers and philosophers for centuries [1]. Despite two decades of research [2, 3, 4, 5, 6, 7], it remains unknown how this could be instantiated at the neural level. To this aim, the present study combined behavior, electrophysiology and neuronal modeling to characterize how the human brain could treat a tool like an extended sensory “organ.” As with the body, participants localize touches on a hand-held tool with near-perfect accuracy [7]. This behavior is owed to the ability of the somatosensory system to rapidly and efficiently use the tool as a tactile extension of the body. Using electroencephalography (EEG), we found that where a hand-held tool was touched was immediately coded in the neural dynamics of primary somatosensory and posterior parietal cortices of healthy participants. We found similar neural responses in a proprioceptively deafferented patient with spared touch perception, suggesting that location information is extracted from the rod’s vibrational patterns. Simulations of mechanoreceptor responses [8] suggested that the speed at which these patterns are processed is highly efficient. A second EEG experiment showed that touches on the tool and arm surfaces were localized by similar stages of cortical processing. Multivariate decoding algorithms and cortical source reconstruction provided further evidence that early limb-based processes were repurposed to map touch on a tool. We propose that an elementary strategy the human brain uses to sense with tools is to recruit primary somatosensory dynamics otherwise devoted to the body.
Data for the EEG experiments and skin-neuron modeling: https://osf.io/c4qmr
Results and Discussion
In somatosensory perception, there is evidence in many species that intermediaries are treated like non-neural sensory extensions of the body [9]. For example, some spiders actively use their web as an extended sensory “organ” to locate prey [10]. Analogously, humans can use tools to sense the properties of objects from a distance [11, 12, 13], such as when a blind person uses a cane to probe the surrounding terrain. This sensorimotor ability is so advanced that humans can almost perfectly localize touch on the surface of a tool [7], suggesting a strong parallel with tactile localization on the body. Characterizing the neural dynamics of tool-extended touch localization provides us with a compelling opportunity to investigate the boundaries of somatosensory processing and hence the question of sensory embodiment: to what extent does the human brain treat a tool like an extended sensory organ?
From a theoretical perspective [7], the sensory embodiment of a tool predicts that—as is the case with biological sense organs—the cerebral cortex (1) rapidly extracts location-based information from changes in a tool’s sensory-relevant mechanical state (e.g., vibrations) and (2) makes this information available to the somatosensory system in an efficient manner. The peripheral code for touch location is likely different for skin (e.g., a place code) and tools (e.g., a temporal code) [7]. Transforming a temporal to a spatial code—a necessary step for tool-extended localization—is a non-trivial task for the brain. We predict that, to do so efficiently, (3) the brain repurposes low-level processing stages dedicated to localizing touch on the body to localize touch on a tool. Direct evidence for sensory embodiment requires understanding how the structural dynamics of tools couple to the neural dynamics of the cortex. Such evidence can be obtained using neuroimaging methods with high temporal resolution. To this aim, we combined electroencephalography (EEG) and computational modeling to test the aforementioned predictions.
The Cerebral Cortex Rapidly Processes Where a Tool Was Touched
In an initial experiment (n = 16), participants localized touches applied on the surface of a 1-m wooden rod (Figure 1A) while we recorded their cortical dynamics using EEG. We designed a delayed match-to-sample task that forced participants to compare the location of two touches (delivered via solenoids; Figure S1A) separated in time (Figure 1B). If the two touches were felt to be in different locations, participants made no overt response. If they were felt to be in the same location, participants used a pedal with their ipsilateral left foot to report whether the touches were close or far from their hand. Participants never used the rod before the experiment and never received performance feedback. As a result, participants had to rely on pre-existing internal models of tool dynamics [14]. Regardless, accuracy was near ceiling for all participants (mean: 96.4% ± 0.71%; range: 89.7%–99.5%), consistent with our prior finding [7].
When a stimulus feature is repeatedly presented to a sensory system, the responses of neural populations representing that feature are suppressed [15]. Effects of repetition are a well-accepted method for timestamping when specific features in a complex input are extracted [16]. Repetition paradigms have previously been used to characterize how sensory signals are mapped by sensorimotor cortices [17, 18, 19, 20]. Our experimental paradigm allowed us to leverage these repetition suppression effects to characterize when the brain extracted where a rod has been touched. Specifically, the amplitude of evoked brain responses reflecting the processing of impact location will be reduced if the rod is hit at the same location twice in a row compared to two distinct locations (Figure 1C).
We first characterized the cortical dynamics of tool-extended touch localization. Touching the surface of the rod led to widespread evoked responses over contralateral regions (Figures S1 and S3), starting ∼24 ms after contact (Figure S1B); this time course is consistent with the known conduction delays between upper limb nerves and primary somatosensory cortex [21]. A nonparametric cluster-based permutation analysis identified significant location-based repetition suppression in a cluster of sensorimotor electrodes between 48 and 108 ms after contact (p = 0.003; Figures 1D–1I, S1C, S1D, and S3; Table S1). This cluster spanned two well-characterized processing stages previously identified for touch on the body: (1) recurrent sensory processing within primary somatosensory (SI) and motor (MI) cortices between 40 and 60 ms after stimulation [22], which has been implicated in spatial processing [23, 24], and (2) feedforward and feedback processing between SI, MI, and posterior parietal regions between 60 and 100 ms after stimulation [25, 26], proposed to contribute to transforming a sensory map into a higher-level spatial representation [18, 27]. This suppression was too quick to reflect signals related to motor preparation/inhibition, which generally occur ∼140 ms after touch [28].
Location-based Repetition Suppression Is Driven by Vibratory Signals
We previously suggested that, during tool-extended sensing, where a rod is touched is encoded pre-neuronally by patterns of vibration (i.e., vibratory motifs; Figures S1E and S1F). When transiently contacting an object, specific resonant modes (100–1,000 Hz for long wooden rods) are selectively excited, giving rise to vibratory motifs that unequivocally encode touch location [7]. These rapid oscillations are superimposed onto a slowly evolving rigid motion that places a load on the participant’s fingers and wrist. Given that the somatosensory system is sensitive to both slow-varying loads (via proprioception) and rapid vibrations imposed to the hand (via touch), these two signals are difficult to disentangle experimentally.
To adjudicate between the contribution of each aspect of the mechanical signal, we repeated experiment 1 with a deafferented participant (DC) who lost proprioception in her right upper limb (33% accuracy in clinical testing) following the resection of a tumor near the right medulla oblongata [29]. Importantly, light touch was largely spared in her right limb (100% accuracy). DC completed the EEG experiment while holding a rod in her deafferented hand and intact left hand (separate blocks). Her behavioral performance was good for both the intact (72%) and deafferented (77%) limbs. Crucially, her neural dynamics exhibited the observed repetition suppression for both limbs, with a magnitude comparable to that of the healthy participants (Figures 1F, 1I, and S2). Though not excluding possible contributions from slow varying rigid motion (when available), this result strongly suggests that the observed suppression was largely driven by information encoded by vibrations.
Processing of Vibratory Motifs Is Temporally Efficient
We used a biologically plausible skin-neuron model [8] to quantify how efficiently the brain extracts touch location on a tool. According to principles of efficient coding, sensory cortices attempt to rapidly and sparsely represent the spatiotemporal statistics of the natural environment with minimal information loss [30]. DC’s results suggest that the brain uses vibratory motifs to extract contact location on a rod. It has been hypothesized that the spiking patterns of Pacinian afferents encode object-to-object contact during tool use [31], a claim that we found model-based evidence for [7]. This temporal code must be decoded in somatosensory processing regions, perhaps as early as the cuneate nucleus [32].
We derived an estimate of “maximal efficiency” by quantifying the time course of location encoding in a simulated population of Pacinian afferents in the hand (Figures 2A and 2B). Support-vector machine (SVM) classification revealed a temporal code that was unambiguous about contact location within 20 ms (Figure 2C). This code was efficient, corresponding to 4.6 ± 1.7 spikes per afferent. Taking into account the known conduction delays between first-order afferents and SI [21], this finding—along with our prior study [7]—suggests that location encoding within 35–40 ms would reflect an efficient representational scheme. The early suppression observed in experiment 1 (Figures 1D–1I) is consistent with this estimate. This suggests that somatosensory cortex views these temporal spike patterns as meaningful tactile features, allowing humans to efficiently use a rod as an extended sensor.
Thursday, December 5, 2019
Confirming the earliest work, against the most recent one, we find that same-sex couples are more likely to break up than different-sex couples; the gap in stability is larger for couples with children
Stability Rates of Same-Sex Couples: With and Without Children. Doug Allen & Joseph Price. Marriage & Family Review, Volume 56, 2020 - Issue 1. https://doi.org/10.1080/01494929.2019.1630048
Abstract: In contrast to earlier studies, several recent ones have claimed that stability rates among same-sex couples are similar to those of different-sex couples. This article reexamines these latest accounts and provides new evidence regarding stability rates using three large, nationally representative datasets from the United States and Canada. Confirming the earliest work, we find that same-sex couples are more likely to break up than different-sex couples. We find that the gap in stability is larger for couples with children, the very group for which concerns about stability are the most important.
Keywords: Children, divorce, same-sex relationships, stability of relationships
Conclusion
We use three large, independent datasets to examine the relationship stability of same-sex couples, taking special note of couples with children. Although there may be some concern regarding the use of independent data, the fact that we find consistent results, while holding constant the same variables in three different legal environments and with data sets constructed in different ways, offers rather compelling evidence of robustness.
Dissolution rates of both same- and different-sex couples vary significantly across panels, in part due to how dissolution and same-sex was measured in each panel, and due to the length of time and time periods the data cover. However, the patterns in the results are strikingly similar. We find not only that same-sex couples are significantly more likely to dissolve their relationship than comparable different-sex couples, but also that this effect is larger for same-sex couples with children. This difference is statistically significant in almost all cases and suggests that parental instability is an important factor through which parents’ sexual orientation influences children’s outcomes. This channel may be the driving force behind recent findings of poorer child outcomes in same-sex families.
We do not want to overstate our results. We must be aware that same-sex couples make up a small fraction of couples in both Canada and in the United States, and that of those couples, only a small fraction have children present in the household. This is clearly a fact in our analysis where the fraction of same-sex couples is very small in each dataset. Furthermore, the definitions of same-sex couples vary across datasets, individuals may have changing incentives to self-report over time, and formally recognized same-sex unions are relatively new. This suggests that the reported number of same-sex unions is fluid and thus will continue to fluctuate. These considerations are also relevant, perhaps more so, for studies using small, biased samples.
However, in the case of relationship stability, the evidence seems quite consistent: same-sex unions appear less stable. This result was found in the first study of Andersson et al., but contrasts with some of the other studies that followed. Our findings therefore (like those of Wiik et al., 2012) reaffirm Andersson et al., and expand the literature by studying the difference between couples without children and couples with children.
The relatively stable rates of heterosexuality, bisexuality, and homosexuality observed across nations for both women and men suggest that non-social factors likely may underlie much variation in human sexual orientation
Prevalence of Sexual Orientation Across 28 Nations and Its Association with Gender Equality, Economic Development, and Individualism. Qazi Rahman, Yin Xu, Richard A. Lippa, Paul L. Vasey. Archives of Sexual Behavior, December 3 2019. https://link.springer.com/article/10.1007/s10508-019-01590-0
Abstract: The prevalence of women’s and men’s heterosexuality, bisexuality, and homosexuality was assessed in 28 nations using data from 191,088 participants from a 2005 BBC Internet survey. Sexual orientation was measured in terms of both self-reported sexual identity and self-reported degree of same-sex attraction. Multilevel modeling analyses revealed that nations’ degrees of gender equality, economic development, and individualism were not significantly associated with men’s or women’s sexual orientation rates across nations. These models controlled for individual-level covariates including age and education level, and nation-level covariates including religion and national sex ratios. Robustness checks included inspecting the confidence intervals for meaningful associations, and further analyses using complete-cases and summary scores of the national indices. These analyses produced the same non-significant results. The relatively stable rates of heterosexuality, bisexuality, and homosexuality observed across nations for both women and men suggest that non-social factors likely may underlie much variation in human sexual orientation. These results do not support frequently offered hypotheses that sexual orientation differences are related to gendered social norms across societies.
Keywords: Sexual orientation Homosexuality Culture Gender roles Gender equality Social construction
Discussion
The central question addressed by the current research was:
Are national factors such as gender equality, economic
development, and individualism-collectivism related to the
national prevalence of various sexual orientations, across
28 nations? Our analyses also tested the frequently offered
hypothesis that sexual orientation rates may be associated
with gender norms and social roles (Bearman & Bruckner,
2002; Greenberg, 1988; Terry, 1999). The use of a large international
dataset allowed us to test whether countries that differed
in gender egalitarianism and rigidity of gender roles (as
indexed by national indicators of gender equality and gender
empowerment) also differed in the prevalence of various
sexual orientations. We found no compelling evidence that
this was the case. While the present results were not significant,
they demonstrate that several theoretically important
predictor variables (national levels of gender equality, economic
development, and individualism) were not much associated
with important outcome variables (sexual identity and
same-sex attractions) in a very large sample with sufficient
statistical power. The non-significant results were also inconsistent
with the notion that women’s sexual identities and
same-sex and other-sex attractions are more linked to cultural
and social factors than men’s were (Bailey et al., 2016;
Baumeister, 2000). Furthermore, there was no evidence that
national indices were more strongly related to identity than
to attraction-based measures of sexual orientation. Finally,
the pattern of associations did not seem to result from the
fact that prevalence rates were more variable, in general, for
women than men across nations. Indeed, when assessed in
terms of sexual identity, prevalence rates for male homosexual
identity were more variable than prevalence rates for
lesbian identity were.
Some factors that may be related to the prevalence of men’s
sexual orientation were not assessed in the current study.
One candidate supported by previous research is participants’
average number of older brothers in a given national sample
(and the correlated factor of the average size of participants’
family of rearing in a given national sample). Many studies
have shown that the more older biological brothers a man has,
the more likely he is to be gay (Blanchard, 2018). This “fraternal
birth order effect” is thought to result from biological
processes—each additional male fetus carried by a woman
increases the likelihood of maternal immunological reactions
against male factors in fetal tissue, and these immunological
reactions then influence the development of subsequent male
fetuses (Bogaert et al., 2018). A prediction that follows from
the fraternal birth order effect is that nations with larger mean
family sizes at the time of participants’ births should, on average,
have higher rates of male but not female homosexuality
among adult probands (Bogaert, 2004). Although not tested
in the current study, this hypothesis suggests the possibility
that biological as well social factors could be associated with
the prevalence of heterosexuality, bisexuality, and homosexuality,
across nations, and furthermore that associations with
biological as well as social factors may sometimes differ for
men and women.
The current study had several limitations. One pertains to
the sexual identity categories used. In some cultures, one’s
degree of sexual attraction to men and women is simply not
a basis upon which individuals construct identities. Cultural
variations in the construal of same-sex and other-sex
attractions have also been affected by our use of an English
language survey. While other cultures may sometime use
sexual identity terms that are comparable to those employed
in Western countries, such terms may have different meanings
across cultures, as for example when a man identifies as
“straight,” but nonetheless engages in sexual activity with
same-sex partners (e.g., Petterson et al., 2016). In some
cultures (e.g., those with “third gender” categories), sexual
orientation might be seen as a basis for identity, but at the
same time, some or all of the Western terms that are commonly
used to denote sexual orientation may not be employed
(e.g., Asthana & Oostvogels, 2001; Petterson et al., 2016).
Similar issues can even characterize some subcultures within
Western nations, in which asking members whether they are
“heterosexual,” “homosexual,” or “bisexual” is discouraged
(e.g., Denizet-Lewis, 2010). In the context of the current
study, it is worth noting that all participants, in fact, identified
themselves using one of the provided sexual identity
terms, and thus they seemed willing to use the categories of
“heterosexual,” “bisexual,” and “homosexual” as a basis for
self-classification.
A second limitation is that the national samples in the BBC
survey were not random or representative. Thus, each national
subsample is not necessarily representative of national patterns
overall. As the participants in all countries come from a sample of
BBC consumers, there may be cross-national homogeneity built
into the sampling frame. As noted earlier, participants tended to
be young, affluent, and educated (as well as able to understand the
English language). Compared to other cross-cultural studies on
college student samples, the BBC data included data from noncollege
populations who came from various locations within the
various countries and who varied in age and various demographic
characteristics.
One obvious direction for future research is to replicate
the current findings with data from representative samples of
men and women from diverse nations. Many of the nations
studied in the current study were European with a number of
notable exceptions (e.g., India, Japan, Malaysia, Philippines,
Singapore, Turkey). The unequal sample sizes across nations
(some nations contained more people than others) is unlikely
to bias the estimation of the parameters of interest. One of
the advantages of using multilevel models is their tolerance
of unequal samples and other unbalanced data structures.
Simulation studies suggest that group-level sample size is
somewhat more important than total sample size, and large
individual-level sample sizes can compensate for small numbers
of groups (for review, see Maas & Hox, 2005). Naturally,
any estimates of grand means (e.g., across all nations) will
be more weighted toward countries with larger sample sizes
which is why researchers should use multilevel models when
nesting is inherent in the study design.
It is also important to note that the concept of national
culture (insofar as that is captured by UN indices) has been
questioned by scholars in personality and social psychology.
While the concept of national cultures is disputed, other
research suggests there may be between-nation differences
in average personality traits and that some of these may
correlate with sociopolitical structures (e.g., having democratic
institutions; Barceló, 2017; Schmitt, Allik, McCrae, &
Benet-Martínez 2007). In this context, Hofstede’s measures
of individualism and collectivism have also been criticized.
As cultures (especially those in closer geographic proximity)
tend to become more similar (perhaps due to economic
factors such as globalization), it is possible that consistency
in psychological traits across cultures may also be driven by
globalized sexual norms. While the analysis presented here
accounts for the statistical dependencies introduced by these
issues, the findings are specific to the BBC sample examined.
Social attitudes toward sexual orientation may also have
changed since the BBC survey was taken. Thus, further tests
of these questions will be needed in other, more representative
and recent cross-cultural datasets.
The use of multilevel models allowed us to use nationlevel
data to draw inferences at the individual level. In other
words, it allowed us to test the potential influence of national
gender equality on individuals’ sexual identity and desire.
However, the relationship between variables could theoretically
be different at other levels of analysis. For example,
societal or structural-level gender egalitarianism could influence
intermediate proximate mechanisms, such as parental
gender socialization or internalization of gender stereotypes
(or other gender norms), which then influence the development
of sexual orientation differences. However, the effects
of factors such as parental socialization on sexual orientation
appear to be weak based on existing research evidence
(Bailey et al., 2016). Furthermore, many country-level variables
may be clustered in world regions (e.g., Europe, North
America). While multilevel model can accommodate such
effects (e.g., by simply adding another data level in a hierarchical
model), it is unlikely that levels of gender egalitarianism
differ sufficiently between countries within a world
region (e.g., between all European countries) for us to detect
such associations with sufficient statistical power.
Finally, it is worth noting that although the national samples
of men and women studied in the BBC survey were not
representative of their larger national populations in some
ways, the male and female samples were nonetheless well
matched on demographic factors such as age and education
levels. Thus, the apparent absence of sex differences in
the current study—e.g., there appeared to be no difference
between men and women in the relation between sociocultural
factors and sexual orientation—was present despite
the fact that male and female samples were matched on key
factors.
In conclusion, our analyses did not yield a significant
association between national indicators of gender equality,
economic development, and individualism-collectivism traits
and identity-based or desire-based measures of sexual orientation
across 28 countries in men and women. This provides
new evidence that questions the power of factors such as
gendered norms, gender roles, and gender socialization to
account for variations in the prevalence of sexual orientations
across nations. Future empirical studies are needed to better
test the extent to which national gender norms and economic
factors are related to variations in the expression of sexual
orientation across nations.
Abstract: The prevalence of women’s and men’s heterosexuality, bisexuality, and homosexuality was assessed in 28 nations using data from 191,088 participants from a 2005 BBC Internet survey. Sexual orientation was measured in terms of both self-reported sexual identity and self-reported degree of same-sex attraction. Multilevel modeling analyses revealed that nations’ degrees of gender equality, economic development, and individualism were not significantly associated with men’s or women’s sexual orientation rates across nations. These models controlled for individual-level covariates including age and education level, and nation-level covariates including religion and national sex ratios. Robustness checks included inspecting the confidence intervals for meaningful associations, and further analyses using complete-cases and summary scores of the national indices. These analyses produced the same non-significant results. The relatively stable rates of heterosexuality, bisexuality, and homosexuality observed across nations for both women and men suggest that non-social factors likely may underlie much variation in human sexual orientation. These results do not support frequently offered hypotheses that sexual orientation differences are related to gendered social norms across societies.
Keywords: Sexual orientation Homosexuality Culture Gender roles Gender equality Social construction
Discussion
The central question addressed by the current research was:
Are national factors such as gender equality, economic
development, and individualism-collectivism related to the
national prevalence of various sexual orientations, across
28 nations? Our analyses also tested the frequently offered
hypothesis that sexual orientation rates may be associated
with gender norms and social roles (Bearman & Bruckner,
2002; Greenberg, 1988; Terry, 1999). The use of a large international
dataset allowed us to test whether countries that differed
in gender egalitarianism and rigidity of gender roles (as
indexed by national indicators of gender equality and gender
empowerment) also differed in the prevalence of various
sexual orientations. We found no compelling evidence that
this was the case. While the present results were not significant,
they demonstrate that several theoretically important
predictor variables (national levels of gender equality, economic
development, and individualism) were not much associated
with important outcome variables (sexual identity and
same-sex attractions) in a very large sample with sufficient
statistical power. The non-significant results were also inconsistent
with the notion that women’s sexual identities and
same-sex and other-sex attractions are more linked to cultural
and social factors than men’s were (Bailey et al., 2016;
Baumeister, 2000). Furthermore, there was no evidence that
national indices were more strongly related to identity than
to attraction-based measures of sexual orientation. Finally,
the pattern of associations did not seem to result from the
fact that prevalence rates were more variable, in general, for
women than men across nations. Indeed, when assessed in
terms of sexual identity, prevalence rates for male homosexual
identity were more variable than prevalence rates for
lesbian identity were.
Some factors that may be related to the prevalence of men’s
sexual orientation were not assessed in the current study.
One candidate supported by previous research is participants’
average number of older brothers in a given national sample
(and the correlated factor of the average size of participants’
family of rearing in a given national sample). Many studies
have shown that the more older biological brothers a man has,
the more likely he is to be gay (Blanchard, 2018). This “fraternal
birth order effect” is thought to result from biological
processes—each additional male fetus carried by a woman
increases the likelihood of maternal immunological reactions
against male factors in fetal tissue, and these immunological
reactions then influence the development of subsequent male
fetuses (Bogaert et al., 2018). A prediction that follows from
the fraternal birth order effect is that nations with larger mean
family sizes at the time of participants’ births should, on average,
have higher rates of male but not female homosexuality
among adult probands (Bogaert, 2004). Although not tested
in the current study, this hypothesis suggests the possibility
that biological as well social factors could be associated with
the prevalence of heterosexuality, bisexuality, and homosexuality,
across nations, and furthermore that associations with
biological as well as social factors may sometimes differ for
men and women.
The current study had several limitations. One pertains to
the sexual identity categories used. In some cultures, one’s
degree of sexual attraction to men and women is simply not
a basis upon which individuals construct identities. Cultural
variations in the construal of same-sex and other-sex
attractions have also been affected by our use of an English
language survey. While other cultures may sometime use
sexual identity terms that are comparable to those employed
in Western countries, such terms may have different meanings
across cultures, as for example when a man identifies as
“straight,” but nonetheless engages in sexual activity with
same-sex partners (e.g., Petterson et al., 2016). In some
cultures (e.g., those with “third gender” categories), sexual
orientation might be seen as a basis for identity, but at the
same time, some or all of the Western terms that are commonly
used to denote sexual orientation may not be employed
(e.g., Asthana & Oostvogels, 2001; Petterson et al., 2016).
Similar issues can even characterize some subcultures within
Western nations, in which asking members whether they are
“heterosexual,” “homosexual,” or “bisexual” is discouraged
(e.g., Denizet-Lewis, 2010). In the context of the current
study, it is worth noting that all participants, in fact, identified
themselves using one of the provided sexual identity
terms, and thus they seemed willing to use the categories of
“heterosexual,” “bisexual,” and “homosexual” as a basis for
self-classification.
A second limitation is that the national samples in the BBC
survey were not random or representative. Thus, each national
subsample is not necessarily representative of national patterns
overall. As the participants in all countries come from a sample of
BBC consumers, there may be cross-national homogeneity built
into the sampling frame. As noted earlier, participants tended to
be young, affluent, and educated (as well as able to understand the
English language). Compared to other cross-cultural studies on
college student samples, the BBC data included data from noncollege
populations who came from various locations within the
various countries and who varied in age and various demographic
characteristics.
One obvious direction for future research is to replicate
the current findings with data from representative samples of
men and women from diverse nations. Many of the nations
studied in the current study were European with a number of
notable exceptions (e.g., India, Japan, Malaysia, Philippines,
Singapore, Turkey). The unequal sample sizes across nations
(some nations contained more people than others) is unlikely
to bias the estimation of the parameters of interest. One of
the advantages of using multilevel models is their tolerance
of unequal samples and other unbalanced data structures.
Simulation studies suggest that group-level sample size is
somewhat more important than total sample size, and large
individual-level sample sizes can compensate for small numbers
of groups (for review, see Maas & Hox, 2005). Naturally,
any estimates of grand means (e.g., across all nations) will
be more weighted toward countries with larger sample sizes
which is why researchers should use multilevel models when
nesting is inherent in the study design.
It is also important to note that the concept of national
culture (insofar as that is captured by UN indices) has been
questioned by scholars in personality and social psychology.
While the concept of national cultures is disputed, other
research suggests there may be between-nation differences
in average personality traits and that some of these may
correlate with sociopolitical structures (e.g., having democratic
institutions; Barceló, 2017; Schmitt, Allik, McCrae, &
Benet-Martínez 2007). In this context, Hofstede’s measures
of individualism and collectivism have also been criticized.
As cultures (especially those in closer geographic proximity)
tend to become more similar (perhaps due to economic
factors such as globalization), it is possible that consistency
in psychological traits across cultures may also be driven by
globalized sexual norms. While the analysis presented here
accounts for the statistical dependencies introduced by these
issues, the findings are specific to the BBC sample examined.
Social attitudes toward sexual orientation may also have
changed since the BBC survey was taken. Thus, further tests
of these questions will be needed in other, more representative
and recent cross-cultural datasets.
The use of multilevel models allowed us to use nationlevel
data to draw inferences at the individual level. In other
words, it allowed us to test the potential influence of national
gender equality on individuals’ sexual identity and desire.
However, the relationship between variables could theoretically
be different at other levels of analysis. For example,
societal or structural-level gender egalitarianism could influence
intermediate proximate mechanisms, such as parental
gender socialization or internalization of gender stereotypes
(or other gender norms), which then influence the development
of sexual orientation differences. However, the effects
of factors such as parental socialization on sexual orientation
appear to be weak based on existing research evidence
(Bailey et al., 2016). Furthermore, many country-level variables
may be clustered in world regions (e.g., Europe, North
America). While multilevel model can accommodate such
effects (e.g., by simply adding another data level in a hierarchical
model), it is unlikely that levels of gender egalitarianism
differ sufficiently between countries within a world
region (e.g., between all European countries) for us to detect
such associations with sufficient statistical power.
Finally, it is worth noting that although the national samples
of men and women studied in the BBC survey were not
representative of their larger national populations in some
ways, the male and female samples were nonetheless well
matched on demographic factors such as age and education
levels. Thus, the apparent absence of sex differences in
the current study—e.g., there appeared to be no difference
between men and women in the relation between sociocultural
factors and sexual orientation—was present despite
the fact that male and female samples were matched on key
factors.
In conclusion, our analyses did not yield a significant
association between national indicators of gender equality,
economic development, and individualism-collectivism traits
and identity-based or desire-based measures of sexual orientation
across 28 countries in men and women. This provides
new evidence that questions the power of factors such as
gendered norms, gender roles, and gender socialization to
account for variations in the prevalence of sexual orientations
across nations. Future empirical studies are needed to better
test the extent to which national gender norms and economic
factors are related to variations in the expression of sexual
orientation across nations.
New Frontiers in Irritability Research
New Frontiers in Irritability Research—From Cradle to Grave and Bench to Bedside. Neir Eshel, Ellen Leibenluft. JAMA Psychiatry, December 4, 2019. https://doi.org/10.1001/jamapsychiatry.2019.3686
We all know what it’s like to be irritable. Our partners walk on eggshells around us. The slightest trigger sets us off. If there’s a punching bag nearby, it had better watch out. Irritability, defined as a low threshold for experiencing frustration or anger, is common. In the right context, irritability can be adaptive, motivating us to overcome barriers or dominate our environment. When prolonged or disproportionate, however, irritability can be counterproductive, causing us to waste our energy on maladaptive behavior.
In recent years, there has been an increase in research on irritability in childhood, with an emerging literature on its neurobiology, genetics, and epidemiology.1 There is even a new diagnosis focused on this symptom, disruptive mood dysregulation disorder (DMDD). However, there is a dearth of irritability research in adults. This is regrettable, because irritability is an important clinical symptom in multiple mental illnesses throughout the life span. From depression to posttraumatic stress disorder, dementia to premenstrual dysphoric disorder, traumatic brain injury to borderline personality disorder, irritability is associated with extensive burdens on individuals, their families, and the general public.
In this Viewpoint we suggest that studying the brain basis for irritability across development and disorder could have substantial clinical benefits. Furthermore, we propose that irritability, like addiction or anxiety, is an evolutionarily conserved focus ready for translational neuroscience.
Diagnosis and Treatment Across the Life Span
Despite its clinical toll, there are few evidence-based treatments for irritability. The only US Food and Drug Administration–approved medications for irritability are risperidone and aripiprazole, which are approved only in the context of autism and are associated with adverse effects that limit their utility. Stimulants, serotonin reuptake inhibitors, and variants of cognitive behavioral therapy and parent management training show promise for different populations, but overall there is a shortage of options, leading many health care professionals to try off-label drug cocktails with unclear efficacy. This situation results in part from our primitive understanding of the phenomenology and brain mechanisms of irritability throughout the life span.
An emerging body of work focuses on measuring irritability in children and adolescents, determining comorbid disorders, and tracking related functional impairment.1 Multiple studies, for example, report that chronically irritable youth are at elevated risk for suicidality, depression, and anxiety in adulthood.2,3 But what are the clinical characteristics and longitudinal course of irritability in adults? Irritability diminishes from toddlerhood through school age, but does it continue to decrease monotonically with age into adulthood? What about the end of life? Irritability and aggression are common in patients with neurodegenerative disorders, but are these symptoms similar to those in a child with DMDD? There has been limited systematic study of irritability in adulthood, and studies that mention irritability in adulthood operationalize the construct in different ways. One study counted 21 definitions and 11 measures of irritability in the psychiatric literature, all of which overlapped with anger and aggression.4 This lack of clarity diminishes our ability to identify biomarkers or track treatment success. Even studies that use childhood irritability to predict adult impairment do not typically measure irritability in adults, thereby obscuring the natural history of irritability as a symptom.5 For the field to progress, it will be crucial to establish standard definitions and measurements spanning childhood through adulthood.
Beyond phenomenology, we need to identify brain signatures associated with the emergence, recurrence, and remission of irritability across the life span and during treatment. Irritability is a prototypical transdiagnostic symptom, but it remains unclear to what extent its brain mechanisms overlap across disorders. For example, in children, data suggest that the brain mechanisms mediating irritability in DMDD, anxiety disorders, and attention-deficit/hyperactivity disorder are similar but differ from those mediating irritability in childhood bipolar disorder.1,6 The frequency of irritable outbursts appears to diminish in step with the maturity of prefrontal regions during childhood.1 Could degeneration in the same structures predict reemergence of irritable outbursts in patients with dementia? Could developmental differences in these regions increase the likelihood of irritability when individuals are sleep deprived or intoxicated later in adolescence or adulthood? Only through fine-grained neuroscientific studies can we disentangle what is unique to the symptom (ie, irritability) and to the disorder (eg, bipolar disorder vs DMDD vs dementia), and develop treatments tailored to an individual’s brain pathology.
Translational Neuroscience and Irritability
In addition to their clinical relevance, neuroscientific studies of irritability can address fundamental questions about brain dysfunction and recovery. Over the past 2 decades, studies have revealed the circuits underlying reward processing, and in particular prediction error, the mismatch between expected and actual reward.7 The neuroscience of aggression has also advanced through the discovery of cells in the amygdala and hypothalamus that form a final common pathway for aggressive behavior.8 Irritability and the concept of frustrative nonreward can tie these 2 fields together.
Frustrative nonreward is the behavioral and emotional state that occurs in response to a negative prediction error, ie, the failure to receive an expected reward. In the classic study by Azrin et al,9 pigeons were trained to peck a key for food reward. After pigeons learned the task, the experimenters removed the reward; then when the pigeons pecked, nothing happened. For the next several minutes, there were 2 changes in the pigeons’ behavior. First, they pecked the key at a higher rate. Second, they became unusually aggressive, damaging the cage and attacking another pigeon nearby. In other words, a negative prediction error led to a state of frustration, which then induced increased motor activity and aggression. Such responses to frustration have been replicated in many species, including chimpanzees, cockerels, salmon, and human children and adults.10 Frustrative nonreward therefore provides an evolutionarily conserved behavioral association between prediction error and aggression. Apart from studies in children,1,6 however, little has been done to probe the neural circuits of frustrative nonreward or of irritability, which can be defined as a low threshold for experiencing frustrative nonreward.
We know, for example, that negative prediction errors cause phasic decreases in dopamine neuron firing, which help mediate learning by reducing the valuation of a stimulus. Does this dip in dopamine level also increase the likelihood of aggression and if so how? The same optogenetic techniques that have demonstrated a causal role for dopamine prediction errors in reward learning could be used to test their role in aggressive behavior. Likewise, multiple nodes in the reward circuit encode the value of environmental stimuli. Could these values modulate the propensity for aggression? Environments of plenty, for instance, may protect against aggressive outbursts, because if there is always more reward available, the missing out factor may not be salient. Conversely, scarcity could make individuals more likely to be aggressive, because if there are few rewards to be had, achieving dominance may be necessary for survival.
Exploring the bidirectional associations between the reward processing and aggression circuits would help us understand state changes in the brain and how environmental context determines our behavior. At the same time, understanding these circuits will lay the groundwork for mechanism-based treatments for irritability.
Conclusions
The neuroscience of irritability is in its infancy and research has focused almost exclusively on children. We now have an opportunity to expand this field to adults, across disorders, and to animal models for more precise mechanistic studies. Through better measurement, careful experimental design, input from theorists and computational psychiatrists, and coordinated efforts across experts in multiple disorders, we can guide the field to maturity.
Benoit Coeure: No euro area country features in the top 10 of the World Bank’s ease of doing business index; many are not even in the top 30.
The single currency: an unfinished agenda. Speech by Benoît Cœuré, ECB. ECB Representative Office in Brussels, Dec 3 2019. https://www.bis.org/review/r191204a.pdf
Excerpts:
Some wounds have still not healed, however. As unsettling as it may sound after so many years of economic hardship, the euro area architecture is still not crisis-proof.
Growth remains cyclically too weak to fully restore fiscal space in countries where public debt is unacceptably high. The profitability of banks remains low and, in many cases, below the cost of equity, reflecting risks to business model sustainability.[3]
And productivity growth, the main component that underpins our living standards and social safety nets, remains low in many Member States. As a consequence, unemployment in some countries, in particular among young people, remains unacceptably high, despite the progress made at the euro area level on average.
True, many other advanced economies are facing similar challenges. But the combination of weak potential growth and high debt is toxic in a monetary union with decentralised fiscal policy and insufficiently integrated financial markets.
It implies that country-specific shocks remain a potential source of instability for the euro area as a whole.
It weakens political support for further integration. And it means that the single monetary policy has to shoulder the burden of macroeconomic stabilisation in the face of adverse shocks.
The arrival of the new European Parliament and Commission provides an important opportunity to address more decisively the remaining vulnerabilities, refocus priorities and sequence actions accordingly. And it presents us with a time frame for achieving these goals.
In my remarks this evening, I will argue that we need to both strengthen the institutional framework to make our currency union more resilient and implement the right policies to boost the growth potential of our economies.
I will argue that flexible and dynamic markets are the first line of defence in the euro area.[4]
They are the key to unlocking sustained productivity growth, and thereby allowing faster normalisation of monetary policy. They also reduce the need for macroeconomic stabilisation and they curb contentious debates about crisis management.
The second line of defence relates to sustainable and growth-enhancing fiscal policies. Countries that have fiscal space should use it to foster investment. Countries where debt is high should calibrate their policies so as to regain fiscal space in the future, limiting the risk they pose to their neighbours. And all countries can improve the quality of their spending.
The third line of defence relates to strengthening our common toolkit – to new policy instruments that are needed to protect the stability of the currency union if shocks are too large to be absorbed by markets or national fiscal policies, and that provide a safety net against poverty and social exclusion.
The first line of defence: integrated and flexible markets
No euro area country features in the top 10 of the World Bank’s ease of doing business index. Many are not even in the top 30.
A consequence of a less business-friendly environment is that business dynamism in Europe is weak.
Compared with the United States, European countries have, on average, larger shares of “static” firms and smaller shares of both growing and shrinking firms.[5]
Low business dynamism feeds and reinforces the misallocation of resources across firms in the euro area. [6]
Empirical evidence shows that an increasing proportion of capital is concentrated in firms that are less productive. In Italy and Spain misallocation is currently higher than at any point in time before the crisis.[7]
The absence of a Schumpeterian process of creative destruction weighs on innovation and growth.
...
There is overwhelming evidence that new firms are more likely to adopt new technologies.
There is a significant link between business entry rates, technology creation and diffusion, and productivity growth.[8]
New and young firms also contribute disproportionally to job creation relative to their share in employment. [9]
...
Several euro area countries lack an effective framework for early private debt restructuring. In Portugal, Greece and Slovakia, for example, it takes more than three years to resolve insolvency. It takes less than one year in Japan, Norway and Canada.[10]
...
But member states don’t walk the talk. The macroeconomic imbalance procedure always lacked teeth and none of the 2018 recommendations for euro area countries have been fully implemented.
Excerpts:
Some wounds have still not healed, however. As unsettling as it may sound after so many years of economic hardship, the euro area architecture is still not crisis-proof.
Growth remains cyclically too weak to fully restore fiscal space in countries where public debt is unacceptably high. The profitability of banks remains low and, in many cases, below the cost of equity, reflecting risks to business model sustainability.[3]
And productivity growth, the main component that underpins our living standards and social safety nets, remains low in many Member States. As a consequence, unemployment in some countries, in particular among young people, remains unacceptably high, despite the progress made at the euro area level on average.
True, many other advanced economies are facing similar challenges. But the combination of weak potential growth and high debt is toxic in a monetary union with decentralised fiscal policy and insufficiently integrated financial markets.
It implies that country-specific shocks remain a potential source of instability for the euro area as a whole.
It weakens political support for further integration. And it means that the single monetary policy has to shoulder the burden of macroeconomic stabilisation in the face of adverse shocks.
The arrival of the new European Parliament and Commission provides an important opportunity to address more decisively the remaining vulnerabilities, refocus priorities and sequence actions accordingly. And it presents us with a time frame for achieving these goals.
In my remarks this evening, I will argue that we need to both strengthen the institutional framework to make our currency union more resilient and implement the right policies to boost the growth potential of our economies.
I will argue that flexible and dynamic markets are the first line of defence in the euro area.[4]
They are the key to unlocking sustained productivity growth, and thereby allowing faster normalisation of monetary policy. They also reduce the need for macroeconomic stabilisation and they curb contentious debates about crisis management.
The second line of defence relates to sustainable and growth-enhancing fiscal policies. Countries that have fiscal space should use it to foster investment. Countries where debt is high should calibrate their policies so as to regain fiscal space in the future, limiting the risk they pose to their neighbours. And all countries can improve the quality of their spending.
The third line of defence relates to strengthening our common toolkit – to new policy instruments that are needed to protect the stability of the currency union if shocks are too large to be absorbed by markets or national fiscal policies, and that provide a safety net against poverty and social exclusion.
The first line of defence: integrated and flexible markets
No euro area country features in the top 10 of the World Bank’s ease of doing business index. Many are not even in the top 30.
A consequence of a less business-friendly environment is that business dynamism in Europe is weak.
Compared with the United States, European countries have, on average, larger shares of “static” firms and smaller shares of both growing and shrinking firms.[5]
Low business dynamism feeds and reinforces the misallocation of resources across firms in the euro area. [6]
Empirical evidence shows that an increasing proportion of capital is concentrated in firms that are less productive. In Italy and Spain misallocation is currently higher than at any point in time before the crisis.[7]
The absence of a Schumpeterian process of creative destruction weighs on innovation and growth.
...
There is overwhelming evidence that new firms are more likely to adopt new technologies.
There is a significant link between business entry rates, technology creation and diffusion, and productivity growth.[8]
New and young firms also contribute disproportionally to job creation relative to their share in employment. [9]
...
Several euro area countries lack an effective framework for early private debt restructuring. In Portugal, Greece and Slovakia, for example, it takes more than three years to resolve insolvency. It takes less than one year in Japan, Norway and Canada.[10]
...
But member states don’t walk the talk. The macroeconomic imbalance procedure always lacked teeth and none of the 2018 recommendations for euro area countries have been fully implemented.
Cohort study of 13 588 adults without dementia at baseline: The Western dietary pattern may not contribute to cognitive decline in later life
Association of Dietary Patterns in Midlife and Cognitive Function in Later Life in US Adults Without Dementia. Jennifer L. Dearborn-Tomazos, Aozhou Wu, Lyn M. Steffen et al. JAMA Netw Open. 2019;2(12):e1916641. December 4, 2019, doi:10.1001/jamanetworkopen.2019.16641
Question What is the association between the Western dietary pattern in adults in midlife and cognitive decline in later life?
Findings In this cohort study of 13 588 adults without dementia at baseline, midlife dietary pattern was not associated with cognitive decline 20 years later.
Meaning The Western dietary pattern may not contribute to cognitive decline in later life.
Abstract
Importance The association of dietary patterns, or the combinations of different foods that people eat, with cognitive change and dementia is unclear.
Objective To examine the association of dietary patterns in midlife with cognitive function in later life in a US population without dementia.
Design, Setting, and Participants Observational cohort study with analysis of data collected from 1987 to 2017. Analysis was completed in January to February 2019. Community-dwelling black and white men and women from Washington County, Maryland; Forsyth County, North Carolina; Jackson, Mississippi; and suburban Minneapolis, Minnesota, participating in the Atherosclerosis Risk in Communities (ARIC) study were included.
Exposures Two dietary pattern scores were derived from a 66-item food frequency questionnaire using principal component analysis. A Western, or unhealthy, dietary pattern was characterized by higher consumption of meats and fried foods. A so-called prudent, or healthier, dietary pattern was characterized by higher amounts of fruits and vegetables.
Main Outcomes and Measures Results of 3 cognitive tests (Digit Symbol Substitution Test, Word Fluency Test, and Delayed Word Recall) performed at 3 points (1990-1992, 1996-1998, and 2011-2013) were standardized and combined to represent global cognitive function. The 20-year change in cognitive function was determined by tertile of diet pattern score using mixed-effect models. The risk of incident dementia was also determined by tertile of the diet pattern score.
Results A total of 13 588 participants (7588 [55.8%] women) with a mean (SD) age of 54.6 (5.7) years at baseline were included; participants in the top third of Western and prudent diet pattern scores were considered adherent to the respective diet. Cognitive scores at baseline were lower in participants with a Western diet (z score for tertile 3 [T3], −0.17 [95% CI, −0.20 to −0.14] vs T1, 0.17 [95% CI, 0.14-0.20]) and higher in participants with a prudent diet (z score for T3, −0.09 [95% CI, −0.12 to −0.06] vs T1, −0.09 [95% −0.12 to −0.06]). Estimated 20-year change in global cognitive function did not differ by dietary pattern (difference of change in z score for Western diet, T3 vs T1: −0.01 [95% CI, −0.05 to 0.04]; and difference of change in z score for prudent diet, T3 vs T1: 0.02 [95% CI, −0.02 to 0.06]). The risk of incident dementia did not differ by dietary pattern (Western hazard ratio for T3 vs T1, 1.06 [95% CI, 0.92-1.22]; prudent hazard ratio for T3 vs T1, 0.99 [95% CI, 0.88-1.12]).
Conclusions and Relevance This study found that the dietary pattern of US adults at midlife was not associated with processing speed, word fluency, memory, or incident dementia in later life.
Introduction
Healthy dietary patterns may protect against dementia and mild cognitive impairment.1,2 Prior studies demonstrate that healthy dietary patterns are associated with increased brain volumes and reduced atrophy compared with less healthy dietary patterns.3,4 Although the mechanism behind a healthy diet and improved brain health are not well understood, 2 plausible mechanisms include reduced vascular injury and a reduction in Alzheimer pathology.5 A healthy dietary pattern reduces hypertension, dysglycemia, hyperlipidemia, and chronic inflammation, which may reduce brain vascular injury.2,6 Second, a healthy diet may, through reduced oxidative stress, reduce the accumulation of proteins involved in Alzheimer disease.5,7,8
Midlife dietary pattern, compared with dietary pattern in later life, may have a stronger association with cognitive decline and dementia because chronic disease or the concern for chronic disease in later life may motivate individuals to improve their diet,9 making it appear that a healthy diet is associated with poor health outcomes. At least 10 prior studies examined associations of dietary patterns later in life with cognitive decline, but far fewer prospectively investigated associations for midlife dietary patterns.9,10 In this study, we examine the association between midlife dietary patterns and cognitive change and incident dementia over 20 years. We hypothesized that a healthy diet at midlife would be associated with less cognitive decline and a lower risk of dementia.
Methods
Study Population
The Atherosclerosis Risk in Communities (ARIC) study is a randomly selected and recruited observational cohort study that began in 1987 with individuals aged 45 to 64 years who were representative of the selected communities. Participants enrolled in the ARIC study were from 4 US communities (Jackson, Mississippi; Forsyth County, North Carolina; Washington County, Maryland; and suburban Minneapolis, Minnesota). A total of 15 792 participants received an initial evaluation, and these participants were re-evaluated in person every 3 years. The study is ongoing with 6 in-person visits completed to date.11 The current analysis includes information collected at visit 1 (1987-1989), visit 2 (1990-1992), visit 4 (1996-1998), and at visit 5 (2011-2013). All patients provided written informed consent. The study was approved by the institutional review boards at all participating institutions. The analysis presented is compliant with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
Exposure: Dietary Patterns
Participants completed a 66-item food frequency questionnaire at baseline (1987-1989).12 We condensed baseline questionnaire items into 20 food and beverage groups and derived 2 dietary pattern scores using principal components analysis with orthogonal rotation.13 Principal components analysis transforms possibly correlated variables (in this case, each of the 20 food groups) into a set of linearly uncorrelated variables (in this case, the 2 dietary patterns). Two distinct dietary patterns emerged with an eigenvalue greater than 2 (eTable 1 in the Supplement). The Western diet pattern explained 12% of the total variance and included higher consumption of meat, refined grains, and processed and fried foods. The so-called prudent diet pattern reflected 10% of the total variance and included higher consumption of fruits and vegetables, fish, chicken, whole grains, dairy, nuts, and alcohol. Study participants received a score for each dietary pattern. The score established how closely they adhered to a Western diet pattern or a prudent diet pattern. Scores ranged from −3.96 to 14.26 (interquartile range [IQR], −1.34 to 0.77) for the Western diet pattern and −3.56 to 10.55 (IQR, −0.98 to 0.79) for the prudent diet pattern. A higher score indicated greater adherence to each particular diet pattern.
Outcomes
Cognitive Change
Cognitive testing was performed at visit 2 (1990-1992), visit 4 (1996-1998), and visit 5 (2011-2013). Three tests were used in the cognitive battery: the Delayed Word Recall (DWR)14 test, the Digit Symbol Substitution (DSS) test, and the Word Fluency (WF) test.15 A test-specific z score representing cognitive function at visits 4 and 5 was calculated by subtracting the baseline population mean from the participant’s raw score and dividing the difference by the baseline population standard deviation. A global z score representing cognitive function at visit 4 and 5 was created as the mean of the 3 test-specific z scores.
Cognitive change was defined as the difference in test-specific and global z scores at each point using random-effects linear regression models to account for the intra-individual correlation of cognitive scores.16
Dementia
Dementia was adjudicated according to an established protocol that included assessments involving 3 levels of ascertainment consisting of in-person assessments, telephone interviews of participants or informants, or surveillance based on hospital discharge codes and death certificates.17 Dementia was adjudicated in 2011 to 2013 and 2016 to 2017. Level 1 included in-person assessment of dementia using an algorithm that incorporated information from the Clinical Dementia Rating Interview; the Mini-Mental State examination; longitudinal cognitive testing at visits 2, 4, 5, and 6; a complete neuropsychological battery at visits 5 and 6; and the Functional Activities Questionnaire.18 Level 2 included participants from level 1 and, in addition, 3 other categorizations: (1) participants who met predefined criteria based on completion of the Telephone Interview for Cognitive Status–modified or the Six-Item Screener, (2) deceased persons classified as having dementia, and (3) informant interviews using the AD8 dementia screening interview, as described elsewhere.18 Level 3 included participants in level 2 in addition to individuals with dementia identified by surveillance using prior hospital discharge codes (International Classification of Diseases, Ninth Revision [ICD-9] or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10]) or death certificate codes for dementia.17 Level 3 was used for this analysis. Level 3 ascertained a dementia status (yes or no) for all participants regardless of study visit completion.
Covariates
Covariates included demographic and lifestyle factors, clinical factors, and apolipoprotein E (APOE) ε4 status. Demographic factors included age, sex, race–study center, and education. Lifestyle factors included activity level, current smoking, current alcohol use, and total energy intake. Clinical factors included body mass index (calculated as weight in kilograms divided by height in meters squared), history of hypertension (yes or no, defined as use of hypertension medication, systolic blood pressure >140 mm Hg, or diastolic blood pressure >90 mm Hg at the baseline visit), diabetes (yes or no, defined as self-reported diabetes diagnosis by physicians, use of diabetes medication, or having fasting glucose level of 126 mg/dL or higher or a nonfasting glucose level of 200 mg/dL or higher at the baseline visit [to convert to millimoles per liter, multiply by 0.0555]), total cholesterol (fasting, mmol/L), history of coronary artery disease, and prevalent stroke through visit 2 defined based on self-report of stroke prior to visit 1 and adjudicated cases between visit 1 and visit 2.
Population Attrition
There was a 15-year gap between visit 4 and visit 5, leading to attrition, largely due to death or disability. At visits 4 and 5, respectively, 73.8% and 41.4% of the original cohort remained. This dropout is likely to be informative,19 as we found that diet scores were associated with loss to follow-up (Table 1). To account for population attrition, we imputed the missing cognitive test results in visits 4 and 5 and missing baseline covariates using multiple imputations with chained equations.20 In the multiple imputations with chained equations, we incorporated the diet scores, all covariates, prior cognitive function measurements, and ancillary information about cognitive status collected prospectively for participants who did not attend visit 5. The ancillary cognitive information was collected from the Clinical Dementia Rating scale from informants of both living participants and deceased participants, the Telephone Interview for Cognitive Status for living participants, and hospitalization discharge codes and death certificates (ICD-9 codes). We imputed the cognitive function for participants who died before visit 5 six months prior to the date of death.21 We conducted the primary analyses with 25 sets of imputations.
Cognitive Change Modeling
In the primary analysis, we evaluated the association of the 2 dietary pattern scores by tertile with cognitive function as measured by the change in global z scores at visits 2, 4, and 5. Mixed-effect models were used to account for the correlation between repeated cognitive test measures over time. We defined a study time metric from visit 2 to the cognitive measurement. We used a linear spline for the time variable with a knot at visit 4 to address potential nonlinearity of cognitive change. We incorporated 2 random slopes, which corresponded to the 2 time-spline terms, and a random intercept, assuming an independent correlation structure. To measure the association between diet scores and cognitive change, we examined the interactions between exposure strata and the time-spline terms modeled as conditional likelihoods. We also included conditional likelihoods for age, sex, education, race–field center, and total energy intake for model 1 and APOE ε4 status, alcohol use history, smoking history, activity level, body mass index, total cholesterol, prevalent coronary heart disease, history of hypertension, diabetes, and stroke for model 2. We included the conditional likelihoods for interactions between time-splines and covariates that contributed to the slope of cognitive change for aforementioned covariates. We estimated the mean cognitive change over 20 years by dietary score tertile, using the coefficients of the 2 time-splines terms and their interactions with diet score tertile. A linear trend was tested across the dietary tertiles using the median score of each tertile modeled as a continuous variable.
We performed 2 secondary analyses. In the first, we further evaluated the association of diet scores with the z score from each of the 3 individual cognitive test results (DSS, DWR, and WF). In the second, we replicated the analyses using nonimputed data. In both analyses, we applied the same methods as in the primary analysis.
Incident Dementia
We next evaluated the association of the 2 dietary pattern scores with incident dementia using Cox proportional hazard models. We adjusted for the baseline covariates age, sex, education, race–field center, and total energy intake for model 1 and APOE ε4 status, alcohol use history, smoking history, activity level, body mass index, total cholesterol, prevalent coronary heart disease, history of hypertension, diabetes, and stroke for model 2.
Statistical Analysis
The analysis for this study was completed in January to February 2019. Baseline characteristics of participants were compared by tertiles of diet score using χ2 or analysis of variance. Two-sided P < .05 was considered statistically significant. Analyses were conducted using Stata statistical software version 14.2 (StataCorp).
Results
Participant Characteristics
A total of 15 792 adults enrolled at study baseline (1987-1989) when they were aged 45 to 64 years. Of these 15 792 participants, 6538 joined the neurocognitive visit from 2011 to 2013. Because of small numbers, and in accordance with usual ARIC practice, we excluded those who were neither white nor black (48 individuals) and the black participants in the Minnesota (22 participants) and Washington County cohorts (33 participants). We further excluded 121 participants for missing dietary data and 387 participants with implausible caloric intake, defined as less than 500 and more than 3500 total calories per day for women and less than 700 and more than 4500 total calories per day for men. We also excluded 1550 participants with incomplete cognitive data at visit 2, among whom 1348 missed visit 2, and 43 participants who were missing key covariates. The analytic population included 13 588 participants.
At the baseline visit, participants in our study had a mean (SD) age of 54.6 (5.7) years, 55.8% were women, and 37.2% had at least some college education or greater (Table 1). The average participant was overweight (mean [SD] body mass index, 27.6 [5.3]) and consumed a mean (SD) of 1629 (605) kcal/d. In all, 57.9% of participants did not complete cognitive testing at visit 5, and 28.6% of participants died between study baseline and visit 5.
Western Diet Pattern
Adherence to the Western diet pattern was defined as participants reaching the third tertile of the Western diet pattern score. Participants with a Western diet pattern had a higher rate of study attrition (Table 1) and were less likely to be women. Participants with a Western diet pattern were more likely to be from Washington County, Maryland, or Jackson, Mississippi, compared with the other 2 sites, more likely to have less than high school education, more likely to be current smokers, and less likely to engage in physical activity. Participants with a Western diet pattern were also more likely to consume a greater number of calories but were not more likely to have hypertension and diabetes.
Cognitive scores at first measurement were lower in participants with a Western diet pattern compared with participants in the first tertile of Western diet pattern score (z score for tertile 3 [T3], −0.17 [95% CI, −0.20 to −0.14] vs T1, 0.17 [95% CI, 0.14-0.20]) (Table 2). The finding of lower cognitive scores in participants with a Western diet pattern was consistent after adjustments for demographic factors and caloric intake (model 1), but was not statistically significant after full adjustments for lifestyle and clinical factors (model 2) (Table 2). Twenty-year change in cognitive scores was less in participants with a Western diet pattern compared with participants in the first tertile of Western diet pattern score; however, this association did not remain after full adjustments (difference of change in z score for Western diet, T3 vs T1: −0.01 [95% CI, −0.05 to 0.04]) (Table 3). When examined independently, only 20-year change in the DSS z score was less in participants with a Western diet pattern compared with participants in the first tertile of Western diet pattern score (meaning less decline), but this association was not significant after adjustments (eTable 2 in the Supplement). Twenty-year change in the DWR or WF was not different in participants with a Western pattern compared with participants in the first tertile of Western diet pattern score (eTable 3 and eTable 4 in the Supplement).
Our secondary analysis using nonimputed data demonstrated the same findings for the Western diet pattern compared with the imputed data set (eTable 5 in the Supplement).
Participants with a Western diet pattern were no more likely to develop dementia 20 years later compared with participants in the first tertile of Western diet pattern score (adjusted hazard ratio for T3 vs T1, 1.06; 95% CI, 0.92-1.22 for T3 vs T1) (Table 4).
Prudent Diet Pattern
Adherence to a prudent diet pattern was defined as participants reaching the third tertile of the prudent diet pattern score (z score for T3, −0.09 [95% CI, −0.12 to −0.06] vs T1, −0.09 [95% −0.12 to −0.06]). Participants with a prudent diet pattern had no difference in study attrition (eTable 6 in the Supplement) and were more likely to be women. Participants with a prudent diet pattern were less likely to be from Jackson, Mississippi, and more likely to be from the other 3 study locations. Participants with a prudent diet pattern were more likely to have a college education or greater and were more likely to be never smokers and engage in physical activity. Participants with a prudent diet pattern were also more likely to consume higher calories and have diabetes but not hypertension.
Cognitive scores at first measurement were higher in participants with a prudent diet pattern compared with participants in the first tertile of prudent diet pattern score (Table 2), but this association did not remain after full adjustments. Twenty-year change in cognitive scores was greater in participants with a prudent diet pattern compared with participants in the first tertile of prudent diet pattern score; however, this association did not remain after full adjustments (difference of change in z score for prudent diet T3 vs T1: 0.02 [95% CI, −0.02 to 0.06]) (Table 3). When examined independently, only 20-year change in the DSS was greater in participants with a prudent diet pattern compared with participants in the first tertile of prudent diet pattern score, but this association was not significant after full adjustments (eTable 2 in the Supplement). Twenty-year change in the DWR or WF was not different in participants with a prudent diet pattern compared with participants in the first tertile of prudent diet pattern score (eTable 3 and 4 in the Supplement).
Our secondary analysis using nonimputed data demonstrated the same findings for the prudent pattern compared with the imputed data set (eTable 5 in the Supplement).
Participants with a prudent diet pattern were not more likely to develop dementia 20 years later compared with participants in the first tertile of prudent diet pattern score (adjusted hazard ratio, 0.99; 95% CI, 0.88-1.12 for T3 vs T1) (Table 4).
Discussion
We did not find an association between dietary patterns and cognitive decline measured over 20 years. A dietary pattern high in meat and fried food intake was associated with lower cognitive test scores at baseline, but differences in demographic characteristics and health behaviors explained this finding. Similarly, a dietary pattern high in fruit and vegetable intake was associated with higher cognitive test scores at baseline, but differences in demographic characteristics and health behaviors explained this finding.
Our results stand in contrast to short-term observational studies. Several observational studies,22-26 ranging in duration from 5 to 7 years, showed modest associations between dietary patterns and cognitive health. One study24 followed 1410 participants over 5 years and found that adherence to a Mediterranean-type dietary pattern was associated with less decline in the Mini-Mental State examination. Another study23 followed more than 2200 participants for 6 years and found that the Western diet was associated with greater cognitive decline and the prudent diet was associated with less cognitive decline as measured by the Mini-Mental State examination.
A recent long-term observational study27 aligns with our results. The Whitehall II study27 measured diet in 1991 to 1993 and dementia surveillance occurred through 2017. The authors found that diet quality at midlife was not associated with incident dementia in long-term follow up. Our results confirm the findings of this study in a US population.
We suggest 3 explanations for the reported differences between short-term studies and studies with long-term follow-up.27 First, it may be that over time, other chronic diseases such as diabetes have a greater impact on cognition compared with diet. Our study only partially accounts for this confounding by adjusting for comorbidities at baseline. Second, participants with an unhealthy diet engage in multiple unhealthy behaviors (eg, smoking and lack of physical activity). It may be difficult to elucidate the independent outcomes associated with diet when multiple lifestyle behaviors contribute to cognitive function. Third, our study does not account for change in dietary intake or the food supply over 20 years.
Two clinical trials28 build on the promising observational science to examine whether dietary changes can protect against cognitive decline and dementia. One intervention28 tested a Mediterranean diet with olive oil or nuts as supplementation in 334 participants at high cardiovascular risk and found improved composite cognitive function compared with the control diet. A second clinical trial, the Mediterranean-Dietary Approaches to Stop Hypertension (MIND) clinical trial, is currently under way. While our study did not find an association of diet with cognitive decline, this should not undermine the potential of dietary change to affect brain health.
Strengths and Limitations
Our study has strengths, one of which is the long duration of follow up. Another is our ability to account for study dropout due to death or loss to follow-up using criterion-standard imputation methods.20
There are also several limitations of our study. First, our definition of achievement of a Western or prudent diet score is based on our tertile cutoffs and may not reflect individual participant identification with the specified dietary pattern. Second, diet was measured 3 years before the first cognitive measurement. The nonconcurrent measurements are unlikely to affect the results because dietary patterns remain relatively stable up to 7 years.29 However, dietary intake likely changes over 20 years owing to change in the food supply and food habits. The ARIC study did not capture diet over the 20 years to test this possibility. In addition, as participants with an unhealthy diet had lower cognition at the time of first assessment, it is possible that diet exerted influence prior to our time of measurement. As diet was not associated with either cognitive trajectories or incident dementia, this is less likely to be the case. We should also note that although study dropout was accounted for, a large proportion of participants did not follow up after 20 years. Finally, as in all observational studies, we are unable to attribute causality to our observations, as the mechanisms between diet and brain health are complex, and the only way to definitively measure the relationship between dietary practices and cognition is in an experimental design in which diet is manipulated; however, long-term follow-up may be expensive.
Conclusions
The results of this cohort study do not support the hypothesis that midlife diet significantly contributes to cognitive decline independent of demographic and behavioral factors. Our finding that participants with an unhealthy diet have lower cognitive function could be attributed to cigarette smoking, eating excess calories, or engaging in less physical activity. Our results suggest that it may be important to address all modifiable risk factors in dietary interventions, supporting the emerging body of multimodal lifestyle and behavioral research.30 A multimodal approach may provide greater risk reduction for cognitive aging.
Question What is the association between the Western dietary pattern in adults in midlife and cognitive decline in later life?
Findings In this cohort study of 13 588 adults without dementia at baseline, midlife dietary pattern was not associated with cognitive decline 20 years later.
Meaning The Western dietary pattern may not contribute to cognitive decline in later life.
Abstract
Importance The association of dietary patterns, or the combinations of different foods that people eat, with cognitive change and dementia is unclear.
Objective To examine the association of dietary patterns in midlife with cognitive function in later life in a US population without dementia.
Design, Setting, and Participants Observational cohort study with analysis of data collected from 1987 to 2017. Analysis was completed in January to February 2019. Community-dwelling black and white men and women from Washington County, Maryland; Forsyth County, North Carolina; Jackson, Mississippi; and suburban Minneapolis, Minnesota, participating in the Atherosclerosis Risk in Communities (ARIC) study were included.
Exposures Two dietary pattern scores were derived from a 66-item food frequency questionnaire using principal component analysis. A Western, or unhealthy, dietary pattern was characterized by higher consumption of meats and fried foods. A so-called prudent, or healthier, dietary pattern was characterized by higher amounts of fruits and vegetables.
Main Outcomes and Measures Results of 3 cognitive tests (Digit Symbol Substitution Test, Word Fluency Test, and Delayed Word Recall) performed at 3 points (1990-1992, 1996-1998, and 2011-2013) were standardized and combined to represent global cognitive function. The 20-year change in cognitive function was determined by tertile of diet pattern score using mixed-effect models. The risk of incident dementia was also determined by tertile of the diet pattern score.
Results A total of 13 588 participants (7588 [55.8%] women) with a mean (SD) age of 54.6 (5.7) years at baseline were included; participants in the top third of Western and prudent diet pattern scores were considered adherent to the respective diet. Cognitive scores at baseline were lower in participants with a Western diet (z score for tertile 3 [T3], −0.17 [95% CI, −0.20 to −0.14] vs T1, 0.17 [95% CI, 0.14-0.20]) and higher in participants with a prudent diet (z score for T3, −0.09 [95% CI, −0.12 to −0.06] vs T1, −0.09 [95% −0.12 to −0.06]). Estimated 20-year change in global cognitive function did not differ by dietary pattern (difference of change in z score for Western diet, T3 vs T1: −0.01 [95% CI, −0.05 to 0.04]; and difference of change in z score for prudent diet, T3 vs T1: 0.02 [95% CI, −0.02 to 0.06]). The risk of incident dementia did not differ by dietary pattern (Western hazard ratio for T3 vs T1, 1.06 [95% CI, 0.92-1.22]; prudent hazard ratio for T3 vs T1, 0.99 [95% CI, 0.88-1.12]).
Conclusions and Relevance This study found that the dietary pattern of US adults at midlife was not associated with processing speed, word fluency, memory, or incident dementia in later life.
Introduction
Healthy dietary patterns may protect against dementia and mild cognitive impairment.1,2 Prior studies demonstrate that healthy dietary patterns are associated with increased brain volumes and reduced atrophy compared with less healthy dietary patterns.3,4 Although the mechanism behind a healthy diet and improved brain health are not well understood, 2 plausible mechanisms include reduced vascular injury and a reduction in Alzheimer pathology.5 A healthy dietary pattern reduces hypertension, dysglycemia, hyperlipidemia, and chronic inflammation, which may reduce brain vascular injury.2,6 Second, a healthy diet may, through reduced oxidative stress, reduce the accumulation of proteins involved in Alzheimer disease.5,7,8
Midlife dietary pattern, compared with dietary pattern in later life, may have a stronger association with cognitive decline and dementia because chronic disease or the concern for chronic disease in later life may motivate individuals to improve their diet,9 making it appear that a healthy diet is associated with poor health outcomes. At least 10 prior studies examined associations of dietary patterns later in life with cognitive decline, but far fewer prospectively investigated associations for midlife dietary patterns.9,10 In this study, we examine the association between midlife dietary patterns and cognitive change and incident dementia over 20 years. We hypothesized that a healthy diet at midlife would be associated with less cognitive decline and a lower risk of dementia.
Methods
Study Population
The Atherosclerosis Risk in Communities (ARIC) study is a randomly selected and recruited observational cohort study that began in 1987 with individuals aged 45 to 64 years who were representative of the selected communities. Participants enrolled in the ARIC study were from 4 US communities (Jackson, Mississippi; Forsyth County, North Carolina; Washington County, Maryland; and suburban Minneapolis, Minnesota). A total of 15 792 participants received an initial evaluation, and these participants were re-evaluated in person every 3 years. The study is ongoing with 6 in-person visits completed to date.11 The current analysis includes information collected at visit 1 (1987-1989), visit 2 (1990-1992), visit 4 (1996-1998), and at visit 5 (2011-2013). All patients provided written informed consent. The study was approved by the institutional review boards at all participating institutions. The analysis presented is compliant with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
Exposure: Dietary Patterns
Participants completed a 66-item food frequency questionnaire at baseline (1987-1989).12 We condensed baseline questionnaire items into 20 food and beverage groups and derived 2 dietary pattern scores using principal components analysis with orthogonal rotation.13 Principal components analysis transforms possibly correlated variables (in this case, each of the 20 food groups) into a set of linearly uncorrelated variables (in this case, the 2 dietary patterns). Two distinct dietary patterns emerged with an eigenvalue greater than 2 (eTable 1 in the Supplement). The Western diet pattern explained 12% of the total variance and included higher consumption of meat, refined grains, and processed and fried foods. The so-called prudent diet pattern reflected 10% of the total variance and included higher consumption of fruits and vegetables, fish, chicken, whole grains, dairy, nuts, and alcohol. Study participants received a score for each dietary pattern. The score established how closely they adhered to a Western diet pattern or a prudent diet pattern. Scores ranged from −3.96 to 14.26 (interquartile range [IQR], −1.34 to 0.77) for the Western diet pattern and −3.56 to 10.55 (IQR, −0.98 to 0.79) for the prudent diet pattern. A higher score indicated greater adherence to each particular diet pattern.
Outcomes
Cognitive Change
Cognitive testing was performed at visit 2 (1990-1992), visit 4 (1996-1998), and visit 5 (2011-2013). Three tests were used in the cognitive battery: the Delayed Word Recall (DWR)14 test, the Digit Symbol Substitution (DSS) test, and the Word Fluency (WF) test.15 A test-specific z score representing cognitive function at visits 4 and 5 was calculated by subtracting the baseline population mean from the participant’s raw score and dividing the difference by the baseline population standard deviation. A global z score representing cognitive function at visit 4 and 5 was created as the mean of the 3 test-specific z scores.
Cognitive change was defined as the difference in test-specific and global z scores at each point using random-effects linear regression models to account for the intra-individual correlation of cognitive scores.16
Dementia
Dementia was adjudicated according to an established protocol that included assessments involving 3 levels of ascertainment consisting of in-person assessments, telephone interviews of participants or informants, or surveillance based on hospital discharge codes and death certificates.17 Dementia was adjudicated in 2011 to 2013 and 2016 to 2017. Level 1 included in-person assessment of dementia using an algorithm that incorporated information from the Clinical Dementia Rating Interview; the Mini-Mental State examination; longitudinal cognitive testing at visits 2, 4, 5, and 6; a complete neuropsychological battery at visits 5 and 6; and the Functional Activities Questionnaire.18 Level 2 included participants from level 1 and, in addition, 3 other categorizations: (1) participants who met predefined criteria based on completion of the Telephone Interview for Cognitive Status–modified or the Six-Item Screener, (2) deceased persons classified as having dementia, and (3) informant interviews using the AD8 dementia screening interview, as described elsewhere.18 Level 3 included participants in level 2 in addition to individuals with dementia identified by surveillance using prior hospital discharge codes (International Classification of Diseases, Ninth Revision [ICD-9] or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10]) or death certificate codes for dementia.17 Level 3 was used for this analysis. Level 3 ascertained a dementia status (yes or no) for all participants regardless of study visit completion.
Covariates
Covariates included demographic and lifestyle factors, clinical factors, and apolipoprotein E (APOE) ε4 status. Demographic factors included age, sex, race–study center, and education. Lifestyle factors included activity level, current smoking, current alcohol use, and total energy intake. Clinical factors included body mass index (calculated as weight in kilograms divided by height in meters squared), history of hypertension (yes or no, defined as use of hypertension medication, systolic blood pressure >140 mm Hg, or diastolic blood pressure >90 mm Hg at the baseline visit), diabetes (yes or no, defined as self-reported diabetes diagnosis by physicians, use of diabetes medication, or having fasting glucose level of 126 mg/dL or higher or a nonfasting glucose level of 200 mg/dL or higher at the baseline visit [to convert to millimoles per liter, multiply by 0.0555]), total cholesterol (fasting, mmol/L), history of coronary artery disease, and prevalent stroke through visit 2 defined based on self-report of stroke prior to visit 1 and adjudicated cases between visit 1 and visit 2.
Population Attrition
There was a 15-year gap between visit 4 and visit 5, leading to attrition, largely due to death or disability. At visits 4 and 5, respectively, 73.8% and 41.4% of the original cohort remained. This dropout is likely to be informative,19 as we found that diet scores were associated with loss to follow-up (Table 1). To account for population attrition, we imputed the missing cognitive test results in visits 4 and 5 and missing baseline covariates using multiple imputations with chained equations.20 In the multiple imputations with chained equations, we incorporated the diet scores, all covariates, prior cognitive function measurements, and ancillary information about cognitive status collected prospectively for participants who did not attend visit 5. The ancillary cognitive information was collected from the Clinical Dementia Rating scale from informants of both living participants and deceased participants, the Telephone Interview for Cognitive Status for living participants, and hospitalization discharge codes and death certificates (ICD-9 codes). We imputed the cognitive function for participants who died before visit 5 six months prior to the date of death.21 We conducted the primary analyses with 25 sets of imputations.
Cognitive Change Modeling
In the primary analysis, we evaluated the association of the 2 dietary pattern scores by tertile with cognitive function as measured by the change in global z scores at visits 2, 4, and 5. Mixed-effect models were used to account for the correlation between repeated cognitive test measures over time. We defined a study time metric from visit 2 to the cognitive measurement. We used a linear spline for the time variable with a knot at visit 4 to address potential nonlinearity of cognitive change. We incorporated 2 random slopes, which corresponded to the 2 time-spline terms, and a random intercept, assuming an independent correlation structure. To measure the association between diet scores and cognitive change, we examined the interactions between exposure strata and the time-spline terms modeled as conditional likelihoods. We also included conditional likelihoods for age, sex, education, race–field center, and total energy intake for model 1 and APOE ε4 status, alcohol use history, smoking history, activity level, body mass index, total cholesterol, prevalent coronary heart disease, history of hypertension, diabetes, and stroke for model 2. We included the conditional likelihoods for interactions between time-splines and covariates that contributed to the slope of cognitive change for aforementioned covariates. We estimated the mean cognitive change over 20 years by dietary score tertile, using the coefficients of the 2 time-splines terms and their interactions with diet score tertile. A linear trend was tested across the dietary tertiles using the median score of each tertile modeled as a continuous variable.
We performed 2 secondary analyses. In the first, we further evaluated the association of diet scores with the z score from each of the 3 individual cognitive test results (DSS, DWR, and WF). In the second, we replicated the analyses using nonimputed data. In both analyses, we applied the same methods as in the primary analysis.
Incident Dementia
We next evaluated the association of the 2 dietary pattern scores with incident dementia using Cox proportional hazard models. We adjusted for the baseline covariates age, sex, education, race–field center, and total energy intake for model 1 and APOE ε4 status, alcohol use history, smoking history, activity level, body mass index, total cholesterol, prevalent coronary heart disease, history of hypertension, diabetes, and stroke for model 2.
Statistical Analysis
The analysis for this study was completed in January to February 2019. Baseline characteristics of participants were compared by tertiles of diet score using χ2 or analysis of variance. Two-sided P < .05 was considered statistically significant. Analyses were conducted using Stata statistical software version 14.2 (StataCorp).
Results
Participant Characteristics
A total of 15 792 adults enrolled at study baseline (1987-1989) when they were aged 45 to 64 years. Of these 15 792 participants, 6538 joined the neurocognitive visit from 2011 to 2013. Because of small numbers, and in accordance with usual ARIC practice, we excluded those who were neither white nor black (48 individuals) and the black participants in the Minnesota (22 participants) and Washington County cohorts (33 participants). We further excluded 121 participants for missing dietary data and 387 participants with implausible caloric intake, defined as less than 500 and more than 3500 total calories per day for women and less than 700 and more than 4500 total calories per day for men. We also excluded 1550 participants with incomplete cognitive data at visit 2, among whom 1348 missed visit 2, and 43 participants who were missing key covariates. The analytic population included 13 588 participants.
At the baseline visit, participants in our study had a mean (SD) age of 54.6 (5.7) years, 55.8% were women, and 37.2% had at least some college education or greater (Table 1). The average participant was overweight (mean [SD] body mass index, 27.6 [5.3]) and consumed a mean (SD) of 1629 (605) kcal/d. In all, 57.9% of participants did not complete cognitive testing at visit 5, and 28.6% of participants died between study baseline and visit 5.
Western Diet Pattern
Adherence to the Western diet pattern was defined as participants reaching the third tertile of the Western diet pattern score. Participants with a Western diet pattern had a higher rate of study attrition (Table 1) and were less likely to be women. Participants with a Western diet pattern were more likely to be from Washington County, Maryland, or Jackson, Mississippi, compared with the other 2 sites, more likely to have less than high school education, more likely to be current smokers, and less likely to engage in physical activity. Participants with a Western diet pattern were also more likely to consume a greater number of calories but were not more likely to have hypertension and diabetes.
Cognitive scores at first measurement were lower in participants with a Western diet pattern compared with participants in the first tertile of Western diet pattern score (z score for tertile 3 [T3], −0.17 [95% CI, −0.20 to −0.14] vs T1, 0.17 [95% CI, 0.14-0.20]) (Table 2). The finding of lower cognitive scores in participants with a Western diet pattern was consistent after adjustments for demographic factors and caloric intake (model 1), but was not statistically significant after full adjustments for lifestyle and clinical factors (model 2) (Table 2). Twenty-year change in cognitive scores was less in participants with a Western diet pattern compared with participants in the first tertile of Western diet pattern score; however, this association did not remain after full adjustments (difference of change in z score for Western diet, T3 vs T1: −0.01 [95% CI, −0.05 to 0.04]) (Table 3). When examined independently, only 20-year change in the DSS z score was less in participants with a Western diet pattern compared with participants in the first tertile of Western diet pattern score (meaning less decline), but this association was not significant after adjustments (eTable 2 in the Supplement). Twenty-year change in the DWR or WF was not different in participants with a Western pattern compared with participants in the first tertile of Western diet pattern score (eTable 3 and eTable 4 in the Supplement).
Our secondary analysis using nonimputed data demonstrated the same findings for the Western diet pattern compared with the imputed data set (eTable 5 in the Supplement).
Participants with a Western diet pattern were no more likely to develop dementia 20 years later compared with participants in the first tertile of Western diet pattern score (adjusted hazard ratio for T3 vs T1, 1.06; 95% CI, 0.92-1.22 for T3 vs T1) (Table 4).
Prudent Diet Pattern
Adherence to a prudent diet pattern was defined as participants reaching the third tertile of the prudent diet pattern score (z score for T3, −0.09 [95% CI, −0.12 to −0.06] vs T1, −0.09 [95% −0.12 to −0.06]). Participants with a prudent diet pattern had no difference in study attrition (eTable 6 in the Supplement) and were more likely to be women. Participants with a prudent diet pattern were less likely to be from Jackson, Mississippi, and more likely to be from the other 3 study locations. Participants with a prudent diet pattern were more likely to have a college education or greater and were more likely to be never smokers and engage in physical activity. Participants with a prudent diet pattern were also more likely to consume higher calories and have diabetes but not hypertension.
Cognitive scores at first measurement were higher in participants with a prudent diet pattern compared with participants in the first tertile of prudent diet pattern score (Table 2), but this association did not remain after full adjustments. Twenty-year change in cognitive scores was greater in participants with a prudent diet pattern compared with participants in the first tertile of prudent diet pattern score; however, this association did not remain after full adjustments (difference of change in z score for prudent diet T3 vs T1: 0.02 [95% CI, −0.02 to 0.06]) (Table 3). When examined independently, only 20-year change in the DSS was greater in participants with a prudent diet pattern compared with participants in the first tertile of prudent diet pattern score, but this association was not significant after full adjustments (eTable 2 in the Supplement). Twenty-year change in the DWR or WF was not different in participants with a prudent diet pattern compared with participants in the first tertile of prudent diet pattern score (eTable 3 and 4 in the Supplement).
Our secondary analysis using nonimputed data demonstrated the same findings for the prudent pattern compared with the imputed data set (eTable 5 in the Supplement).
Participants with a prudent diet pattern were not more likely to develop dementia 20 years later compared with participants in the first tertile of prudent diet pattern score (adjusted hazard ratio, 0.99; 95% CI, 0.88-1.12 for T3 vs T1) (Table 4).
Discussion
We did not find an association between dietary patterns and cognitive decline measured over 20 years. A dietary pattern high in meat and fried food intake was associated with lower cognitive test scores at baseline, but differences in demographic characteristics and health behaviors explained this finding. Similarly, a dietary pattern high in fruit and vegetable intake was associated with higher cognitive test scores at baseline, but differences in demographic characteristics and health behaviors explained this finding.
Our results stand in contrast to short-term observational studies. Several observational studies,22-26 ranging in duration from 5 to 7 years, showed modest associations between dietary patterns and cognitive health. One study24 followed 1410 participants over 5 years and found that adherence to a Mediterranean-type dietary pattern was associated with less decline in the Mini-Mental State examination. Another study23 followed more than 2200 participants for 6 years and found that the Western diet was associated with greater cognitive decline and the prudent diet was associated with less cognitive decline as measured by the Mini-Mental State examination.
A recent long-term observational study27 aligns with our results. The Whitehall II study27 measured diet in 1991 to 1993 and dementia surveillance occurred through 2017. The authors found that diet quality at midlife was not associated with incident dementia in long-term follow up. Our results confirm the findings of this study in a US population.
We suggest 3 explanations for the reported differences between short-term studies and studies with long-term follow-up.27 First, it may be that over time, other chronic diseases such as diabetes have a greater impact on cognition compared with diet. Our study only partially accounts for this confounding by adjusting for comorbidities at baseline. Second, participants with an unhealthy diet engage in multiple unhealthy behaviors (eg, smoking and lack of physical activity). It may be difficult to elucidate the independent outcomes associated with diet when multiple lifestyle behaviors contribute to cognitive function. Third, our study does not account for change in dietary intake or the food supply over 20 years.
Two clinical trials28 build on the promising observational science to examine whether dietary changes can protect against cognitive decline and dementia. One intervention28 tested a Mediterranean diet with olive oil or nuts as supplementation in 334 participants at high cardiovascular risk and found improved composite cognitive function compared with the control diet. A second clinical trial, the Mediterranean-Dietary Approaches to Stop Hypertension (MIND) clinical trial, is currently under way. While our study did not find an association of diet with cognitive decline, this should not undermine the potential of dietary change to affect brain health.
Strengths and Limitations
Our study has strengths, one of which is the long duration of follow up. Another is our ability to account for study dropout due to death or loss to follow-up using criterion-standard imputation methods.20
There are also several limitations of our study. First, our definition of achievement of a Western or prudent diet score is based on our tertile cutoffs and may not reflect individual participant identification with the specified dietary pattern. Second, diet was measured 3 years before the first cognitive measurement. The nonconcurrent measurements are unlikely to affect the results because dietary patterns remain relatively stable up to 7 years.29 However, dietary intake likely changes over 20 years owing to change in the food supply and food habits. The ARIC study did not capture diet over the 20 years to test this possibility. In addition, as participants with an unhealthy diet had lower cognition at the time of first assessment, it is possible that diet exerted influence prior to our time of measurement. As diet was not associated with either cognitive trajectories or incident dementia, this is less likely to be the case. We should also note that although study dropout was accounted for, a large proportion of participants did not follow up after 20 years. Finally, as in all observational studies, we are unable to attribute causality to our observations, as the mechanisms between diet and brain health are complex, and the only way to definitively measure the relationship between dietary practices and cognition is in an experimental design in which diet is manipulated; however, long-term follow-up may be expensive.
Conclusions
The results of this cohort study do not support the hypothesis that midlife diet significantly contributes to cognitive decline independent of demographic and behavioral factors. Our finding that participants with an unhealthy diet have lower cognitive function could be attributed to cigarette smoking, eating excess calories, or engaging in less physical activity. Our results suggest that it may be important to address all modifiable risk factors in dietary interventions, supporting the emerging body of multimodal lifestyle and behavioral research.30 A multimodal approach may provide greater risk reduction for cognitive aging.
Contrary to expectations, didn't find a significant relation between friending behavior of participants and fictitious Facebook dimensions of gender, profile photos, education status & relationship status
Would You like to Be My Facebook Friend? Cemil Akkaş, Hülya Bakırtaş. Sexuality & Culture, December 5 2019. https://link.springer.com/article/10.1007/s12119-019-09684-6
Abstract: This research seeks to understand how people respond to demographic factors and different types of Facebook profile. Using a 2 × 3 × 2 × 2 between-subjects experimental design, the research explores the relationship between gender of a fictitious Facebook account (female, male), attraction levels of the profile photo (attractive, normal and default), education status (university, default) and relationship status (in a relationship, default). Additionally, this process has been applied in both field research (Study 1) and laboratory (Study 2). A beauty survey was applied to determine the profile photos to be used in these fictitious accounts. Friendship requests were sent to participants in the two different environments (field and lab) by fictitious Facebook accounts, and results were monitored and analyzed. Whilst some research has been carried out on online friendship, no study exists that involves the role of the environment. The results of this study indicate that the environment plays an important role in friendship acceptance behavior. Another important finding was that the gender of participants is the most significant determinant in friendship acceptance behavior in both field and laboratory. However, the relationship between class and income levels of participants and behavior of accepting friendship request was not significant. Contrary to expectations, this study did not find a significant relation between friending behavior of participants and fictitious Facebook dimensions of gender, profile photos, education status and relationship status.
Keywords: Social networks Friendship Facebook Friending
Abstract: This research seeks to understand how people respond to demographic factors and different types of Facebook profile. Using a 2 × 3 × 2 × 2 between-subjects experimental design, the research explores the relationship between gender of a fictitious Facebook account (female, male), attraction levels of the profile photo (attractive, normal and default), education status (university, default) and relationship status (in a relationship, default). Additionally, this process has been applied in both field research (Study 1) and laboratory (Study 2). A beauty survey was applied to determine the profile photos to be used in these fictitious accounts. Friendship requests were sent to participants in the two different environments (field and lab) by fictitious Facebook accounts, and results were monitored and analyzed. Whilst some research has been carried out on online friendship, no study exists that involves the role of the environment. The results of this study indicate that the environment plays an important role in friendship acceptance behavior. Another important finding was that the gender of participants is the most significant determinant in friendship acceptance behavior in both field and laboratory. However, the relationship between class and income levels of participants and behavior of accepting friendship request was not significant. Contrary to expectations, this study did not find a significant relation between friending behavior of participants and fictitious Facebook dimensions of gender, profile photos, education status and relationship status.
Keywords: Social networks Friendship Facebook Friending
In Brain and Behavior: A multimethod investigation of motor inhibition in professional drummers
Boom Chack Boom—A multimethod investigation of motor inhibition in professional drummers. Lara Schlaffke Sarah Friedrich Martin Tegenthoff Onur Güntürkün Erhan Genç Sebastian Ocklenburg. Brain and Behavior, December 4 2019. https://doi.org/10.1002/brb3.1490
Abstract
Introduction: Our hands are the primary means for motor interaction with the environment, and their neural organization is fundamentally asymmetric: While most individuals can perform easy motor tasks with two hands equally well, only very few individuals can perform complex fine motor tasks with both hands at a similar level of performance. The reason why this phenomenon is so rare is not well understood. Professional drummers represent a unique population to study it, as they have remarkable abilities to perform complex motor tasks with their two limbs independently.
Methods: Here, we used a multimethod neuroimaging approach to investigate the structural, functional, and biochemical correlates of fine motor behavior in professional drummers (n = 20) and nonmusical controls (n = 24).
Results: Our results show that drummers have higher microstructural diffusion properties in the corpus callosum than controls. This parameter also predicts drumming performance and GABA levels in the motor cortex. Moreover, drummers show less activation in the motor cortex when performing a finger‐tapping task than controls.
Conclusion: In conclusion, professional drumming is associated with a more efficient neuronal design of cortical motor areas as well as a stronger link between commissural structure and biochemical parameters associated with motor inhibition.
1 INTRODUCTION
Our hands are the primary means of interaction with the environment. A key aspect of hand use in humans is its asymmetrical organization. While most individuals can perform easy motor tasks with two hands at a similar level, only very few individuals can perform complex fine motor tasks with both hands equally well. Most individuals strongly prefer one hand (often called the dominant hand) over the other hand. Typically, each individual has a distinct handedness and prefers either the left or the right hand for complex fine motor tasks, for example writing (Güntürkün & Ocklenburg, 2017; Ocklenburg, Hugdahl, & Westerhausen, 2013). Handedness is thus one of the most pronounced and most widely investigated aspects of hemispheric asymmetries. A ratio of 90% right‐handed to 10% left‐handed people is constant for the past 5,000 years over all continents (Coren & Porac, 1977) and is noticeable even in utero (Hepper, Shahidullah, & White, 1990).
Each hand is controlled by the contralateral motor cortex. Neuronal correlates of handedness are mostly investigated by examining brain activity during more or less complex hand movement tasks. Such activities with the dominant hand are largely regulated by the contralateral hemisphere, whereas motor tasks with the nondominant hand are controlled more bilaterally by both hemispheres (van den Berg, Swinnen, & Wenderoth, 2011; Grabowska et al., 2012). The corpus callosum, as the major connecting pathway between hemispheres, was shown to have substantial influence on the characteristics of handedness (Hayashi et al., 2008; Westerhausen et al., 2004). Right‐handed people show a strong ipsilateral motor cortex de‐activation, when performing tasks with their dominant hand (Genç, Ocklenburg, Singer, & Güntürkün, 2015). In contrast, in left‐handed people, ipsilateral activations/de‐activation are equally pronounced, independent of the used hand. These findings demonstrate the correlation between ipsilateral activations and transcallosal inhibitions (Tzourio‐Mazoyer et al., 2015). Furthermore, patients with callosal agenesis, a hereditary condition in which the corpus callosum is absent in the brain, show a stronger tendency toward both‐handedness, for example not having a dominant hand (Ocklenburg, Ball, Wolf, Genç, & Güntürkün, 2015). Therefore, inhibitory functions of the corpus callosum represent an important aspect when understanding the neuronal correlates of handedness (Genç et al., 2015; Ocklenburg, Friedrich, Güntürkün, & Genç, 2016).
Since handedness can be partly altered through training (Perez et al., 2007), its constituent neural fundaments can change by learning. Neuroplasticity describes the adaption and cortical reorganization for example after training or learning a new skill. Functional plasticity of motor skills has been in the focus of neuroscientific research for decades. Already in the 1990s, it has been shown that playing the violin as a professional is influencing the somatosensory representations of the left (nondominant) hand (Elbert, Pantev, Wienbruch, Rockstroh, & Taub, 1995). Being able to play a music instrument on a professional level can also influence visuo‐motor (Buccino et al., 2004; Stewart et al., 2003; Vogt et al., 2007) as well as audio‐motor processes (Bangert et al., 2006; Baumann, Koeneke, Meyer, Lutz, & Jäncke, 2005; Baumann et al., 2007; Parsons, Sergent, Hodges, & Fox, 2005).
Up to now, musical training‐driven plasticity was primarily centered on changes of cortical gray matter. However, most musical instruments are played with both hands, increasing the demand for fast, precise and uncoupled movements of both hands. When playing piano, both hands are recruited in an equally demanding manner and sometimes with different rhythms, whereas playing a stringed instrument requires distinct motor activities for the same rhythm. In contrast, when drumming, both hands and even legs have to perform similar motor tasks, however with distinct rhythms. Therefore, drummers are well suited as subjects for the investigation of structural correlates of transcallosal inhibition.
While it is very difficult for an untrained person to play a ¾ beat with one hand and a 4/4 beat with the other at the same time, this is an easy task for trained drummers. Research in split‐brain patients indicates that this remarkable ability to uncouple the motor trajectories of the two hands is likely related to inhibitory functions of the corpus callosum. Franz, Eliassen, Ivry, and Gazzaniga (1996) investigated bimanual movements in split‐brain patients and healthy controls and found that the controls showed deviations in the trajectories when the two hands performed movements with different spatial demands (Franz et al., 1996). In contrast, split‐brain patients did not produce spatial deviations. This suggests that movement interference in controls is mediated by the corpus callosum and that professional drummers likely show an experience‐dependent change in callosal structure and/or function that enables them to perform two different motor trajectories with the two hands at the same time. Thus, drumming requires neuroplasticity of whiter matter pathways. This is what we set out to study.
The structural, functional, and biochemical correlates of this remarkable ability of professional drummers are still completely unclear, but unraveling them would yield important insights into the general neuronal foundations of motoric decoupling. Therefore, the present study was aimed at investigating professional drummers for structural, functional, and biochemical differences to untrained controls, linked to transcallosal inhibition. To this end, we used a state‐of‐the‐art multimethod neuroimaging approach. We assessed the microstructure of the corpus callosum using DTI to reveal possible alterations of callosal anatomy between groups (Friedrich et al., 2017; Genç, Bergmann, Singer, & Kohler, 2011a; Genç, Bergmann, Tong, et al., 2011b; Westerhausen et al., 2004). Moreover, we assessed the biochemical correlates of GABA spectroscopy to test long‐term changes of inhibitory motor control (Stagg, 2014), as GABA levels in motor regions are highly associated with BOLD activations and motor learning. Specifically, lower GABA levels are associated with an increased degree of motor learning (Ziemann, Muellbacher, Hallett, & Cohen, 2001), while individuals with higher baseline levels of M1 GABA have slower reaction times and smaller task‐related signal changes (Stagg, Bachtiar, & Johansen‐Berg, 2011). Last, we also scanned participants using a fMRI finger‐tapping task to use a well‐established quantitative framework producing different behavioral complexities (Genç et al., 2015; Haaland, Elsinger, Mayer, Durgerian, & Rao, 2004). We assumed that drummers should show differences from nonmusical controls reflecting a more efficient neural organization on the structural, functional, and biochemical modality.
Abstract
Introduction: Our hands are the primary means for motor interaction with the environment, and their neural organization is fundamentally asymmetric: While most individuals can perform easy motor tasks with two hands equally well, only very few individuals can perform complex fine motor tasks with both hands at a similar level of performance. The reason why this phenomenon is so rare is not well understood. Professional drummers represent a unique population to study it, as they have remarkable abilities to perform complex motor tasks with their two limbs independently.
Methods: Here, we used a multimethod neuroimaging approach to investigate the structural, functional, and biochemical correlates of fine motor behavior in professional drummers (n = 20) and nonmusical controls (n = 24).
Results: Our results show that drummers have higher microstructural diffusion properties in the corpus callosum than controls. This parameter also predicts drumming performance and GABA levels in the motor cortex. Moreover, drummers show less activation in the motor cortex when performing a finger‐tapping task than controls.
Conclusion: In conclusion, professional drumming is associated with a more efficient neuronal design of cortical motor areas as well as a stronger link between commissural structure and biochemical parameters associated with motor inhibition.
1 INTRODUCTION
Our hands are the primary means of interaction with the environment. A key aspect of hand use in humans is its asymmetrical organization. While most individuals can perform easy motor tasks with two hands at a similar level, only very few individuals can perform complex fine motor tasks with both hands equally well. Most individuals strongly prefer one hand (often called the dominant hand) over the other hand. Typically, each individual has a distinct handedness and prefers either the left or the right hand for complex fine motor tasks, for example writing (Güntürkün & Ocklenburg, 2017; Ocklenburg, Hugdahl, & Westerhausen, 2013). Handedness is thus one of the most pronounced and most widely investigated aspects of hemispheric asymmetries. A ratio of 90% right‐handed to 10% left‐handed people is constant for the past 5,000 years over all continents (Coren & Porac, 1977) and is noticeable even in utero (Hepper, Shahidullah, & White, 1990).
Each hand is controlled by the contralateral motor cortex. Neuronal correlates of handedness are mostly investigated by examining brain activity during more or less complex hand movement tasks. Such activities with the dominant hand are largely regulated by the contralateral hemisphere, whereas motor tasks with the nondominant hand are controlled more bilaterally by both hemispheres (van den Berg, Swinnen, & Wenderoth, 2011; Grabowska et al., 2012). The corpus callosum, as the major connecting pathway between hemispheres, was shown to have substantial influence on the characteristics of handedness (Hayashi et al., 2008; Westerhausen et al., 2004). Right‐handed people show a strong ipsilateral motor cortex de‐activation, when performing tasks with their dominant hand (Genç, Ocklenburg, Singer, & Güntürkün, 2015). In contrast, in left‐handed people, ipsilateral activations/de‐activation are equally pronounced, independent of the used hand. These findings demonstrate the correlation between ipsilateral activations and transcallosal inhibitions (Tzourio‐Mazoyer et al., 2015). Furthermore, patients with callosal agenesis, a hereditary condition in which the corpus callosum is absent in the brain, show a stronger tendency toward both‐handedness, for example not having a dominant hand (Ocklenburg, Ball, Wolf, Genç, & Güntürkün, 2015). Therefore, inhibitory functions of the corpus callosum represent an important aspect when understanding the neuronal correlates of handedness (Genç et al., 2015; Ocklenburg, Friedrich, Güntürkün, & Genç, 2016).
Since handedness can be partly altered through training (Perez et al., 2007), its constituent neural fundaments can change by learning. Neuroplasticity describes the adaption and cortical reorganization for example after training or learning a new skill. Functional plasticity of motor skills has been in the focus of neuroscientific research for decades. Already in the 1990s, it has been shown that playing the violin as a professional is influencing the somatosensory representations of the left (nondominant) hand (Elbert, Pantev, Wienbruch, Rockstroh, & Taub, 1995). Being able to play a music instrument on a professional level can also influence visuo‐motor (Buccino et al., 2004; Stewart et al., 2003; Vogt et al., 2007) as well as audio‐motor processes (Bangert et al., 2006; Baumann, Koeneke, Meyer, Lutz, & Jäncke, 2005; Baumann et al., 2007; Parsons, Sergent, Hodges, & Fox, 2005).
Up to now, musical training‐driven plasticity was primarily centered on changes of cortical gray matter. However, most musical instruments are played with both hands, increasing the demand for fast, precise and uncoupled movements of both hands. When playing piano, both hands are recruited in an equally demanding manner and sometimes with different rhythms, whereas playing a stringed instrument requires distinct motor activities for the same rhythm. In contrast, when drumming, both hands and even legs have to perform similar motor tasks, however with distinct rhythms. Therefore, drummers are well suited as subjects for the investigation of structural correlates of transcallosal inhibition.
While it is very difficult for an untrained person to play a ¾ beat with one hand and a 4/4 beat with the other at the same time, this is an easy task for trained drummers. Research in split‐brain patients indicates that this remarkable ability to uncouple the motor trajectories of the two hands is likely related to inhibitory functions of the corpus callosum. Franz, Eliassen, Ivry, and Gazzaniga (1996) investigated bimanual movements in split‐brain patients and healthy controls and found that the controls showed deviations in the trajectories when the two hands performed movements with different spatial demands (Franz et al., 1996). In contrast, split‐brain patients did not produce spatial deviations. This suggests that movement interference in controls is mediated by the corpus callosum and that professional drummers likely show an experience‐dependent change in callosal structure and/or function that enables them to perform two different motor trajectories with the two hands at the same time. Thus, drumming requires neuroplasticity of whiter matter pathways. This is what we set out to study.
The structural, functional, and biochemical correlates of this remarkable ability of professional drummers are still completely unclear, but unraveling them would yield important insights into the general neuronal foundations of motoric decoupling. Therefore, the present study was aimed at investigating professional drummers for structural, functional, and biochemical differences to untrained controls, linked to transcallosal inhibition. To this end, we used a state‐of‐the‐art multimethod neuroimaging approach. We assessed the microstructure of the corpus callosum using DTI to reveal possible alterations of callosal anatomy between groups (Friedrich et al., 2017; Genç, Bergmann, Singer, & Kohler, 2011a; Genç, Bergmann, Tong, et al., 2011b; Westerhausen et al., 2004). Moreover, we assessed the biochemical correlates of GABA spectroscopy to test long‐term changes of inhibitory motor control (Stagg, 2014), as GABA levels in motor regions are highly associated with BOLD activations and motor learning. Specifically, lower GABA levels are associated with an increased degree of motor learning (Ziemann, Muellbacher, Hallett, & Cohen, 2001), while individuals with higher baseline levels of M1 GABA have slower reaction times and smaller task‐related signal changes (Stagg, Bachtiar, & Johansen‐Berg, 2011). Last, we also scanned participants using a fMRI finger‐tapping task to use a well‐established quantitative framework producing different behavioral complexities (Genç et al., 2015; Haaland, Elsinger, Mayer, Durgerian, & Rao, 2004). We assumed that drummers should show differences from nonmusical controls reflecting a more efficient neural organization on the structural, functional, and biochemical modality.
Declining Sexual Activity and Desire in Women: Findings from Representative German Studies in 2005 & 2016
Declining Sexual Activity and Desire in Women: Findings from Representative German Surveys 2005 and 2016. Juliane Burghardt et al. Archives of Sexual Behavior, December 4 2019. DOI 10.1007/s10508-019-01525-9
Abstract: We estimate (1) sexual activity and sexual desire in women living with and without a partner across the age range in Germany and (2) changes over 11 years. A representative survey of 345 (response rate: 65%) women between 18 and 99 years from 2016 was compared to a survey of 1314 women age 18–91 from 2005 (response rate: 53%). Sexual activity was assessed as having been physically intimate with someone in the past year; frequency of sexual desire was rated for the past 4 weeks. In 2016, the great majority of women living with a partner were sexually active and indicated sexual desire until the age of 60, which decreased thereafter. Compared to 2005, fewer women cohabited with a partner. Across the age range, women living without a partner reported considerably less sexual activity and desire. The overall proportion of women reporting partnered sexual activity decreased from 67% to 62% in 2016, and absent sexual desire increased from 24% to 26%. Declines of sexual activity and desire affected mostly young and middle-aged women. The decline of sexual activity and desire seems to be due to a reduced proportion of women living with a partner. There was also a generation effect with younger and middle-aged women without a partner becoming less sexually active and experiencing less desire compared to the previous survey. While surveys were methodologically comparable, interpretations are limited by the absence of longitudinal data.
Keywords: Sexual desire Sexual activity Partnership Representative sample
Discussion
In 2016, 60% of women from a population-based German
sample reported sexual activity during the last year. Partnership
was an important factor: Eighty-seven percent of women
with a partner reported having been sexually active in the past
year, this applied to only 37% without a partner. A considerable
proportion of women living with a partner reported
sexual activity in old age (27% > 70 years), whereas among
women without a partner sexual activity decreased earlier.
For instance, over 41 years, the majority of women (59%)
reported having been sexually inactive during the past year,
and hardly any were active among the elderly.
Between 2005 and 2016, the overall proportion of sexually
active women decreased by 5%. However, this reduction did
not occur among women with a partner, whose sexual activity
remained high and stable between 2005 (85%) and 2016
(87%). The decline in sexual activity can be attributed to
both a decline of women living with a partner by 7%, which
manifested itself among all age groups except for the oldest
(over 71 yr.) and a decrease in sexual activity among women
living without a partner. Among these women, sexual activity
decreased from 42 to 37%. This decline was most pronounced
among women aged 18–60; no decline occurred among older
women regardless of partnership.
Our findings of reduced sexual activity are consistent
with American studies (Twenge et al., 2016, 2017). Thus,
despite differences between Germany and the U.S. regarding
cultural norms and use of contraception, the decrease seemed
to be a more general development. In contrast to Twenge
et al. (2017), we found this decline to occur mostly among
individuals living with a partner. The difference between
these findings could be based on differences in the way of
measuring sexual activity. While Twenge et al. (2017) asked
participants how often they did “have sex during the last
12 months,” we asked whether participants “were (physically)
intimate with someone” within this period.
Our findings on frequency of sexual desire mirrored those
of sexual activity, with desire also decreasing between 2005
and 2016. Overall, the frequency of absent sexual desire
increased by almost 3% (23.5–26.4%). This decline was the
strongest among women below 50 years and those living
without a partner. The decrease in sexual interest contrasts
with Lindau and Gavrilova (2010), who reported constant
sexual interest in women between 1995 and 2002, which
might be an artefact of using different items to measure
sexual interest within the two compared surveys or a more
recent development.
The similarity between the findings regarding sexual
activity and sexual desire was also marked by a strong correlation
between the two variables. The correlation was higher
among non-partnered women than among partnered women.
Though this correlation does not allow causal interpretations,
it attests to the relevance of the partner to prolonged sexual
activity. Further, the correlation decreased between 2005 and
2016. This may be the first evidence that a change in sexual
desire does not fully explain the decrease in sexual activity
over this decade.
It remains unclear, whether the decline in sexual activity
in women without a partner is compensated by increased
individual, non-partnered sexual activity (e.g. online sex use
with masturbation) or maybe increasing acceptance of absent
sexual interest. Alternatively, the decrease in women living
with a partner may indicate social changes, which decreased
the value of partnered activity and increasing solitary recreational
activities, for instance by media use (Stiftung Zukunftsfragen,
2016). Another important question is whether the
decrease in both sexual activity and interest creates sexual
distress or dissatisfaction (Hayes et al., 2008) or is instead
accepted as a different form of lifestyle.
Our data mirror the general trend of decreasing numbers
of married and cohabitating couples (Fry, 2016). The analysis
included all cohabiting couples by combining the variables
“married, living together” and “living with a partner”. However,
this did not include unmarried committed couples who
do not live together. Comparing committed couples that do
versus do not live together might distinguish between effects
of commitment and partner availability. Further, it remains
unclear whether the availability of a partner preserves sexual
activity and interest, or whether sexual desire sustains/
establishes a partnership, or alternative processes intervene.
However, the findings are in line with the previous models
on women’s sexual desire, which stated that women’s sexual
desire is often responsive and relies on positive non-sexual
outcomes provided by the partner (e.g. trust, emotional intimacy,
communication). These outcomes motivate women
to seek sexually arousing cues to trigger responsive desire in
addition or in the absence of spontaneous desire. This behaviour
has the potential to stabilize sexual activity in a partnership
even in the absence of spontaneous desire (Basson,
2000). The lower correlation of desire and activity among
partnered women fits with Basson’s observation that women
do not perceive responsive desire as “true” desire. Future
research should elaborate on these perceptions.
The cross-sectional design of our two surveys limits the
data interpretation. Despite including the entire age range,
we cannot analyse individual’s life trajectories. In contrast
to Lindau and Gavrilova (2010), we used identical measures
and sampling procedures to create representative samples
in both surveys. Unlike studies that limit sexual activity to
intercourse, our items purposefully included a wide range of
potentially relevant sexual behaviours (“have you been intimate
with someone…”) to cover a broad range of partnered
sexual activities (Mercer et al., 2013). Twenge et al. (2016)
argued that the decreased sexual frequency may be explained
by differences in definitions of sex (in contrast to including
only vaginal-penile penetration). However, our results match
their findings using a broader measure of sexual activity. The
low non-responder rate (< 1%) indicates that participants felt
comfortable to answer the questions, which we believe to be
based on the item introduction. This supports the validity of
the findings. Future studies should evaluate socioeconomic,
social, ethical and religious influences as well as working
conditions on sexuality and their interplay with partnerships.
Abstract: We estimate (1) sexual activity and sexual desire in women living with and without a partner across the age range in Germany and (2) changes over 11 years. A representative survey of 345 (response rate: 65%) women between 18 and 99 years from 2016 was compared to a survey of 1314 women age 18–91 from 2005 (response rate: 53%). Sexual activity was assessed as having been physically intimate with someone in the past year; frequency of sexual desire was rated for the past 4 weeks. In 2016, the great majority of women living with a partner were sexually active and indicated sexual desire until the age of 60, which decreased thereafter. Compared to 2005, fewer women cohabited with a partner. Across the age range, women living without a partner reported considerably less sexual activity and desire. The overall proportion of women reporting partnered sexual activity decreased from 67% to 62% in 2016, and absent sexual desire increased from 24% to 26%. Declines of sexual activity and desire affected mostly young and middle-aged women. The decline of sexual activity and desire seems to be due to a reduced proportion of women living with a partner. There was also a generation effect with younger and middle-aged women without a partner becoming less sexually active and experiencing less desire compared to the previous survey. While surveys were methodologically comparable, interpretations are limited by the absence of longitudinal data.
Keywords: Sexual desire Sexual activity Partnership Representative sample
Discussion
In 2016, 60% of women from a population-based German
sample reported sexual activity during the last year. Partnership
was an important factor: Eighty-seven percent of women
with a partner reported having been sexually active in the past
year, this applied to only 37% without a partner. A considerable
proportion of women living with a partner reported
sexual activity in old age (27% > 70 years), whereas among
women without a partner sexual activity decreased earlier.
For instance, over 41 years, the majority of women (59%)
reported having been sexually inactive during the past year,
and hardly any were active among the elderly.
Between 2005 and 2016, the overall proportion of sexually
active women decreased by 5%. However, this reduction did
not occur among women with a partner, whose sexual activity
remained high and stable between 2005 (85%) and 2016
(87%). The decline in sexual activity can be attributed to
both a decline of women living with a partner by 7%, which
manifested itself among all age groups except for the oldest
(over 71 yr.) and a decrease in sexual activity among women
living without a partner. Among these women, sexual activity
decreased from 42 to 37%. This decline was most pronounced
among women aged 18–60; no decline occurred among older
women regardless of partnership.
Our findings of reduced sexual activity are consistent
with American studies (Twenge et al., 2016, 2017). Thus,
despite differences between Germany and the U.S. regarding
cultural norms and use of contraception, the decrease seemed
to be a more general development. In contrast to Twenge
et al. (2017), we found this decline to occur mostly among
individuals living with a partner. The difference between
these findings could be based on differences in the way of
measuring sexual activity. While Twenge et al. (2017) asked
participants how often they did “have sex during the last
12 months,” we asked whether participants “were (physically)
intimate with someone” within this period.
Our findings on frequency of sexual desire mirrored those
of sexual activity, with desire also decreasing between 2005
and 2016. Overall, the frequency of absent sexual desire
increased by almost 3% (23.5–26.4%). This decline was the
strongest among women below 50 years and those living
without a partner. The decrease in sexual interest contrasts
with Lindau and Gavrilova (2010), who reported constant
sexual interest in women between 1995 and 2002, which
might be an artefact of using different items to measure
sexual interest within the two compared surveys or a more
recent development.
The similarity between the findings regarding sexual
activity and sexual desire was also marked by a strong correlation
between the two variables. The correlation was higher
among non-partnered women than among partnered women.
Though this correlation does not allow causal interpretations,
it attests to the relevance of the partner to prolonged sexual
activity. Further, the correlation decreased between 2005 and
2016. This may be the first evidence that a change in sexual
desire does not fully explain the decrease in sexual activity
over this decade.
It remains unclear, whether the decline in sexual activity
in women without a partner is compensated by increased
individual, non-partnered sexual activity (e.g. online sex use
with masturbation) or maybe increasing acceptance of absent
sexual interest. Alternatively, the decrease in women living
with a partner may indicate social changes, which decreased
the value of partnered activity and increasing solitary recreational
activities, for instance by media use (Stiftung Zukunftsfragen,
2016). Another important question is whether the
decrease in both sexual activity and interest creates sexual
distress or dissatisfaction (Hayes et al., 2008) or is instead
accepted as a different form of lifestyle.
Our data mirror the general trend of decreasing numbers
of married and cohabitating couples (Fry, 2016). The analysis
included all cohabiting couples by combining the variables
“married, living together” and “living with a partner”. However,
this did not include unmarried committed couples who
do not live together. Comparing committed couples that do
versus do not live together might distinguish between effects
of commitment and partner availability. Further, it remains
unclear whether the availability of a partner preserves sexual
activity and interest, or whether sexual desire sustains/
establishes a partnership, or alternative processes intervene.
However, the findings are in line with the previous models
on women’s sexual desire, which stated that women’s sexual
desire is often responsive and relies on positive non-sexual
outcomes provided by the partner (e.g. trust, emotional intimacy,
communication). These outcomes motivate women
to seek sexually arousing cues to trigger responsive desire in
addition or in the absence of spontaneous desire. This behaviour
has the potential to stabilize sexual activity in a partnership
even in the absence of spontaneous desire (Basson,
2000). The lower correlation of desire and activity among
partnered women fits with Basson’s observation that women
do not perceive responsive desire as “true” desire. Future
research should elaborate on these perceptions.
The cross-sectional design of our two surveys limits the
data interpretation. Despite including the entire age range,
we cannot analyse individual’s life trajectories. In contrast
to Lindau and Gavrilova (2010), we used identical measures
and sampling procedures to create representative samples
in both surveys. Unlike studies that limit sexual activity to
intercourse, our items purposefully included a wide range of
potentially relevant sexual behaviours (“have you been intimate
with someone…”) to cover a broad range of partnered
sexual activities (Mercer et al., 2013). Twenge et al. (2016)
argued that the decreased sexual frequency may be explained
by differences in definitions of sex (in contrast to including
only vaginal-penile penetration). However, our results match
their findings using a broader measure of sexual activity. The
low non-responder rate (< 1%) indicates that participants felt
comfortable to answer the questions, which we believe to be
based on the item introduction. This supports the validity of
the findings. Future studies should evaluate socioeconomic,
social, ethical and religious influences as well as working
conditions on sexuality and their interplay with partnerships.
Large US-representative adolescent sample: A Flynn Effect was found for IQs ≥ 130, a negative one for ≤ 70; this challenge the practice of generalizing IQ trends with non-representative data samples
The Flynn effect for fluid IQ may not generalize to all ages or ability levels: A population-based study of 10,000 US adolescents. Jonathan M. Platt et al. Intelligence, Volume 77, November–December 2019, 101385. https://doi.org/10.1016/j.intell.2019.101385
Highlights
• When outdated norms are used, the Flynn Effect inflates IQs and potentially biases intellectual disability diagnosis
• In a large US-representative adolescent sample, a Flynn Effect was found for IQs ≥ 130, and a negative effect for IQs ≤ 70
• IQ changes also differed substantially by age group
• A negative Flynn Effect for those with low intellectual ability suggests widening disparities in cognitive ability
• Findings challenge the practice of generalizing IQ trends based on data from non-representative samples
Abstract: Generational changes in IQ (the Flynn Effect) have been extensively researched and debated. Within the US, gains of 3 points per decade have been accepted as consistent across age and ability level, suggesting that tests with outdated norms yield spuriously high IQs. However, findings are generally based on small samples, have not been validated across ability levels, and conflict with reverse effects recently identified in Scandinavia and other countries. Using a well-validated measure of fluid intelligence, we investigated the Flynn Effect by comparing scores normed in 1989 and 2003, among a representative sample of American adolescents ages 13–18 (n = 10,073). Additionally, we examined Flynn Effect variation by age, sex, ability level, parental age, and SES. Adjusted mean IQ differences per decade were calculated using generalized linear models. Overall the Flynn Effect was not significant; however, effects varied substantially by age and ability level. IQs increased 2.3 points at age 13 (95% CI = 2.0, 2.7), but decreased 1.6 points at age 18 (95% CI = −2.1, −1.2). IQs decreased 4.9 points for those with IQ ≤ 70 (95% CI = −4.9, −4.8), but increased 3.5 points among those with IQ ≥ 130 (95% CI = 3.4, 3.6). The Flynn Effect was not meaningfully related to other background variables. Using the largest sample of US adolescent IQs to date, we demonstrate significant heterogeneity in fluid IQ changes over time. Reverse Flynn Effects at age 18 are consistent with previous data, and those with lower ability levels are exhibiting worsening IQ over time. Findings by age and ability level challenge generalizing IQ trends throughout the general population.
Keywords: IntelligenceFlynn effectAdolescenceIntellectual disabilities
Cool charts and tables at the publisher's link above. Excerpts:
5. Discussion
The present study utilized data from a large US-representative
sample of adolescents to describe changes in IQ between 1989 and
2003. There were three central findings: 1) Overall, there was no evidence
of a Flynn Effect during the study period; 2) however, overall IQ
trends masked substantial heterogeneity in the presence and direction
of the Flynn Effect by both ability level and age; and 3) there was no
variation in the Flynn effect as a function of other sociodemographic
characteristics.
The overall lack of a Flynn Effect in our sample is concordant with
trends in the K-BIT, KBIT-2, the Kaufman Assessment Battery for
Children (K-ABC and KABC-II), and other individually administered
screening tests reported in a previous meta-analysis (Trahan et al.,
2014). It also conforms with the conclusion that gains have decreased
in more recent decades (Pietschnig & Voracek, 2015). However, studies
using other tests (e.g., Wechsler scales) did find substantial Flynn
Effects (Pietschnig & Voracek, 2015; Trahan et al., 2014). Explanations
for the Flynn Effect are diverse. Although genetic explanations focusing
on factors such as hybrid vigor (Mingroni, 2007; Rodgers & Wänström,
2007) have been proposed, environmental explanations predominate
(Dickens & Flynn, 2001), emphasizing societal changes in perinatal
nutrition (Lynn, 2009) and nutrition in general (Colom, Lluis-Font, &
Andrés-Pueyo, 2005), education (Teasdale & Owen, 2005), reduced
number of siblings (Sundet, Borren, & Tambs, 2008), the prevalence of
parasites and the burden of disease (Daniele & Ostuni, 2013; Eppig,
Fincher, & Thornhill, 2010), and increased environmental complexity
(Schooler, 1998).
By contrast, other studies have reported reverse Flynn Effects. In
discussing these negative trends in Scandinavian countries, Lynn and
colleagues hypothesized that they may be due to greater fertility among
low SES groups, immigrants, and older adults (Dutton et al., 2016;
Dutton & Lynn, 2013). However, a recent analysis in Norway to test
these claims largely rejects their hypotheses, reporting that Flynn
Effects were not consistent within families over time (Bratsberg &
Rogeberg, 2018). Further, a recent meta-analysis found no substantial
role of fertility on test score changes across an array of studies
(Pietschnig & Voracek, 2015), and recent empirical evidence suggests
that immigration effects do not play a meaningful role in explaining
Flynn Effect reversals (Pietschnig, Voracek, & Gittler, 2018).
We add to the evidence reported in previous studies, by reporting
heterogeneity in the Flynn Effect by ability level and age. We find
support for a reverse Flynn Effect for those of low ability and older age,
and a positive Flynn Effect for those of high ability and younger age.
These results have several implications. First, they signal a widening
disparity in the US in terms of cognitive ability, with those at the lower
end of the ability dimension not only exhibiting less gains than those at
the higher ends, but reversing direction entirely. Second, these results
have implications for considering demographic differences when adjusting
IQ test scores in the population.
Improvements in education, nutrition, prenatal and post-natal care,
and overall environmental complexity over the past century are
thought to contribute to the Flynn Effect in the overall population
(Dickens & Flynn, 2001; Lynn, 2009; Schooler, 1998; Teasdale & Owen,
2005). However, the disparities by ability level that we identified
suggest that the benefits from these societal improvements have been
more dramatic for those at the highest ability levels, potentially because
they are better able to take advantage of these societal changes. This
interpretation is in line with Fundamental Cause Theory (Phelan, Link,
& Tehranifar, 2010), which argues that when new knowledge or technology
is introduced into a society, those with the highest status are
most likely to take advantage first and benefit. Disproportionate utilization
by those with higher abilities may widen intellectual disparities,
leaving those at the lowest ability levels worse off than before. We note,
however, that the Flynn Effect did not differ across other measures of
status, such as poverty and parental education. The correlation analyses
we conducted revealed a positive association of moderate magnitude
between IQ and the size of Flynn Effect, for every age group between 13
and 18, regardless of whether that group showed an overall positive or
negative Flynn Effect. One possible interpretation of this pattern is that
adolescents with high fluid intelligence, not necessarily those with the
highest access to resources, have benefitted most from societal progress
over time.
Previous research on the stability of the Flynn Effect across ability
levels has produced inconsistent and inconclusive results (McGrew,
2015; Weiss, 2010). Sometimes it has been higher at low IQs, and
sometimes a reverse Flynn Effect has been found in high IQ samples
(Spitz, 1989; Teasdale & Owen, 1989; Zhou et al., 2010). A meta-analysis examining ability level as a moderator variable did not observe a
Flynn Effect for those with low IQ (Trahan et al., 2014). However,
previous studies differ in quality (Trahan et al., 2014) and often rely on
small sample sizes at the lower end of the IQ distribution (Zhou et al.,
2010). Specifically, Trahan and colleagues noted, “the distribution of
Flynn effects that we observed at lower ability levels might be the result
of artifacts found in studies of groups within this range of ability” (p.
1349).
We also identified variation in the Flynn Effect by age. The positive
Flynn Effect of 2.3 points per decade at age 13 approximately equals the
value obtained in a summary of studies of Raven's matrices for nearly
250,000 children in 45 countries (Brouwers, Van de Vijver, & Van
Hemert, 2009) and in a meta-analysis of about 14,000 children and
adults in the US and UK (Trahan et al., 2014). However, the 2-point
value is smaller than the traditional 3 points for global intelligence and
4 points for fluid intelligence (Pietschnig & Voracek, 2015). Likewise,
the reverse Flynn Effect that occurred at ages 15–18 was similar to
effects reported in Scandinavian countries among young adult males
during the same time period (Bratsberg & Rogeberg, 2018; Dutton &
Lynn, 2013; Sundet et al., 2004; Teasdale & Owen, 2005, 2008), and in
other countries as well, such as France (adults tested on WAIS-III and
WAIS-IV) and Estonia (young adults tested on Raven's Matrices)
(Dutton et al., 2016). The age effects are discordant with previous
metaanalyses. Pietschnig and Voracek (2015) evaluated age effects and
found stronger gains for adults than children. In their meta-analysis,
Trahan et al. (2014) did not find a significant relationship between
Flynn Effect and age in their examination of the mean ages across
heterogeneous and often small samples. Our methodology differed from
the techniques used in both meta-analyses, as we studied large samples
that were homogeneous by age.
The notable differences we identify among narrowly defined age
groups may be related to cognitive and neurodevelopmental changes
that occur during adolescence. Fluid reasoning abilities and cognitive
abilities that support reasoning (e.g., rule representation) develop rapidly during early adolescence (Crone et al., 2009; Crone, Donohue,
Honomichl, Wendelken, & Bunge, 2006; Ferrer, O'Hare, & Bunge, 2009;
Žebec, Demetriou, & Kotrla-Topić, 2015). Brain regions that play a
central role in reasoning and problem solving, including the dorsolateral and ventrolateral prefrontal cortex and superior and inferior
parietal cortex, also exhibit dramatic changes in structure and function
across adolescence (Bunge, Wendelken, Badre, & Wagner, 2004; Ferrer
et al., 2009; Gogtay et al., 2004; Wendelken, Ferrer, Whitaker, & Bunge,
2015; Wright, Matlen, Baym, Ferrer, & Bunge, 2008). The notably different Flynn Effects by age in our study caution against generalizing
findings for a specific sub-group (such as conscripted young adult
males, which comprise the Scandinavian samples) to the nation as a
whole (Dutton & Lynn, 2013).
The present study identified no meaningful relationship between
Flynn Effect and poverty, parental education other sociodemographic
variables and background factors, including parental nationality, birth
order, family size, age of birth mother and father. This finding is notable given that these demographic variables are associated with IQ
level (von Stumm & Plomin, 2015), including in our sample (Platt,
Keyes, et al., 2018).
The results of this study should be considered in light of several
limitations. First, the study data were obtained 15 years ago. However,
this period was an ideal time to evaluate the presence of a reverse Flynn
Effect in the US, given the reverse effects found in Denmark, Norway,
Finland, and several other countries (Dutton et al., 2016; Teasdale &
Owen, 2008). In more recent years, no reverse Flynn Effect has been
observed for Wechsler's scales, as gains on the WAIS-IV (Wechsler,
2008) and WISC-V (Wechsler, 2014). Full Scale IQ have been close to
the hypothesized value of 3 points per decade (J Grégoire & Weiss,
2019; Jacques Grégoire, Daniel, Llorente, & Weiss, 2016 Weiss,
Gregoire, & Zhu, 2016; Zhou et al., 2010), especially when test content
is held constant (J Grégoire & Weiss, 2019; Weiss et al., 2016).
Second, the K-BIT nonverbal test is a screening test that measures a
single cognitive ability. It is, however, an analog of Raven's popular
matrices test which is commonly used in Flynn Effect studies (Brouwers
et al., 2009; Flynn, 1998; Pietschnig & Voracek, 2015). The Flynn Effect
is known to differ for different cognitive abilities (e.g., fluid intelligence, short-term memory) (Pietschnig & Voracek, 2015; Teasdale
& Owen, 2008), which may contribute to heterogeneity in findings
across studies with differing IQ measures. However, the K-BIT and
KBIT-2 nonverbal IQ is substantially correlated with comprehensive IQ
tests, such as the Wechsler's Full Scale IQ (mid-.50s to mid-70s)
(Canivez et al., 2005; Kaufman & Kaufman, 1990, 2004), though it is
lower than the correlation between different comprehensive test batteries (Kaufman, 2009; Wechsler, 2014). The present findings are descriptive and any practical application regarding the adjustment of IQs
must be made with the awareness that clinical diagnosis, such as the
identification of individuals with intellectual disabilities, must be based
on comprehensive IQ tests such as Wechsler's scales or the WoodcockJohnson, which assess multiple cognitive abilities.
Third, the study included only adolescents, which represents a
narrow period that may not capture meaningful developmental
changes. Indeed, fluid reasoning changes between ages 13–18 are
minimal (Wechsler, 2008, 2014), including in the present 2003 K-BIT
norms sample (Keyes et al., 2016) and the original 1989 norms sample
Kaufman & Kaufman (1990, Table 4.7). This age pattern may partially
explain why we found no overall Flynn Effect in this sample.
Fourth, different procedures were used to develop the 1989 and
2003 norms. The 1989 norms were estimated based on aggregated data
across all age groups, in order to stabilize norms at all ages (Angoff &
Robertson, 1987). Although slightly different statistical techniques
were used to develop the 2003 norms, the general approach to norms
development was similar between samples, and one test author (ASK)
was involved in the development of both sets of norms. Both samples
were representative of the US distributions of sociodemographic, economic, and other key background variables at the time (Kaufman &
Kaufman, 1990; Kessler, Avenevoli, Costello, et al., 2009). Further, both
sets of norms are based on six-month age bands. These samples are at
least as convergent as similar studies comparing samples used to develop original vs. revised norms. Previous studies have differed substantially by key sociodemographic distributions, such as the WISC and
WISC-R (Wechsler, 1949, 1974), which were key samples in the development of the Flynn Effect theory (Flynn, 1984). In the present
study, we adjusted the Flynn Effect for an array of background variables
to further minimize any differences between the 1989 and 2003 norms
samples that may confound the Flynn Effect estimates.
Fifth, the Flynn Effect has had a non-linear trajectory over the past
century (Pietschnig & Voracek, 2015). Because our study included IQ
measurements at only two time points, we were not able to test the
linearity of change over time.
This study is strengthened by the use of a large and representative
adolescent sample, with IQs measured with reasoning items that are
widely accepted as prototypical measures of fluid intelligence (Dutton
et al., 2016). The use of two sets of norms based on a single
administration of a test avoids practice effects and bias that may arise
from use of different versions of a test.
In conclusion, this study reports important heterogeneity in the
Flynn Effect among a nationally-representative sample of US adolescents.
We confirmed previous reports of reverse Flynn Effects among
large samples of older adolescent males, and extended the same pattern
to females. We also found important differential Flynn Effects by ability
level. These results add to a growing body of evidence suggesting that
Flynn Effect findings from narrow age bands or ability levels may
produce divergent findings that do not generalize to the overall population.
However, given the potential life or death implications of this
research in determining intellectual status in capital punishment cases,
the strength of evidence needed for definitive conclusions is extremely
high. At this time, we do not have sufficient evidence to recommend
differential adjustments to IQ scores. Additional research is needed to
replicate the current findings on the full age range and across comprehensive
measures of intelligence.
Highlights
• When outdated norms are used, the Flynn Effect inflates IQs and potentially biases intellectual disability diagnosis
• In a large US-representative adolescent sample, a Flynn Effect was found for IQs ≥ 130, and a negative effect for IQs ≤ 70
• IQ changes also differed substantially by age group
• A negative Flynn Effect for those with low intellectual ability suggests widening disparities in cognitive ability
• Findings challenge the practice of generalizing IQ trends based on data from non-representative samples
Abstract: Generational changes in IQ (the Flynn Effect) have been extensively researched and debated. Within the US, gains of 3 points per decade have been accepted as consistent across age and ability level, suggesting that tests with outdated norms yield spuriously high IQs. However, findings are generally based on small samples, have not been validated across ability levels, and conflict with reverse effects recently identified in Scandinavia and other countries. Using a well-validated measure of fluid intelligence, we investigated the Flynn Effect by comparing scores normed in 1989 and 2003, among a representative sample of American adolescents ages 13–18 (n = 10,073). Additionally, we examined Flynn Effect variation by age, sex, ability level, parental age, and SES. Adjusted mean IQ differences per decade were calculated using generalized linear models. Overall the Flynn Effect was not significant; however, effects varied substantially by age and ability level. IQs increased 2.3 points at age 13 (95% CI = 2.0, 2.7), but decreased 1.6 points at age 18 (95% CI = −2.1, −1.2). IQs decreased 4.9 points for those with IQ ≤ 70 (95% CI = −4.9, −4.8), but increased 3.5 points among those with IQ ≥ 130 (95% CI = 3.4, 3.6). The Flynn Effect was not meaningfully related to other background variables. Using the largest sample of US adolescent IQs to date, we demonstrate significant heterogeneity in fluid IQ changes over time. Reverse Flynn Effects at age 18 are consistent with previous data, and those with lower ability levels are exhibiting worsening IQ over time. Findings by age and ability level challenge generalizing IQ trends throughout the general population.
Keywords: IntelligenceFlynn effectAdolescenceIntellectual disabilities
Cool charts and tables at the publisher's link above. Excerpts:
5. Discussion
The present study utilized data from a large US-representative
sample of adolescents to describe changes in IQ between 1989 and
2003. There were three central findings: 1) Overall, there was no evidence
of a Flynn Effect during the study period; 2) however, overall IQ
trends masked substantial heterogeneity in the presence and direction
of the Flynn Effect by both ability level and age; and 3) there was no
variation in the Flynn effect as a function of other sociodemographic
characteristics.
The overall lack of a Flynn Effect in our sample is concordant with
trends in the K-BIT, KBIT-2, the Kaufman Assessment Battery for
Children (K-ABC and KABC-II), and other individually administered
screening tests reported in a previous meta-analysis (Trahan et al.,
2014). It also conforms with the conclusion that gains have decreased
in more recent decades (Pietschnig & Voracek, 2015). However, studies
using other tests (e.g., Wechsler scales) did find substantial Flynn
Effects (Pietschnig & Voracek, 2015; Trahan et al., 2014). Explanations
for the Flynn Effect are diverse. Although genetic explanations focusing
on factors such as hybrid vigor (Mingroni, 2007; Rodgers & Wänström,
2007) have been proposed, environmental explanations predominate
(Dickens & Flynn, 2001), emphasizing societal changes in perinatal
nutrition (Lynn, 2009) and nutrition in general (Colom, Lluis-Font, &
Andrés-Pueyo, 2005), education (Teasdale & Owen, 2005), reduced
number of siblings (Sundet, Borren, & Tambs, 2008), the prevalence of
parasites and the burden of disease (Daniele & Ostuni, 2013; Eppig,
Fincher, & Thornhill, 2010), and increased environmental complexity
(Schooler, 1998).
By contrast, other studies have reported reverse Flynn Effects. In
discussing these negative trends in Scandinavian countries, Lynn and
colleagues hypothesized that they may be due to greater fertility among
low SES groups, immigrants, and older adults (Dutton et al., 2016;
Dutton & Lynn, 2013). However, a recent analysis in Norway to test
these claims largely rejects their hypotheses, reporting that Flynn
Effects were not consistent within families over time (Bratsberg &
Rogeberg, 2018). Further, a recent meta-analysis found no substantial
role of fertility on test score changes across an array of studies
(Pietschnig & Voracek, 2015), and recent empirical evidence suggests
that immigration effects do not play a meaningful role in explaining
Flynn Effect reversals (Pietschnig, Voracek, & Gittler, 2018).
We add to the evidence reported in previous studies, by reporting
heterogeneity in the Flynn Effect by ability level and age. We find
support for a reverse Flynn Effect for those of low ability and older age,
and a positive Flynn Effect for those of high ability and younger age.
These results have several implications. First, they signal a widening
disparity in the US in terms of cognitive ability, with those at the lower
end of the ability dimension not only exhibiting less gains than those at
the higher ends, but reversing direction entirely. Second, these results
have implications for considering demographic differences when adjusting
IQ test scores in the population.
Improvements in education, nutrition, prenatal and post-natal care,
and overall environmental complexity over the past century are
thought to contribute to the Flynn Effect in the overall population
(Dickens & Flynn, 2001; Lynn, 2009; Schooler, 1998; Teasdale & Owen,
2005). However, the disparities by ability level that we identified
suggest that the benefits from these societal improvements have been
more dramatic for those at the highest ability levels, potentially because
they are better able to take advantage of these societal changes. This
interpretation is in line with Fundamental Cause Theory (Phelan, Link,
& Tehranifar, 2010), which argues that when new knowledge or technology
is introduced into a society, those with the highest status are
most likely to take advantage first and benefit. Disproportionate utilization
by those with higher abilities may widen intellectual disparities,
leaving those at the lowest ability levels worse off than before. We note,
however, that the Flynn Effect did not differ across other measures of
status, such as poverty and parental education. The correlation analyses
we conducted revealed a positive association of moderate magnitude
between IQ and the size of Flynn Effect, for every age group between 13
and 18, regardless of whether that group showed an overall positive or
negative Flynn Effect. One possible interpretation of this pattern is that
adolescents with high fluid intelligence, not necessarily those with the
highest access to resources, have benefitted most from societal progress
over time.
Previous research on the stability of the Flynn Effect across ability
levels has produced inconsistent and inconclusive results (McGrew,
2015; Weiss, 2010). Sometimes it has been higher at low IQs, and
sometimes a reverse Flynn Effect has been found in high IQ samples
(Spitz, 1989; Teasdale & Owen, 1989; Zhou et al., 2010). A meta-analysis examining ability level as a moderator variable did not observe a
Flynn Effect for those with low IQ (Trahan et al., 2014). However,
previous studies differ in quality (Trahan et al., 2014) and often rely on
small sample sizes at the lower end of the IQ distribution (Zhou et al.,
2010). Specifically, Trahan and colleagues noted, “the distribution of
Flynn effects that we observed at lower ability levels might be the result
of artifacts found in studies of groups within this range of ability” (p.
1349).
We also identified variation in the Flynn Effect by age. The positive
Flynn Effect of 2.3 points per decade at age 13 approximately equals the
value obtained in a summary of studies of Raven's matrices for nearly
250,000 children in 45 countries (Brouwers, Van de Vijver, & Van
Hemert, 2009) and in a meta-analysis of about 14,000 children and
adults in the US and UK (Trahan et al., 2014). However, the 2-point
value is smaller than the traditional 3 points for global intelligence and
4 points for fluid intelligence (Pietschnig & Voracek, 2015). Likewise,
the reverse Flynn Effect that occurred at ages 15–18 was similar to
effects reported in Scandinavian countries among young adult males
during the same time period (Bratsberg & Rogeberg, 2018; Dutton &
Lynn, 2013; Sundet et al., 2004; Teasdale & Owen, 2005, 2008), and in
other countries as well, such as France (adults tested on WAIS-III and
WAIS-IV) and Estonia (young adults tested on Raven's Matrices)
(Dutton et al., 2016). The age effects are discordant with previous
metaanalyses. Pietschnig and Voracek (2015) evaluated age effects and
found stronger gains for adults than children. In their meta-analysis,
Trahan et al. (2014) did not find a significant relationship between
Flynn Effect and age in their examination of the mean ages across
heterogeneous and often small samples. Our methodology differed from
the techniques used in both meta-analyses, as we studied large samples
that were homogeneous by age.
The notable differences we identify among narrowly defined age
groups may be related to cognitive and neurodevelopmental changes
that occur during adolescence. Fluid reasoning abilities and cognitive
abilities that support reasoning (e.g., rule representation) develop rapidly during early adolescence (Crone et al., 2009; Crone, Donohue,
Honomichl, Wendelken, & Bunge, 2006; Ferrer, O'Hare, & Bunge, 2009;
Žebec, Demetriou, & Kotrla-Topić, 2015). Brain regions that play a
central role in reasoning and problem solving, including the dorsolateral and ventrolateral prefrontal cortex and superior and inferior
parietal cortex, also exhibit dramatic changes in structure and function
across adolescence (Bunge, Wendelken, Badre, & Wagner, 2004; Ferrer
et al., 2009; Gogtay et al., 2004; Wendelken, Ferrer, Whitaker, & Bunge,
2015; Wright, Matlen, Baym, Ferrer, & Bunge, 2008). The notably different Flynn Effects by age in our study caution against generalizing
findings for a specific sub-group (such as conscripted young adult
males, which comprise the Scandinavian samples) to the nation as a
whole (Dutton & Lynn, 2013).
The present study identified no meaningful relationship between
Flynn Effect and poverty, parental education other sociodemographic
variables and background factors, including parental nationality, birth
order, family size, age of birth mother and father. This finding is notable given that these demographic variables are associated with IQ
level (von Stumm & Plomin, 2015), including in our sample (Platt,
Keyes, et al., 2018).
The results of this study should be considered in light of several
limitations. First, the study data were obtained 15 years ago. However,
this period was an ideal time to evaluate the presence of a reverse Flynn
Effect in the US, given the reverse effects found in Denmark, Norway,
Finland, and several other countries (Dutton et al., 2016; Teasdale &
Owen, 2008). In more recent years, no reverse Flynn Effect has been
observed for Wechsler's scales, as gains on the WAIS-IV (Wechsler,
2008) and WISC-V (Wechsler, 2014). Full Scale IQ have been close to
the hypothesized value of 3 points per decade (J Grégoire & Weiss,
2019; Jacques Grégoire, Daniel, Llorente, & Weiss, 2016 Weiss,
Gregoire, & Zhu, 2016; Zhou et al., 2010), especially when test content
is held constant (J Grégoire & Weiss, 2019; Weiss et al., 2016).
Second, the K-BIT nonverbal test is a screening test that measures a
single cognitive ability. It is, however, an analog of Raven's popular
matrices test which is commonly used in Flynn Effect studies (Brouwers
et al., 2009; Flynn, 1998; Pietschnig & Voracek, 2015). The Flynn Effect
is known to differ for different cognitive abilities (e.g., fluid intelligence, short-term memory) (Pietschnig & Voracek, 2015; Teasdale
& Owen, 2008), which may contribute to heterogeneity in findings
across studies with differing IQ measures. However, the K-BIT and
KBIT-2 nonverbal IQ is substantially correlated with comprehensive IQ
tests, such as the Wechsler's Full Scale IQ (mid-.50s to mid-70s)
(Canivez et al., 2005; Kaufman & Kaufman, 1990, 2004), though it is
lower than the correlation between different comprehensive test batteries (Kaufman, 2009; Wechsler, 2014). The present findings are descriptive and any practical application regarding the adjustment of IQs
must be made with the awareness that clinical diagnosis, such as the
identification of individuals with intellectual disabilities, must be based
on comprehensive IQ tests such as Wechsler's scales or the WoodcockJohnson, which assess multiple cognitive abilities.
Third, the study included only adolescents, which represents a
narrow period that may not capture meaningful developmental
changes. Indeed, fluid reasoning changes between ages 13–18 are
minimal (Wechsler, 2008, 2014), including in the present 2003 K-BIT
norms sample (Keyes et al., 2016) and the original 1989 norms sample
Kaufman & Kaufman (1990, Table 4.7). This age pattern may partially
explain why we found no overall Flynn Effect in this sample.
Fourth, different procedures were used to develop the 1989 and
2003 norms. The 1989 norms were estimated based on aggregated data
across all age groups, in order to stabilize norms at all ages (Angoff &
Robertson, 1987). Although slightly different statistical techniques
were used to develop the 2003 norms, the general approach to norms
development was similar between samples, and one test author (ASK)
was involved in the development of both sets of norms. Both samples
were representative of the US distributions of sociodemographic, economic, and other key background variables at the time (Kaufman &
Kaufman, 1990; Kessler, Avenevoli, Costello, et al., 2009). Further, both
sets of norms are based on six-month age bands. These samples are at
least as convergent as similar studies comparing samples used to develop original vs. revised norms. Previous studies have differed substantially by key sociodemographic distributions, such as the WISC and
WISC-R (Wechsler, 1949, 1974), which were key samples in the development of the Flynn Effect theory (Flynn, 1984). In the present
study, we adjusted the Flynn Effect for an array of background variables
to further minimize any differences between the 1989 and 2003 norms
samples that may confound the Flynn Effect estimates.
Fifth, the Flynn Effect has had a non-linear trajectory over the past
century (Pietschnig & Voracek, 2015). Because our study included IQ
measurements at only two time points, we were not able to test the
linearity of change over time.
This study is strengthened by the use of a large and representative
adolescent sample, with IQs measured with reasoning items that are
widely accepted as prototypical measures of fluid intelligence (Dutton
et al., 2016). The use of two sets of norms based on a single
administration of a test avoids practice effects and bias that may arise
from use of different versions of a test.
In conclusion, this study reports important heterogeneity in the
Flynn Effect among a nationally-representative sample of US adolescents.
We confirmed previous reports of reverse Flynn Effects among
large samples of older adolescent males, and extended the same pattern
to females. We also found important differential Flynn Effects by ability
level. These results add to a growing body of evidence suggesting that
Flynn Effect findings from narrow age bands or ability levels may
produce divergent findings that do not generalize to the overall population.
However, given the potential life or death implications of this
research in determining intellectual status in capital punishment cases,
the strength of evidence needed for definitive conclusions is extremely
high. At this time, we do not have sufficient evidence to recommend
differential adjustments to IQ scores. Additional research is needed to
replicate the current findings on the full age range and across comprehensive
measures of intelligence.
On psychological researcher's strategic behavior: High prevalence of effect declines with each new study of some question, yielding a ratio of 2:1; these declines are systematic, strong, and ubiquitous
Effect Declines are Systematic, Strong, and Ubiquitous: A Meta-Meta- Analysis of the Decline. Jakob Pietschnig et al. Front. Psychol., Nov 2019, doi: 10.3389/fpsyg.2019.02874
Abstract: Empirical sciences in general and psychological science in particular are plagued by replicability problems and biased published effect sizes. Although dissemination bias-related phenomena such as publication bias, time-lag bias, or visibility bias are well-known and have been intensively studied, another variant of effect distorting mechanisms, so-called decline effects, have not. Conceptually, decline effects are rooted in low initial (exploratory) study power due to strategic researcher behavior and can be expected to yield overproportional effect declines. Although decline effects have been documented in individual meta-analytic investigations, systematic evidence for decline effects in the psychological literature remains to date unavailable. Therefore, we present in this meta-meta-analysis a systematic investigation of the decline effect in intelligence research. In all, data from 22 meta-analyses comprising 36 meta-analytical and 1,391 primary effect sizes (N = 697,000+) that have been published in the journal Intelligence were included in our analyses. Two different analytic approaches showed consistent evidence for a higher prevalence of cross-temporal effect declines compared to effect increases, yielding a ratio of about 2:1. Moreover, effect declines were considerably stronger when referenced to the initial primary study within a meta-analysis, yielding about twice the magnitude of effect increases. Effect misestimations were more substantial when initial studies had smaller sample sizes and reported larger effects, thus indicating suboptimal initial study power as the main driver of effect misestimations in initial studies. Post-hoc study power comparisons of initial versus subsequent studies were consistent with this interpretation, showing substantially lower initial study power of declining, than of increasing effects. Our findings add another facet to the ever accumulating evidence about non-trivial effect misestimations in the scientific literature. We therefore stress the necessity for more rigorous protocols when it comes to designing and conducting primary research as well as reporting findings in exploratory and replication studies. Increasing transparency in scientific processes such as data sharing, (exploratory) study preregistration, but also self- (or independent) replication preceding the publication of exploratory findings may be suitable approaches to strengthen the credibility of empirical research in general and psychological science in particular.
Keywords: decline effect, meta-meta-analysis, Dissemination bias, effect misestimation, Intelligence
Abstract: Empirical sciences in general and psychological science in particular are plagued by replicability problems and biased published effect sizes. Although dissemination bias-related phenomena such as publication bias, time-lag bias, or visibility bias are well-known and have been intensively studied, another variant of effect distorting mechanisms, so-called decline effects, have not. Conceptually, decline effects are rooted in low initial (exploratory) study power due to strategic researcher behavior and can be expected to yield overproportional effect declines. Although decline effects have been documented in individual meta-analytic investigations, systematic evidence for decline effects in the psychological literature remains to date unavailable. Therefore, we present in this meta-meta-analysis a systematic investigation of the decline effect in intelligence research. In all, data from 22 meta-analyses comprising 36 meta-analytical and 1,391 primary effect sizes (N = 697,000+) that have been published in the journal Intelligence were included in our analyses. Two different analytic approaches showed consistent evidence for a higher prevalence of cross-temporal effect declines compared to effect increases, yielding a ratio of about 2:1. Moreover, effect declines were considerably stronger when referenced to the initial primary study within a meta-analysis, yielding about twice the magnitude of effect increases. Effect misestimations were more substantial when initial studies had smaller sample sizes and reported larger effects, thus indicating suboptimal initial study power as the main driver of effect misestimations in initial studies. Post-hoc study power comparisons of initial versus subsequent studies were consistent with this interpretation, showing substantially lower initial study power of declining, than of increasing effects. Our findings add another facet to the ever accumulating evidence about non-trivial effect misestimations in the scientific literature. We therefore stress the necessity for more rigorous protocols when it comes to designing and conducting primary research as well as reporting findings in exploratory and replication studies. Increasing transparency in scientific processes such as data sharing, (exploratory) study preregistration, but also self- (or independent) replication preceding the publication of exploratory findings may be suitable approaches to strengthen the credibility of empirical research in general and psychological science in particular.
Keywords: decline effect, meta-meta-analysis, Dissemination bias, effect misestimation, Intelligence
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