Extended Penfield’s findings of the primary somatosensory cortex’s homunculus to the higher level of somatosensory processing suggest a major role for somatosensation in human cognition

The "creatures" of the human cortical somatosensory system: Multiple somatosensory homunculi. Noam Saadon-Grosman, Yonatan Loewenstein, Shahar Arzy Author Notes. Brain Communications, fcaa003, January 17 2020, https://doi.org/10.1093/braincomms/fcaa003

Abstract: Penfield’s description of the “homunculus”, a “grotesque creature” with large lips and hands and small trunk and legs depicting the representation of body-parts within the primary somatosensory cortex (S1), is one of the most prominent contributions to the neurosciences. Since then, numerous studies have identified additional body-parts representations outside of S1. Nevertheless, it has been implicitly assumed that S1’s homunculus is representative of the entire somatosensory cortex. Therefore, the distribution of body-parts representations in other brain regions, the property that gave Penfield’s homunculus its famous “grotesque” appearance, has been overlooked. We used whole-body somatosensory stimulation, functional MRI and a new cortical parcellation to quantify the organization of the cortical somatosensory representation. Our analysis showed first, an extensive somatosensory response over the cortex; and second, that the proportional representation of body-parts differs substantially between major neuroanatomical regions and from S1, with, for instance, much larger trunk representation at higher brain regions, potentially in relation to the regions’ functional specialization. These results extend Penfield’s initial findings to the higher level of somatosensory processing and suggest a major role for somatosensation in human cognition.

The division-of-labor may result in modular & assortative social network of strong associations among those performing the same task: DOL & political polarization may share a common mechanism

Social influence and interaction bias can drive emergent behavioural specialization and modular social networks across systems. Christopher K. Tokita and Corina E. Tarnita. Journal of The Royal Society Interface, January 8 2020. https://doi.org/10.1098/rsif.2019.0564

Abstract: In social systems ranging from ant colonies to human society, behavioural specialization—consistent individual differences in behaviour—is commonplace: individuals can specialize in the tasks they perform (division of labour (DOL)), the political behaviour they exhibit (political polarization) or the non-task behaviours they exhibit (personalities). Across these contexts, behavioural specialization often co-occurs with modular and assortative social networks, such that individuals tend to associate with others that have the same behavioural specialization. This raises the question of whether a common mechanism could drive co-emergent behavioural specialization and social network structure across contexts. To investigate this question, here we extend a model of self-organized DOL to account for social influence and interaction bias among individuals—social dynamics that have been shown to drive political polarization. We find that these same social dynamics can also drive emergent DOL by forming a feedback loop that reinforces behavioural differences between individuals, a feedback loop that is impacted by group size. Moreover, this feedback loop also results in modular and assortative social network structure, whereby individuals associate strongly with those performing the same task. Our findings suggest that DOL and political polarization—two social phenomena not typically considered together—may actually share a common social mechanism. This mechanism may result in social organization in many contexts beyond task performance and political behaviour.

4. Discussion

Our main result demonstrates that, in the presence of homophily with positive influence, the feedback between social influence and interaction bias could result in the co-emergence of DOL and modular social network structure. These results reveal that self-organized specialization could give rise to modular social networks without direct selection for modularity, filling a gap in our knowledge of social organization [55] and mirroring findings in gene regulatory networks, which can become modular as genes specialize [56]. The co-emergence requires both social influence and interaction bias but, if the level of social influence is too high, its pressure leads to conformity, which homogenizes the society. Because this feedback between social influence and interaction bias has also been shown to drive political polarization [22–25], our results suggest a shared mechanism between two social phenomena—polarization and DOL—that have not traditionally been considered together and raise the possibility that this mechanism may structure social systems in other contexts as well, such as in the case of emergent personalities [11,29–31]. Furthermore, the ubiquity of this mechanism may help explain why social systems often have a common feature—modular network structure—that is shared with a range of other biological and physical complex systems [57].

Intriguingly, although our results suggest that diverse forms of behavioural specialization—and the associated modular, assortative social networks—might arise from a common mechanism, depending on their manifestation, they can be either beneficial or detrimental for the group. For example, DOL and personality differences have long been associated with beneficial group outcomes in both animal [5,58–60] and human societies [61] (although it can sometimes come at the expense of group flexibility [62]). Moreover, the modularity that co-occurs in these systems is also often framed as beneficial, since it can limit the spread of disease [63] and make the social system more robust to perturbation [55]. On the contrary, political polarization is typically deemed harmful to democratic societies [64]. Thus, an interesting question for future research arises: if a common mechanism underlies the emergence of behavioural specialization and the co-emergence of a modular social network structure in multiple contexts, why would group outcomes differ so dramatically? Insights may come from studying the frequency of co-occurrence among various forms of behavioural specialization. If the same mechanism underlies behavioural specialization broadly, then one would expect multiple types of behavioural specialization (e.g. in task performance, personality, decision-making) to simultaneously arise and co-occur in the same group or society, as is the case in some social systems, where certain personalities consistently specialize on particular tasks [9,10] or in human society, where personality type and political ideology appear correlated [65]. Then, the true outcome of behavioural specialization for the group is the net across the different types co-originating from the same mechanism and cannot be inferred by investigating any one specific instantiation of behavioural specialization.

While DOL emerged when homophily was combined with positive influence, other combinations of social influence and interaction bias may nevertheless be employed in societies to elicit other group-level phenomena. For instance, under certain conditions, a society might benefit from uniform rather than divergent, specialized behaviour. This is the case when social insect colonies must relocate to a new nest, a collective decision that requires consensus-building [66]. To produce consensus, interactions should cause individuals to weaken their commitment to an option until a large majority agrees on one location. Heterophily with positive influence—preferential interactions between dissimilar individuals that reduce dissimilarity—achieves this dynamic and is consistent with the cross-inhibitory interactions observed in nest-searching honeybee swarms [67]: scouts interact with scouts favouring other sites and release a signal that causes them to stop reporting that site to others. One could imagine that similar dynamics might also reduce political polarization.

Recent work has shown that built environments—physical or digital—can greatly influence collective behaviour [16,18,68–70], but the mechanisms underlying this influence have remained elusive. By demonstrating the critical role of interaction bias for behavioural outcomes, our results provide a candidate mechanism: structures can enhance interaction bias among individuals and thereby amplify the behavioural specialization of individuals. For example, nest architecture in social insect colonies alter collective behaviour [68] and social organization [18] possibly because the nest chambers and tunnels force proximity to individuals performing the same behaviour and limit interactions with individuals performing other behaviours. Similarly, the Internet and social media platforms have changed the way individuals interact according to interest or ideology [16,69,70]: selective exposure to certain individuals or viewpoints creates a form of interaction bias that our results predict would increase behavioural specialization, i.e. political bias. Thus, our model predicts that built environments should increase behavioural specialization beyond what would be expected in more ‘open’, well-mixed environments. This prediction has evolutionary consequences: a nest can increase behavioural specialization without any underlying genetic or otherwise inherent, diversity. Such consequences would further consolidate the importance of built environments—specifically, nests—for the evolution of complex societies. It has been previously argued that the construction of a nest may have been a critical step in the evolution of stable, highly cooperative social groups [71]. Subsequent spatial structuring of the nest would then, according to our findings, bring further benefits to nascent social groups in the form of increased behavioural specialization, e.g. DOL, even in the absence of initial behavioural and/or trait heterogeneity.

Finally, our results shed light on how plastic traits can result in scaling effects of social organization with group size, a finding that tightens theoretical links between the biological and social sciences. Founding sociological theorist, Emile Durkheim, posited that the size of a society would shape its fundamental organization [3]: small societies would have relatively homogeneous behaviour among individuals, but DOL would naturally emerge as societies grew in size and individuals differentiated in behaviour due to social interactions. Similar to Durkheim's theoretical framing, John Bonner famously posited that complexity, as measured by the differentiated types of individuals (in societies) or cells (in multicellular aggregations), would increase as groups grew in size [72]. Bonner argued that the differentiation among individuals was not due to direct genetic determinism but was instead the result of plasticity that allowed individuals to differ as groups increased in size. Our model supports these qualitative predictions and even predicts a rapid transition in organization as a function of group size that results from socially influenced plasticity at the level of the individual. Previous theoretical work showed that DOL could exhibit group size scaling effects even with fixed traits, but these increases in DOL quickly plateaued past relatively small group sizes [5,39]. Our model, along with models of self-reinforced traits [38], demonstrates how DOL could continue to increase at larger group sizes, a pattern observed empirically in both animal [49,73] and human societies [74,75]. For other forms of behavioural specialization, such as emergent personalities or political polarization, the effect of group size is understudied; however, our results suggest similar patterns. Our model further demonstrated that group size can affect social network structure, a dynamic that has only been preliminarily investigated empirically so far [76]. Leveraging new technologies—such as camera-tracking algorithms and social media—that can simultaneously monitor thousands of individuals and their interactions to investigate the effect of group size on societal dynamics could have significant implications as globalization, urbanization and technology increase the size of our social groups and the frequency of our interactions.

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Modularity is a form of community structure within a group in which there are clusters of strongly connected nodes that are weakly connected to nodes in other clusters. Using each simulation's time-aggregated interaction matrix A, we calculated modularity with the metric developed by Clauset et al. [77]. A modularity value of 0 indicates that the network is a random graph and, therefore, lacks modularity; positive values indicate deviations from randomness and the presence of some degree of modularity in the network.

Frequency of non-random interactions reveals the degree to which individuals are biasing their interactions towards or away from certain other individuals. For a random, well-mixed population, the expected frequency of interactions between any two individuals is pinteract = 1 − (1 − 1/(n − 1))2. For our resulting social networks, we compared this expected well-mixed frequency to the value of each entry aik in the average interaction matrix resulting from the 100 replicate simulations per group size. To determine whether the deviation from random was statistically significant, we calculated the 95% confidence interval for the value of each entry aik in the average interaction matrix. If the 95% confidence interval for a given interaction did not cross the value pinteract, that interaction was considered significantly different than random.

Assortativity is the tendency of nodes to attach to other nodes that are similar in some trait (e.g. here, threshold bias). We measured assortativity using the weighted assortment coefficient [78]. This metric takes values in the range [− 1, 1], with positive values indicating a tendency to interact with individuals that are similar in traits and negative values indicating a tendency to interact with individuals that are different. A value of 0 means random traits-based mixing among individuals.

US: The share of job vacancies requiring a bachelor’s degree increased by more than 60 percent between 2007 and 2019, with faster growth in professional occupations and high-wage cities

Structural Increases in Skill Demand after the Great Recession. Peter Q. Blair, David J. Deming. NBER Working Paper No. 26680. January 2020. https://www.nber.org/papers/w26680

Abstract: In this paper we use detailed job vacancy data to estimate changes in skill demand in the years since the Great Recession. The share of job vacancies requiring a bachelor’s degree increased by more than 60 percent between 2007 and 2019, with faster growth in professional occupations and high-wage cities. Since the labor market was becoming tighter over this period, cyclical “upskilling” is unlikely to explain our findings.

1 Introduction

The yearly wage premium for U.S. workers with a college degree has grown rapidly in

recent decades: from 40 percent in 1980 to nearly 70 percent in 2017 (Autor, Goldin, and

Katz 2020). Over the same period, the share of adults with at least a four-year college

degree doubled, from 17 to 34 percent (Snyder, de Brey, and Dillow, 2019) (Digest of

Education Statistics, 2020). In the “education race” model of Tinbergen (1974), these two

facts are explained by rapidly growing relative demand for college-level skills. If the

college premium grows despite a rapid increase in the supply of skills, this must mean

that the demand for skills is growing even faster.

The education race model provides a parsimonious and powerful explanation of US

educational wage differentials over the last two centuries (Katz and Murphy 1992; Goldin

and Katz 2008; Autor, Goldin, and Katz 2020). Yet one key limitation of the model is that

skill demand is not directly observed, but rather inferred as a residual that fits the facts

above. How do we know that the results from the education race model are driven by

rising employer skill demand, as opposed to some other unobserved explanation?

We study this question by using detailed job vacancy data to estimate the change in

employer skill demands in the years since the Great Recession. Our data come from the

labor market analytics firm Burning Glass Technologies (BGT), which has collected data

on the near-universe of online job vacancy postings since 2007.

Our main finding is that skill demand has increased substantially in the decade following the Great Recession. The share of online job vacancies requiring a bachelor’s degree

increased from 23 percent in 2007 to 37 percent in 2019, an increase of more than 60 percent. Most of this increase occurred between 2007 and 2010, consistent with the finding

that the Great Recession provided an opportunity for firms to upgrade skill requirements

in response to new technologies (Hershbein and Kahn 2018).

We present several pieces of evidence suggesting that the increase in skill demand is

structural, rather than cyclical. We replicate the findings of Hershbein and Kahn (2018)

and Modestino, Shoag, and Ballance (2019), who show that skill demands increased more

in labor markets that were harder hit by the Great Recession. However, when we extend

the sample forward and adjust for differences in the composition of online vacancies, we

find that this cyclical “upskilling” fades within a few years. In its place, we find longrun structural increases in skill demand across all labor markets. In fact, we show that

the increase in skill demand post-2010 is larger in higher-wage cities. We also find much

larger increases in the demand for education in professional, high-wage occupations such

as management, business, science and engineering.

Previous work using the BGT data has found that it is disproportionately comprised of

high-wage professional occupations, mostly because these types of jobs were more likely

to be posted online (e.g., Deming and Kahn 2018). As online job advertising has become

more common, the BGT sample has become more representative, and the firms and jobs

that are added later in the sample period are less likely to require bachelor’s degrees and

other advanced skills.

We adjust for the changing composition of the sample in two ways. First, we weight

all of our results by the employment share of each occupation as well as the size of the

labor force in each city in 2006. This ensures that our sample of vacancies is roughly

representative of the national job distribution in the pre-sample period. Second, our preferred empirical specification controls for occupation-by-MSA-by-firm fixed effects. This

approach accounts for compositional changes over time in the BGT data.

Our results suggest that increasing demand for educated workers is likely a persistent

feature of the U.S. economy post-recession. Recent work has documented a slowdown

in the growth of the college wage premium since 2005 (Beaudry, Green, and Sand 2016;

Valletta 2018; Autor, Goldin, and Katz 2020). Yet this slowdown has occurred during a

period of rapid expansion in the supply of skills. We find rapid expansion in the demand

for skills, suggesting that education and technology are “racing” together to hold the

college wage premium steady.1

Non-conscious prioritization speed is not explained by variation in conscious cognitive speed, decision thresholds, short-term visual memory, and by the three networks of attention (alerting, orienting and executive)

Sklar, Asael, Ariel Goldstein, Yaniv Abir, Ron Dotsch, Alexander Todorov, and Ran Hassin. 2020. “Did You See It? Robust Individual Variance in the Prioritization of Contents to Conscious Awareness.” PsyArXiv. January 20. doi:10.31234/osf.io/hp7we

Abstract: Perceptual conscious experiences result from non-conscious processes that precede them. We document a new characteristic of the human cognitive system: the speed with which the non-conscious processes prioritize percepts to conscious experiences. In eight experiments (N=375) we find that an individual’s non-conscious prioritization speed (NPS) is ubiquitous across a wide variety of stimuli, and generalizes across tasks and time. We also find that variation in NPS is unique, in that it is not explained by variation in conscious cognitive speed, decision thresholds, short-term visual memory, and by the three networks of attention (alerting, orienting and executive). Finally, we find that NPS is correlated with self-reported differences in perceptual experience. We conclude by discussing the implications of variance in NPS for understanding individual variance in behavior and the neural substrates of consciousness.


NPS=non-conscious prioritization speed


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And then, you suddenly become aware: it might be of a child running into the road in front of your car, your friend walking on the other side of the street, or a large spider in your shoe. On the timeline that stretches between non-conscious processes and the conscious experiences that emerge from them, this paper focuses on the moment in which your conscious experiences begin: just when you become aware of the child, your friend or the spider. Before this point in time processing is strictly non-conscious, after this moment conscious processing unfolds.

For many, the idea that non-conscious processing generates visual awareness is unintuitive. Imagine suddenly finding yourself in Times Square. You may imagine opening your eyes and immediately experiencing busy streets, flashing ads and moving people, all at once. Intuitively, we feel our experience of the world is immediate and detailed. Yet this intuition is wrong; the literature strongly suggests that conscious experiences are both limited in scope (e.g., Cohen, Dennett, & Kanwisher, 2016; Elliott, Baird, & Giesbrecht, 2013; Wu & Wolfe, 2018) and delayed (e.g., Dehaene, Changeux, Naccache, & Sergent, 2006; Libet, 2009; Sergent, Baillet, & Dehaene, 2005). The feeling that we consciously experience more than we actually do is perhaps the most prevalent psychological illusion, as it is omnipresent in our every waking moment (e.g., Cohen et al., 2016; Kouider, De Gardelle, Sackur, & Dupoux, 2010)1.

Measurements of the “size” or “scope” of conscious experience indicate a rather limited number of objects can be experienced at any given time (e.g., Cohen, Dennett, & Kanwisher, 2016; Elliott, Baird, & Giesbrecht, 2013; Wu & Wolfe, 2018). Other objects, the ones not consciously experienced, are not necessarily entirely discarded. Such objects may be partially experienced (Kouider et al., 2010) or integrated into a perceptual ensemble (Cohen et al., 2016) yet neither constitutes fully conscious processing.

Considerable research effort has identified what determines which visual stimuli are prioritized for conscious experience. This work found that both low-level features (e.g. movement, high contrast) and higher-level features (e.g., expectations, Stein, Sterzer, & Peelen, 2012; emotional value, Zeelenberg, Wagenmakers, & Rotteveel, 2006) influence the prioritization of stimuli for awareness.

Evidently, the process that begins with activation patterns in the retina and ends with a conscious percept has a duration (e.g., Dehaene, Changeux, Naccache, & Sergent, 2006; Libet, 2009; Sergent, Baillet, & Dehaene, 2005). Considering the above examples, clearly this duration may have important consequences. If you become aware quickly enough, you are more likely to slam the brakes to avoid running over the child, call out to your friend, or avoid a painful spider bite.

Here, we focus on this aspect of how conscious experiences come about using a novel perspective. Specifically, we examine individual variability in the speed with which our non-conscious processes prioritize information for conscious awareness (i.e., do some individuals become aware of stimuli more quickly than others?). Examination of individual differences provides rich data for psychological theories (for a recent example see de Haas, Iakovidis, Schwarzkopf, & Gegenfurtner, 2019), an acknowledgement that has recently gained renewed interest (e.g., Bolger, Zee, Rossignac-Milon, & Hassin, 2019). We report 8 experiments documenting robust differences, and examine possible mechanisms that may bring these differences about.

To examine non-conscious prioritization speed (NPS) we use two long-duration masking paradigms. The main paradigm we employ is breaking continuous flash suppression (bCFS; Tsuchiya & Koch, 2005). In bCFS, a stimulus is presented to one eye while a dynamic mask is presented to the other eye (see Figure 1). This setup results in long masking periods, which may last seconds. Participants are asked to respond when they become conscious of any part of the target stimulus. This reaction time, the duration between the initiation of stimulus presentation and its conscious experience, is our measure of participants' NPS.

Like many others (e.g., Macrae, Visokomogilski, Golubickis, Cunningham, & Sahraie, 2017; Salomon, Lim, Herbelin, Hesselmann, & Blanke, 2013; Yang, Zald, & Blake, 2007), we hold that bCFS is particularly suited for assessing differences in access to awareness for two reasons. First, CFS allows for subliminal presentations that can last seconds. Thus, unlike previous masking techniques, it allows for a lengthy non-conscious processing. Second, bCFS allows one to measure spontaneous emergence into awareness, focusing on the moment in which a previously non-conscious stimulus suddenly becomes conscious2.

Crucially, to overcome the limitations associated with using just one paradigm, we use another long duration masking technique that has the same advantage, Repeated Mask Suppression (RMS; Abir & Hassin, in preparation). Using two different paradigms allow us to generalize our conclusions beyond the specific characteristics and limitations of each of the paradigms.

In eight experiments, we document large differences between individuals in NPS. Across experiments, we show that some people are consistently faster than others in becoming aware of a wide variety of stimuli, including words, numbers, faces, and emotional expressions.

Moreover, this individual variance is general across paradigms: Participants who are fast prioritizers in one paradigm (CFS; Tsuchiya & Koch, 2005) are also fast when tested using a different suppression method (RMS; Abir & Hassin, in preparation; see Experiment 3), a difference which is stable over time (Experiment 7). We extensively examined possible sources of this individual trait. Our experiments establish that NPS cannot be explained by variation in conscious cognitive speed (Experiment 4), detection threshold (Experiment 5), visual short-term memory (Experiment 6), and alerting, orienting and executive attention (Experiment 7). Finally, we find that differences in NPS are associated with self-reported differences in the richness of experience (Experiment 8). Based on these results we conclude that NPS is a robust trait and has subjectively noticeable ramifications in everyday life. We discuss possible implications of this trait in the General Discussion.


Discussion

Overall, the current findings paint a clear picture. In eight experiments we discovered a highly consistent, stable and strong cognitive characteristic: NPS. NPS manifested in a large variety of stimuli – from faces and emotional expressions, through language to numbers. It was stable over time (20 minutes) as well as measurement paradigm (bCFS vs. bRMS). We additionally found NPS to be independent of conscious speed, short-term visual memory, visual acuity and three different attentional functions and largely independent of conscious detection thresholds.

In previous research differences in suppression time between stimuli (e.g. upright faces, Stein et al., 2012; primed stimuli, Lupyan & Ward, 2013) have been used as a measure of stimuli’s priority in access to awareness. In such research, individual variance in participants' overall speed of becoming aware of stimuli is treated, if it is considered at all, as nuisance variance during analysis (e.g., Gayet & Stein, 2017). A notable exception to this trend is a recent article (Blake, Goodman, Tomarken, & Kim, 2019) that documented a relationship between individual differences in the masking potency of CFS and subsequent binocular rivalry performance. Here, we greatly extend this recent result as we show that individual variance in NPS is highly consistent across stimuli and time, generalizes beyond bCFS, and is not explained by established individual differences in cognition.

Because of its effect on conscious experience, it is easy to see how NPS may be crucial for tasks such as driving or sports, and in professions such as law enforcement and piloting, where the duration required before conscious processing initiates can have crucial and predictable implications. In fact, NPS may be an important factor in any task that requires both conscious processing and speeded reaction. Understanding NPS, its underlying processes and downstream consequences, is therefore a promising avenue for further research.

Another promising direction would be to examine NPS using neuroscience tools, especially with respect to the underpinnings of conscious experience. First, understanding what neural substrates underpin individual differences in NPS may shed new light on the age-old puzzle of what determines our conscious stream. Second, understanding NPS may shed new light on some of the currently intractable problems in the field of consciousness research, such as separating neural activity that underlies consciousness per se, from neural activity that underlies the non-conscious processes that precede or follow it (De Graaf, Hsieh, & Sack, 2012). Thus, understanding NPS may provide missing pieces for many puzzles both in relation to how conscious experience arises and in relation to how it may differ between individuals, and what the consequences of such differences might be.

Another nation in which the Flynn effect (IQ in Romania was increasing with approximately 3 IQ points/decade) seems to reverse: The continuous positive outlook is in question as modern generations show signs of IQ “fatigue”

Time and generational changes in cognitive performance in Romania. George Gunnesch-Luca, Dragoș Iliescu. Intelligence, Volume 79, March–April 2020, 101430. https://doi.org/10.1016/j.intell.2020.101430

Highlights
•    The Flynn effect can be also observed in Romanian samples.
•    IQ in Romania is increasing with approximately 3 IQ points per decade.
•    Both period and generational effects contribute to the overall effect.
•    The continuous positive outlook is in question as modern generations show signs of IQ “fatigue”.

Abstract: The Flynn effect describes sustained gains in cognitive performance that have been observed in the past century. These improvements are not evenly distributed, with strong variations across regions or groups. To this effect, we report time and generational trends in IQ development in Romania. Using pooled repeated cross-sectional data ranging from 2003 to 2018 (N = 12,034), we used Hierarchical Age-Period-Cohort Models (HAPC) on data measured with the Multidimensional Aptitude Battery II. The results show an increase in measured performance of about one third of an IQ point per year, mainly driven by individual level effects and with additional variance attributable to generational (cohort) and period effects.

Check also Cohort differences on the CVLT-II and CVLT3: evidence of a negative Flynn effect on the attention/working memory and learning trials. Lisa V. Graves, Lisa Drozdick, Troy Courville, Thomas J. Farrer, Paul E. Gilbert & Dean C. Delis. The Clinical Neuropsychologist, Dec 12 2019. https://www.bipartisanalliance.com/2019/12/usa-evidence-of-negative-flynn-effect.html

Associations between cognitive ability & education, from middle childhood to old age, as well as their links with wealth, morbidity and mortality: The strong genetic basis in the association is amplified by environmental experiences

Cognitive ability and education: how behavioural genetic research has advanced our knowledge and understanding of their association. Margherita Malanchini et al. Neuroscience & Biobehavioral Reviews, January 20 2020. https://doi.org/10.1016/j.neubiorev.2020.01.016

Highlights
•    The evidence reviewed points to a strong genetic basis in the association between cognitive ability and academic performance, observed from middle childhood to old age.
•    Over development, genetic influences are amplified by environmental experiences trhigh gene-environment interplay.
•    The strong stability and heritability of academic performance is not driven entirely by cognitive ability.
•    Other educationally-relevant noncognitive characteristics contribute to accounting for the genetic variation in academic performance beyond cognitive ability.
•    Overall, genetic research has provided compelling evidence that has resulted in greatly advancing our knowledge and understanding of the association between cognitive ability and learning.
•    Considering both cognitive and noncognitive skills as well as their biological and environmental underpinnings will be fundamental in moving towards a comprehensive, evidence-based model of education.

Abstract: Cognitive ability and educational success predict positive outcomes across the lifespan, from higher earnings to better health and longevity. The shared positive outcomes associated with cognitive ability and education are emblematic of the strong interconnections between them. Part of the observed associations between cognitive ability and education, as well as their links with wealth, morbidity and mortality, are rooted in genetic variation. The current review evaluates the contribution of decades of behavioural genetic research to our knowledge and understanding of the biological and environmental basis of the association between cognitive ability and education. The evidence reviewed points to a strong genetic basis in their association, observed from middle childhood to old age, which is amplified by environmental experiences. In addition, the strong stability and heritability of educational success are not driven entirely by cognitive ability. This highlights the contribution of other educationally relevant noncognitive characteristics. Considering both cognitive and noncognitive skills as well as their biological and environmental underpinnings will be fundamental in moving towards a comprehensive, evidence-based model of education.

High intellect/imagination predicted digital aggression in lab and Twitter; low conscientiousness predicted digital aggression on Twitter and self-reports

A Multi-Method Investigation of the Personality Correlates of Digital Aggression. M. Kim, SL. Clark, MB. Donnellan, SA. Burt. Journal of Research in Personality, January 20 2020, 103923. https://doi.org/10.1016/j.jrp.2020.103923

Highlights
•    A multi-method investigation of the personality correlates of digital aggression.
•    The ‘Big 5’ differentially predicted all three digital aggression measures.
•    High intellect/imagination predicted digital aggression in lab and Twitter.
•    Low conscientiousness predicted digital aggression on Twitter and self-reports.
•    Personality predictors of digital aggression may be context-specific.

Abstract: Digital aggression (DA) refers to the use of computer-mediated technologies to harm others. A handful of previous studies have provided mixed results regarding the personality correlates of DA. We clarified these findings by analyzing the associations between three measures of DA (behavioral, Twitter, and self-report) and the Big Five traits using data from 1167 undergraduate participants. Big Five personality trait measures predicted all three DA measures, but results varied across particular assessments of DA. These results point to possible moderators and potentially important differences within the broader construct of DA.

Ideological orientations are substantially heritable, but the public is largely non-ideological; what happens is that ideological orientations are extraordinarily heritable for the most informed citizens, much less for the others

Genes, Ideology, & Sophistication. Nathan P. Kalmoe. Jan 2020. https://www.dropbox.com/s/4q14j1qwx94ub7d/Kalmoe%20-%20Genes%2C%20Ideology%2C%20%26%20Sophistication.pdf?dl=0

Abstract: Twin studies show that ideological orientations are substantially heritable, but how does that comport with evidence showing a largely non-ideological public? This study integrates these two important literatures and tests whether political sophistication is necessary for genetic predispositions to find expression in political attitudes and their organization. Data from the Minnesota Twin Study show that ideological orientations are extraordinarily heritable for the most informed citizens—far more so than full-sample averages in past tests show—but barely heritable among the rest. This holds true for the Wilson-Patterson ideological index scores and a related measure of ideological consistency, and somewhat less so for individual W-P items. Heritability for ideological identification is non-monotonic across knowledge; partisanship is most heritable for the least knowledgeable. The results resolve the tension between the two fields by showing that political knowledge is required to link genetic predispositions with specific attitudes.


DISCUSSION
I set out to test whether average heritability estimates differ by levels of political knowledge, as prodigious literature on the limits of mass belief systems suggest they might. The results grandly support these expectations: High-knowledge twin pairs (top 21%) show heritability estimates ranging from 49-82% (average 65%) across a variety of ideology estimates. In contrast, the least knowledgeable half of the sample showed comparable estimates of 0-40% (average 18%). To sum it up: ideological orientations appear extraordinarily heritable for the most sophisticated citizens—far more so than full-sample averages in past tests show—but hardly heritable at all among the rest.

How well does this twin sample reflect the national population? Arceneaux and colleagues (2012) show Minnesota Twin Study respondents are older and more educated than the American public, on average, but they are similarly interested in politics and similarly unconstrained in attitudes, like national samples. That suggests these tests are a reasonable base from which to infer general population dynamics, at least as they relate to political sophistication.

Converse (2000) argued that ideological tests must always account for the public’s huge
variance in political knowledge—and that doing otherwise risked concealing more than it revealed.
Simply put, average ideological estimates ignore qualitative differences in the nature of belief
systems. The tests here show the utility of extending Converse’s exhortation to estimates of genetic influence. Low-knowledge citizens may also carry heritable ideological predispositions, but those proto-orientations lie dormant without the sophistication and engagement to connect them to concrete sociopolitical attitudes and broader liberal-conservative belief systems. Political knowledge is necessary for that political development. Merging two important and related but isolated fields adds insight into the origins of ideological beliefs and the conditions for genetic influence in politics.