Study: Language (not geography) major force behind India’s gene flow
The study found that people who spoke the same language tended to be more closely related despite living far apart.
29 January, 2021
Credit: Adobe Photo
- Studies focusing on European genetics have found a strong correlation between geography and genetic variation.
- Looking toward India, a new study found a stronger correlation between gene variation and language as well as
- social structure.
- Understanding social and cultural influences can help expand our knowledge of gene flow through human history.
<p>When we think about our ancestors, our minds tend to wander to geography. We introduce our progenitors by noting they were Norwegian, Brazilian, Indonesian, or members of an American Native tribe. <a href="https://bigthink.com/technology-innovation/genetic-privacy?rebelltitem=1#rebelltitem1" target="_self">Personal genetic tests</a>, such as those offered by Ancestry and 23andMe, offer customers a travel log of their lineages' global journeys. And some of our more obvious phenotypic markers, such as hair and <a href="https://humanorigins.si.edu/evidence/genetics/human-skin-color-variation/modern-human-diversity-skin-color" target="_blank" rel="noopener noreferrer">skin color</a>, evolved in relationship with the lands our ancestors called home.</p><p>Lost within this land-locked focus is the fact that social and cultural factors—how our ancestors cohabitated and interacted with each other—also influence gene flow. In doing so, these factors shaped our evolution and genetic diversity. As a new study has found, for the peoples of the Indian subcontinent, such social and cultural factors may be more important to their genetic variation than the deserts, grasslands, and tropical forests between them.</p>
A new kind of mother tongue
<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTU0ODY2MS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYzODQ4MjEyMH0.Ag7iKSgWxyUn6-v3wbIk7ADkxtbyiuUaodlxjRYmDkk/img.jpg?width=980" id="e0037" class="rm-shortcode" data-rm-shortcode-id="0624bd5ae5c2c18e87d89e6549ef3131" data-rm-shortcode-name="rebelmouse-image" data-width="815" data-height="450" />A map showing the locations of 33 Indian populations alongside plot graphs showing the relations between sociolinguistic groups and genetic structures.
<p>The <a href="https://academic.oup.com/mbe/advance-article/doi/10.1093/molbev/msaa321/6108106?login=true" target="_blank" rel="noopener noreferrer">new study</a>, published in <a href="https://academic.oup.com/mbe" target="_blank" rel="noopener noreferrer">Molecular Biology and Evolution</a>, began when Aritra Bose, who earned his doctorate at Purdue in genetics and data science, was researching the close ties between genes and geography in Europe. Originally from Calcutta, India, Bose wondered if such a strong link would be true of his home country. He teamed up with Peristera Paschou, a population geneticist and associate professor of biological sciences at Purdue University, and Petros Drineas, associate head of Purdue's Department of Computer Science, to find out.</p><p>"Our genome carries the signature of our ancestors, and the genetic structure of modern populations has been shaped by the forces of evolution. What we are looking for is what led different groups of people to come together and what drove them apart," Paschou, who led the study with Drineas, said in <a href="https://www.purdue.edu/newsroom/releases/2021/Q1/new-study-ties-indias-genetic-diversity-to-language,-not-geography.html" target="_blank" rel="noopener noreferrer">a press release</a>. "To understand the genetics of human populations, we created a model that allows us to consider jointly many different factors that may have shaped genetics."</p><p>The researchers developed a computer model called COGG (Correlation Optimization of Genetics and Geodemographics) to analyze population genetic substructure. They then feed COGG a dataset featuring 981 individuals from 90 Indian groups, further merging that with a dataset of 1,323 individuals from 50 Eurasian populations. The model crunched the numbers and found something surprising.</p><p>Studies looking at European populations have typically found a strong correlation between genotype and geography. As <a href="https://www.nationalgeographic.com/science/phenomena/2008/09/01/european-genes-mirror-european-geography/" target="_blank" rel="noopener noreferrer">one National Geographic</a><u> </u>writer put it when discussing <a href="https://www.nature.com/articles/nature07331" target="_blank" rel="noopener noreferrer">a study published in Nature</a>: "The result was startling—the genetic and geopolitical maps of Europe overlap to a remarkable degree. On the two-dimensional genetic map, you can make out Italy's boot and the Iberian peninsula [sic] where Spain and Portugal sit. The Scandinavian countries appear in the right order and in the south-east, Cyprus sits distinctly off the 'coast' of Greece."</p><p>Such a confluence of the geo and the genome was not found in the India study; in fact, the analysis showed a weak correlation between genotype and geography. Instead, it was shared language that proved the major genetic link.</p><p>The researchers found that people who speak the same language were much more likely to be closely related, regardless of where they lived on the subcontinent. For example, their analysis showed that Indo-European and Dravidian speakers shared genetic drift with Europeans, while Tibeto-Burman speaking tribes shared it with East Asians.</p><p>Social structure also showed a stronger correlation than geography in their analysis. The researchers hypothesized this correlation originated from the social stratification imposed by <a href="https://www.bbc.com/news/world-asia-india-35650616" target="_blank" rel="noopener noreferrer">India's caste system</a>. </p><p>For several thousands of years, the caste system divided Hindus into hierarchical groups based on their karma (work) and dharma (duty). Marriage was strictly limited within one's caste, resulting in a long history of endogamy. Though the caste system was effectively expunged in 1950 by the Indian government, such endogamy held sway over Indian society long enough to have a powerful effect on the country's historic gene flow.</p><p>"Our results clearly show that endogamy and language families are pivotal in studying the genetic stratification of Indian populations," the researchers write in the study.</p>New dimensions for understanding ancestry
<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="b2f6780bd878e2434da8e19bff5481d8"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/hu4pjmBTN2Y?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p>None of this is to say that geography played no part in the ancestral gene flow of India, nor that social and cultural factors didn't influence genotypes across Europe. They most certainly did. That Nature study, for example, discovered genetic clusters in Switzerland that were language-based. And Europe's geographic distribution may have more to do with historical sociopolitical realities than environmental ones.</p><p>The point of both studies, however, is not to tie our genetic history to land or language, but to understand how genes flowed throughout historical societies.</p><p>"It sheds light on how genetics work in our society," Bose said in the same release. "This is the first model that can take into account social, cultural, environmental and linguistic factors that shape the gene flow of populations. It helps us to understand what factors contribute to the genetic puzzle that is India. It disentangles the puzzle."</p><p>With an improved knowledge of historic gene flow, scientists may be able to further biomedical research to better detect rare genetic variants, assess individual risks to certain diseases, and predict which populations may be more or less susceptible to particular drugs. By opening the avenues we use to understand our genetic history, we can hopefully advance such knowledge and understanding.</p>
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Cancel culture vs. toleration: The consequences of punishing dissent
When we limit the clash of ideas, we ultimately hinder progress for the entire society.
04 November, 2020
Credit: Анатолий Шаповал via AdobeStock / Big Think
- Pluralism is the idea that different people, traditions, and beliefs not only can coexist together in the same society but also should coexist together because society benefits from the vibrant workshopping of ideas.
- Cancel culture is a threat to a liberal society because it seeks to shape the available information rather than seek truth.
- Practicing toleration for those ideas does not mean merely putting up with them but actually acknowledging the ideas with an open spirit, as Chandran Kukathas, professor at Singapore Management University, says.
<p><br></p>
<p>"Cancel culture now poses a real threat to intellectual freedom in the United States," Jonathan Rauch, distinguished fellow at the Institute for Humane Studies, <a href="https://www.persuasion.community/p/the-cancel-culture-checklist-c63" target="_blank">writes</a> in <em>Persuasion</em>. Rauch cites a Cato Institute <a href="https://www.cato.org/publications/survey-reports/poll-62-americans-say-they-have-political-views-theyre-afraid-share" target="_blank">poll</a> that found a third of Americans worry their careers will be harmed if they express their real political opinions. Canceling is different than healthy criticism, Rauch writes, because canceling "is about shaping the information battlefield, not seeking truth; and its intent—or at least its predictable outcome—is to coerce conformity[.]"</p>
<p>And conformity is a death knell for liberalism. In a homogenous society—one in which everyone has roughly the same background, religion, values, and goals—people will generally agree on what it means to be a good person and live a good life. But a key tenet of liberalism is pluralism: the idea that different people, traditions, and beliefs not only <em>can</em> coexist together in the same society but also <em>should</em> coexist together because society benefits from vibrant heterogeneity. </p>
<p>"Liberal thinking really arises out of a reflection on the fact that people disagree substantially about things," Chandran Kukathas, professor at Singapore Management University, says in a Big Think <a href="https://www.youtube.com/watch?v=yDLhVZ-RB3o" target="_blank" rel="noopener noreferrer">video</a> on pluralism and toleration. "They have different ways of life."</p>
<blockquote>[Cancel culture] is about shaping the information battlefield, not seeking truth; and its intent—or at least its predictable outcome—is to coerce conformity[.]</blockquote>
<p>Throughout history, men and women who've changed the world have been living examples of pluralism—people whose lives and minds were unique products of a diverse, interconnected world. Alexander Hamilton was, as the musical <em>Hamilton</em> says, "a bastard, orphan, son of a whore and a Scotsman, dropped in the middle of a forgotten spot in the Caribbean" before he came to the colonies. Marie Curie (neé Skłodowska) was the daughter of two Polish teachers, one atheist and one Catholic, and attended an underground university in Warsaw before immigrating to Paris. Sergey Brin was born in the Soviet Union to Jewish parents before his family fled persecution and came to the United States, where Brin co-founded Google.</p><p>A pluralistic society nourishes innovation and progress, where diverse people with unique life experiences develop and share ideas. If people stayed in discrete, homogenous communities, how many world-changing lives and ideas would never have existed?</p><p>Critics might say: It's one thing to welcome people from diverse backgrounds into your society; it's another to welcome diverse ideas, even if some are offensive or harmful.</p><p>But our vibrant, evolving world depends on diverse ideas and cultures. In a homogenous society, ideas and customs can be stagnant for generations. But in a pluralistic society, ideas and customs evolve by being brought into constant contact with alternative ideas and customs. In <em>On Liberty</em>, John Stuart Mill <a href="https://www.gutenberg.org/files/34901/34901-h/34901-h.htm" target="_blank">writes</a>:</p><p style="margin-left: 20px;">…the peculiar evil of silencing the expression of an opinion is that it is robbing the human race; posterity as well as the existing generation; those who dissent from the opinion, still more than those who hold it. If the opinion is right, they are deprived of the opportunity of exchanging error for truth: if wrong, they lose, what is almost as great a benefit, the clearer perception and livelier impression of truth, produced by its collision with error.</p><p>For humanity to benefit from pluralism—to benefit from the exchange of cultures and the collision of ideas—we must practice toleration. We must respect the rights of our colleagues and neighbors to think and live differently than we do.</p><p>When someone practices toleration, Kukathas says, they don't just put up with something but actually acknowledge it "with a kind of open spirit." Intentional, meaningful tolerance includes making an effort to understand others' points of view. We don't have to agree, but we should seek to understand. And, ultimately, we have to tolerate ideas we disagree with if we want to live in a flourishing and peaceful society.</p><p>This is what cancel culture robs society of—the healthy and essential practice of toleration, without which pluralism and a peaceful society cannot be sustained. </p>
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Six ways machine learning threatens social justice
Machine learning is a powerful and imperfect tool that should not go unmonitored.
15 October, 2020
Credit: Monopoly919 on Adobe Stock
- When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage.
- Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque.
- In this article, I cover six ways that machine learning threatens social justice and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism.
<p>When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage. Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque. In this article, I cover six ways that machine learning threatens social justice and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism.</p><p>When you use machine learning, you aren't just optimizing models and streamlining business.<strong> You're governing. </strong>In essence, the models embody policies that control access to opportunities and resources for many people. They drive consequential decisions as to whom to investigate, incarcerate, set up on a date, or medicate – or to whom to grant a loan, insurance coverage, housing, or a job.<br></p><p>For the same reason that machine learning is valuable—that it drives operational decisions more effectively—it also wields power in the impact it has on millions of individuals' lives. Threats to social justice arise when that impact is detrimental, when models systematically limit the opportunities of underprivileged or protected groups.</p>
Here are six ways machine learning threatens social justice
<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNDUyMDgxNC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY0MzM0NjgxOH0.zHvEEsYGbNA-lnkq4nss7vwVkZlrKkuKf0XASf7A7Jg/img.jpg?width=980" id="05f07" class="rm-shortcode" data-rm-shortcode-id="a7089b6621166f5a2df77d975f8b9f74" data-rm-shortcode-name="rebelmouse-image" data-width="1000" data-height="563" />Credit: metamorworks via Shutterstock
<p><strong></strong><strong>1) </strong><strong>Blatantly discriminatory models</strong> are predictive models that base decisions partly or entirely on a protected class. Protected classes include race, religion, national origin, gender, gender identity, sexual orientation, pregnancy, and disability status. By taking one of these characteristics as an input, the model's outputs – and the decisions driven by the model – are based at least in part on membership in a protected class. Although models rarely do so directly, there is <a href="https://www.youtube.com/watch?v=eSlzy1x6Fy0" target="_blank">precedent</a> and <a href="https://www.youtube.com/watch?v=wfpNN8ASIq4" target="_blank">support</a> for doing so.</p><p>This would mean that a model could explicitly hinder, for example, black defendants for being black. So, imagine sitting across from a person being evaluated for a job, a loan, or even parole. When they ask you how the decision process works, you inform them, "For one thing, our algorithm penalized your score by seven points because you're black." This may sound shocking and sensationalistic, but I'm only literally describing what the model would do, mechanically, if race were permitted as a model input. </p><p><strong>2) Machine bias</strong>. Even when protected classes are not provided as a direct model input, we find, in some cases, that model predictions are still inequitable. This is because other variables end up serving as proxies to protected classes. This is <a href="https://coursera.org/share/51350b8fb12a5937bbddc0e53a4f207d" target="_blank" rel="noopener noreferrer">a bit complicated</a>, since it turns out that models that are fair in one sense are unfair in another. </p><p>For example, some crime risk models succeed in flagging both black and white defendants with equal precision – each flag tells the same probabilistic story, regardless of race – and yet the models falsely flag black defendants more often than white ones. A crime-risk model called COMPAS, which is sold to law enforcement across the US, falsely flags white defendants at a rate of 23.5%, and Black defendants at 44.9%. In other words, black defendants who don't deserve it are <a href="https://coursera.org/share/df6e6ba7108980bb7eeae0ba22123ac1" target="_blank" rel="noopener noreferrer">erroneously flagged almost twice as much</a> as white defendants who don't deserve it.</p><p><strong>3) Inferring sensitive attributes</strong>—predicting pregnancy and beyond. Machine learning predicts sensitive information about individuals, such as sexual orientation, whether they're pregnant, whether they'll quit their job, and whether they're going to die. Researchers have shown that it is possible to <a href="https://youtu.be/aNwvXhcq9hk" target="_blank" rel="noopener noreferrer">predict race based on Facebook likes</a>. These predictive models deliver dynamite.</p><p>In a particularly extraordinary case, officials in China use facial recognition to <a href="https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html" target="_blank" rel="noopener noreferrer">identify and track the Uighurs, a minority ethnic group</a> systematically oppressed by the government. This is the first known case of a government using machine learning to profile by ethnicity. One Chinese start-up valued at more than $1 billion said its software could recognize "sensitive groups of people." It's website said, "If originally one Uighur lives in a neighborhood, and within 20 days six Uighurs appear, it immediately sends alarms" to law enforcement.</p><span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="10134f9910c910d2002e2c1984069791"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/IHE63fxpHCg?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p><strong></strong><strong>4) A lack of transparency.</strong> A computer can keep you in jail, or deny you a job, a loan, insurance coverage, or housing – and yet <a href="https://www.youtube.com/watch?v=XDonDVGWhAE&feature=youtu.be" target="_blank">you cannot face your accuser</a>. The predictive models generated by machine learning to drive these weighty decisions are generally kept locked up as a secret, unavailable for audit, inspection, or interrogation. Such models, inaccessible to the public, perpetrate a lack of due process and a lack of accountability.</p><p>Two ethical standards oppose this shrouding of electronically-assisted decisions: 1) <em>model transparency</em>, the standard that predictive models be accessible, inspectable, and understandable. And 2) <em>the right to explanation</em>, the standard that consequential decisions that are driven or informed by a predictive model are always held up to that standard of transparency. Meeting those standards would mean, for example, that a defendant be told which factors contributed to their crime risk score -- which aspects of their background, circumstances, or past behavior caused the defendant to be penalized. This would provide the defendant the opportunity to respond accordingly, establishing context, explanations, or perspective on these factors.</p><p><strong>5) Predatory micro-targeting.</strong> Powerlessness begets powerlessness – and that cycle can magnify for consumers when machine learning increases the efficiency of activities designed to maximize profit for companies. Improving the micro-targeting of marketing and the predictive pricing of insurance and credit can magnify the cycle of poverty. For example, highly-targeted ads are more adept than ever at <a href="https://youtu.be/LeZv2RanMRQ" target="_blank">exploiting vulnerable consumers</a> and separating them from their money.</p><p>And insurance pricing can lead to the same result. With insurance, the name of the game is to charge more for those at higher risk. Left unchecked, this process can quickly slip into predatory pricing. For example, a churn model may find that elderly policyholders don't tend to shop around and defect to better offers, so there's less of an incentive to keep their policy premiums in check. And pricing premiums based on other life factors also contributes to a cycle of poverty. For example, individuals with poor credit ratings are charged more for car insurance. In fact, a low credit score can increase your premium more than an at-fault car accident.</p><p><strong>6) The coded gaze.</strong> If a group of people is underrepresented in the data from which the machine learns, the resulting model won't work as well for members of that group. This results in <a href="https://www.youtube.com/watch?v=UG_X_7g63rY" target="_blank" rel="noopener noreferrer">exclusionary experiences and discriminatory practices</a>. This phenomenon can occur for both facial image processing and <a href="https://www.pnas.org/content/117/14/7684" target="_blank" rel="noopener noreferrer">speech recognition</a>.</p>
Recourse: Establish machine learning standards as a form of social activism
<p>To address these problems, take on machine learning standardization as a form of social activism. We must establish standards that go beyond nice-sounding yet vague platitudes such as "be fair", "avoid bias", and "ensure accountability". Without being precisely defined, these catch phrases are subjective and do little to guide concrete action. Unfortunately, such broad language is fairly common among the principles released by many companies. In so doing, companies protect their public image more than they protect the public.<br></p><p>People involved in initiatives to deploy machine learning have a powerful, influential voice. These relatively small numbers of people mold and set the trajectory for systems that automatically dictate the rights and resources that great numbers of consumers and citizens gain access to.</p><p>Famed machine learning leader and educator Andrew Ng drove it home: "AI is a superpower that enables a small team to affect a huge number of people's lives... Make sure the work you do leaves society better off."</p><p>And Allan Sammy, Director, Data Science and Audit Analytics at Canada Post, clarified the level of responsibility: "A decision made by an organization's analytic model is a decision made by that entity's senior management team."</p><p>Implementing ethical data science is as important as ensuring a self-driving car knows when to put on the breaks.</p><p>Establishing well-formed ethical standards for machine learning will be an intensive, ongoing process. For more, <a href="https://youtu.be/ToSj0ZkJHBQ" target="_blank">watch this short video</a>, in which I provide some specifics meant to kick-start the process.</p><p><em>Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who makes machine learning understandable and captivating. He is the founder of the long-running <a href="https://www.predictiveanalyticsworld.com/" target="_blank" rel="noopener noreferrer">Predictive Analytics World</a> and the <a href="https://www.deeplearningworld.com/" target="_blank" rel="noopener noreferrer">Deep Learning World</a> conference series and the instructor of the end-to-end, business-oriented Coursera specialization </em><em><a href="http://www.machinelearning.courses/" target="_blank" rel="noopener noreferrer">Machine learning for Everyone</a>. Stay in touch with Eric on Twitter <a href="https://twitter.com/predictanalytic" target="_blank">@predictanalytic.</a></em><a href="http://www.machinelearning.courses/" target="_blank" rel="noopener noreferrer"></a></p>
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How socioeconomic status negatively impacts children's brains
A new study shows how poor children are negatively impacted neurologically.
07 October, 2020
Credit: alexfan32 / Shutterstock
- Children in poor neighborhoods exhibit abnormal activation of motivational circuits in their brains.
- The neurological impact increases the likelihood of criminal behavior and substance abuse later in life.
- Researchers suggest focusing on shaping the environment to set up the child for success.
<p>A <a href="https://www.sciencedirect.com/science/article/abs/pii/S0022103173800058" target="_blank">1973 experiment</a> produced interesting data about noise and education—and, by extension, socioeconomic status. The Bridge apartment complex sits directly over Interstate 95 in Manhattan. Researchers noticed the echo chamber effect generated by highway noise negatively impacted children's ability to read. Children living on lower floors experienced serious problems due to their inability to concentrate. On higher floors, which were both more expensive and subject to less ambient noise, children didn't have the same difficulties.</p><p>The longstanding myth that every American has the same opportunities needs to be abandoned. Black, LatinX, and Native American communities are being <a href="https://www.npr.org/sections/health-shots/2020/05/30/865413079/what-do-coronavirus-racial-disparities-look-like-state-by-state" target="_blank">disproportionately affected</a> by COVID-19. Residents of economically disadvantaged neighborhoods have less access to health care and public services. This is true of anyone in these economic categories, but these categories tend to include the aforementioned communities. </p><p>A <a href="https://journals.sagepub.com/doi/abs/10.1177/0956797620929299" target="_blank">new study</a>, published in the journal <em>Psychological Science</em>, adds to the growing literature of socioeconomic trauma. Researchers from The University of Mexico found that children in poor neighborhoods exhibit abnormal activation of motivational circuits in their brain, putting them at greater risk for mental health and social problems. </p><p>The focus of this study was the brain's reward system. A link between reward and motivation is well established. For example, getting into flow states <a href="https://medium.com/centered-blog/how-flow-states-help-you-find-meaning-in-work-and-life-64a5c814cfa5" target="_blank">requires</a> immediate feedback and achievable tasks. The reward—a psychological state in which time dissolves—results with regular training. The takeaway: an achievable reward awaits your effort. </p><p>Rewards are thin or nonexistent for children in disadvantaged neighborhoods. Forget flow; mere survival is in question. The team scanned the brains of 6,396 children (ages 9-10) while they completed an anticipation task that required them to either quickly win or refrain from losing a reward. </p>
<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="f423b07981d29296763866605a081b7d"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/7O7BMa9XGXE?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span><p>The results show that children from poorer zip codes (household income of less than $35,000 or in the $35,000-$50,000 range) suffered greater internalizing and externalizing psychological problems than children from wealthier (six-figure) neighborhoods. The internalizing-problems domain includes anxious-depressed symptoms, withdrawn-depressed symptoms, and somatic complaints (such as stomachaches), while the externalizing-problems domain includes attentional difficulties, aggression, and rule-breaking behaviors.</p><p>The team discovered decreased activation of the ventral and dorsal striatum, as well as the pallidum—motivational circuitry. Disadvantaged children were unable to anticipate rewards in the same manner as children from wealthier neighborhoods, and therefore unlikely to put in the same effort. </p><p>The team writes that this data suggest an increased likelihood of attention-deficit/hyperactivity disorder, along with impaired reward-motivated behavior—factors that increase the likelihood of criminal behavior and substance abuse later in life. </p><p>In sum, the team suggests changing the environment, not seeking out self-help coaches. </p><p style="margin-left: 20px;">"This suggests that interventions to reduce externalizing in children from deprived neighborhoods would do well to focus on shaping the environment to set up the child for success, rather than providing, for example, verbal instruction to change goal-directed behavior." </p><p>Mental health is rarely an individual matter. Your environment plays a far greater role in health than your genes. We've long denied this reality as a culture, pretending the "bootstraps" mentality applies equally to everyone. Decades of data show that to be false. Until we provide environments that allow all children an opportunity to thrive, studies like this will continue to highlight the dangers of economic and racial inequality on public health. </p><p>--</p><p><em>Stay in touch with Derek on <a href="http://www.twitter.com/derekberes" target="_blank">Twitter</a>, <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank" rel="noopener noreferrer">Facebook</a> and <a href="https://derekberes.substack.com/" target="_blank" rel="noopener noreferrer">Substack</a>. His next book is</em> "<em>Hero's Dose: The Case For Psychedelics in Ritual and Therapy."</em></p>
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Art will never die. So why does it need philanthropy?
We wouldn't want to live without it, so how can we create art that's durable?
03 May, 2020
- You cannot kill the arts. This is particularly true when you talk about poetry, which does well in a world of social media as its easy to digest in its short form.
- Measuring success in art can be tricky, though. Impact and influence can be felt immediately, so how does art find that everlasting durability?
- Philanthropy can encourage and enable art, and as a result, potentially lengthen its lifespan. If we can find ways to measure art in its own terms, we can effectively give a platform to new voices who complete the cultural picture.
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