However, the ability to lie and engage in other forms of deception is also a source of great social power, as it allows people to shape interactions in ways that serve their interests: They can evade responsibility for their misdeeds, take credit for accomplishments that are not really theirs, and rally friends and allies to the cause. As such, it’s an important step in a child’s development and there are cognitive building blocks that must be in place in order to successfully lie.
One way research psychologists have sought to understand the
reasoning behind the choice to lie versus tell the truth is to go back to when we first learn this skill in childhood. In some studies, researchers ask children to play a game in which they can obtain a material reward by lying. In other studies children are faced with social situations in which the more polite course of action involves lying instead of telling the truth. For example, an experimenter will offer an undesirable gift such as a bar of soap and ask the child whether he or she likes it. Yet another method is to ask parents to keep a written record of the lies that their children tell.
In our recent study, my colleagues and I sought to understand children’s thinking processes when they were first figuring out how to deceive other people, which for most children is around age three and a half. We were interested in the possibility that certain types of social experiences might speed up this developmental timeline.
We invited young children to play a simple game they could win only by deceiving their opponent: Children who told the truth won treats for the experimenter and those who lied won treats for themselves.
In this game, the child hides a treat in one of two cups while an experimenter covers her eyes. The experimenter then opens her eyes and asks the child where the treat is hidden, and the child responds by indicating one of the two cups. If the child indicates the correct cup, the experimenter wins the treat, and if the child indicates the incorrect one, the child wins the treat.
Children played 10 rounds of this game each day for 10 consecutive days. This method of closely observing children over a short period of time allows for fine-grained tracking of behavioral changes, so researchers can observe the process of development as it unfolds.
We tested children around the time of their third birthday, which is before children typically know how to deceive. We found that, as
expected, when children first started playing the game most of them made no effort to deceive, and lost to the experimenter every time. However, within the next few sessions most children discovered how to deceive in order to win the game – and after their initial discovery they used deception consistently.
Not all children figured out how to deceive at the same rate. At one extreme, some figured it out on the first day; at the other
extreme, some were consistently losing the game even at the end
of the 10 days.
We discovered that the rate at which individual children learned to deceive was related to certain cognitive skills. One of these skills – what psychologists call theory of mind – is the ability to understand that others don’t necessarily know what you know. This skill is needed because when children lie they intentionally communicate information that differs from what they themselves believe. Another one of these skills, cognitive control, allows people to stop themselves from blurting out the truth when they try to lie. The children who figured out how to deceive the most quickly had the highest levels of both of these skills.
Our findings suggest that competitive games can help children gain the insight that deception can be used as a strategy for personal gain – once they have the underlying cognitive skills to figure this out.
It’s important to keep in mind that the initial discovery of deception is not an endpoint. Rather, it’s the first step in a long developmental trajectory. After this discovery, children typically learn when to deceive, but in doing so they must sort through a confusing array of messages about the morality of deception. They usually also learn more about how to deceive. Young children often inadvertently give away the truth when they try to dupe others, and they must learn to control their words, facial expressions and body language to be convincing.
As they develop, children often learn how to employ more nuanced forms of manipulation, such as using flattery as a means to curry favor, steering conversations away from uncomfortable topics and presenting information selectively to create a desired impression. By mastering these skills, they gain the power to help shape social narratives in ways that can have far-reaching consequences for themselves and for others.
By Gail Heyman, Professor of Psychology, University of California, San Diego
This article was originally published on The Conversation. Read the original article.
For him, our ideas about our ancestors have changed, proving once more that evolution was a theory, and therefore we should be free to teach other theories alongside evolution in our classrooms.
How to respond? The usual answer is that we should teach students the meaning of the word “theory” as used in science – that is, a hypothesis (or idea) that has stood up to repeated testing. Pence’s argument will then be exposed to be what philosophers call an equivocation – an argument that only seems to make sense because the same word is being used in two different senses.
Evolution, Pence argues, is a theory, theories are uncertain, therefore evolution is uncertain. But evolution is a theory only in the scientific sense of the word. And in the words of the National Academy of Sciences, “The formal scientific definition of theory is quite different from the everyday meaning of the word. It refers to a comprehensive explanation of some aspect of nature that is supported by a vast body of evidence.” Attaching this label to evolution is an indicator of strength, not weakness.
If you take this approach, you have failed to understand the purpose of Pence’s rhetoric, or why it is so appealing to creationists. Pence is an accomplished politician, and knows exactly how to appeal to his intended audience. He is also an accomplished trial lawyer, which makes him a conjuror with words, and like any skilful conjuror he has pulled off his trick by distraction. Pence has drawn us into a discussion about words, when our focus should be on the evidence.
I would suggest the opposite approach. The problem is not really with the word “theory” at all. Students will have learned its meaning in the same way they learn meanings in general: by seeing how the word is used.
They will have heard of atomic theory, which no one has seriously doubted for over a century. And what about the theory of gravity? Finally, they may have seen how Darwin himself uses the expression “my theory”, although at the time it was neither comprehensive nor well supported (there were huge gaps in the fossil record), to refer in a very general way to his linked ideas about mutability of species, common descent, and the power of natural selection.
So if anyone says, “Evolution is a theory”, don’t give them a lecture on the meaning of the word “theory”. If you do, you’ve fallen into the trap of making it seem that how we define words should affect how we see reality. You will be fighting on ground of your opponent’s choosing, since arguing about how to apply words is the stock in trade of theologians, preachers and lawyers like Mike Pence.
The correct response is to say that evolution is a theory – like gravity is a theory – and then redirect attention to the evidence. And that evidence is overwhelming.
Start with family relationships. Carl Linnaeus showed how living things can be classified into species, genera, families and so on, and Darwin pointed out that this is exactly the structure we would expect from a family tree. All dogs are canines, so dogs share an ancestor with foxes; all canines are carnivora, so dogs share a more remote ancestor with bears; all carnivora are mammals, so dogs and sheep are, albeit more remotely, related, and so on.
Then look at the discovery over the past few decades of family relationships at the molecular level, and the fact that the molecular family tree matches that based on anatomical resemblances.
Observe the fossil record. Once lamentably full of gaps (Darwin was among the lamenters), it is now densely populated. A century ago, it still made sense to point to the “missing link” between humans and pre-human apes. Now we know of several different hominin species living alongside each other, and the problem becomes one of distinguishing our grandparents from our great uncles. And yes, there are missing links in the chain, but without evolution we would not have a chain at all.
And then there’s biogeography: for example, why marsupials are only found in South America and Australasia, and except for a few species that made their way across the Isthmus of Panama, are never found elsewhere.
Plus we can actually observe evolution, and study it in the field or in the lab. The emergence of pesticide resistance is evolution in action, as shown in the justly famous Harvard/Technion demonstration “evolution on a plate”. So is the delightful Russian experiment of breeding tame foxes. Artificial selection, just as much as natural selection, is evolution in action.
And finally, and most convincingly, we must look at the way that these different lines of evidence mesh together. We can apply biogeography to the fossil record, and link it to what we know about the movements of the continents. Using the methods of molecular biology, we can identify and time the mutations that led different species to diverge from their common ancestor, and match the timing against the fossil record.
Thus the fossil record, deep anatomical resemblances, and DNA evidence agree in showing that whales, for instance, are closely related to hoofed mammals, diverging from them in the Eocene period. There are many other examples of such consistency.
Then, and only then, pause to explain how a scientific theory is an interlocking connection of ideas that explain things about the world, and that evolution is one of the most successful examples. And challenge the Mike Pences of this world to spell out exactly what they would like to see taught alongside the Theory of Evolution – and why.
By Paul Braterman, Hon. Research Fellow; Professor Emeritus, University of Glasgow
This article was originally published on The Conversation. Read the original article.
The World Health Organisation recommends that people reduce their daily intake of added sugars (that is, all sugar, excluding the sugar that is naturally found in fruit, vegetables and milk) to less than 5% of their total energy intake. However, people in the UK consume double – in the US, triple – that amount of sugar. Three-quarters of these added sugars come from sweet food and beverages, such as cakes and soft drinks. The rest come from other processed foods, such as ketchup.
At the same time, one in six people worldwide suffers from a common mental disorder, such as a mood or anxiety disorder. Could there be a link between high sugar consumption and common mental disorders?
Earlier research, published in 2002, examined the link between depression and sugar consumption in six countries. The researchers, from Baylor College in the US, found that higher rates of refined sugar consumption were associated with higher rates of depression.
Since then, a handful of studies have investigated the link between added sugar consumption and subsequent depression. In 2011, researchers in Spain found that when they grouped participants based on their commercial baked food consumption, those who ate the most baked food had a 38% increased chance of developing depression compared with those in the group with the lowest intake. The association remained even after accounting for health consciousness and employment status.
In 2014, researchers studied the association between sweetened beverages in a large US group. They found that sugar-sweetened and artificially sweetened drinks (diet drinks) could increase a person’s risk of developing depression. And, more recently, a 2015 study, including nearly 70,000 women, found higher chances of depression in those with a high added sugar intake, but not in those with a high intake of naturally occurring sugars, such as those found in fruit.
We are still not sure what causes depression, but some researchers believe that biological changes are at the root of it. Some of these changes could be influenced by sugar and sweet taste. For example, a study in rats found that diets high in sugar and fat can reduce a protein called BDNF that influences the growth and development of nerve cells in the brain. This protein is thought to be involved in the development of depression and anxiety.
Another possible biological cause is inflammation. High sugar diets can increase inflammation – a protective reaction of the body, normally directed against microorganisms or foreign substances. While common signs of inflammation, such as redness, are far from a mood disorder, the symptoms that keep us in bed with a cold are much closer, such as low energy and being unable to concentrate. Ongoing research suggests that mood disorders could be linked with inflammation, at least in some cases.
Dopamine is another possible culprit. A study using rats earned headlines for suggesting sweet foods could be as addictive as cocaine. This might be due to affects on dopamine, a brain chemical involved in the reward system. Dopamine is also thought to influence mood. And addiction is itself associated with a higher risk of developing a mood disorder.
Finally, sugar intake could be associated with other factors, such as obesity, which itself is related to mood.
But these associations could also reflect a reverse phenomenon: low mood could make people change their diet. Sweet foods could be used to soothe bad feelings by providing a short-term mood boost. And low mood and anxiety could make simple tasks, such as grocery shopping or cooking, so difficult and exhausting for the sufferer that they might start to avoid them. Instead, they might opt for junk food, takeaways and ready meals – all of which have a high sugar content.
For our latest study, my colleagues and I put the reverse association idea to the test. We used sugar intake from sweet food and drinks to predict new and recurrent mood disorders in a group of British civil servants. We also investigated whether having a mood disorder would make people more inclined to choose sweet foods and drinks.
We found that men without a mood disorder who consumed over 67g of sugar had a 23% increased risk of suffering from a mood disorder five years later, compared with those who ate less than 40g. This effect was independent of the men’s socioeconomic status, physical activity, drinking, smoking, other eating habits, body fatness and physical health.
We also found that men and women with a mood disorder and a high intake of sugar from sweet food and drinks were at higher risk of becoming depressed again five years later, compared with those who consumed less sugar. But this association was partly explained by their overall diet.
We found no evidence for a potential reverse effect: participants did not change their sugar intake after suffering from mood disorders.
Despite our findings, a number of questions remain about whether sugar makes us sad, whether it affects men more than women, and whether it is sweetness, rather than sugar itself, that explains the observed associations. What is certain, though, is that sugar is associated with a number of health problems, including tooth decay, type 2 diabetes and obesity. So cutting down on sugar is probably a good idea, regardless of whether it causes mood disorders or not.
By Anika Knüppel, PhD Candidate in Epidemiology and Public Health, UCL
This article was originally published on The Conversation. Read the original article.
Between 27 and 45.8 million individuals worldwide are trapped in some form of modern-day slavery. The victims are forced into slavery as sex workers, beggars and child soldiers, or as domestic workers, factory workers and laborers in manufacturing, construction, mining, commercial fishing and other industries.
Human trafficking occurs in every country in the world, including the U.S. It’s a hugely profitable industry, generating an estimated US$150 billion annually in illegal profits per year. In fact, it’s one of the largest sources of profit for global organized crime, second only to illicit drugs.
Analytics, the mathematical search for insights in data, could help law enforcement combat human trafficking. Human trafficking is essentially a supply chain in which the “supply” (human victims) moves through a network to meet “demand” (for cheap, vulnerable and illegal labor). Traffickers leave a data trail, however faint or broken, despite their efforts to operate off the grid and in the shadows.
There is an opportunity – albeit a challenging one – to use the bits of information we can get on the distribution of victims, traffickers, buyers and exploiters, and disrupt the supply chain wherever and however we can. In our latest study, we have detailed how this might work.
In most countries, resources to fight human trafficking are woefully inadequate. Agencies strive to use them as effectively and efficiently as possible, and often find themselves fighting for scarce funding and support. A government, for example, may need to decide how best to fund or schedule labor inspectors to detect child labor in the manufacturing industry. An organization with limited resources may need insight into which prevention program to run, or what type of awareness campaign to implement.
We can use data to identify populations most at-risk and target prevention campaigns to those populations. Risk factors for being drawn into trafficking include poverty, unemployment, migration and escape from political conflict or war. Experiences with organized crime and natural disasters can also change to a person’s risk.
Trafficking often begins with fraudulent recruitment methods, such as promises of employment or romance. Data can help identify specific economically depressed areas, where we can deploy awareness campaigns and social service support.
In operations research, scientists apply mathematical methods to answer complex questions about patterns in data and predict future trends or behaviors. Analytical tools similar to those used in transportation, manufacturing and finance can help us decide where to best allocate resources and help locate shelters for victims.
Trafficking networks are dynamic. Traffickers are likely to frequently change distribution and transportation routes to avoid detection, leaving law enforcement and analysts with incomplete information as they attempt to identify and dismantle trafficking networks.
However, researchers can help by tracking subtle trends in data at various locations; at access points where we actually come in contact with victims, such as the emergency room; and in the activity of local law enforcement.
In the sex trade, for example, clues may be found in patterns of petty theft, by looking at transactional data from purchases at retail outlets. Victims sometimes steal essential supplies that traffickers may not provide for them such as feminine hygiene products, soap and toothpaste. Trends in the use of cash for transactions normally made with debit or credit cards – hotel bookings, for example – may also raise a red flag.
Traffickers advertise on social media and internet-based sites. Analytics could seek patterns in photos through facial recognition software, comparing images from missing person reports or trafficking ads.
Sex trafficking activity, in particular, leaves traces in the public areas of the internet, mostly in the form of advertisements and escort ads. Advertisers tend to use social networks and dating websites, while more proficient traffickers frequently alter their online presence to try to elude identification.
Machine learning – a type of artificial intelligence where computers teach themselves to do tasks, such as recognize images – can be used to detect online trafficking activity. Recent advances in matrix completion, a type of machine learning, could even help clean up falsified information or make predictions about missing data.
Traffickers are also known to take advantage of increased demand for commercial sexual exploitation during major events, including conventions and large sporting events. Analyses that look at both location and timing of online ads could help law enforcement detect and possibly interdict transportation of victims to the event. They could also suggest when and where policymakers should focus intervention efforts.
Interrupting the flow of people, money and other components of trafficking is critical to identifying trafficking networks, disrupting their infrastructure at the source and eliminating them.
Unfortunately, network interruption requires the cooperation of authorities and the public surrounding the network. In some countries, such as Nepal and Costa Rica, officials are threatened or bribed into ignoring or otherwise allowing human trafficking. There is often inadequate regulatory oversight of industries known to use trafficked laborers. Traffickers can easily fabricate or alter a victim’s identification documents, rendering them invisible to overburdened authorities.
To help authorities identify trafficking operations to target, researchers could turn to network analysis, a mathematical way of representing real world systems and their interactions. For example, network analysis can be used to map out the dynamics of users and their connections embedded in social networks, such as Facebook and Twitter. This can possibly identify at-risk persons or, alternatively, traffickers or customers.
Social network analysis could also help to determine which contacts have a critical influence over others. This may enable early identification of either a victim or trafficking transaction.
Human trafficking is a serious crime and an appalling violation of human rights. Almost every country is affected by human trafficking as a source of victims, a transit point, or a destination and location of abuse. These new mathematical tools show great potential both to interrupt the human trafficking cycle and to provide the information needed to help victims escape to safety.
By Renata Konrad, Assistant Professor of Operations and Industrial Engineering, Worcester Polytechnic Institute and Andrew C. Trapp, Associate Professor of Operations and Industrial Engineering, Worcester Polytechnic Institute
This article was originally published on The Conversation. Read the original article.
