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How Our Autistic Ancestors Played An Important Role In Human Evolution

When you think of someone with autism, what do you think of? It might be someone with a special set of talents or unique skills such as natural artistic ability or a remarkable memory. It could also be someone with enhanced abilities in engineering or mathematics, or an increased focus on detail. The Conversation

This is because despite all the negative stories of an epidemic of autism most of us recognise that people with autism spectrum conditions bring a whole range of valued skills and talents both technical and social to the workplace and beyond.

Research has also shown that a high number of people not diagnosed with autism have autistic traits. So although many of these people have not been officially diagnosed, they might be were they to go for autism-related tests. These people were unaware they have these traits, dont complain of any unhappiness, and tend to feel that many of their particular traits are often an advantage.

The origins of autism

This is what we mean when we talk about the autism spectrum we are all a bit autistic and we all fit somewhere along a spectrum of traits.

And we know through genetic research that autism and autistic traits have been part of what makes us human for a long time.

Research has shown that some key autism genes are part of a shared ape heritage, which predates the split that led us along a human path. This was when our ancient ape ancestors separated from other apes that are alive today. Other autism genes are more recent in evolutionary terms though they are still more than 100,000-years-old.

Research has also shown that autism for the most part is highly hereditary. Though a third of the cases of autism can be put down to the random appearance of genetic mistakes or spontaneously occurring mutations, high rates of autism are generally found in certain families. And for many of these families this dash of autism can bring some advantages.

All of this suggests that autism is with us for a reason. And as our recent book and journal paper show, ancestors with autism played an important role in their social groups through human evolution because of their unique skills and talents.

Ancient genes

Going back thousands of years, people who displayed autistic traits would not only have been accepted by their societies, but could have been highly respected.

Many people with autism have exceptional memory skills, heightened perception in realms of vision, taste and smell and in some contexts, an enhanced understanding of natural systems such as animal behaviour. And the incorporation of some of these skills into a community would have played a vital role in the development of specialists. It is very likely these specialists would then have become vitally important for the survival of the group.

One anthropological study of reindeer herders said:

The extraordinary old grandfather had a detailed knowledge of the parentage, medical history and moods of each one of the 2,600 animals in the herd.

He was more comfortable in the company of reindeer than of humans, and always pitched his tent some way from everyone else and cooked for himself. His son worked in the herd and had been joined for the summer by his own teenage sons, Zhenya and young Sergei.

Autistic traits in art

Further evidence can be found in traits shared between some cave art and talented autistic artists such as those paintings found in the Chauvet Cave, in southern France. This contains some of the best preserved figurative cave paintings in the world.

The paintings show exceptional realism, remarkable memory skills, strong attention to detail, along with a focus on parts rather than wholes.

These autistic traits can also be found in talented artists who dont have autism but they are much more common in talented autistic artists.

Rewriting history

But unfortunately despite the potential evidence, archaeology and narratives about human origins have been slow to catch up. Diversity has never been a part of our reconstructions of human origins. It has taken researchers a long time to move beyond the image of a man evolving from an ape-like form that we so typically associate with evolution.

It is only relatively recently that women have been recognised as playing a key role in our evolutionary past before this evolution narratives tended to focus on the role of men. So its no wonder that including autism something which is still seen as a disorder by some is considered to be controversial.

And this is undoubtedly why arguments about the inclusion of autism and the way it must have influenced such art have been ridiculed.

But given what we know, it is clearly time for a reappraisal of what autism has brought to human origins. Michael Fitzgerald, the first professor of child and adolescent psychiatry in Ireland to specialise in autism spectrum disorder, boldly claimed in an interview in 2006 that:

All human evolution was driven by slightly autistic Aspergers and autistic people. The human race would still be sitting around in caves chattering to each other if it were not for them.

And while I wouldnt go that far, I have to agree that without that dash of autism in our human communities, we probably wouldnt be where we are today.

Penny Spikins, Senior Lecturer in the Archaeology of Human Origins, University of York

This article was originally published on The Conversation. Read the original article.

Read more: http://www.iflscience.com/plants-and-animals/how-our-autistic-ancestors-played-an-important-role-in-human-evolution/

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Read This Inspiring “Letter To My Past Self” From A Student Who Escaped Afghanistan To Study Science

The right to an education is article 26 of the Universal Declaration of Human Rights. Sadly, like all the other articles, this is often ignored, particularly for women.

In some countries, girls are not only denied an education but are threatened if they try to obtain one. Last year IFLScience heard the story of Lema, an Afghan girl who stopped going to school after her life was threatened, but taught herself to read English at home from a newspaper. From the age of 13 Lema used the power of the Internet to access online education systems from home, and she fell in love with physics and mathematics, spending years developing her knowledge of these fields as well as could be done remotely.

Afghanistan doesn’t have a SAT testing site, so the only way Lema could win entry to American universities was to be smuggled into Pakistan. The trip was so hazardous family members tried to get her to give up on her dream of studying abroad, but an uncle agreed to take her on a trip he was making for other reasons. The journey was so long that when she arrived the site was full, but SAT management decided if ever there was a time to bend the rules this was it, and allowed her to sit the test.

Despite her lack of formal education, Lema did well enough that she was offered a place at several American universities. Unfortunately, her initial attempts to get a visa to study in America were rejected.

We first heard about Lema’s story through a lobbying campaign to get her visa approved, combined with fundraising to pay for travel and tuition. We wanted to get behind this campaign. However, publicity about her story also attracted the attention of enemies of girls’ education, and there were fears of reprisals against her family. Additional media was thought likely to increase the danger, so naturally, we didn’t run anything.

Now, a year later, we are delighted to learn Lema has made it to a university in America, and is loving it, although we still can’t use her real name as fears of reprisals to her family are still ongoing. She has even been granted a full scholarship, so is no longer seeking financial support. On the other hand, she does want to inspire other girls to know what is possible, and sent us the letter to her former self, published on the next page, in the hope it can reach others who are told maths and physics are not for them.

As you can imagine, we are deeply honored that she chose IFLScience to get her message to the world, and to learn she loves reading us.

A Letter to My Past Self

To the girl who is just beginning to dream about the stars and galaxies in a corner of the world where dreaming is forbidden to her. To the girl who does not know yet what her existence means to her and everyone else. To the girl who wants to run, who wants to fly, who even wants to fall, but never wants to stop. You will get your wings.

I know its hard to imagine a life of self-esteem and respect now, but I want you to know it is possible. They will ruffle your feathers, try to get you to stop learning, but you will learn how to ignore it. I know you hear now, Your Xs and Ys wont help you if you mother-in-law asks you to cook. Your wings are clipped by your three-year-old nephew, so arrogant of his gender that even at that age he asks, Why are you studying? You are a girl! It hurts, I know, and you might not have the answer today, but you will. Someday soon youll answer that little guys big question: I am studying math and science to temporarily liberate myself from a life of bigotry and difficulty from a life reduced to just cooking and assimilating into a society where I am told womans place is in the house or in the grave. I am studying to travel the world and read about enlightenment.

You give off incredible light in a part of the world still enshrouded in darkness, where people fear becoming blind by the brightness of the light. You will be warned, too. Keep challenging their lies. Stare them in the eyes and show them that your brain has more than enough capacity to understand infinities and integrals and think about the universe. Every equation you learn will take you one step closer to understanding a universe full of wonder, to finding your own meaning. I have to tell life will reward your struggle, even if you dont see it now. I know you dont expect anything in return in the pursuit of learning other than a temporary mental freedom, but you will be emboldened by strangers who appreciate the struggle of your flight. You will make friends and share ideas. You will leave the nest, flying to heights you never imagined possible.

To my younger self, keep growing the effort is worth it. In the words of Ovid, Be patient and tough; someday this pain will be useful to you.

Read more: http://www.iflscience.com/physics/read-this-inspiring-letter-to-my-past-self-from-a-student-who-escaped-afghanistan-to-study-science/

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Paradoxes Of Probability And Other Statistical Strangeness

The Conversation

Statistics is a useful tool for understanding the patterns in the world around us. But our intuition often lets us down when it comes to interpreting those patterns. In this series we look at some of the common mistakes we make and how to avoid them when thinking about statistics, probability and risk. The Conversation


You dont have to wait long to see a headline proclaiming that some food or behaviour is associated with either an increased or a decreased health risk, or often both. How can it be that seemingly rigorous scientific studies can produce opposite conclusions?

Nowadays, researchers can access a wealth of software packages that can readily analyse data and output the results of complex statistical tests. While these are powerful resources, they also open the door to people without a full statistical understanding to misunderstand some of the subtleties within a dataset and to draw wildly incorrect conclusions.

Here are a few common statistical fallacies and paradoxes and how they can lead to results that are counterintuitive and, in many cases, simply wrong.


Simpsons paradox

What is it?

This is where trends that appear within different groups disappear when data for those groups are combined. When this happens, the overall trend might even appear to be the opposite of the trends in each group.

One example of this paradox is where a treatment can be detrimental in all groups of patients, yet can appear beneficial overall once the groups are combined.

How does it happen?

This can happen when the sizes of the groups are uneven. A trial with careless (or unscrupulous) selection of the numbers of patients could conclude that a harmful treatment appears beneficial.

Example

Consider the following double blind trial of a proposed medical treatment. A group of 120 patients (split into subgroups of sizes 10, 20, 30 and 60) receive the treatment, and 120 patients (split into subgroups of corresponding sizes 60, 30, 20 and 10) receive no treatment.

The overall results make it look like the treatment was beneficial to patients, with a higher recovery rate for patients with the treatment than for those without it.

image-20170330-8593-t93w83.pngThe Conversation, CC BY-ND

However, when you drill down into the various groups that made up the cohort in the study, you see in all groups of patients, the recovery rate was 50% higher for patients who had no treatment.

image-20170330-30365-f2956l.pngThe Conversation, CC BY-ND

But note that the size and age distribution of each group is different between those who took the treatment and those who didnt. This is what distorts the numbers. In this case, the treatment group is disproportionately stacked with children, whose recovery rates are typically higher, with or without treatment.


Base rate fallacy

What is it?

This fallacy occurs when we disregard important information when making a judgement on how likely something is.

If, for example, we hear that someone loves music, we might think its more likely theyre a professional musician than an accountant. However, there are many more accountants than there are professional musicians. Here we have neglected that the base rate for the number of accountants is far higher than the number of musicians, so we were unduly swayed by the information that the person likes music.

How does it happen?

The base rate fallacy occurs when the base rate for one option is substantially higher than for another.

Example

Consider testing for a rare medical condition, such as one that affects only 4% (1 in 25) of a population.

Lets say there is a test for the condition, but its not perfect. If someone has the condition, the test will correctly identify them as being ill around 92% of the time. If someone doesnt have the condition, the test will correctly identify them as being healthy 75% of the time.

So if we test a group of people, and find that over a quarter of them are diagnosed as being ill, we might expect that most of these people really do have the condition. But wed be wrong.


image-20170329-1664-htfx0x.pngIn a typical sample of 300 patients, for every 11 people correctly identified as unwell, a further 72 are incorrectly identified as unwell. The Conversation, CC BY-ND


According to our numbers above, of the 4% of patients who are ill, almost 92% will be correctly diagnosed as ill (that is, about 3.67% of the overall population). But of the 96% of patients who are not ill, 25% will be incorrectly diagnosed as ill (thats 24% of the overall population).

What this means is that of the approximately 27.67% of the population who are diagnosed as ill, only around 3.67% actually are. So of the people who were diagnosed as ill, only around 13% (that is, 3.67%/27.67%) actually are unwell.

Worryingly, when a famous study asked general practitioners to perform a similar calculation to inform patients of the correct risks associated with mammogram results, just 15% of them did so correctly.


Will Rogers paradox

What is it?

This occurs when moving something from one group to another raises the average of both groups, even though no values actually increase.

The name comes from the American comedian Will Rogers, who joked that when the Okies left Oklahoma and moved to California, they raised the average intelligence in both states.

Former New Zealand Prime Minister Rob Muldoon provided a local variant on the joke in the 1980s, regarding migration from his nation into Australia.

How does it happen?

When a datapoint is reclassified from one group to another, if the point is below the average of the group it is leaving, but above the average of the one it is joining, both groups averages will increase.

Example

Consider the case of six patients whose life expectancies (in years) have been assessed as being 40, 50, 60, 70, 80 and 90.

The patients who have life expectancies of 40 and 50 have been diagnosed with a medical condition; the other four have not. This gives an average life expectancy within diagnosed patients of 45 years and within non-diagnosed patients of 75 years.

If an improved diagnostic tool is developed that detects the condition in the patient with the 60-year life expectancy, then the average within both groups rises by 5 years.

image-20170328-21243-1wcp3a8.pngThe Conversation, CC BY-ND


Berksons paradox

What is it?

Berksons paradox can make it look like theres an association between two independent variables when there isnt one.

How does it happen?

This happens when we have a set with two independent variables, which means they should be entirely unrelated. But if we only look at a subset of the whole population, it can look like there is a negative trend between the two variables.

This can occur when the subset is not an unbiased sample of the whole population. It has been frequently cited in medical statistics. For example, if patients only present at a clinic with disease A, disease B or both, then even if the two diseases are independent, a negative association between them may be observed.

Example

Consider the case of a school that recruits students based on both academic and sporting ability. Assume that these two skills are totally independent of each other. That is, in the whole population, an excellent sportsperson is just as likely to be strong or weak academically as is someone whos poor at sport.

If the school admits only students who are excellent academically, excellent at sport or excellent at both, then within this group it would appear that sporting ability is negatively correlated with academic ability.

To illustrate, assume that every potential student is ranked on both academic and sporting ability from 1 to 10. There are an equal proportion of people in each band for each skill. Knowing a persons band in either skill does not tell you anything about their likely band in the other.

Assume now that the school only admits students who are at band 9 or 10 in at least one of the skills.

If we look at the whole population, the average academic rank of the weakest sportsperson and the best sportsperson are both equal (5.5).

However, within the set of admitted students, the average academic rank of the elite sportsperson is still that of the whole population (5.5), but the average academic rank of the weakest sportsperson is 9.5, wrongly implying a negative correlation between the two abilities.

image-20170329-1649-h3kvxl.pngThe Conversation, CC BY-ND


Multiple comparisons fallacy

What is it?

This is where unexpected trends can occur through random chance alone in a data set with a large number of variables.

How does it happen?

When looking at many variables and mining for trends, it is easy to overlook how many possible trends you are testing. For example, with 1,000 variables, there are almost half a million (1,000×999/2) potential pairs of variables that might appear correlated by pure chance alone.

While each pair is extremely unlikely to look dependent, the chances are that from the half million pairs, quite a few will look dependent.

Example

The Birthday paradox is a classic example of the multiple comparisons fallacy.

In a group of 23 people (assuming each of their birthdays is an independently chosen day of the year with all days equally likely), it is more likely than not that at least two of the group have the same birthday.

People often disbelieve this, recalling that it is rare that they meet someone who shares their own birthday. If you just pick two people, the chance they share a birthday is, of course, low (roughly 1 in 365, which is less than 0.3%).

However, with 23 people there are 253 (23×22/2) pairs of people who might have a common birthday. So by looking across the whole group you are testing to see if any one of these 253 pairings, each of which independently has a 0.3% chance of coinciding, does indeed match. These many possibilities of a pair actually make it statistically very likely for coincidental matches to arise.

For a group of as few as 40 people, it is almost nine times as likely that there is a shared birthday than not.

image-20170329-1664-1tb8sti.pngThe probability of no shared birthdays drops as the number of people in a group increases. The Conversation, CC BY-ND

Stephen Woodcock, Senior Lecturer in Mathematics, University of Technology Sydney

This article was originally published on The Conversation. Read the original article.

Read more: http://www.iflscience.com/editors-blog/paradoxes-of-probability-and-other-statistical-strangeness/

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