by: Brianna Chambers, Cox Media Group National Content DeskUpdated: Sep 3, 2017 - 8:33 PM

Charlottes Web,Stellaluna andThe Ugly Duckling are among the innumerable childrens books written to teach kids lessons through situations and images involving animals.

But a new study says books that feature humans learning lessons, instead of animal characters, stick with children more and allow for more insight into application of values and morals.

>> Read more trending news

The study, conducted by researchers at the University of Torontos Ontario Institute for Studies in Education (OISE) and published in the journal Developmental Science,found that children who read a book with human characters were more affected than those who read a book with animal characters.

In an experiment, nearly 100 children between the ages of 4 and 6 were read one of three books:Little Raccoon Learns to Shareby Mary Packard, which illustrates a fictional raccoon who learns that sharing makes one feel good and proves beneficial to all involved in the action; a version of the story in which the animal illustrations were replaced with human characters; and a control book about seeds.

The experiment found that children who were read the book with the human characters were more willing to share later in the day than those who were read the book with animal characters. Andthere was no difference in generosity between children who read the book with anthropomorphized animal characters and the control book; both groups showed a decrease in sharing behavior, the researchers found.

Reading a book about sharing had an immediate effect on childrens pro-social behavior, according to the study. However, the type of story characters significantly affected whether children became more or less inclined to behave pro-socially. After hearing the story containing real human characters, young children became more generous. In contrast, after hearing the same story but with anthropomorphized animals or a control story, children became more selfish.

A growing body of research has shown that young children more readily apply what theyve learned from stories that are realistic ... (but) this is the first time we found something similar for social behaviors, said Patricia Ganea, who led the study, according to The Guardian. The finding is surprising given that many stories for young children have human-like animals.

Read more atThe Guardianand read the study here.

2017 Cox Media Group.

View original post here:
Children's books with humans, not animals more effective, study says - KIRO Seattle

Behavioral health is rather specific, and technology-powered distant care is only cautiously developing in this realm. While providers recognize the need to employ technology for treating patients with anxiety, chronic stress, eating disorders, substance abuse, and other conditions, it is challenging to create a solution capable of effective intervention in human behavior that brings measurable and positive outcomes.

But there's more to this challenge. Behavioral disorders often go hand-in-hand with physical conditions. For example, a study initiated by the Robert Wood Johnson Foundation revealed that patients with asthma are nearly 2.5 times more likely to develop depression. Another viewpoint published in JAMA states that diabetes patients are twice as likely to suffer from a major depressive disorder during their lifetime.

Accordingly, some patients have to simultaneously take care of their physical and behavioral conditions, which is a huge burden. Good news is, technology is here to back up patients' efforts in-between support group meetings and one-on-ones. We are talking about patient-generated health data (PGHD) and its processing through the health data analytics methods.

Why PGHD for Behavioral Health?

Behavior is a constantly changing aspect of identity, which gives hope to patients who are feeling helpless in controlling their anxiety, depression, eating disorders, or other problems. But to initiate a positive change in a patient's condition, providers need more data. EHR data is great as a foundation for a patient profile, but it isn't enough to show gradual progress in treatment and establish short-term goals for patients to achieve.

PGHD can help in supporting patients with behavioral disorders in their daily struggle. It includes subjective and objective data collected by a patient (or their family) using wearables or medical devices, and is usually shared with caregivers through mHealth apps.

Subjective Data

A patient's self-evaluation is critical to successful treatment and recovery, be it depression, eating disorder, substance abuse, or another behavioral health condition. While each condition might require additional data on patients' feelings and emotions, the general list of subjective items to report can include:

Additionally, some objective information can be turned subjective with advanced wearable technology. For example, Spire tracks breathing patterns and analyzes them to understand how an individual feels, even before someone can recognize their own emotional state.

By continuously defining and reporting emotions, both patients and providers can understand certain patterns of how well the patient dealt with anxiety last week and how helpful the support group is (looking at the overall mood after each session). Moreover, strong negative trends in subjective items can indicate that the patient is on the edge of relapse, and the provider would get an automated notification about the possible problem. In this case, the caregiver would be able to discuss the patient's problem and take necessary actions, such as scheduling an appointment.

Objective Data

From the behavioral health perspective, objective data is supportive to the subjective data, a physiological reflection of a patient's mental and emotional state. The following vitals can help a caregiver understand the full picture of a patient's progress and current health status:

The readings from smart trackers can be aggregated and sent to the provider's health data analytics system to analyze the results and match them with previous measurements. If the analysis reveals any negative trends (e.g., weight loss dynamics for a patient with anorexia or decrease in activity because of a reduced number of steps), an application will notify the care team about possible risks to a patient's health status (via emails or text messages).

PGHD Supports Patients

Mental health is about keeping people strong and resourceful in the face of challenges. But with anxiety or depression crawling inside their mind, food becoming an obsession, or substance addiction developing, individuals can't think straight and can't live their lives to the fullest.

While there are various ways to help patients recover from disorders, including support groups, medications, and one-on-ones with a psychologist or psychiatrist, most of these measures are short-term interventions. Patients, in their turn, need continuous support, and PGHD can enable it.

A patient will be able to see the summary of their progress via their mHealth app. They can track mood swings during the month, relate anxiety bursts to insomnia cases, or celebrate the weight gain trend (this can be especially motivating for patients with anorexia) -- all backed up with automated push notifications if there's anything to worry about.

Not pretending to be a full-blown substitute for therapy, PGHD serves as a bridge between care points. This way, both a patient and their provider will be informed of the individual's overall progress with ups and downs, streamlining the process of tracking achievements, recognizing plateaus and, ultimately, patient recovery.

Lola Koktysh is a healthcare industry analyst at ScienceSoft, an IT consulting company headquartered in McKinney, Texas, where she focuses on healthcare IT including the industry's challenges and technology solutions.

2017-09-02T16:00:00-0400

See the article here:
Will Behavioral Health Benefit from Patient-Generated Data? - MedPage Today

Society is comprised of various individuals with a unique set of hopes, dreams and emotions. Attempting to understand each person's motives is a complexing feat to say the least. Author Duane Shoebridge tackles the challenge head-on and helps readers decipher why we do the things we do in his new book, "Getting Around the Humans: Figuring Out Why People Do What They Do."

"Getting Around the Humans" educates readers on how to discern the root of human behavior. Shoebridge identifies three primary desires - wealth, riches and honor - and shares how these wants are translated into action and help shape personality. He shows how to identity these concepts and how to interact with others who prioritize such desires differently. Featuring a Biblical perspective, "Getting Around the Humans" sheds light on why we do what we do.

"Each human being has a distinctive combination of aspirations, wants and hopes that they can only fully comprehend," Shoebridge said. "Getting Around the Human teaches readers to recognize these desires - both in ourselves and others - and work with their strengths and weaknesses."

"Getting Around the Humans: Figuring Out Why People Do What They Do"By Duane ShoebridgeISBN: 9781512779493 (hardcover), 9781512779486 (softcover), 9781512779479 (e-book)Available at Amazon, Barnes and Noble and Westbow Press

About the authorDuane Shoebridge is a husband and a homeschool father of three. He has his bachelor's in psychology and Bible from Northwestern College. His passion has been in youth ministry since 1990. He has been working as a business information consultant and programmer for a business he started since 2010.

Review Copies & Interview Requests:LAVIDGE - PhoenixSaTara Williams480-998-2600 x 586swilliams(at)lavidge.com

General Inquiries:LAVIDGE - PhoenixJacquelyn Brazzale480-998-2600 x 569jbrazzale(at)lavidge.com

Original post:
New Book Examines the Core of Human Behavior - Broadway World

"Mom, can you do something, please..."

Jael's voice trailed off as she spoke to her mother on phone. She started sobbing even before she hung up.

Life had not been kind to Jael. She felt older than her 42 years of age. Where did she go wrong?

She closed her eyes and allowed her mind to wander. She grew up in an average family. Her mother was a teacher while her father was a businessman. Her father was now deceased and her mother - who had already retired from her teaching job - was in charge of the family businesses.

A Happy Marriage

Jael's marriage was full of ups and downs. She met Mike when still at the university and they started dating almost immediately. He was really charming and treated her like a queen. They moved in together a few months after her graduation and legalized their union less than a year later.

Mike treated her really well, taking her out for surprise dinners and occasionally even taking her away for weekends to exotic locations in different parts of the country. This did not stop even after the children came. The family lived quite well and life was good.Mike and Jael were blessed with three children; two girls and one boy.

All No Longer Well?

The first indication that all was not well in their lives happened after 15 years of marriage. There were many times that Mike's phone would ring and he would not pick the calls. Then, Mike changed his telephone line without any explanation. When he told Jael that he had a new cell phone number, she was surprised. She asked him why he would change his number yet most of his contacts did not have the new number. He could not explain but told her that it was no big deal.

Things just did not look the same. Mike's circles had changed. He no longer seemed to keep the same company like before. He had also become secretive. Suddenly, Jael realized that her husband was slowly becoming a stranger. She started wondering whether he was in an extra-marital relationship that he was trying to hide from her.

It did not take long for her to fit the jigsaw puzzle. She started receiving telephone calls from people who were known to both of them, requesting her to tell Mike to switch on his phone for they were trying to get in touch with him. She would tell him but he would not comment.

With time, the message in the calls changed to telling her to let Mike know that they were expecting the payments as agreed. When she would give Mike the messages he would not comment.

Trouble With The Law

It was not until the day that a colleague from Mike's office called Jael to inform her that Mike had been arrested. That was the beginning of a long journey of turmoil for Jael and her family. The family got auctioned twice within a space of one year and Mike got arrested a number of times. Their lives turned into a nightmare.

It was a rude awakening to Jael to discover that a lot of what she believed about Mike was fake, including his academic credentials. He even had a fake identity and some people knew him by names she did not know. In short, Mike's life was largely a lie and he had misrepresented himself to many different people mainly to extort money from the unsuspecting people.

The first few times her family got into trouble, her mother and her siblings put some cash together and bailed her family out. They helped out a number of times till it dawned on them that Mike's problems were beyond what they could handle.

Jael would call her family members and beg them to help but they totally refused to get involved. She eventually surrendered to fate. Mike was found guilty of a number of crimes and sentenced to prison.

Rebuilding

Jael started to rebuild her life from humble beginnings. She moved to a cheaper house that she could afford to pay for. She struggled to keep the children in school and often paid school fees in installments. The high life they had lived for years gradually became a distant dream.

The children struggled to adjust to their new status and it was initially difficult for Jael to cope with the backlash. They got angry, became rebellious, got into trouble in school and in the neighborhood and disobeyed her. It was a very difficult road for her family but she took it one day at a time.

A man is the head of his household. He provides direction and leadership for his family. To learn more about how to effectively lead a family, here are useful tipssecure-your-family's-future-through-strong-leadership.

We often like to quote about the two becoming one in marriage. So, now that you are married, do you take responsibility for the behavior of your spouse, whom you consider to be your better half?

Two people meet when they are already adults, fall in love and decide to get married. In a few exceptional cases, couples have known each other from a young age, sometimes from childhood.

Should You Take Responsibility For Your Spouse's Behavior?

You are married probably to the love of your life. Is it your fault that your spouse is cheating on you, disrespects or abuses you or probably engages in criminal activities? Is it your behavior that taught him or her to be that way, to treat you that way or to have a certain attitude towards family responsibilities?

Human behavior refers to the sum total of actions and emotions associated with a human being. It is complicated. For those who think that everything human behavior is simply a matter of good or bad choices, that is oversimplifying a complex topic.

So, What Shapes Human Behavior?

1.Genetics

Genetics refers to the traits we inherit from our parents. Genetic influence on behavior has been studied using identical twins who were adopted by different families at birth such that besides inheritance, everything in their upbringing environment was different. Siblings who were adopted sometimes discover each other as adults only to find out that they have a lot of similarities and not just in terms of physical appearance.

2.Social Norms

An individual's behavior is shaped by the group one is a part of. That is why people from the same cultural or religious group have similar attitudes and practices such as what they consider an acceptable dressing code. There is warmth in a sense of belonging and human beings make effort to fit in or to find acceptance, even when the practices of the group might be destructive to them. Norms also govern families.

3.Attitudes

Attitudes have roots in past experiences and conditioning. An individual associates certain things with certain experiences. For example, a child associates going out to the park with pleasure and going to the dentist with pain. Changing one's attitude takes a conscious effort to question the norms. Negative attitudes can be changed by evaluating reasons behind the attitudes.

4.Mind and Body

Behavior is affected by what is going on in our bodies. Hormonal changes at certain periods of time such as teenage, pregnancy, during certain times in women's monthly cycle and during menopause; affect behavior.

Nutrition also affects behavior and that is the genesis of the saying 'a hungry man is an angry man. Hunger or having a brain that is starved of nutrients affects mood negatively and can make one quick to anger. Conditions such as having a brain tumor in certain areas of the brain or having low levels of feel-good neurotransmitters in the brain can affect mood negatively.

The mind and body are connected and influence each other. That is why we talk of 'a healthy mind in a healthy body'.

5.Coping Mechanisms

All human beings face difficulties and challenges from time to time but some cope better than others. Coping mechanisms are dependent on one's overall personality and lessons learned in life. There are people who train themselves in coping mechanisms such breathing in and out before reacting when provoked or engaging in vigorous physical activity when angry. Coping mechanisms can be trained as part of upbringing, through therapy or one can learn them independently.

When two people get married, the behavior of each of them is already fully established; it is not taught by the spouse. Much as one can do their best to influence the spouse positively, there is a lot that is already deeply entrenched in the individual that might not be possible to change, unless through therapy.

Achieving behavior change takes work; it is not handed to anyone on a silver platter. It, therefore, depends on whether the individual is ready to pay the price of change or not. People do not change themselves because someone else told them to change.

Read the rest here:
Are you responsible for your spouse's behavior? - The Standard

Ongoing advances in understanding the functional connections within the brain are producing exciting insights into how the brain circuits function together to support human behaviorand may lead to new discoveries in the development and treatment of psychiatric disorders, according to a review and update in the Harvard Review of Psychiatry.

Advanced neuroimaging techniques provide a new basis for studying circuit-level abnormalities in psychiatric disorders, according to the special perspectives article by Deanna M. Barch, PhD, of Washington University in St. Louis. She writes, "These advances have provided the basis for recent efforts to develop a more complex understanding of the function of brain circuits in health and of their relationship to behaviorproviding, in turn, a foundation for our understanding of how disruptions in such circuits contribute to the development of psychiatric disorders."

Functional Connectivity Data Point to New Understanding of Psychopathology

In recent years, large-scale research projects including the Human Connectome Project (HCP) have focused on defining and mapping the functional connections of the brain. The result is an extensive body of new evidence on functional connectivity and its relationship to human behavior.

In her article, Dr. Barch focuses on a technique called resting-state functional connectivity MRI (rsfcMRI), which measures how spontaneous fluctuations in blood oxygen level-dependent signals are coordinated across the brain. Analysis of rsfcMRI and other data in large numbers of subjects from the HCP will provide new insights into a wide range of psychiatric disorders, such as depression and anxiety, substance use, and cognitive impairment.

Recent studies have found that spontaneous activity from networks of regions across the brain are highly correlated even at rest (that is, when the person is not performing a specifically targeted task). This "resting state" activity may consume around 20 percent of the body's total energyeven though the brain is only two percent of total body mass, according to Dr. Barch. "Ongoing resting-state activity may provide a critical and rich source of disease-relate variability."

One key question is what constitutes the "regions" that make up the neural circuits of the brain. Recent rsfcMRI mapping studies have identified between 180 and 356 different brain regions, including many common regions that can be mapped across individuals. Future studies will look at whether these regions differ in shape, size, or location in people with psychiatric disordersand whether these differences contribute to changes in the formation and function of brain circuits.

Some brain networks identified by rsfcMRI may play important roles in the functions and processes commonly impaired in psychiatric disorders. These include networks involved in cognitive (thinking) function, attention to internal emotional states, and the "salience" of events in the environment. Many questions remain as to how these brain networks are related to behavior in general, and to psychiatric disorders in particular.

Some researchers are using HCP data to study behavioral factors relevant to psychiatric issues, including cognitive function, mood, emotions, and substance use/abuse. Other studies are looking for rsfcMRI patterns related to individual differences in depression or anxiety, and their connections to various brain networks.

Dr. Barch's research focuses on brain networks affecting the relationship between cognitive function and "psychotic-like" experiences. She notes that work on individual differences in functional connectivity in the HCP dataset is just getting startedthe full HCP dataset was made publicly available in the spring of 2017.

"The hope is that these analyses will shed new light on how behavior of many different forms is related to functional brain connectivity, ultimately providing a new window for understanding psychopathology," Dr. Barch writes. Continued studies of the relationships between brain circuitry and behavior might eventually lead to new therapeutic targets and new approaches to treatment monitoring and selection for patients with psychiatric disorders.

Explore further: Manipulating brain network to change cognitive functions: New breakthrough in neuroscience

More information: Deanna M. Barch. Resting-State Functional Connectivity in the Human Connectome Project, Harvard Review of Psychiatry (2017). DOI: 10.1097/HRP.0000000000000166

The rest is here:
New findings on brain functional connectivity may lend insights into mental disorders - Medical Xpress

It is no secret most robots will be far better at making products and goods thantheir human counterparts ever will be. According to Grabit, the companysline of robots is capable of working at 20 times the pace of anaverage human. These robots are designed to build pairs of Nike shoes. Flooding the market with the finished product may help to push the shoesaverage price down by quite a bit. It is one of those developments people will both love and hate at the same time.

Very few people consider how much work goes into the process of putting a pair of shoes together. The amount of labor required for this specific purpose should not be overlooked. The upper part of the shoe which sits on top of your foot is actually the most laborious task ofall to complete. It is not comprised of one single piece of material. Humans often have to put together a few dozen individual pieces in order to create this part of the shoe. Up until now, no robot hadbeen able to produceadequate results whenputting this part together.

Grabit claims that has now changed. The company has built a robot which is capable of fully assembling pairs of Nike shoesquickly. Considering how Nike, Inc. invested millions in this company, it is about time those efforts pay off. It is worth noting how the robots rely on static electricity known as electroadhesion to help manipulate objects in unique ways. This allows the robots to assemble every single part of a shoe with relative ease. It does so at 20 times the pace of a human worker, which is both amazing and terrifying.

So far, a few Nike facilities have been equipped with Grabit robots to fully test their performance over time. It is expected around 12 of these machines will be operating across both Mexico and China before December 31st of this year. Thiswould certainly allow Nike to shake up itsmanufacturing process quite a bit and bring it closer to itsmajor consumer market. If this trial were successful, it could mean positive things for the industrys negative association with child labor as well.

Automation is coming to the manufacturing sector. So far, no major companies have deployed such technology on any sizeable scale, though. Robotic arms have been the main area of focus for the time being, although other technologies are being considered as well. Entrusting robots with more meticulous work is a big gamble by Nike, but so far, the companysefforts are paying off. Only time will tell whether or not their gut feeling was the right one, though.

Grabits robots do not mimic human behavior. They use flat pads of electrodes which create an electrical field adhering to virtually any surface one can think of. This is very different from most robotic hand-oriented projects in the industry right now. It is more likehow one would expect robots to behave, rather than an imitation of human workforce. It does not appear the company faces any major competition right now, which makes them rather unique for the time being.

Moreover, Grabits robots can work together with human operators, which is another big selling point. The software decides how the various pieces shouldbe stacked, and lights up positions for the human partner to set things down. The system currently requiresone human employee to monitor one machine per shift, which should improve overall productivity by up to 2,000%. This is a very interesting development that shows that not all robots are designed to take human jobs.

See more here:
Grabit's Robots Produce Nike Shoes 20 Times Faster Than Humans Do - The Merkle

What do you need to build a self-driving car? Roboticists and computer scientists have generally settled on similar requirements. Your autonomous vehicle needs to know where the boundaries of the road are. It needs to be able to steer the car and hit the brakes. It needs to know the speed limit, be able to read street signs, and detect if a traffic light is red or green. It needs to be able to react quickly to unexpected objects in its path, and it gets extra points if it knows where it is on a map.

All of those skills are important and necessary. But by building from a list of technical requirements, researchers neglect the single most important part of real-world driving: our intuition. Using it to determine the motivations of those around us is something humans are so effortlessly good at that its hard to even notice were doing it, nonetheless program for it.

A self-driving car currently lacks the ability to look at a personwhether theyre walking, driving a car, or riding a bikeand know what theyre thinking. These instantaneous human judgments are vital to our safety when were drivingand to that of others on the road, too.

As the CTO and cofounder of Perceptive Automata, an autonomous-vehicle software company started by Harvard neuroscientists and computer scientists, I wanted to see how often humans make these kinds of subconscious calls on the road. I took a camera out to a calm intersection near my former lab at Harvard with no traffic signals. It is not by any stretch of the imagination as congested or difficult as an intersection in downtown Boston, let alone Manhattan or Mexico City. But in 30 seconds of video, it is still possible to count more than 45 instances of one person intuiting whats in the mind of another. These non-verbal, split-second intuitions could be that person is not going to yield, that person doesnt know Im here, or that person wouldnt jaywalk while walking a dog. Is that bicyclist going to turn left or stop? Is that pedestrian going to take advantage of their right-of-way and cross? These judgments happen instantaneously, just watch.

We have lots of empirical evidence that humans are incredibly good at intuiting the intentions of others. The Sally-Anne task is a classic psychology experiment. Subjectsusually childrenwatch a researcher acting out a scene with dolls. A doll named Sally hides a marble in a covered basket. Sally leaves the room. While Sally is gone, a second dollAnnesecretly moves the marble out of the basket and into a closed box. When the first doll comes back, children are asked where she will look for the marble. Its easy to say, Well, of course shell still look in the basket, as Sally couldnt have known that the marble had moved while she was gone. But that of course is hiding an immensely sophisticated model. Children have to know not only that Sally is aware of some things and not of others, but that her awareness only updates when she is able to pay attention to something. They also have to know that her mental state is persistent, even when she leaves the room and comes back. This task has been repeated many times in labs around the world, and is part of the standard toolkit researchers use to understand if somebodys social intuitions are intact.

The ability to predict the mental state of others is so innate that we even apply it to distinctly non-human objects. The Heider-Simel experiment shows how were prone to ascribe perceived intent even to simple geometric shapes. In this famous study, a film shows two triangles and a circle moving around the screen. With essentially no exceptions, most people construct and elaborate narrative about the goals and interactions of the geometric shapes: One is a villain, one a protector, the third a victim who grows courageous and saves the dayall these mental states and narratives just from looking at geometric shapes moving about. In the psychological literature, this is called an impoverished stimulus.

Our interactions with people using the road are an example of an impoverished stimulus, too. We only see a pedestrian for a few hundred milliseconds before we have to decide how to react to them. We see a car edging slightly into a lane for a half second and have to decide whether to yield to them. We catch a fleeting glimpse of a cyclist and judge whether they know were making a right turn. These kinds of interactions are constant, and they are at the very core of driving safely and considerately.

And computers, so far, are hopeless at navigating them.

The perils of lacking an intuition for state of mind are already evident. In the first at-fault crash of a self-driving vehicle, a Google self-driving car in Mountain View incorrectly assumed that a bus driver would yield to it, misunderstanding both the urgency and the flexibility of a human driver trying to get around a stopped vehicle. In another crash, a self-driving Uber in Arizona was hit by a turning driver who expected that any oncoming vehicles would notice the adjacent lanes of traffic had slowed down and adjust its expectations of how turning drivers would behave.

Why are computers so bad at this task of mind reading if its so easy for people? This circumstance comes up so often in AI development that it has a name: Moravecs Paradox. The tasks that are easiest for people are often the ones that are the hardest for computers. Were least aware of what our minds do best, said the late AI pioneer Marvin Minsky. Were more aware of simple processes that dont work well than of complex ones that work flawlessly.

So how do you design an algorithm to perform a task if you cant say with any certainty what the task entails?

The usual solution is to define the task as simply as possible and use what are called deep-learning algorithms that can learn from vast quantities of data. For example, when given a sufficient number of pictures of trees (and pictures of things that are not trees), these computer programs can do a very good job of identifying a tree. If you boil a problem down to either proving or disproving an unambiguous fact about the worldthere is a tree there, or there is notalgorithms can do a pretty good job.

The only way to solve these problems is to deeply understand human behavior by characterizing it carefully using the techniques of behavioral science.But what to do about problems where basic facts about the world are neither simple nor accessible? Humans can make surprisingly accurate judgments about other humans because we have an immensely sophisticated set of internal models for how those around us behave. But those models are hidden from scrutiny, hidden in the black boxes of our minds. How do you label images with the contents of somebodys constantly fluid and mostly nonsensical inner monologue?

The only way to solve these problems is to deeply understand human behaviornot just by reverse-engineering it, but by characterizing it carefully and comprehensively using the techniques of behavioral science. Humans are immensely capable but have opaque internal mechanisms. We need to use the techniques of human behavioral research in order to build computer-vision models that are trained to capture the nuances and subtleties of human responses to the world instead of trying to guess what our internal model of the world looks like.

First, we need to work out how humans worksecond comes training the machines. Only with a rich, deep characterization of the quirks and foibles of human ability can we know enough about the problem were trying to solve in order to build computer models that can solve it. By using humans as the model for ideal performance, we are able to gain traction on these difficult tasks and find a meaningful solution to this intuition problem.

And we need to solve it. If self-driving cars are going to achieve their promise as a revolution in urban transportationdelivering reduced emissions, better mobility, and safer streetsthey will have to exist on a level playing field with the humans who already use those roads. They will have to be good citizens, not only skilled at avoiding at-fault accidents, but able to drive in such a way that their behavior is expected, comprehensible, and clear to other vehicles drivers and the pedestrians and cyclists sharing space with them.

Follow Sam on Twitter. Learn how to write for Quartz Ideas. We welcome your comments at ideas@qz.com.

Original post:
Self-driving cars still can't mimic the most natural human behavior - Quartz

Imagine a thriving community built around manufacturing jobswhere the production methods and processes not only minimize wasteand mitigate negative environmental impacts, but also addresshealth risks posed to residents and workers.

How do we get there? Who needs to have a seat at the table?

A new partnership, facilitated by The JPB Foundation, aims toaddress these questions and more through the formation of theCollaboratory for a Regenerative Economy (CoRE). Led by UBsDepartment of Materials Design and Innovation (MDI), CoRE is acollaboration with Clean Production Action and Niagara Share.

CoRE will bring together scientists, manufacturers, communitypartners and other key stakeholders to understand the challenges inbuilding a self-sustaining economy in rapidly expanding andevolving industries.

This is an unusual project with its emphasis onthe interplay between science, technology, and their interactionwith human behavior to impact social change, says KrishnaRajan, Erich Bloch Endowed Chair of MDI.

While the initial focus of the project is on solar panelmanufacturing, the findings will serve as a testbed that can laterbe scaled and used for other industries.

Our project seeks to lower the barriers to the adoptionof production processes that are environmentally friendly and offerthe potential to improve community health, Rajan says.

We will use cutting-edge discoveries in materials scienceand engineering to develop innovative and transformative approachesto design data-driven, green-manufacturing processes that willreduce the use of toxic chemicals and/or those derived from fossilfuels in the solar panel manufacturing industry, hesays.

This data-driven approach to designing alternate materials forindustrial use will include human and environmental healthfactors.

Our aim is to not only reduce the use of harmfulchemicals in industrial production, but also reduce the healthhazards arising from the exposure to toxic chemicals, both duringproduction and when products are decommissioned, says MarkRossi, executive director of Clean Production Action, which isbased in Somerville, Massachusetts.

Since low-income families make up a significant portion of thefrontline communities that are impacted by industrial and energyproduction, the project aligns closely with The JPBFoundations focus on health and poverty.

This unique partnership among the academic researchcommunity, non-governmental and community outreach organizationsbrings together complementary expertise in research, marketanalysis, policy formulation and social innovation tosupport the transition toward a safer materials economy,says Liesl Folks, dean of the School of Engineering and AppliedSciences.

Key features of the CoRE initiative include industry andcommunity-targeted workshops, an MDI Summer Institute and atraineeship program that links MDI students with communityorganizations and other constituencies.

A change agent program will provide industry andcommunity leaders with the tools needed to understand and analyzethese technologies, the inherent risks and cost-benefits involved,and the best methods for adopting new approaches.

The project embraces both scientific advancements andsocial innovation, underlining the importance of bringing togetherpeople and resources in new, more effective ways to createresilient networks that can drive new innovation and value for ourcommunities, businesses and local economies, says AlexandraMcPherson, principal of Niagara Share, a Buffalo-basednonprofit.

Adds Robin Schulze, dean of the College of Arts and Sciences:This project aligns with MDIs core mission ofaddressing societal needs through significant acceleration ofdesign and discovery of new materials in a socially responsiblemanner.

UBs Department of Materials Design and Innovation is acollaboration between the College of Arts and Sciences and theSchool of Engineering and Applied Sciences.

Read more from the original source:
Safer manufacturing through materials science - University at Buffalo Reporter

Advisers to retirement plans are familiar with the biggest challenge facing their retirement plan sponsor clients: How to motivate employees to participate?

Various enrollment methods have been developed to encourage participation and consider basic human behavior, but each carries tradeoffs. Standard enrollment and auto-enrollment have been widely adopted, but active-choice enrollment has been an area somewhat unexplored.

A traditional enrollment process allows participants to choose to participate in, or opt-in to, a retirement plan.

This method requires employee initiation. Unfortunately, when this decision is left entirely up to employees, some basic behaviors take over. These include loss aversion, present bias and procrastination.

Loss aversion happens when employees weigh out the costs and benefits of setting aside part of their paycheck every month to save for retirement. The perceived losses receive undue importance when compared with the expected benefits. In simpler terms, giving up income today is a bigger deal than receiving income in the future.

Present bias is this same idea of unequal importance of costs and benefits, but compounded by time. The costs are borne immediately and the benefits are not realized until much further in to the future. The delay in benefit when it comes to retirement savings is decades long. Employees may not be able to extrapolate their decisions this far into the future.

Procrastination will also come into play, causing decisions about saving for retirement to be put off until employees feel they make enough money to save, know enough about how much to save, or have enough time to make these crucial savings decisions.

These psychological forces are hard for all humans to overcome and the default of not enrolling in the retirement savings plan is, to many potential participants, the path of least resistance.

One solution to the motivation conundrum is the auto-enrollment process. This process allows a plan sponsor to automatically enroll eligible employees into the plan unless an employee affirmatively elects otherwise.

When employees do not have to act to be enrolled, participation can increase by up to 50% as compared to traditional enrollment, while giving employees the flexibility to not participate if they decide the retirement plan or automatic savings rate is not right for them.

However, many default savings rates prove to be sticky; participants consider them an implicit recommendation and are reluctant to stray from it. Default savings rates are most commonly set at 2-4%, which is not the optimal long-term savings rate for most participants. Loss aversion and present bias appear when participants weigh the costs and benefits of increasing savings above the default rate. Conversely, these same default savings rates may not be affordable for certain participants.

In addition to the psychological struggles that auto-enrollment presents, certain plan sponsors feel that auto-enrolling participants is too paternalistic. The politics of a plan sponsor taking the choice out of participation can be tricky or impossible to navigate.

A different pathActive-choice enrollment requires employees to actively make the decision to participate, or not, in their retirement plan.

This differs from the other enrollment processes, which allow participants to default into a state of participation. By mandating employees to decide, plan sponsors may better serve employees who might struggle with procrastination, and can offer more tailored engagement than an auto-enroll process may provide.

Start Slideshow

The retirement, technology, voluntary, wellness and overall winners are taking charge of the future of benefits.

Usually, the active choice is presented to employees along with other new-hire materials that must be completed or returned within a specified period. Employees decide between two options: Enroll or Waive; Yes, I want to participate or No, I do not want to participate, etc. If participants choose to participate, they are prompted to make contribution rate decisions immediately or in the near future.

By making the decision time-bound, employees are more likely to make an instant decision regarding their savings. This can result in a positive or negative effect. On the one hand, it starts employee saving as early as possible, maximizing compounding. Additionally, employees enrolled through active choice have been found to immediately choose a savings rate that took traditional enrolled participants more than two years to reach through contribution increases.

But the immediate decision can place unprepared employees in a situation where they are not informed enough to make the proper savings rate decision for their specific circumstance. Financially illiterate employees may be better served through a carefully chosen default savings rate and auto-enrollment.

Enhanced active voiceActive choice can be elaborated upon to include language that invokes an emotional response from potential participants. Known as enhanced active choice, this process adds descriptive language to the decision. Instead of choice between Enroll, or Waive, the participant must choose between such statements as Yes, I want to plan for a successful retirement by saving and investing money today. and No, I do not want to save today and understand that this choice may negatively affect my retirement.

This style of active choice taps into human instinct, highlighting the possible positive and negative effects of this choice in a visceral way. Plan sponsors who adopt enhanced active choice should be sure to not use language that implies any guarantee of success or failure.

The principles behind active-choice enrollment relieve many pain points that exist in other enrollment methods. Active-choice enrollment increases plan participation. By allowing employees to choose, plans will have more engaged, empowered participants. By requiring employees to choose for themselves, and making it easy to enroll, plans can help participants sidestep behavioral roadblocks and end up on track for a more successful retirement.

Go here to see the original:
Views Active-choice: An enrollment alternative worth considering - Employee Benefit Adviser (registration)

Gentrification brings with it new restaurants, businesses and housing but often pushes out longtime residents. Jay Lazarin/Getty Images hide caption

Gentrification brings with it new restaurants, businesses and housing but often pushes out longtime residents.

Gentrification of neighborhoods can wreak havoc for those most vulnerable to change.

Sure, access to services and amenities rise in a gentrifying neighborhood. That is a good thing. But those amenities won't do you much good if you're forced to move because of skyrocketing housing costs.

That is why neighborhood and housing advocacy groups have spent decades searching for ways to protect longtime residents from the negative effects of gentrification.

But how can you tell if a neighborhood is gentrifying? Is it the art gallery that appears next to the bodegas? Is it the hipster coffee shop opening up where the old deli used to be? Maybe it's the expensive new condos rising up across from the older row houses? The problem with any of these obvious indicators is that by the time they appear, it may already be too late. The tide of living expenses in a given neighborhood may already be rising so fast that there is little that local groups, city planners or outside agencies can do. If you're poor or working class, it's just time to leave.

But what if there were a way to see gentrification long before the coffee shops, condos and Whole Foods appear? What if city planners and neighborhoods had an early warning system that could sniff out the changes just as they begin? In that way, cities might prepare for the coming changes securing a diverse range of housing options before land and rent prices shoot through the roof.

A neighborhood early warning system like this has been a dream for city planners for decades. The first versions of it stretch back as far as the 1980s. Now, though, with the rise of big data, this dream has taken a giant step forward toward becoming a reality. As with all things big data, however, taking that step comes with both considerable promise and peril.

Big data is a shorthand term for the insane amounts of information being generated by human beings in our digital world. From cellphones to credit card transactions to social media, we are all leaving digital contrails of almost all of our activity in the world. Learning how to harvest and analyze these digital traces en masse holds the promise of allowing data scientists to see how societies operate at a resolution that was simply impossible before. And seeing hidden patterns in gentrification may be exactly the kind of task big data and data science are best at.

So what does it take to see gentrification before it happens? The most obvious indicator is housing prices. Cities have always done a pretty good job of keeping track of property sales. That is why those records have, for many decades, been the primary data set for studies of neighborhood change. But big data has already swept through the housing price field, as apps like Zillow and Trulia allow anyone access to real estate information going back years. Using a data science technique called machine learning, computers can analyze patterns in these real estate records and extract future trends allowing companies to try to predict what your house will be worth next year.

But even if it works, this kind of "predictive analytics" for housing prices is too blunt an instrument to predict which neighborhoods might gentrify. To really develop an early warning system, data scientists need to go deeper into human behavior. Going deeper, however, means getting new kinds of data.

Evictions of both people and businesses might be one of the best representations of how gentrification negatively affects a neighborhood. But unlike real estate transactions, most cities do a terrible job of keeping track of who, where and why evictions are initiated. Getting that data used to mean a trip to city hall to dig through the musty records department. Because of this, evictions remain invisible to data scientists in their search for gentrification indicators.

A different kind of problem is faced by urban scientists who want to see who exactly is moving into, and out of, the neighborhoods. How does the economic and racial profile of a neighborhood change when gentrification occurs? Data from the U.S. census contains a wealth of information relevant to this question but it comes just once a decade. That is too slow to catch the details of a changing neighborhood. Social scientists also have what is called the American Community Survey, which is done every year. But it's a fraction of the size of the census and, like a bad cellphone camera, it doesn't have the resolution scientists need to see the spatial details of how neighborhoods change.

The difficulties in these tools limited earlier heroic attempts at building a neighborhood early warning system. But with big data, the situation has radically changed. Rather than asking a handful of people a few direct questions about their lives, these days we're all leaving volumes of answers about ourselves in the data we generate just, for example, by using our phones.

Consider a study from October 2015 that used Twitter to look at how residents of different neighborhoods moved around the city of Louisville, Ky. For generations, Louisville residents have seen Ninth Street as the boundary of the poorer African-American neighborhood to the west and wealthier white neighborhood to the east. But by carefully tracking tweets that were geotagged, meaning they contained location information, researchers could study mobility patterns of residents in the different neighborhoods. In particular, they found that Twitter users from the western neighborhoods were far more likely to be found in different regions of the city than residents of the eastern neighborhoods. In this way, the researchers found that the traditional boundaries of the neighborhoods could be redrawn based on the way people actually behaved rather than just "common wisdom."

The Louisville research highlights how studies of what is called "human mobility" can provide ground-truth insights into how neighborhoods function for the people who use them. In the future, perhaps, it will be possible to identify gentrifying neighborhoods by looking for unexpected patterns in how people travel into and out them on a daily basis. Studies of the mixing of ethic groups in Estonia tracked changes in neighborhood composition between the daytime and nighttime hours as well as weekday vs. weekend. By analyzing these patterns over months or years, it may be possible to see the "signal" of gentrification appear as people who normally would not be visiting a neighborhood begin making more frequent appearances.

With an early warning system in place, neighborhood advocates would have the opportunity to implement policies ranging from reserving affordable housing units to educating residents of their renting rights to helping small businesses negotiate long-term lease extensions.

And given that gentrification represents a small problem compared with existing urban poverty, early warning systems could also be applied to the other direction of neighborhood change. Using big data alongside traditional social science methods, it may be possible to identify neighborhoods at risk of decline. In this way, predictive analytics would let residents and city officials take steps to keep these at-risk neighborhoods healthy through early intervention in the availability of services or policing.

The methods of big data might even allow neighborhood equality to be crowdsourced. A recent study using data from cellphones and credit card transactions tracked shopping trips across a range of rich and poor neighborhoods in Spain. By rewiring just 5 percent of those shopping trips to more economically challenged neighborhoods, the researchers found income disparity could be significantly flattened. That means that by changing the destination of just 5 out of 100 of our shopping trips, we might all be a source for positive change.

But, as is becoming clear with everything to do with big data, while advances hold great promise for dealing with neighborhood change, they also hold significant peril. The great hope of urban advocates is to democratize data and its analysis tools, allowing residents and other stakeholders to see more clearly how a neighborhood is changing. But knowledge of those changes might act in a way that accelerates them. Seeing gentrification early may spur more development more quickly. Seeing neighborhoods decline early may provide more disincentive for investment.

As the first wave of optimism for big data passes, both researchers and users have become more realistic about its possibilities. But with eyes wide open, we may be at the beginning of seeing human communities in an entirely new way. From this new vantage point we will, hopefully, have new tools to ensure their health and well-being, even in the midst of change.

(Special thanks to Solomon Greene of the Urban Research Institute for his help with this post.)

Adam Frank is a co-founder of the 13.7 blog, an astrophysics professor at the University of Rochester, a book author and a self-described "evangelist of science." You can keep up with more of what Adam is thinking on Facebook and Twitter: @adamfrank4

Read more from the original source:
What Does It Take To See Gentrification Before It Happens? - NPR