Humans prefer attractive faces over unattractive ones. Our preference for attractive faces exists from early infancy and is robust across age, gender and ethnicity. The quest to define facial beauty either by the size or shape of isolated facial features, for example, eyes or lips or by the spatial relations between facial features dates back to antiquity, when the Ancient Greeks believed beauty was represented by a golden ratio of 1:1.618. Although there is little support for the golden ratio, studies have shown that averaging a group of faces results in a synthetic face more attractive than any of the originals. Furthermore, a sufficiently large increase in the distance between the eyes and mouth of an individual face can make the face appear grotesque. Any individual’s facial attractiveness can be optimized when the spatial relations between facial features approximate those of the average face. However, no evidence to date has confirmed this suggestion.
Two types of alterations can be made to the spatial relations between facial features of any individual face. One may alter the vertical distance between the eyes and the mouth; this alteration results in a change in the ratio of this distance to the face length, which is measured by the distance between the hairline and the chin. The ratio is henceforth referred to as the length ratio. The other alteration is to change the horizontal distance between the pupils; this change alters the ratio between this distance and the face width, which is measured between the inner edges of the ears. This ratio is henceforth referred to as the width ratio.
Using a regression analysis to determine the exact relation between the attractiveness score and length ratio, it is found that facial attractiveness follows a curvilinear function with length ratio. Face with an average length ratio is rated as more attractive than faces with other length ratios. This is further supported by the finding that attractiveness scores for faces without an average length ratio were significantly less than the mean attractiveness score for the faces with an average length ratio.
When an optimally attractive state for an individual face in terms of both length and width ratios is examined, it is found that facial attractiveness follows a curvilinear function with the width ratio. When an individual face’s length ratio is already optimal, the optimal width ratio maximizing its attractiveness is 46. Attractiveness scores for faces without an average width ratio were significantly less than the mean attractiveness score for the faces with an average width ratio. Attractiveness scores for faces without an average length ratio were significantly less than the mean attractiveness score for the faces with an average length ratio indicating preference for an ideal length ratio is independent of the width ratio.
In each individual face, there exists an optimally attractive state in terms of both length and width ratios. When the face’s eye-to-mouth distance is 36 percent of the face length and interocular distance is 46 percent of the face width, the face reaches its optimal attractiveness given its unique facial features. Further, although the absolute level of attractiveness may vary with differences in facial features, the optimal length and width ratios remain constant. These optimal, golden ratios correspond with those of an average face. Critically, this preference for average ratios reflects a true preference for the average and not a regression toward the mean. These results may explain some basic daily observations, such as why some hairstyles can make an unattractive face appear more attractive or vice versa. Changing one’s hairstyle may alter the perceived face length or face width, as well as their related length and width ratios, therefore affecting the perceived attractiveness of the face.
Many experiments on attractiveness involve comparing faces that differ in both facial features and spatial relations, but the presence of features that vary in attractiveness could obscure any effect of variation in feature spatial relation on attractiveness. Also, prior research comparing an average face to individual faces failed to discover the ideal length and width ratios for any individual face because the averaging process tends to not only average the spatial relations between facial features but also smoothes the facial features and skin texture. This smoothing effect could artificially increase the attractiveness of the average face, obscuring the effect of average spatial relations on facial attractiveness.
Identifying the optimal length and width ratios for individual facial beauty have attracted a tremendous amount of pursuit, but yet eluded discovery for centuries. Furthermore, the present findings suggest that although different faces vary greatly in absolute attractiveness, for any particular face, there is an optimal spatial relation between facial features that will reveal its intrinsic beauty.
It should be noted that the optimal spatial relations found can also coexist with preferences for sexually dimorphic features. A woman who has large lips, suggesting a strong mating potential, with average length and width ratios will always be more attractive than a woman with narrow lips and average length and width ratios. It is unknown, however, whether the preference for average length and width ratios is stronger than the desire for a pronounced sexually dimorphic trait. In other words, a woman with large lips and unattractive length and width ratios may or may not be preferred to a woman with narrow lips and ideal length and width ratios. Future research is necessary to assess the nature of this trade-off.
By definition, eye-mouth-eye angle involves both horizontal and vertical components. The preference for an average length ratio is independent of the width ratio. Therefore, it is important to note that despite the similarity between the two measures, they may actually measure two very different aspects of the face. While eye-mouth-eye angle provides information on the spatial relations between internal facial features, it also assesses the relation between the internal features and the external facial contour. Since faces are perceived holistically, it is important to consider the facial elements in the context of the whole face. It is possible for the length and width ratios to vary, while eye-mouth-eye angle stays the same, and vice versa. In the context of the whole face length ratios and width ratios appear independent, but within the localized area of the eyes and mouth, there may be an interaction between length and width.
Why should we find a face with an average length and width ratio attractive? Two existing theories provide explanations at two different levels. At the evolutionary level, it has been suggested that humans prefer to reproduce with other healthy mates. Generations of healthy mate selection may act as an evolutionary averaging process. This process leads to the propagation of healthy individuals with physical characteristics, including faces that approximate the population average. As a result, we are biologically predisposed to find average faces attractive. At the cognitive level, it is well established that after exposure to a series of exemplars from one object category, we form a prototype, that is to say, an average for this category. One robust consequence of prototype formation is that we find the prototype more attractive than any individual category members because the prototype is easier to process. Due to this same cognitive averaging mechanism, the average face is perceived as more attractive than any individual face. It is suggested that while the two theories provide different levels of explanation, they may work together to account for our preferences for the optimal length and width ratios for facial beauty. The evolutionary process predisposes us to find average length and width ratios attractive. The cognitive process prescribes what the average length and width ratios are by averaging the ratios of individual faces we have encountered to date.
Our ancestors evolutionarily split from those of rhesus monkeys about 25 million years ago. Since then, brain areas have been added, have disappeared or have changed in function. This hoists the question, ‘have evolution given humans’ unique brain structures?’ Scientists have entertained the idea before but conclusive evidence was lacking. By combining different research methods, scientists have a first piece of evidence that could prove that humans have unique cortical brain networks.
Functional brain scans in humans and rhesus monkeys at rest and while watching a movie was compared with regards to both the place and the function of cortical brain networks. Scans showed even at rest, the brain is very active. Different brain areas that are active simultaneously during rest form are called ‘resting state’ networks. In general, these resting state networks in humans and monkeys are shockingly similar, but scientists found two networks unique to humans and one unique network in the monkey.
When watching a movie, the cortex processes an enormous amount of visual and auditory information. The human-specific resting state networks react to this stimulation in a totally different way than any part of the monkey brain. This signifies that humans also have a different function than any of the resting state networks found in the monkey. Put differently, brain structures that are unique in humans are anatomically absent in the monkey and there are no other brain structures in the monkey that have similar function. Human unique brain areas are primarily situated high at the back and at the front of the cortex and are most likely associated to explicit human cognitive capabilities, such as human-specific intelligence.
Scientists used Functional Magnetic Resonance Imaging scans to visualize brain activity. Functional Magnetic Resonance Imaging scans map functional activity in the brain by detecting changes in blood flow. The oxygen content and the amount of blood in a given brain area vary according to a particular task, thus permitting goings on to be followed.
The Bedside-to-Bench Program funds research teams seeking to translate basic scientific findings into therapeutic interventions for patients and to increase understanding of important disease processes. The Bedside-to-Bench Program accomplishes this mission by addressing barriers, such as the traditional silos between basic and clinical researchers in biomedical research, which can hinder progress toward finding new therapeutics for patients in need. Bedside-to-Bench teams involve basic and clinical researchers, often from different National Institutes of Health Institutes and Centers. In 2006, the Bedside-to-Bench program’s charge was expanded to unite the efforts of intramural and extramural National Institutes of Health researchers. Intramural science refers to research that takes place on National Institutes of Health campus under the auspices of federal employees, while extramural research is funded by National Institutes of Health and conducted by investigators and institutions outside of National Institutes of Health.
The Bedside-to-Bench program exemplifies the benefits associated with intramural – extramural collaborations; the extramural community gains access to the Clinical Center’s unique resources and the intramural community can pursue innovative research with extramural investigators. Projects are funded by various National Institutes of Health offices and institutes, have represented several research categories such as AIDS, rare diseases, behavioral and social sciences, minority health and health disparities, women’s health, rare diseases drug development, pharmacogenomics, and general.
Through the end of the 2012 program cycle, about 700 principal and associate investigators have collaborated on 209 funded projects with approximately $48M distributed in total bedside-to-bench funding. The introduction of extramural collaborations in 2006 has resulted in partnerships at 74 institutions, 27 of which are Clinical and Translational Science Award sites.
Modern medicine keeps unraveling new ways to investigate autoimmunity, leading to the production of boundless amounts of genetic, cellular and imaging data. Although the precision with which this information can define the etiology and mechanisms of a particular autoimmune disease is encouraging, much work lies ahead until all the knowledge acquired can be translated into the clinic. In ‘Bedside to Bench’, Calliope A. Dendrou, John I. Bell and Lars Fugger discuss the promises and limitations of genome-wide and next-generation genetic studies to provide further understanding of mechanisms driving autoimmune disorders and the role of experimental medicine in the new era of integrative clinical practice and personalized medicine.
In ‘Bench to Bedside’, Lawrence Steinman argues the concept of a ‘hub and spoke’ pattern of T cell activation and organ targeting in multiple sclerosis, inflammatory bowel disease and type 1 diabetes. This paradigm suggests new ways to develop drugs to keep autoreactive T cells in the organ where activation occurs and preclude them from reaching the target organ and cause disease.
Research shows mice brains are ‘very wired up’ at birth, and suggests experience selects which connections to keep. Ask the average person in the street how the brain develops, and they’ll likely tell you that the brain’s wiring is built as newborns first begin to experience the world. With more experience, those connections are strengthened, and new branches are built as they learn and grow. A study conducted in Harvard Lab, however, suggests just the opposite is true.
As reported on June 7 in the journal Neuron, a team of scientists led by Jeff Lichtman, the Jeremy R. Knowles Professor of Molecular and Cellular Biology, has shown that just days before birth, mice undergo an explosion of neuromuscular branching. At birth, some muscle fibers are contacted by as many as 10 nerve cells. Within days, however, all but one of those connections will be pruned away.
By the time mammals and humans are first coming into the world, when they can do almost nothing; the brain is probably very wired up. Through experience, the brain works to select, out of this mass of possible circuits, a small subset and everything else that could have been there is gone. It is hard for anyone to see in this perspective. In simple muscles, every nerve cell branches out and contacts every muscle fiber. That is, the wiring diagram is as diffuse as possible. But by the end, only two weeks later, every muscle fiber is the lifelong partner of a single nerve cell, and 90 percent of the wires will disappear.
Scientists, including Lichtman, had shown as early as 1970 that mice undergo an early developmental period in which target cells including muscle fibers and some neurons are contacted by multiple nerve cells before being reduced to a single connection. The early studies and the current work are fraught by the same problem – technological challenges make it difficult to identify individual nerve cells in earlier stages of life. Though the use of mice have been genetically-engineered to express fluorescent protein molecules in nerve cells has made it easier for scientists to identify nerve cells, it remains taxing to study early stages of development because the fluorescent labeling in the finest nerve cell wires often becomes so weak as to be invisible. Scientists typically begin studying these mice at about a week after birth, but as scientists started to look at earlier and earlier stages, the fluorescent color was coming up ever more weakly. If one goes up from post-natal day seven to post-natal day four, there are very few labeled cells. If one went to post-natal day zero, there are none.
Eventually, J.D. Wylie, one of the lead scientists, took a new idea – using antibodies to label nerve cells – and a bit of luck for the research to pay off. Scientists were just very lucky that one of the first animals they looked at, they saw a labeled axon. Once scientists saw it, they knew it was just a matter of time until they got another, but it wasn’t until J.D. Wylie did 50 more than scientists found it, so to get the 20 or so examples J.D. Wylie had thousands of mice to be looked at. If J.D. Wylie had not seen that first one, scientists might have given up on this. It takes a lot of effort and work, but showed something that scientists have never seen before, which is a remarkable amount of connectivity.
Identifying the axons was only the first step. To fully understand how widely diffuse the branching becomes early on, scientists had to count how many different nerve cells were contacting muscle fibers. To accomplish that feat, Juan Carlos Tapia, the other genius used a new technique the lab had developed for serial electron microscopy that allowed him to capture images of as many as 10 axons connecting to a single muscle fiber. After reaching its peak at birth, scientists found the branching was quickly pruned back, until just a single nerve axon remained connected to each muscle fiber. Though there isn’t a definitive answer to what is driving that pruning process, Lichtman said there is strong suggestive evidence that points to experience.
Scientists think that experience must be the engine that allows some branches to survive and the vast majority to disappear. If this were a stereotypical developmental program, one might imagine that it might trim off whole parts of the trunk and branches, but when scientists looked at where the ten percent of surviving branches are located, they saw the trunk and branches extended over the same area, it simply had fewer branches. It has chosen, at the terminal level, which branches to keep and which not to.
In future studies, scientists plan to study how those decisions are made, work that could potentially lead to insight into a number of disorders, including autism. One theory people have talked about, whether autism could be a disorder where connections that should have been trimmed back weren’t, and as a result stimuli are much more intense than they should be. There are stories about children with autism spectrum disorders who cannot run in their bare feet on grass, because it’s just too painful. The study spotlights the unique developmental strategy undertaken by all mammals, including humans. This is a strategy to generate a nervous system that is tuned to the world it finds itself in. Interestingly, this is not the predominant strategy of nervous systems on the planet. Most animals – insects for example – come into the world knowing, based on their genetic heritage, exactly how to behave.
It seems like an impossibility – why would the best brains seem to be the most backward, and take the longest to figure out how to do things? Rather than allowing our genes to tyrannize our behavior, we more than any other animal are under the tyranny of the environment we find ourselves in. If you start with a nervous system that allows for any wiring diagram, you need only choose the right option for a particular environment. That’s why humans today are behaving differently than our grandparents, and our grandparents are different from people 1,000 years ago, or 10,000 years ago. Whereas a fruit fly today and a fruit fly 1,000 years ago is behaving the same way.