The Complexity of Beauty

    The concept of beauty has preoccupied many scientists and philosophers for centuries. The viewpoint of beauty studies measurable features of stimuli that can be based on people’s preferences when they notice that something or someone is beautiful. Symmetry seems to be an important stimulus when it comes to beauty, but other factors that stimulate beauty are color, balance, complexity, and curvature. Some of the findings that have been discussed so far are based on the work of a scientist named Noberto M. Grzywac, who takes a deep dive into this concept. He introduces the processing fluency theory, which states that “the easier it is for a person to perceive the process and the properties of a stimulus, the greater its aesthetic response will be." In other terms, to define something as beautiful is directly related to how easily our brains find it to think about and be able to mentally process and understand it.     The Association for Psychological Science published an article, "I Like Your Face—Why? A New Computer Model Pinpoints the Reasons This article discusses what makes a person’s face attractive. Most studies look at the features of an average face as well as whether those features fit conventionally in both genders, where men have masculine characteristics and women have feminine characteristics. This study was done by psychologists Christopher P. Said and Alexander Todorov. A method they used is called "face space,” which is a computerized map that makes people's facial features into dimensions. They divided those dimensions into two categories: shape and reflectance. The shape category, for example, looks at the size of certain facial features, while reflectance focuses on the darkness and color of those features. The participants analyzed each computerized picture and pointed out what facial features they noticed the most, to see if that correlated with their preference to identify if the person is attractive or not. The results, as stated in the article "In female faces, what males typically want is the shape to be feminine and the reflectance to be feminine,” said Said. “For male faces, women want the reflectance to be masculine but the shape to be feminine.” In other words, both masculine and feminine traits of attractiveness were opposite from each other based on the participant's preferences.

    The results of the study can be correlated back to Noberto M. Grzywac's findings in the processing fluency theory because when the participants were looking at these facial images, they were looking at reflectance and shape to determine attractiveness in both genders. These two specific stimuli are part of the fluency theory, but they are named complexity and color in Grzywac terms to determine what makes something beautiful. In our neural circuitry, when we look at visual properties, they correlate with stimuli like symmetry, complexity, balance, etc. It helps process information through cognitive and neural mechanisms. So when the participants were judging the picture and seeing which facial parts they found attractive in the back of their minds, they were judging if they were liking or disliking certain attributes, which follows the principles of our cognitive reasoning. The participants based their reasoning of liking or disliking by the universal standards of what beauty is. When the brain recognizes a noticeable trait that is not like other facial images, it helps them process information faster, which is shown to increase attractiveness. In conclusion, beauty is in the eye of the beholder, but when people classify something as beautiful, it's more than a preference; it's scientific.

References:

“I Like Your Face-but Why? A New Computer Model Pinpoints the Reasons.” Association for Psychological Science - APS, 19 Apr. 2011, https://www.psychologicalscience.org/news/releases/i-like-your-facebut-why-a-new-computer-model-pinpoints-the-reasons.html. Aleem, Hassan, et al. “Is Beauty in the Eye of the Beholder or an Objective Truth? A Neuroscientific Answer.” Springer Series on Bio- and Neurosystems, 2019, pp. 101–110., https://doi.org/10.1007/978-3-030-24326-5_11.