Computational approaches to aesthetics emerged amid efforts to use computer science methods "to predict, convey, and evoke emotional response to a piece of art. It this field, aesthetics is not considered to be dependent on taste but is a matter of cognition, and, consequently, learning. In 1928, the mathematician George David Birkhoff created an aesthetic measure "M = O/C" as the ratio of order to complexity. Since about 2005, computer scientists have attempted to develop automated methods to infer aesthetic quality of images. Typically, these approaches follow a machine learning approach, where large numbers of manually rated photographs are used to "teach" a computer about what visual properties are of relevance to aesthetic quality. A study by Y. Li and C.J. Hu employed Birkhoff's measurement in their statistical learning approach where order and complexity of an image determined aesthetic value. The image complexity was computed using information theory while the order was determined using fractal compression. There is also the case of the Acquine engine, developed at Penn State University, that rates natural photographs uploaded by users. There have also been relatively successful attempts with regard to chess and music. Computational approaches have also been attempted in film making as demonstrated by a software model developed by Chitra Dorai and a group of researchers at the IBM T.J. Watson Research Center. The tool predicted aesthetics based on the values of narrative elements. A relation between Max Bense's mathematical formulation of aesthetics in terms of "redundancy" and "complexity" and theories of musical anticipation was offered using the notion of Information Rate. Evolutionary aesthetics refers to evolutionary psychology theories in which the basic aesthetic preferences of "Homo sapiens" are argued to have evolved in order to enhance survival and reproductive success. One example being that humans are argued to find beautiful and prefer landscapes which were good habitats in the ancestral environment. Another example is that body symmetry and proportion are important aspects of physical attractiveness which may be due to this indicating good health during body growth. Evolutionary explanations for aesthetical preferences are important parts of evolutionary musicology, Darwinian literary studies, and the study of the evolution of emotion. As well as being applied to art, aesthetics can also be applied to cultural objects, such as crosses or tools. For example, aesthetic coupling between art-objects and medical topics was made by speakers working for the US Information Agency Art slides were linked to slides of pharmacological data, which improved attention and retention by simultaneous activation of intuitive right brain with rational left. It can also be used in topics as diverse as cartography, mathematics, gastronomy, fashion and website design.