DATA ANALYSIS IN THE ARTS Painting by numbers


From Baroque painters to the aesthetics of environment, Felix Grant turns his attention to statistical analysis in the arts


O


ne of the staple exercises in statistics education is to make a light-hearted foray into that old academic wrangle: were the


Shakespeare plays and sonnets really written by William Shakespeare or by [insert your favourite candidate here]? Various metrics are analysed by whatever techniques are being taught, with a view to assessing similarity and difference. Real literary academics, of course, have


visited the same methods with serious intent, and similar debates exist within the visual plastic arts. Was this unsigned painting produced by old master X, or by unknown Y? A very well-known example of such a


dispute concerns the early 17th century Baroque painter, Artemisia Gentileschi. In the last 50 years she has been rehabilitated in art history circles and is now widely recognised, but for centuries much of her work was wrongly attributed to others. Te most frequent misattributions were to her father Orazio (which most experts now regard as blatantly ridiculous but which can be blamed on signatures) or to Michelangelo Merisi da Caravaggio, who painted in a superficially similar style a generation earlier. Reattribution was in most cases visual by expert witnesses, but in a few cases appeal was made to more objective means. Disputes still occur, however, and as recently


as January of this year a bitter disagreement between two national art institutions, with significant financial implications, was finally settled. Te process is plastered with nondisclosure clauses, but people love to talk about their interesting cases, whether in art or science or finance. In this case, the resolution of the quarrel was based on evidence from a number of computerised data analytic methods in a university statistical science back room. Te painting in question (not, I hasten to emphasise, illustrated here) was an obscure minor piece and experts disagreed over whether it had been created by Gentileschi, by Caravaggio, or by some unknown artist imitating one of those. Te investigation involved isolating


discriminant variables from hundreds of metrics sampled across numerous works by the two artists, then comparing the specifics of the


SCIENTIFIC COMPUTING WORLD


Artemisia Gentileschi, La Pittura, c1638. Oil on canvas, 986mm × 752mm. Part of The Royal Collection.


disputed item with their general distributions. Some of those metrics were purely physical: thickness of paint film, for example, or exactly how that thickness is sub composed from glaze or scumble layers, microstructure of the underlying gesso primer layer, or the depth and structure of surface texturing. Others concerned qualities such as reflectivity or range and adjacency of hue. Others again were perceptual or representational; Caravaggio, for instance, particularly in representations of women, consistently rendered the human body with higher aspect ratios in all of its component parts than Gentileschi. Te final judgement was that this particular


painting differed, over more than 20 discriminant variables and at the five per cent significance level or better in each case, from other works by either artist and could not, therefore, be realistically attributed to either. So far, so concrete, and its principles would


not have been alien to statisticians a century ago. Te same can be said for similar data analytic approaches to forgery detection. At the opposite end of the scale are studies of how art is perceived by, and affects, the human viewer. Tese range from neurological investigations of perception itself to impacts on health and once again statistical views of the evidence are essential to separate overlapping and conceptually fuzzy bodies of experimental or observational data. Te idea that successful art depends upon


inherent triggers within us is not, in itself, new. It is at least as old as the classical Greek philosophers, who tried to incorporate it within their unifications of human and cosmic systems. William Hogarth’s Te analysis of Beauty[1] tried to introduce quantitative definitions 250 years ago, and Jay Appleton’s Te experience of landscape[2]


was a comprehensive attempt to tie aesthetics into Darwinian neurobiology four BEYOND THE NUMBERS A STATISTICS SPECIAL 23


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