Ongoing developments in machine learning are transforming how researchers analyze large-scale visual data, particularly in the realm of historical political economy. Valentine Figuroa, a researcher from MIT, is spearheading efforts to tap into the rich information encoded within paintings housed in museums and private collections. This approach aims to create a comprehensive framework that allows scholars to assess the meanings embedded in these artworks and the assumptions that guide their interpretation.
To explore this concept, Figuroa has developed a framework that leverages a database of 25,000 European paintings spanning from 1000 CE to the First World War. The research focuses on three distinct applications that reveal various types of information conveyed through paintings, including depicted content, communicative intent, and incidental information. Each application further investigates a cultural transformation that characterized the early-modern period in Europe.
Examining the Civilizing Process in European Art
The first application revisits the notion of a European “civilizing process,” which refers to the internalization of stricter behavioral norms coinciding with the rise of state power. Figuroa analyzes paintings depicting meals to determine if they reflect increasingly intricate forms of etiquette over time. By examining visual representations of social dining practices, Figuroa aims to provide insights into changing societal expectations and the evolution of manners influenced by political developments.
Portraits and Public Image of Political Elites
The second aspect of this research delves into the portrayal of political elites through portraits, shedding light on how these figures sculpted their public personas. The analysis highlights a significant long-term shift in representation, moving from traditional chivalric depictions to more rational and bureaucratic portrayals of men. This change underscores the evolving relationship between political authority and the public image, revealing how visual culture mirrored broader societal transformations.
Secularization Reflected in Artistic Trends
The third application documents a long-term process of secularization, which can be measured by the changing proportions of religious paintings in the art landscape. This trend began prior to the Reformation and accelerated in its aftermath. By examining the decline of religious imagery in favor of secular themes, Figuroa’s research provides evidence of shifting cultural values and the increasing influence of secularism in European society.
Figuroa’s work exemplifies how integrating machine learning techniques with traditional humanities research can yield new insights into historical narratives. By establishing a framework for interpreting artworks, this research opens the door for future studies that can further enrich our understanding of the political and cultural landscapes of past societies. As machine learning continues to advance, the potential for uncovering hidden dimensions in historical data remains vast and promising.
