Experimental philosophers aim to use empirical methods to inform philosophical enquiries. Since many of the early, prominent, influential works make heavy use of the contrastive vignette technique, it is easy — especially for those already hostile to experimental philosophy — to think that experimental philosophers don’t do anything but CVT studies.
That’s a mistake. Methodologically, experimental philosophers are a heterogeneous bunch. Meskin, Phelan, Moore, and Kieran conducted an experiment that involved visual priming. Chandra Sripada used structural equation modeling to shed new light on the side-effect effect. And, to come back to one part of the title of this blog post, Nicole Hassoun and collaborators harnessed the “big data” from the micro-lending institution Kiva to uncover how people think about distributing aid.
Onto the other part of the title: aesthetics. Can “big data” inform philosophical aesthetics?
Before that question can be answered, we need to consider another, even more pressing question. Where can such data come from?
Any proposed “big data” study faces a difficult challenge right from the start: getting the data. The kind of information that is most accessible, such as the ones that can be gleaned from art history textbooks, is also inevitably heavily biased toward the relatively few iconic artworks. Datasets constructed this way would be unlikely to give a fully informative picture of the artworld. So when it comes to highly abstract philosophical questions, say about the nature of art, such datasets seem to be only of limited use.
Where else can we look? Thankfully, as I learned from the wonderful blog Museum 2.0, many museums — notably the Tate — are now making datasets of their collections openly accessible. Although the Tate dataset has only been available since the beginning of this month, there are already fascinating, if preliminary, findings from it. (Of course, researchers also encountered some serious problems in the mean time: notably, not all objects in an art museum’s collection are artworks.)
As a first-pass reaction, there seems to be much that can be learned from “big data aesthetics”. What say you, readers? Which questions in philosophical aesthetics, on your view, would be most amenable to empirical investigations using the museum collection datasets?
[x-posted at Experimental Philosophy Blog]