– combining
graphic and spatial design
– with research and
speculative storytelling

– exploring futures in
its plurality  











︎ APOPHENIA


     2018 – RES 


-–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––-
Apophenia is an ongoing research project on perception, human and non-human alike, and investigates the (mis)recognition of patterns in visual and transformed data to speculate on future use.

The project is inspired by ‘A Sea of Data: Apophenia and Pattern (Mis-)Recognition’, Hito Steyerl.

“‘A Sea of Data: Apophenia and Pattern (Mis-)Recognition’ is a 2016 essay by Hito Steyerl. She talks about separating signal and noise. The idea of having too much data, and too much information communicating just as little as not enough data, and what is seen as noise and what is seen as signal, is something that keeps cropping up in my own work.

In the essay, Steyerl talks about different methods of extracting data from the ‘sea of data’. One of these is apophenia, or the perception of patterns in random sets of data- for example seeing animals in cloud formations. This takes on a political dimension when government analysts use this process- Hito Steyerl uses the example of analysts accidentally bombing civilians. She says ‘Who is ‘signal’ and who disposable ‘noise’. She also mentions a mythical story about a possible Ancient Greek process of separating signal and noise by Jacques Ranciere in which sounds produced by educated noise are perceived as speech, whereas sounds produced by females, slaves and foreigners are seen as garbled noise. The ‘others’ are seen as irrelevant, irrational and producers of inconsequential noise. This questions who is recognised on a political level, and who’s opinions are counted.“

The included poster presents a contextual framework connecting three main references, that together summarise the findings of this project. This (mis)recognition of data is explored physically through the scanning, rendering and printing of ceramic pieces that underwent modifications through data loss.