Short abstract:

This paper examines how narrowing conceptions of attention and distraction have emerged from neurotechnologies that measure and train attention in education contexts. These conceptions are relevant to neuroscientifically informed indices economic productivity, ‘brain capital’.

Long abstract:

Neurotechnology innovations in mobile neuromonitoring and brain-computer interfaces are changing how human brains and behaviours are understood and acted upon, while also catalysing ambitions to condition brains for learning and productivity. This paper examines neurotechnologies that capture, monitor, measure and train attention aimed for use in education contexts, some of which promise to improve learning outcomes and to address ADHD symptoms. I explore what conceptions of attention and distraction have emerged from research using such technologies, along with understandings of the learning brain. I show how such understandings are narrowed and normativised in the process of their becoming variables to be measured and visualised via neuroimaging headsets, and controlled via training and nudging students’ attention towards intended targets. These conceptions are relevant to providing measurements for neuroscientifically informed indices economic productivity, ‘brain capital’ (OECD, 2021; Smith et al, 2021). This paper forms part of the Leverhulme Trust funded project 'Biology, Data Science and the Making of Precision Education’ (PI: Ben Williamson; CoIs: Jessica Pykett and Martyn Pickersgill).