'Attention as an object of knowledge, intervention and valorisation: exploring data-driven neurotechnologies and imaginaries of intensified learning' by Dimitra Kotouza, Martyn Pickersgill, Jessica Pykett and Ben Williamson in (2025) Critical Studies in Education comments
Innovations in mobile neuromonitoring and brain–computer interfaces are increasingly used to inform understandings of human brains and behaviours while also catalysing imaginaries of neuroscientifically measured and enhanced economic productivity. In this paper, we focus on neurotechnologies that claim to capture, monitor, measure and train learners’ attention. We analyse a corpus of relevant scientific, governance, and commercial texts to explore how they reconfigure learners’ attention as an object of knowledge, intervention, and valorisation. We demonstrate that outcomes- driven neuroscience research and technological development tends to split attention into optimal and undesirable forms: externally versus internally oriented, and synchronised versus unsynchronised with others, which become the variables of intervention to optimise attention. Commercial wearables, in turn, envelop desirable forms of attention under logics of brain control, social discipline, and valorisation. This process is enacted within an international context of speculation on neurotechnology investments and their anticipated outcome of enhancing future human productivity. Circumscribing desirable forms of learner attention and subjectivity, these technologies provide expanded means to mould and monitor learners’ attention towards performativity regimes of economised education governance while enabling profit-making based on learners’ activity.
They note
Neurotechnological research and development has received billions of dollars of state funding across the world in recent decades via large-scale brain science projects. This reflects, and further intensifies, a focus on neuroscience as an object of significant social and political investment and concern (Pickersgill, 2023). Today, the increasing portability of neuroimaging and its integration with software allow analysing and visualising ‘millisecond-by-millisecond’ brain data thought to reflect cognitive processes (Davidesco et al., 2021, p. 650). These advances have been hailed as capable of ‘unlocking the secrets of how the human brain works’ (Mathieson et al., 2021, p. 8).
One focus among many has been the field termed ‘educational neuroscience’. While this has for some years asserted that its epistemic products can provide new insights to teachers (Howard-Jones et al., 2021), well-funded neurotechnological advances enable neuroscience to propose going beyond these founding ambitions. Combined with learning analytics and AI, technologies are anticipated by some proponents to deliver algorithmically personalised education alongside various other tools to enhance learning capacity (Davidesco et al., 2021, p. 650; Edelenbosch et al., 2015).
Within the US especially, the notion of ‘attention’ in education is emphasised through research, policy, and economic infrastructures that promote neurotechnologies to measure, monitor, and enhance learners’ attentiveness. Research in this area involves tried- and-tested techniques such as electroencephalography (EEG) – which visualises brain electrical activity – and functional magnetic resonance imaging (fMRI), a technique to represent blood flow as a means of generating information about brain activity. Learners’ attentional control is increasingly presented as a datafied object of knowledge and intervention in the broader context of concern around ADHD and digital distraction. Using fMRI, cognitive neuroscience is said to have revealed the ‘attention networks’ in which children with diagnoses of ADHD (Attention Deficit Hyperactivity Disorder) are thought to have a ‘deficit’ (Posner, 2013, p. 15). Further, various attention-targeting neurotechnologies currently in development promise to improve attention and address ADHD symptoms. Indeed, the relevance of cognitive neuroscience to education has often been advocated in the context of rising levels of ADHD diagnoses and parental concerns that ‘typically developing children’ will not be able to resist the temptations of the ‘digital age’ (Posner, 2013, p. 14). Perhaps ironically, then, social anxieties around attention have shaped interest in, and a market for, neurotechnological interventions within education. Such developments resonate with broader trajectories of governance. Since the 1980s, educational governance internationally has been increasingly guided by economic theories of human capital (Roberts-Holmes & Moss, 2021). This ‘economisation of education’ renders it as the process of individuals acquiring skills and personal qualities to form a productive workforce (Connell, 2013). Subsequently, it also involved economists’ interest in ‘the neuroscience of human capability formation’ (Heckman, 2007, p. 13250). Education is repeatedly submitted to what Foucault (2008, p. 247) has called a ‘permanent economic tribunal’, in which governance structures – such as OECD’s international student assessment – metricise, standardise, and compare schools and educational systems, subjectifying through the proliferation of numbers (Grek & Ydesen, 2021).
Under this regime, the learning process has become an object not only of economic management but also of the scientific management of learning. This is expressed, for instance, as policy interest in the ‘learning sciences’ and most prominently in educational neuroscience (De Vos, 2016; Pykett & Disney, 2016). Internationally, organisations like the OECD and UNESCO have generated policy- influencing agendas for educational transformation around the promises of neuroscience and neurotechnology. For the OECD, optimised human cognitive performance – figured as ‘brain capital’ – is thought necessary for long-term productivity and economic prosperity, and is to be measured and improved by neuroscientific instruments (OECD, 2021; Smith et al., 2021). Educational neurotechnology, in this policy discourse, is imagined as having an ‘immense potential to improve student learning and cognition’ (UNESCO, 2023, p. 6).
Anticipatory enthusiasm about the possibilities of neurotechnology in education redoubles existing socio-cultural fascination with the neurological and its attendant subjectivities (De Vos, 2016; Pickersgill et al., 2011; Pykett & Disney, 2016; Rose & Abi-Rached, 2013; Vidal & Ortega, 2017). Neuroscience is expected to help ‘stretch’ children and provide tools to improve their learning outcomes, not least by finding therapeutic solutions to increasing rates of ADHD diagnoses (Cortese et al., 2023). Coupled with the growing market in learning analytics – which monitor university students’ data traces and use algorithms to assess engagement, predict performance and help and prevent lapses and drop-outs (Kotouza et al., 2021) – these technologies enable new ways to directly monitor, assess, and train attention in educational settings. This leads us to ask: what might be the implications for the shaping (and reduction) of students into attentive and productive learner subjects? More specifically, what aspects of this subjectivity are targeted via scientific and economic forms of control, and in what ways?
This article is a step towards answering such questions, by concentrating on the production of knowledge and technology in neuroscience that addresses attention in education. It is informed by critical studies of the implications of neurotechnologies for knowledge production, governance and contemporary forms of economic organisation (e.g. Pickersgill, 2023), as we outline conceptually in the following section. Specifically, by building on such theorisations of the epistemic, interventionist and economic dimensions of neuroscience, we explore how ‘attention’ is (re)configured as: (a) an object of knowledge, by examining how concepts of attention and distraction have shifted in educational neuroscience studies; (b) an object of intervention, by looking at how those concepts are operant within emerging neurotechnologies used to measure, monitor, and enhance learners’ attention; and (c) an object of valorisation, by tracing how attention neurotechnologies enable learners’ attentional activity to enter, in different forms, circuits of valorisation.