Distended Nervous System: Networked Media and its Neurological Turns

Despite having celebrated ‘the decade of the brain’ from 1990–1999, Catherine Malabou reminds us that we still do not know what we should do with our brains.¹ Yet philosophy is perhaps confronting such risks too late, since an entire technics of search, query, databasing, pattern matching and machine learning is already attempting to determine what our brains must do. Such a technics seeks out that share of mind that is not, or not yet, conscious, staking a claim on what we should be thinking and feeling, how we should be behaving, before we register that this entanglement is something we might desire.

Take, for example, Google’s aspirations beyond its constitution as the ‘ultimate’search engine. Its construction of, and investment in, ‘the future’ rests on its capacity to reterritorialize this proto-mind space. This reterritorialization is attempted via a raft of machine learning techniques from data mining to dataset training, which claim to reveal dimensions of thought and behavior.² This move away from search per se, toward prediction of what ‘users’ desire before they even know what they want, signals an insidious foray into the non-conscious and affective terrain of precognition and all its liminality. Networked platforms and corporations thus turn toward systems, tools and processes of what I call ‘neuro-perception’; that is, a paradigm of perception that sees human thought and action proceeding from the reterritorialisation of the pre-cognitive and pre-perceptual by the research and development vectors of contemporary networked corporations.³ The occupation of the ‘neural’ through an entire technics of machine learning, data mining and artificial intelligence emanating from networked corporations lays claim to what are actually the indeterminate non-predictive capacities of organic neurobiological processes. In neuroscience this non-predictability is frequently referred to as the brain’s plasticity.

The reterritorializing vectors of networked corporatism, its platforms, research and mediality present us with a distended rather than extended nervous system. Marshall McLuhan had suggested we were already reaching toward such a state during the 1960s.⁴ A ‘Neuro’ everything in, and of, contemporary media bloats our brains and bodies. Yet McLuhan was more prescient than he is often credited with, having foreseen the commercial grab at our nerves:

Once we have surrendered our senses and nervous systems to the private manipulation of those who would try to benefit from taking a lease on our eyes and ears and nerves, we don’t really have any rights left. Leasing our eyes and ears and nerves to commercial interests is like handing over the common speech to a private corporation, or like giving the earth’s atmosphere to a company as a monopoly.⁵

But the conception of media as prostheses, or extensions, of our perception and sensorimotor systems does not immediately get at the sense in which the networked turns towards and against the neuro swell encephalitically—from a space simultaneously constituted by and deemed already anterior to both perception and motoricity.

Distensions of the nerves suggest, instead, something pushing outwards that is already immanent to that system—this indeed is where networked media hope to insert themselves. Yet, rather than examine how this intercalary event of media might have come to have occurred, critics of networked media have likewise turned to the neuro for evidence of the perils of contemporary computational life. Whether the turn is towards or away from networked media, computation, or associated areas of research and development such as machine learning, the neural has steadily gained status as the anterior ground of a now epigenetic mediality.

Turning ‘Neuro’Across the Neural Spectrum

Over the last few years, ‘the neurological turn’ in humanitiesand social science discourses has gathered speed, particularly in analyses of screens and other new media technologies. Bythe neurological turn, I am referring mainly to the recourse to neuroscience by non-scientists, journalists and commentators as evidence of the ways in which, variously, the Internet, gaming, screens in general, databases and artificial intelligence, as well as all manner of informatic devices are changing the ‘wiring’ in our brains. Probably the best known of these turns has been the journalistic writing of Nicholas Carr, whose article for the Atlantic magazine, “Is Google Making Us Stupid? What the Internet is doing to our brains” was so widely discussed in both print media and the blogosphere that it garnered its own Wikipedia entry.⁶ Observing that his own reading habits seemed to have changed as a result of constant skimming and hyperlinking in the online context, Carr, in spite of making his name as a blogger/journalist, bemoaned the loss of meditative, deep thought about the world, “Once I was a scuba diver in the sea of words. Now I zip along the surface like a guy on a Jet Ski.”⁷ Although in this initial article Carr did not offer any neuroscientific evidence for the rewiring that was apparently taking place in his grey matter (synapses) he referred to the work of Maryanne Wolf, which itself sits on the spectrum of the turn towards the neuro. A developmental psychologist, Wolf also worried about our failing capacities to think and read with any depth in an age of surface-oriented media.⁸ Carr later went on to develop his argument in a book, The Shallows (2010), armed with a swathe of neuroscientific ‘evidence’ demonstrating the internet’s neuroanatomical impact.⁹ Furnishing images of the brain gleaned from functional Magnetic Resonance Imaging (fMRI) studies, taken in vivo (in the living) while the subjects of experiments surfed the net, Carr drew on work conducted by psychiatrists who are likewise convinced that the impact of contemporary technologies on our ‘wiring’ seem like incontrovertible truth of a rapid rewiring.¹⁰

This ‘turn’ toward neuroscience has struck those in the humanities of a certain generation—and with a vested interest in ‘literature’ and scholarship—the hardest. Although not quite in the same vein, Katherine Hayles has likewise fretted about the potential loss of deep thought and reading.¹¹ And along somewhat different lines, Bernard Stiegler sees a new formation of biopower—psychopower—emerging, whose pharmacological and neural marketing technics work at the level of the neuronal capture of an entire younger generation’s attentional capacities.¹² This is a rather amorphous crowd admittedly. Yet, this increasing shift toward neuroscience to bolster evidence for the decline in literary and cognitive standards have registered across contemporary media analysis. Geert Lovink has located ‘a neurological turn in internet criticism’, and points out that such a turn has been taking place in a German-speaking context.¹³ The neurological turn, he argues, is evidence of an obsession with mind and consciousness generally by media outlets, it is evidenced by the quantity of reportage about neuroscience and neuro-imaging, which ranges from locating the brain’s ‘center’ for happiness using fMRIs to the role of mirror neurons in all and any examples of human cognition.

Like Lovink, I share a concern with the ways in which analysis of the effects of contemporary media resorts to neuroscience, drawing especially on the indexical power attributed to images of the brain to prove that our minds are ‘devolving’ in some shape or form. But, I think that such amassing of images and studies in Internet and media analysis requires further attention. We should not turn away from neuroscience altogether and engage only in social and political critiques of Internet, gaming, or screen media trends. If the ‘neurological turn’ in and against networked media is a nebulous forking pathway, stretching its tendrils across a range of heterogeneous neurosciences, neuroscientists, networked media entities and media theorists, it is nonetheless materially embedded in the techniques and visuals of functional magnetic resonance imaging. It would be more useful to develop a technomaterialist understanding of functional Magnetic Resonance Imaging (fMRI) together with Magnetic Resonance Imaging (MRI) and an understanding of the way these dovetail with the conventions, forms and materialities of contemporary imaging. For example, the promise offered by fMRIs of providing anin vivo lens; a real time mind movie; or even the soundtrack to the brain’s activities. As non-scientifically trained thinkers, we need a greater understanding of what constitutes the materialities of the neuroscientific image and how such materialities engage with the ubiquity of ‘real time’ media imaging vectors.

I will also argue that the neurological turn against, in particular, networked media comprises not so much a different path but, rather, is part of a ‘neural’ continuum in which the technics of contemporary media are more and more imbricated. Along this continuum, we find a more generalized uptake of the neural as a means for extending a kind of symbiosis between new forms of software, computational architecture and soft ‘thought.’As it turns out, the recent push toward developing a general and global ‘artificial intelligence’ to accompany and ultimately overtake the online search provided by networked corporations such as Google, also turns toward the neural. As George Dyson revealed, as far back as 2005, after visiting Google Head Quarters in Mountain View California, the corporation had already begun its quest to capture the world’s data in order to build a form of distributed artificial intelligence: “We are not scanning all those books to be read by people,” explained one of my hosts after my talk. “We are scanning them to be read by an AI.”¹⁴ This may seem a long way from fears about the Internet rewiring the brain. Yet, I want to suggest, it occupies part of a broad neural spectrum that pervades networked media research and development. A neural spectrum in which, at one end, it is asserted that our media rot our brains; and, at the other, a more subtle insertion, in which networked media interstitially territorialize circuits of thought and action. Google’s shift from search to AI—pre-empted in 2005, and announced more formally by Eric Schmidt Google’s CEO in 2010—stakes a claim for a new space of soft thought that will, eventually, not take over from human thinking in this paradigm but will have already insinuated itself as antecedent to organic neural activity.¹⁵

Google is not alone in using a branch of AI, specifically machine learning, to extract patterns from data; Facebook is also developing various aspects of pattern recognition including image, voice and facial recognition.¹⁶ In using a number of machine learning operations, particularly in Google’s development of its Prediction API and Facebook’s Generative Adversarial Nets approach, the research and development arms of these networked corporations have come to claim an expanding area of the neuro spectrum. The development of neural network-driven approaches to artificial intelligence expressly tie networked corporations’ research trajectories to a desire to become an information architecture functioning before we humans consciously think, search, and act. This area of the neuro spectrum, I will suggest later, is not so much the space where cognition is occurring but instead the territory of the pre-cognitive: that grey area of the ‘just before’ of consciousness and intentionality, and a space into which networked corporations increasingly want to insinuate themselves. All those ‘we recommend’ emails, those ‘like’ icons and those privacy settings we forget to activate are harbingers of a ‘neuro-perceptual’ soft apparatus that will lay claim to know what we want to think; where we want to go; what we want to purchase, before we do. As it turns out then, the neurological turn against contemporary media may have little impact on the same media’s overall predilections for an increasing share of the neuro spectrum’s bandwidth.

The fMRIs Carr furnishes as evidence of the way ‘the internet rots our brain’ come from a neuro-sociological study exposing subjects both ‘naive’ and ‘savvy’ to online ‘hypermedia’.¹⁷ In these studies, the psychiatrist Gary Small’s resulting fMRIs function as visual indicators of structural neuro-anatomical change; before and after shots that document the fundamental fact that something has occurred to alter the neuro-anatomical structure of the brain. Importantly, the indexical claim made by such an image rests on three important functions attributed to neuro-imaging, and especially imaging processes such as fMRI. The first is that neuro-imaging infallibly accesses brain processes and maps these processes in a manner similar to photography and cartography.¹⁸ That is, it provides direct vision or correspondence to an area, which, due to scale, lack of accessibility or technical deficiencies, the naked eye cannot see unaided. As Dumit has argued, neuro-imaging has come to assume the evidential status of older visual technical modes of imaging.¹⁹ The second is that neuro-images claim to capture the neural correlates of mental processes, especially in studies such as those conducted by Small on the ‘browsing’ brain or by claiming that an intention to lie correlates with certain areas of neural excitation. Thus, in locating anatomical change or activity in the brain, as is the case with Positron Emission Tomography (PET) or fMRI, inferences are made to states of mental and emotional life.²⁰ The third, resting on both previous claims, is that as an imaging technique such as fMRI accesses the invisible neurobiological cornices of our behavior, it does so in real time. It furnishes us with motion capture of something we are not visibly, intentionally, or actually capable of accessing, yet is said to be taking place within us. It is this third claim, tethered as it is to a contemporary media reality formed by the moving technical image of real time events, that gives fMRI its newly emerging status as a pre-indexical, pre-emptive action, like being about to tell a lie, before it has even happened.

In a different use of fMRIs, which deploys a ‘generic’ fMRI image as a ‘sample’, we find neuro-imaging being used to persuade people of the efficacy of a commercially available ‘neuro’ product. On the homepage of the No Lie MRI company, which hosts a suite of test centers across the US, catering increasingly to the legal profession, a sequence of images shows changing areas of the brain, ‘lit’ up as a result of an fMRI being run on a subject.²¹ These fMRIs are geared toward capturing the neural response involved in intentionally telling a lie when a participant is asked a series of questions. The subject’s in vivo neural responses are then measured to see if there is a level of excitation of neurons in areas of the brain associated with anticipation and intention, suggesting the subject is intending to lie. Here the fMRI operates as a visual index of process—the brain caught in the act of anticipation, of something to come.

As a record of process, of something that is in the middle, and about to be, the fMRI is indicative of a changing brain as it mobilizes the subject in order to not speak the truth. The claim made by a company such as No Lie fMRI—that an fMRI visualizes anticipation—indicates the ways in which the neurological image also comes to occupy a space similar to Google’s predictive software research and develops the continuum of a cultural ‘neuralism’. But as we shall also see, fMRIs are not visual images in the same way that even the digital photograph is a visual image. As its name states, the fMRI is a functional image: that is, it dynamically images cerebral blood activity as a function of time. When we simply compare a before and after image, as Carr’s referencing of Small’s study reenacts, we freeze frame the functional and durational aspects of this imaging process and focus instead on what comparisons the two images seem to morphologically proffer.²² If we want to deploy fMRIs in a non-therapeutic and cultural context, we must be savvier with our relation to the more dynamic and plastic character of these complex computational artifacts.

The materiality of fMRIs must here be taken into account; in particular, we should note that an fMRI (as is the case for Magnetic Resonance Imaging generally) is not an optically generated image. In order to become an image as such, its non-optically generated data must be transposed into an image space. Like MRIs, fMRIs measure the combination of magnetic signals emitted from hydrogen nuclei in water from the area of the body being imaged. Magnetic field gradients are captured in the scanning process, their frequencies and rate of change are related to the position where the signal is picked up by the scanner.

The magnetic signals captured—in fMRIs these are emitted over time as the cerebral blood flow changes in response to metabolic stimulation—are composed of a series of sine waves, with individual frequencies and amplitudes. These frequencies and amplitudes are computed using a process called the Fourier transform, which converts signals from the time domain into the frequency domain. The frequencies are then separated out and their amplitudes are plotted as an image. A number of manipulations in the Fourier transform space that allow for smoothing of the final image data, elimination of noise via, for example, high pass filters and so forth, take place before the ‘image’ of an fMRI is generated. What is scanned and what is done computationally to the signal captured are fundamentally non-optical and the images that eventuate map the rate of change as a function of time. What we are looking at in this form of neuro-imaging is first and foremost a temporally imputed image scape.

Soft Nerves, Hard Data

The uses of fMRIs, both as evidence of media changing brain structure and as imaging of the interiority of subjectivity in process, raise a number of political and conceptual issues about the status and deployment of the neurological as material artifact. I will return to this later, but for the moment I want to draw attention to how the diffuse mobilization of techniques such as fMRI signals an increasing turn toward the neural as a means of thinking, acting in, negotiating and shaping the contours of contemporary media and culture. Thought, focus, attention—especially of the so-called ‘iGeneration’ of 18 to 24 year-olds—and the synapses themselves have increasingly been characterised as deteriorating, in a state of crisis or under attack, targeted by economies, techniques and cultures of fragmenting and accelerating media. Computational culture’s revving up and dumbing down tendencies effect a significant shift, according to Carr, away from depth and substance toward the flatlands of surface insignificance, ‘the shallows’:

our online habits continue to reverberate in the workings of our brain cells even when we’re not at a computer. We’re exercising the neural circuits devoted to skimming and multitasking while ignoring those used for reading and thinking deeply.²³

Amongst the different factions participating in the turn to the neurological in media analysis, for those at the alarmist end of the spectrum the software and hardware of media technologies are literally turning our ‘wetware’ to mush and bloating rather than extending our central nervous system.

The most strident of these claims—particularly by Carr, Greenfield, and earlier Wolf as to the actual ‘re-wiring’ of the brain’s structure by exposure to the computer screen and internet—also signals a turn away from contemporary media technologies and culture. Both Stiegler and Hayles’ arguments are more nuanced. Nonetheless, they conjoin with Carr and Greenfield inasmuch as the vector they follow deploys neuroscience evidentially yet simultaneously deploys nebulous ‘entities’such as ‘the mind’, ‘attention’, ‘generations’, ‘youth’ and ‘the internet’. But it is precisely the unproblematic unity and homogeneity of such entities that is questioned by a number of contemporary neuroscientists: Vilanyur Ramachandran’s behavioral neurology or Steven Rose’s neurobiology, for example.²⁴ It is important, then, to pay attention to the variety of neuroscience being evoked in non-scientific turns to the neurological against networked media.

The other turn that I want to chart in tandem with the above is the turn in networked media itself toward systems, tools and processes of what I will call ‘neuro-perception.’ This includes, for example, the development and implementation of Google’s Prediction API tool (part of its self-conscious switch in systems development from ‘search’ to artificial intelligence), which is, in effect, a marriage of cloud computing with predictive AI. This is part of networked entities and corporations’ growth and use of data mining systems that learn from, and model, data in order to predict future directions. In fact, this turn has been taking place for some time. During the 1990s, when we were experiencing an explosion in the life sciences and artificial life, artificial intelligence research was nevertheless ticking along. But as John Johnston has suggested, AI priorities shifted largely away from the construction of human-machine intelligence and the goal of creating an artificially intelligent ‘mind,’ over to ‘practical’ applications for industry and for the military.²⁵ A raft of ‘smart applications’ were tried and tested such as electronic fraud detection, voice and face recognition and data mining systems.

This, too, signals a neural turn but a qualitatively different one from the idea that networks and media are rotting our brains. Rather, this R&D is imbricated in generating and deploying a distributed, networked architecture and infrastructure that still owes a debt to mid-20th century cybernetic insights.

In particular, to Warren McCulloch and Walter Pitts’ conception of the movement of the brain’s electrochemical impulses through neural circuits as a form of biological computation.²⁶ Importantly, McCulloch and Pitts drew a formal analogy between the activity of neurons acting in neurophysiological networks in order to receive and transmit electrical signals and the ‘activity’ of logical propositions and their networks of relations: The ‘all or none’ law of nervous activity is sufficient to insure that the activity of any neuron may be represented as a proposition. Physiological relations existing among nervous activities correspond, of course, to relations among the propositions; and the utility of the representation depends upon the identity of these relations with those of the logic of propositions.²⁷

In fact, McCulloch and Pitts shed less light on the brain’s activities and more on a mode of thinking computationally beyond simple input-output models of information processing. Their paper gave impetus to the modeling of artificial rather than biological neural networks and initiated research into questions of modeling learning and adaptation in AI. What this analogy between physical and artificial neurons facilitated was a kind of backwards and forwards mapping that has also entwined and harnessed computational models of thinking to the activity of (idealized) neural correlates: “they simplified and idealized the known properties of networks of neurons so that certain propositional inferences could be mapped onto neural events and vice versa.”²⁸ Perhaps, in spite of the fact that McCulloch and Pitts were aware that the ‘neural’they were referring to had been largely abstracted from its biology, the far reaching implications of their analogy has meant that computational neural modeling is underpinned by the positing of some association to a neurophysiological base.

As Johnston has argued, this enmeshing of the neurobiological with the computational has continued to provide an AI research direction that is different from the concentration upon language, general intelligence and symbolic processing.²⁹ In a range of contemporary AI contexts that deal with large datasets, it is generally acknowledged that various models and manipulations of artificial neural networks provide the best paradigms and applications for machine learning applications that involve pattern recognition.³⁰ A deficiency of McCulloch and Pitts’ early model of neural networks was that the artificial networks modeled were simply too small to compute at either a satisfactory machine-based rate or compare with the operations of the billions of biological neurons that support the generation of actual neural patterning across their networks.³¹ In fact, neural networks as they now feature in AI have become largely non-biological and increasingly concerned and interlinked with branches of statistics and data analysis. This branch of AI’s orientation toward machine learning now finds itself at home in information networks such as the Internet and in databases. In part this is due to the fact that large enough datasets and, especially with the development of shared online platforms, enough instances of distributed parallel processing networked nodes can ‘collectively’ combine to create a kind of vast quasi-AI. Or, at least this seems to be the dream of networked corporations such as Google, as we can glean from another infamous Eric Schmidt-‘ism’: in five years, Google will have built ‘the product I’ve always wanted to build—we call it ‘serendipity”’, he said, adding that it will ‘tell me what I should be typing.’³²

From one angle it appears that deployment of machine learning techniques across, especially online, industries signals that biological neurality may have fled the scene of such models. Yet, Google’s search aspirations rest precisely upon a reterritorialisation of mind and intelligence, in which a raft of machine learning techniques from data mining through to dataset training reclaim the non-cognitive dimensions of brain and thought for the technics of artificial neural networks. The neural has definitively re-entered the fray when aspects of machine learning come to be applied, as they are with Google’s Prediction API, to anticipating or pre-empting the domain of pre-cognitive thought-action relationships such as ‘the serendipitous’ or ‘anticipation/prediction’. I want to suggest, then, that networked media really are re-turning to the neural in their exploration of techniques for machine learning. What they hope to territorialise via creating an AI that can, for example, ‘read’ large datasets, is a kind of intelligence that exists interstitially in the nebulous spaces before conscious (human) thought clearly emerges.

This vector of distributed and networked intelligence is at work in the current research and development of corporations such as Google as it transforms search mechanisms into a much further reaching, ever present, predictive form of AI. As Eric Schmidt has more recently claimed:

We’re still happy to be in search, believe me. But one idea is that more and more searches are done on your behalf without you needing to type […] I actually think most people don’t want Google to answer their questions[…]. They want Google to tell them what they should be doing next.³³

In the case of its Prediction API, Google releases its data mining and prediction tool to users on the basis that their data gets stored on Google servers.³⁴ Effectively, what occurs is that data becomes less distributed and more concentrated within the proprietorial grasp and confines of particular networked corporations. This stored data also becomes the testing field for a tool enabled with machine learning capacities that is also Google’s property. This has major implications for networked culture and for it political economy.

The Prediction API turns out to be a way of initiating a new pathway for, or at least transforming, the usual user-developer assemblage in computational culture. Rather than simply providing content (from user) for an application (by developer), the machine learning architecture of the Prediction API, is driven by a recursive adaptation of the data, or content, by and into the development of the application itself. There are of course precedents here across all kinds of software development communities—gaming and open source code for example. But machine learning changes the game plan—it automates the development process making it in some fundamental ways non-participatory. What crucially differentiates this neural vector from the one that concerns Carr, Greenfield and others, is that here we are dealing with dispersed, pervasive and entangled interconnectivities. The relative unity of phenomena such as deep ‘thought,’ focused ‘attention,’ mind and brain as structure, ‘the young brain,’ the old brain, the younger generation, the older generation (and so on) posited by the ‘anti-networkers’such as Carr et. al., give way to vectors and relationalities populating networked media such as artificial neurons, data-neural architectures, pattern and, increasingly, prediction that precedes reflective or intentional thought. While we stumble around as clueless cohabitants of radically distributed, embedded, networked ecologies, Nicholas Carr is mainly worried about what is going on inside his head.

But there’s more than a turn here and more at stake than a difference in neurological model and application. A staggering leap has taken place. How did we travel from pattern to prediction? How is it that for networked media corporations like Google, Facebook and Amazon search and browsing and social media are morphing into forms of AI that anticipate your next move? And what implications does this assemblage of ‘machine-us’intelligence, riding the slippery slope of the predictive, have for the molecularity of thought and perception and, equally, for its molar socio-technical conjunctions? Networked media are largely made of, and by, us—they are the product of our connective, informatic cognitive musings in online space. As we contribute content, we make, for instance, a ‘Google-us’ earth.³⁵ But, we also discard the integrity of our ‘selves’‘minds’‘actions,’ as the molecules of our exoskeleton are divested and become not ours. Importantly, alongside the amorphousness of such entanglements, we may well feel ambivalent about such networked coporations’shift to an AI that predicts what we want to do next. We have previously imagined artificially intelligent entities as benign or malevolent agents.³⁶ But we hadn’t culturally configured an AI so distributed and diffuse, yet so modulating of both cognition and perception. We hadn’t perhaps dreamt of neuro-perception as the emerging vector guiding networked corporations.

From Neuro-turns to Neuropolitics

This is where the neurological turns, in and against networked media, bifurcate. How and where neurological architectures and processes are situated and deployed—‘in the brain’ or relationally and transversally across brains and media—differ in the critiques by Carr et. al, on the one hand, and the R&D direction of networked corporations’ neuro-perceptual territorialisations, on the other. Both the neurological turn against networked media, and the neuro-perceptual turn toward prediction use neuroscience and artefacts such as fMRIs. Yet they remain worlds apart. The critique of networked media based upon a conception of damage done to thought, attention and youth by Carr and Greenfield radically misconstrues and misconceives the neuropolitics of contemporary networked media. Lovink asks where, in all this, is the economic and political analysis of Google and other networked corporations, and of the colonization of content, real time and labor?³⁷

Although concurring with his questions, I nonetheless think we need to be simultaneously asking specific questions about the neural micropolitics at play here and to try to figure out their conjunctions and relays with molar dimensions of the economic and political. Stiegler’s critique of contemporary media, to be fair, does take aim at psychopower and its ‘noopolitics’. He argues that the consumer, or market related, elements of contemporary biopower—including such phenomena as neural marketing and the pharmacological economies and subjectivations produced such as Attention Deficit Disorder (ADD) in children—operate at the level of ‘attention capture.’³⁸ But how does attention get captured? What are the technics of this capture? What indeed might a more networked or ecological understanding of, and approach to, media and to the media artefacts of neuroscience bring to understanding the capturing, sequestering and inflecting of attention? William Connolly’s nomenclature of ‘neuropoltics’is more useful than Stiegler’s ‘noopolitics’ because it gets at the transversal and dynamic meshwork of neuro- logical, affective, perceptual, cognitive and socio-technical components at stake in thinking through what thought might be in relation to contemporary culture and politics:

[T]he inventive and compositional dimensions of thinking are essential to freedom of the self and to cultivation of generosity in ethics and politics. Thinking participates in that uncertain process by which new possibilities are ushered into being.³⁹

Such a neuropolitics inhabits the deployment of neuro-images by those who both turn away from networked media and by the deployment of neuro-imaging in social and cultural contexts to stake a claim on the pre-cognitive. By understanding the fMRI as a material assemblage of converging socio-technical machines when deployed in such ways (that is, beyond its diagnostic uses), it is possible to trace just such relays between the molecular and molar at work in current neuropolitics.

Soft Imaging the Brain

In the study by Gary Small used by Carr, evidence of the internet’s ability to ‘rewire’the plastic and malleable circuitry of the brain is furnished, as we compare the before and after snapshots of a plastic brain that has surfed the internet for a period of time. Initially, the brain of the ‘naïve’ user is relatively unwired for hypermedia engagement. Fast forward five hours per day over five days of web surfing and this naïve brain’s ‘wiring’ lights up differently—incontrovertible evidence that neuro-anatomical change has taken place due to networked media engagement! In a voice poised between wonder and terror at the incredible swiftness of such ‘reprogramming on the fly’, Carr remarks, ‘The human brain is almost infinitely malleable’.⁴⁰ Yet, as with all before and after shots, change itself can only be inferred. What is captured in these before and after juxtapositions of snapshots selected from an fMRI sequence, then, is the fact that change has happened, indexed to the implication that what we are seemingly looking at in these images is that change rendered in a neuro-anatomical structure.

In No Lie MRI’s description of its product online, it tempts us with the capture of a lie as it happens, by providing a kind of stop-frame animation version of the fMRI sequence. Although this could be viewed as an extension of the before and after technique, what is going on here is different from Small’s study. Here, we are in the midst of capturing process: ‘Differential activation during the telling of a lie’.⁴¹ The mode of attention being captured here is infinitesimally more fleeting, and faster than the voluntary decision to click on a web link as operative in Small’s study. No Lie MRI claims to image and capture the brain during the unfolding of intentionality.

That is, its promise rests upon the delivery of real time image processing of the pre-, or in-, voluntary. It seeks to capture the movement and change that takes place as the non-conscious intention to not tell the truth is neurally initiated. It stakes this claim on the belief that what the fMRI captures are the micro-processes of neurological change itself such as neural firing.

But what does an fMRI actually visualize? Nothing quite so simple or complex as wiring and firing or intentionality. To begin with, and as I mentioned earlier, an fMRI is not specifically an optically constituted image. Like MRIs, fMRIs measure a combination of signals from all over the object (the part of the body). What is being scanned and what is done to the signal captured computationally is in quite a fundamental way non-imagistic. It is closer, if anything, to the computational processing of audio signal. When dealing with the actual transduction of this data into image, we need to additionally to think through what it means to image a process of cerebral change. The areas of ‘color’ we often see in magnetic resonance imaging are converted from grayscale in the first transduced images (that is, after the Fourier transform space has been transformed into image data), and these map a ‘capture’ of hemodynamic response. We see the surplus of oxyhemoglobin (oxygenated blood) remaining in the veins, measured as a ratio of the increase to—in the case of fMRIs of the brain—decrease of cerebral blood flows. Active neurons require both glucose and oxygen in order to fire, and an fMRI traces the movement of blood transporting glucose and oxygen through the vascular system necessary for firing. But, as is usual in the area of neurological imaging, what is being imaged is up for question. Are we seeing the trace of the activity of neurons themselves, for example, or are we seeing the trace of activity caused by neurotransmitters, which likewise require cerebral blood flow? An fMRI cannot distinguish these substantially—it is a mapping of oxygenated blood flow, that is, of processes. Furthermore, although the spatial resolution of the fMRI is remarkable, its temporal resolution is relatively poor with an estimated 4-5 second period needed for image capture during which changes in blood flow may have occurred at least twice over.

These remain areas of neuroscientific dispute. But what we can be sure we are not seeing in those color patches is a neuron, a wire, a circuit, a network or a restructure of structure itself. We are looking at a mathematically inflected (ratio of increase to decrease), recolored, afterimage selected out of dynamic processuality. Interestingly, the more the fMRI becomes visual (and especially when it statically becomes ‘an’image or even two comparable images as in Small’s study), the less indexical it can be said to be, given that its initial data comprises signal generated by relations imputed to wave differentials. As ‘an’ imaging of the brain, then, we need to understand the final startling ‘images’that purport to locate emotions, states and changes as datasets of transduced cross-processed signal. What is important in this cross-processing for neurosicentific (medical and radiographic, that is) diagnosis via neuro-imaging, is that relations between data variables such as frequency, amplitude and position are maintained in the transduction. The fMRI is therefore quite far removed from a pictorial semiosis structured by the relation between a signifier and signified (referent) and much more akin to a topological mode of constituting an image. That is, the relations in the set (of data) are maintained although the data is itself transduced and deformed.

If we were to deploy a Peircean semiotics here we could say that the fMRI is less indexical and more iconic. But to take this further, fMRIs comprise a special case of the icon: the diagram. For Peirce, the icon was itself subsumed under the second category of his trichotomous classification of signs, in which signs were to be ordered by the ways they denoted their objects.⁴² Icons are a kind of sign, which share the qualities of their objects, such as resemblance. Diagrams are ‘in the main’ a kind of icon that resembles not the object itself but the relations necessary for generating an object.⁴³ The Peircean diagram, while iconic, is not strictly speaking indexical as its mimetic properties are not caused by the object to which it refers.⁴⁴ Peirce overlays image and object diagrammatically; the consistency of their resemblances lie not with cause but rather in the event of their overlay, or we might say, in their cross-processing. We see in Peirce this discovery of the diagram as a method for moving reasoning away from embodying meaning or functioning as description, model or illustration, toward the diagrammatic as an event that generates novelty: ‘A diagram is an icon or schematic image embodying the meaning of a general predicate; and from the observation of this icon we are supposed to construct a new general predicate.’⁴⁵ This also shifts the diagram away from the representation of something toward a more ontogenetic or process-based generativity. Although this is not the place to conduct a sustained argument about the qualities of soft imaging, we could speculate as to whether any fundamentally non-optically generated computational image—that is, imaging that is computed (rather than say scanned for optical information) as a function of mathematical transforms—is always non-representational and comprised firstly of relations, and then information or data.

Moreover, what an fMRI indicates is that we are in the middle of something. The image is not simply a tracing of action but a forwarding: one fMRI snapshot refers to its others both captured and not captured (those that would have occurred in the 4-5 second gap between ‘snapshots’), and tells us something else will be about to have become. This is vitally important because if the fMRI indicates that the brain can change because of its exposure to media or because it is about to lie, it is because it indicates nothing more than that the brain is always changing, always in process—something to which rewiring and lying are likewise prone. In a non-therapeutic context, the visual status of the fMRI must be seen more diagrammatically, and less cartographically. Here, I invoke Deleuze and Guattari’s sense of the operations of the diagram, that functions not simply on what is actually seen but what a diagram simultaneously virtually expresses:

A diagram has neither substance nor form, neither content nor expression […]. A matter-content having only degrees of intensity, resistance, conductivity, heating, stretching, speed, or tardiness; and a function-expression having only ‘tensors’, as in a system of mathematical, or musical, writing.⁴⁶

Thinking an image diagrammatically, then, draws attention to its potential unfoldings not all of which will, in actuality, unfold. For Deleuze and Guattari, the diagram has a processual aesthetic that lies in its assembling of intensive elements. We might try to conceive the mode of imaging that an fMRI produces differently by following these diagrammatic axes, when we see them deployed in social and cultural contexts. This would require transversally drawing out the virtualities rather than representational vectors of neural processes: the brain looks like its moving toward this but may also be moving toward that; there’s a burst of activity here but then nanoseconds later there’s another burst elsewhere. We would have to think the still neural image as always already moving. Whereas the fMRI when presented as a single image of brain change traces activity that’s occurred, if re-imagined as a tracing of process it could also sketch a virtual future, moving us forward indeterminately to what might be unfolding multifariously somewhere else.

But the fMRI corralled into ‘demonstrating’structural change—as with Nicholas Carr’s deployment of Small’s studies of structural change in web surfers’ brains—loses its virtualities. It loses the potential to express the brain changing again in response to… well, less exposure to the web, exposure to noise in the street, exposure to anything whatsoever! Losing a relation to its virtualities means it also loses its relations to the material processes that neuroscience now ascribes to brains: plasticity and dynamism. In No Lie MRI’s techniques and modus operandi for the fMRI, the indeterminacy of a dynamic, distributed brain-mind is sacrificed, neural processes are fixed, and they feed into a neuro-perceptual politics similar to that of networked media corporations such as Google. Here, the fluidity of the spectrum comprising attentional capacities is whittled down into bounded territories, captured as linear and one-directional flows such as anticipation-intention and prediction-consumption. What both No Lie MRI and Google seek, is to insert themselves at a molecular level in the temporal intervals between what is felt at a lived relational level and what we know or how we will then act, harnessing attention to prediction so that we inhabit an environment where ‘they’ feel what we are going to do before we feel or know it. An insertion into the middle of something happening—the readiness potential of the now—in order to fold in and effectively close out openness to an indeterminate futurity.

1. Catherine Malabou, What Should We Do with Our Brain? (New York, NY: Fordham University Press, 2008).
2. See, for example, the following entries from Google’s research blog: Alexander Mordvintsev et. al., “Inceptionism: Going Deeper into Neural Networks,” Google Research Blog, 2015, accessed August 22, 2015, http://googleresearch.blogspot.com.au/2015/06/inceptionism-going-deeper-into-neural.html; Christian Szegedy, “Building a Deeper Understanding of images” Google Research Blog, 2014, accessed August 22, 2015, http://googleresearch.blogspot.com.au/2014/09/building-deeper-understanding-of-images.html. In general the research undertaken on various aspects of machine learning at Google, relies upon the cloud infrastructure of Google’s Prediction API, which is located at: https://cloud.google.com/prediction/. I shall discuss this later in this essay.
3. Anna Munster, An Aesthesia of Newtorks: Conjunctive Experience in Art and Technology, (Cambridge, MA: MIT Press, 2013), 126–129.
4. Marshall McLuhan, Understanding Media: The Extensions of Man (Santa Rosa, CA: Gingko Press, 2003), 68.
5. Ibid., 99.
6. Nicholas Carr, “Is Google Making us Stupid?,” The Atlantic Magazine (July/August 2008a), accessed August 22, 2015, http://www.theatlantic.com/magazine/archive/2008/07/is-google-making-us-stupid/306868/ The Wikipedia entry is in addition to an entry on Carr himself. See, “Is Google Making us Stupid?,” Wikipedia: The Free Encyclopedia, 2008–15, last modified June 10 2015, http://en.wikipedia.org/wiki/Is_Google_Making_Us_Stupid%3F
7. “Is Google Making us Stupid?,” op. cit.
8. Maryanne Wolf, Proust and the Squid: The Story and Science of the Reading Brain (London: Harper, 2007).
9. Nicholas Carr, The Shallows: What the Internet is doing to our Brains (New York, NY: W.W. Norton and Company, 2010).
10. Carr’s primary source is the work of Gary Small and Gigi Vorgan which can be found in their book, iBrain: Surviving the Technological Alteration of the Modern Mind (New York, NY: Harper, 2009).
11. Katherine Hayles, “Hyper and Deep Attention: The Generational Divide in Cognitive Modes,” Profession (2007), 187–199.
12. Bernard Stiegler, Taking Care of Youth and the Generations, Stanford: Stanford University Press, 2010, and “Biopower, psychopower and the logic of the scapegoat,” Ars Industrialis: Association internationale pour une politique industrielle des technologies de l’esprit, last modified 2008, http://www.arsindustrialis.org/node/2924
13. Geert Lovink, “MyBrain.net: The colonization of real-time and other trends in Web 2.0,” Eurozine (March 18, 2010), accessed August 22, 2015, http://www.eurozine.com/articles/2010-03-18-lovink-en.html
14. Google employee quoted in George Dyson, “Turing’s Cathedral,” Edge: The Third Culture (2005), accessed August 22, 2015, http://www.edge.org/3rd_culture/dyson05/dyson05_index.html
15. Eric Schmidt quoted in Holman Jenkins Jr., “Google and the Search for the Future,” The Wall Street Journal (August 14, 2010), accessed August 22, 2015, http://online.wsj.com/article/SB1000142405274870490110
16. Google’s neural network researchers claim to have taught complexly layered and iterative neural networks to generate representational images from visual noise. Further, these researchers also suggest that locating the possibility of image generation—that is, visual pattern that results in representation of recognizable physical phenomena—might reveal insight into how human neural processes lead to creativity. See, Alexander Mordvintsev et. al., “Inceptionism…” op. cit. The approach taken by Facebook, on the other hand,is toward the iterative production of increasingly ‘naturalistic’ images from ‘noise’ created by pitting neural networks against each other. Hence the name Generative Adversarial Networks. See, Emily Denton, et.al, “Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks,” Facebook Research: Publications, June 18, 2015, accessed August 22, 2015, https://scontent.fadl1-1.fna.fbcdn.net/hphotos-xfa1/t39.2365-6/11404858_446018348908666_724767519_nDeep_Generative_Image_Models_using_a_Laplacian_Pyramid_of_Adversarial_Networks.pdf
17. The Shallows, op.cit, 120-126.
18. Joseph Dumit, “Objective Brains, Prejudicial Images,” Science in Context 12, 1 (1999), 173–201, 177
19. Ibid.
20. See also Joseph Dumit, Picturing Personhood: Brain Scans and Biomedical Identity (Princeton, NJ: Princeton University Press, 2004), 6.
21. The image sequence appears on the bottom of the ‘Product Overview’ page of No Lie MRI’s website at: http://noliemri.com/products/Overview.htm
22. “Objective Brains, Prejudicial Images,” op.cit, 187–89.
23. Nicholas Carr, “The Web Shatters Focus, Rewires Brains,” Wired (May 24, 2010), accessed August 22, 2015, http://www.wired.com/magazine/2010/05/ff_nicholas_carr/all/1
24. Ramachandran’s position is more complex than neural reductionism and posits the existence of a dynamic bodily schema operating in conjunction with the neural. See Vilanyur Ramachandran and Sandra Blackslie, Phantoms in the Brain (London: Harper, 1998). Rose is perhaps one of the most open of neuroscientists to the complex interrelation of the neural, chemical, genetic, historical and cultural ecology of the brain. See, for example, Steven Rose, The 21st Century Brain: Explaining, Mending and Manipulating the Mind (London: Vintage, 2006).
25. John Johnston, The Allure of Machinic Life (Cambridge MA: MIT Press, 2008), 386.
26. Warren S. McCulloch and Walter Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biology 52, 1–2 (1943), 115–133.
27. Ibid., 117.
28. Gualtiero Piccinini, “The First Computational Theory of Mind and Brain: a Close Look at McCulloch and Pitts’s ‘‘Logical Calculus of Ideas Immanent in Nervous Activity,” Synthese 141 (2004), 175–215, 176.
29. The Allure of Machinic Life, op.cit, 386–88.
30. George F. Luger and William A. Stubblefield, Artificial Intelligence: Structures and Strategies for Complex Problem Solving (Reading, UK: Addison-Wesley, 1998), 759.
31. The Allure of Machinic Life, op.cit, 36.
32. Eric Schmidt quoted in Elinor Mills, ‘Will Search Keep Google on the Throne?’, CNET News, May 10, 2006, accessed August 22, 2016, http://news.cnet.com/Will-search-keep-Google-on-the-throne/2100-1032_3-6070774.html
33. Eric Schmidt quoted in ‘Google and the Search for the Future’, op.cit.
34. My two sources for information about Google’s Prediction API have been the Google Labs document archive for the application itself available at: https://cloud.google.com/prediction/docs and a video of a presentation about smart applications and the Prediction API by Google developers Travis Green, Max Lin, Robert Kaplow, Jóhannes Kristinsson, Ryan McGee, uploaded to YouTube, 13 May 2011, http://www.youtube.com/watch?v=FJDP_0Mrb-w&feature=youtu.be
35. William Gibson makes this point in pointing toward the kinds of entanglements of us-networked corporations that online media platforms are now producing. See William Gibson, “Google’s Earth,” New York Times, August 31, 2010, accessed August 22, 2015, http://www.nytimes.com/2010/09/01/opinion/01gibson.html?_r=6
36. Phantoms in the Brain, op.cit. See above footnote 24.
37. “MyBrain.net,” op.cit.
38. Taking Care of Youth and the Generations, 90 and “Biopower, psychopower and the logic of the scapegoat,” op.cit.
39. William Connolly, Neuropolitics: Thinking, Culture, Speed, Minneapolis, MN: University of Minnesota Press, 2002), 1.
40. “The Web Shatters Focus, Rewires Brains,” op.cit.
41. http://noliemri.com/products/Overview.htm
42. Charles Sanders Peirce, Collected Papers, eds. Charles Hartshorne and Paul Weiss, Volumes 1–8 (Cambridge, MA: Harvard University Press, 1932 and 1933), 243–63.
43. Ibid., 531.
44. Ibid., p.230.
45. Charles Sanders Peirce, The Essential Peirce: Selected Philosophical Writings Volume 1: 1867-1893, ed. N. Houser (Indianapolis, IN: Indiana University Press, 1998), 303.
46. Gilles Deleuze and Félix Guattari, A Thousand Plateaus, trans. B. Massumi (London: Althone Press, 1987), 162.