Original Article
Science, Technology, & Human Values
1-23
The Algorithms ª The Author(s) 2021
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DOI: 10.1177/01622439211025632
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Johannes Bruder1,2
Abstract
This paper analyzes notions and models of optimized cognition emerging at
the intersections of psychology, neuroscience, and computing. What I
somewhat polemically call the algorithms of mindfulness describes an ideal
that determines algorithmic techniques of the self, geared at emotional
resilience and creative cognition. A reframing of rest, exemplified in cor-
porate mindfulness programs and the design of experimental artificial neural
networks sits at the heart of this process. Mindfulness trainings provide
cues as to this reframing, for they detail each in their own way how
intermittent periods of rest are to be recruited to augment our cognitive
capacities and combat the effects of stress and information overload. They
typically rely on and co-opt neuroscience knowledge about what the brains
of North Americans and Europeans do when we rest. Current designs for
artificial neural networks draw on the same neuroscience research and
incorporate coarse principles of cognition in brains to make machine
learning systems more resilient and creative. These algorithmic techniques
are primarily conceived to prevent psychopathologies where stress is
1Institute of Experimental Design and Media Cultures/Critical Media Lab, FHNW Academy of
Art and Design, Basel, Switzerland
2Milieux – Institute for Arts, Culture, Technology, Concordia University, Montreal, Quebec,
Canada
Corresponding Author:
Johannes Bruder, Institute of Experimental Design and Media Cultures/Critical Media Lab,
FHNW Academy of Art and Design, Freilager-Platz 1, CH-4002 Basel, Switzerland.
Email: johannes.bruder@fhnw.ch
2 Science, Technology, & Human Values XX(X)
considered the driving force of success. Against this backdrop, I ask how
machine learning systems could be employed to unsettle the concept of
pathological cognition itself.
Keywords
attention, rest, sleep, information overload, cognitive neuroscience,
artificial intelligence
Introduction
In their best-selling book Peak Performance, science writers Brad Stulberg
and Steve Magness (2017b) refer to a friend who rarely replies to texts, for
his employer has “nailed the recipe for stress” (p. 76). When Stulberg and
Magness wrote their book, Adam (name changed) was an engineer at Goo-
gle’s self-driving car project and his attention was ever absorbed by projects
“where struggle and productive failure aren’t consequences of the work, but
rather the driving forces behind it” (p. 76). Yet, according to the authors,
Google has meanwhile understood that keeping their employees busy is
only half the battle. “Without rest,” Stulberg and Magness write, “Google
wouldn’t end up with innovation. Instead, it’d end up with a workforce that
is broken down and burnt out” (p. 76).
Books like Peak Performance and Google’s Search Inside Yourself
(SIY) program described within exemplify a movement toward technolo-
gical fixes for working at or over capacity in North American and European
societies.1 Corporate mindfulness trainings and meditation apps, for
instance, promise to help prevent attentional lapses and detail each in their
own way how periods of idleness are to be recruited to combat the effects of
stress and information overload. In this context, mindfulness practices are
reconceived as techniques of the self that augment cognitive labor. I argue
that the rising interest in mindfulness practices is symptomatic of an interest
in recruiting rest or the flipside of what we know as attention “to task” for
enhanced information processing on behalf of the cognitive worker.
In the first part of the paper, I draw on analyses of corporate mindfulness
and meditation apps to show how rest is reframed within. This links mind-
fulness trainings and apps to research on wakeful rest in cognitive neu-
roscience, which I sketch in the second part of the paper. Thanks to
investigations into the brain’s so-called resting state starting in the 1990s,
the brain of cognitive neuroscience has gradually been reconceived as
Bruder 3
industrious and never idle. Said research led to a reframing of the resting
state as a mode of information processing where our brains not only
recharge their cognitive capacities but deviate from the “narrow if-then
highway” (Stulberg and Magness 2017b, 88)
Google’s artificial intelligence division DeepMind, among others, has
taken up these ideas and adopted coarse principles of the resting brain to
improve the capabilities of their machine learning systems. In the final
section of the paper, I elaborate on this algorithmic implementation of rest
to show how rest is therein further removed from idleness and reconceived
as a mechanism that supposedly augments information processing and
keeps the effects of information overload at bay. In contrast to mindfulness
trainings and apps, Google DeepMind’s developments do not (yet) have an
immediate effect on the behavior of individuals. Similar to the Facebook
patent applications analyzed by Tero Karppi, these algorithmic models are
critical media that process our current realities at the level of discourse: they
give us “a set of ideas who the user is, what they do, and who they can
become . . . ” (Karppi 2020, 49). Ongoing exchanges between computer
science and neuroscience hence pass as epistemic experiments, which begin
to bleed into psychologies and pedagogies that shape work-lives in North
American and European societies. How can we make use of this knowledge
in different ways to unsettle the concept of cognitive labor and the user
instead of further internalizing the subjectivities that corporate mindfulness
and experimental machine learning systems project onto us?
Mindfulness, Incorporated
In two articles published in 2018, a group of researchers who subscribe to
conducting contemplative neuroscience voiced their concerns regarding the
ongoing muddling of the ancient Buddhist practice of mindfulness through
corporate programs and apps. The authors write that many of the practices
that undergird mindfulness “arose in religious and spiritual contexts where
the motivations and goals for what could and would be achieved through
meditation differed greatly from secular Western notions of health, well-
being, and flourishing” (Van Dam et al. 2018b, 68). Even if reliable mea-
sures of “flourishing” and “well-being” could be attained, it would remain
unclear, from a psychological standpoint, whether the practices conceived
as mindfulness trainings are well-suited to attaining such high-level goals.
Theirs is a rebuttal to an ongoing commodification of mindfulness prac-
tices, which finds support also in their own fields. For instance, neuroscien-
tist Alissa Mrazek and colleagues argue that apps provide “an
4 Science, Technology, & Human Values XX(X)
unprecedented opportunity to deliver high-quality training to an increas-
ingly internet-connected global audience” (Mrazek et al. 2019, 81). They
emphasize the reach and seamlessness of apps in comparison to classic,
place-based psychiatric therapy, which is becoming ever more important in
workplaces that are primarily or entirely digital and screen-based.
In general, the two camps do not disagree about the value of mindfulness
practices, yet they are divided over the question whether mindfulness
should be reconceived to neatly fit into the packed schedules of North
American and European executives and white-collar workers. “With the
current use of umbrella terms,” the contemplative neuroscientists write,
“a 5-minute meditation exercise from a popular phone application might
be treated the same as a 3-month meditation retreat (both labeled as medita-
tion) and a self-report questionnaire might be equated with the character-
istics of someone who has spent decades practicing a particular type of
meditation (both labeled as mindfulness)” (Van Dam et al. 2018a, 38).
Contemplative neuroscientists are concerned that mindfulness might turn
into a mere technological fix for the problems that creative economies and
digital cultures have wrought.
In this context, mindfulness trainings and apps sit next to digital detox
programs (Beattie and Cassidy 2020; Sutton 2020), conceived to counter
the effects of affective bonds introduced through the patents, policies, and
business models of digital platforms (Baym, Wagman, and Persaud 2020).
Apps promise to alleviate the effects of “how our cognitive capacities are
captured and modulated on these platforms at the level of affective flow”
(Karppi 2018, 10-11). In contrast to digital detox programs, however, mind-
fulness trainings and apps are typically designed to allow for affective
bonds to be sustained. The very same devices that first induce digital dis-
tractions are employed to support changes in behavior and promise
“attention by design” (Jablonsky forthcoming).2
Corporate mindfulness trainings pursue similar objectives and recon-
ceive mindfulness as a behavioral technique. For instance, Workfulness, the
corporate well-being program of Scandinavian telecommunication com-
pany Telenor, has been developed for companies that seek to create a
healthy digital working environment by reducing digital distractions in the
workplace: it “aims at refocusing the employees’ attention to more con-
structive behavior” (Guyard and Kaun 2018, 543-44). Central to Google’s
mindfulness strategy—which is prominently featured in Stulberg and Mag-
ness’s book—is their own SIY program. SIY supposedly supports employ-
ees in sustaining peak performance by strengthening their “ability to stay
present and be aware of what’s happening as it’s happening” (Search Inside
Bruder 5
Yourself Leadership Institute 2019). Ruchika Sikri, Google’s Well-being
Learning Strategy Lead, explains these tactics by comparing the mind to a
snow globe:
We’re constantly shaking it with information overload, distractions and task
switching. This results in reduced clarity of our priorities and a lack of focus.
By practicing a brief meditation (as short as five minutes!)—we can let the
“snow’’ settle and see things more clearly and vividly. Clarity of mind can
help us prioritize what’s important, solve problems better, figure out new
strategies or uncover issues we may have ignored. (Parcerisa 2019)
Google’s SIY, Telenor’s Workfulness, and other corporate programs
position mindfulness as an intermittent mode of rest, which departs from
traditional understandings of rest as the cessation of labor or nonactivity.
They emphasize intellectual flexibility and emotional resilience in work-
places, digital and analog (Cook 2016; Ferguson 2016; Parviainen and Kor-
telainen 2019). Instead of disconnecting entirely, cognitive workers are called
upon to go off-line and regenerate their cognitive capacities to reconnect to
the world. What was once a meditation practice that requires years of training
is now often recruited to support “fitness for work” (Hull and Pasquale 2018)
and has therefore taken on “feats of athleticism” (Gregg 2018).
Janice Maturano, a former Vice President and Deputy General Counsel
at the American consumer food manufacturer General Mills and founder of
The Institute for Mindful Leadership, states: “Just as we understand that
there are innate capacities of our bodies that can be trained to make us more
resilient, flexible and stronger, we now know from neuroscience research
that there is universal training we can experience that can cultivate and
strengthen our mind’s capacities. And, the good news is that it doesn’t
require any special equipment or a gym membership” (The Institute for
Mindful Leadership 2016). That is, rest is not only recruited to regain focus
and attention—it allows switching into modes of thought that complement
the fast-paced cognitive labor that companies, such as Google, have
branded. In Stulberg and Magness’s (2017a) words, rest “isn’t lazily sloth-
ing around; it’s an active process in which physical and psychological
growth occurs. To reap the benefits of stress, you need to rest.”
Corporate mindfulness projects an industrious subject that is never really
idle—a mode of subjectivity that is in fact firmly rooted in cognitive neu-
roscience research on the brain’s “resting state” (Callard and Margulies
2010). Stulberg and Magness, for instance, prominently reference the work
of neuroscientist Marcus Raichle, who began studying the brain “at rest” in
6 Science, Technology, & Human Values XX(X)
the 1990s and has since continued this line of research by investigating the
brain’s “default mode” of operation. This change of perspective—from rest
to default mode—is based on a “flipping of contrasts” in the psychology
laboratory that occurred in the 1990s (Callard and Margulies 2011).
Rest, Reframed
In 2007, neuroscientists Alexa Morcom and Paul Fletcher (2007) pub-
lished an article in the highly influential journal Neuroimage that lamen-
ted the ever-growing interest in what the brain does when we rest.
Although a resting subject’s brain might well be active, the authors saw
no reason why such activity should be conceived as the source of crea-
tivity and subjectivity, as some of their fellow neuroscientists would have
it. Morcom and Fletcher appeared to be particularly disturbed by the fact
that participants were by then increasingly asked to simply rest during
experiments.
I myself remember many off-the-record conversations in neuroscience
labs from roughly ten years ago, where researchers mocked resting state
research as mere experimental laziness. Most experiments I witnessed
doing fieldwork in cognitive neuroscience labs had been dominated by
problems, instructions, or stimuli defined by the experimenter and executed
by the volunteer. Any measurements of mental and cognitive activity—
whether via electroencephalography, positron-emissions tomography, or
functional magnetic resonance imaging (fMRI)—were typically conducted
if and when the volunteer was occupied with an experimental task.
In fact, experimental psychology and neuroscience had since the late
nineteenth century been characterized by “an uncanny proximity between
subjective responses to a task delivered in the laboratory and one prescribed
on the shop floor” (Morrison et al. 2019, 64). Nevertheless, asking volun-
teers to put their brains “at rest” in the fMRI scanner had always been an
important element of brain imaging studies, for brain activity at rest was
conceived as “control condition” and thus “the flipside of a range of
focused, controlled and externally oriented processes: an image in negative
of the aware and externally attentive brain” (Alderson-Day and Callard
2016, 12). In the 1990s, neuroscientists developed a vested interest in
distinguishing the components of resting state activity, and initially they
simply looked the other way. Instead of subtracting the activities of the
brain at rest from what happens when the volunteer’s brain is hard at work,
they started to search for brain regions that show increased activity during
Bruder 7
periods of rest and found a network of brain regions we now know as the
“default network.”
In this process, what had been considered as mere background noise that
tends to obscure the cognitive activity of the brain became the target of
analysis—an “an organized, baseline default mode of brain function that is
suspended during specific goal-directed behaviors” (Raichle et al. 2001,
676). Crucially, cognitive neuroscientists gave up on the idea that the
default mode is bound to extended periods of rest and began to more
generally analyze mental processes that had been disregarded by cognitive
neuroscientists, for they were considered unrelated to active, cognitive
processes, and hard to summon in the laboratory. They developed strategies
that would keep volunteers from following the traditional, attentive routines
of the psychology lab so that they stay “off task.”
The goal of these experimental strategies gradually changed, from iden-
tifying brain regions that are active when we rest to creating the conditions
where volunteers’ minds could stray. The underlying cognitive activity is
now variously referred to as self-generated, task-independent, stimulus-
independent, unconstrained, or spontaneous thought. The rising interest in
these phenomena and the brain’s default mode was based on the idea that
“conscious experience is relatively more dependent on the individual’s
concerns, preoccupations and hopes (i.e., self-generated), rather than imme-
diate perceptual input (i.e., perceptually generated)” (Callard et al. 2013, 1).3
The described, experimental shifts in brain imaging have since contributed to
the idea that brains are, in fact, entirely unrestful and allowed for a default
mode phenomenology to emerge.
Default Mode Phenomenology
In 2010, resting state forerunner Marcus Raichle (Raichle) published a
paper on the brain’s “dark energy.” The metaphor latched onto the similar
concept of dark energy in physics, which allows one to speak about phe-
nomena that cannot be reliably measured or explained, although its effects
can be observed. The metaphor was introduced as an auxiliary hypothesis to
explain why the expansion of our universe keeps on accelerating; in neu-
roscience, it helped explain why the brain remains active when the demands
of the environment abate.
The dark energy metaphor grasped the growing interest in the contents of
self-generated mental activity, which researchers had largely ignored—also
since experimenters technically need the help of volunteers to know when it
occurs and catch their mind wandering.4 In the process of mind wandering,
8 Science, Technology, & Human Values XX(X)
memories are recalled for simulations of the future based on experiences in
the past, which is why we sometimes imagine lying on a pristine beach
while staring into gray, postindustrial landscapes that whizz by the windows
of a commuter train.5
More than fantasy and (day) dreaming, mind wandering has been linked
to the process of drifting away and interrupting whatever activity had been
carried out before. In psychology, it had been conceptualized as task-
unrelated since laboratory practices “have repeatedly assumed that experi-
mental subjects must have some task to wander from” (Morrison et al. 2019,
67). The so-called executive failure theory, for instance, postulates that
mind wandering amounts to a loss of focus and control over our mental
activity (McVay and Kane 2010). Psychologists Jennifer McVay and
Michael Kane offered that mind wanderers are characteristically unable
to stay on task, unconsciously drift away and fail to return their attention
to the outside world, and unable to take notice of their mental absence.
What is to be avoided, from this perspective, is getting lost in one’s
thoughts, whether these are positively connotated as in daydreaming, nega-
tively connotated as in depressive rumination, or entirely erratic as in psy-
chotic episodes (Wotruba et al. 2014).
Yet, the differentiation between highly valued attentiveness and patho-
logical introversion has been complicated throughout the last two decades.
The fact that humans are mind wandering up to 50 percent of their waking
life alone speaks against the cognitive insignificance or any pathological
character of mind wandering per se. Proponents of the “perceptual
decoupling” hypothesis suggest that mind wandering is characteristic of a
cognitive state where we attend to normally sub- or nonconscious processes
that occupy parts of our brain throughout the day and not only when we rest
(Baird et al. 2014; Hove et al. 2016). That is, neuroscientists meanwhile
believe that mind wandering might be indicative of a subconscious, yet
system-critical mode of information processing, which occupies our atten-
tion whenever we indulge in our thoughts, but generally benefits our ability
to keep fixed and “on task” (Shepherd 2019).
Uncontrolled mind wandering is billed as the source of cognitive pathol-
ogies such as attention deficit hyperactivity disorder, autism, depressive
rumination, schizophrenia, and obsessive thought (Tang, Hölzel, and Pos-
ner 2015). If mind wandering is voluntary, however, it is linked to creative
thinking, imagining the future, social problem-solving, memory consolida-
tion, and a general openness to new experiences (Beaty et al. 2018; Murphy
et al. 2018). In this case, mind wandering amounts to a particularly vivid
form of “off-line thought” (Smallwood and Schooler 2015) or “off-line
Bruder 9
perception” (Fazekas, Nanay, and Pearson 2021). In other words, a phe-
nomenological ambiguity sits at the heart of the concept: mind wandering is
considered a source of pathology if it cannot be controlled, but it can be
beneficial and productive if it is “goal-directed” (Christoff et al. 2016).
Kieran Fox, one of the currently most prolific researchers in the field of
contemplative neuroscience, explains this phenomenological ambiguity of
mind wandering in an interview with ALIUS Bulletin:
I don’t think of mind-wandering as a conscious state—I think of these processes
as more or less ongoing, below the level of awareness, competing with other
inputs and signals in the brain for our attention. We can tune in and pay attention
to them, or not, and sometimes the thoughts will be strong enough or emotionally
salient enough to grab our attention even when we don’t want them to. I think of
the stream of inner thought in a way similar to other perceptual channels; for
instance, you are constantly receiving a stream of auditory information, even
when you’re asleep, but your brain is very good at blocking out probably 99% of
this information as totally irrelevant, and you never become conscious of it . . . . I
suspect the brain is constantly generating thoughts, imagery, and so on at a
“subthreshold” level as well, and noticing it is more a matter of this content
catching our attention and becoming illuminated by our conscious awareness
than of entering a particular conscious state where mind-wandering then starts or
is allowed to take place. (Fox and Koroma 2018, 4)
In general, this reframing of mind wandering as a form of subconscious
information processing is a welcomed de-pathologizing gesture. It liberates
self-generated mental activity from the stigma that emerged thanks to task-
focused experimentation in psychology labs and links it to the concept of
system maintenance via the metaphor of a default mode.
At the same time, this reframing suggests putting the burden of manag-
ing attention on the individual. In the context of trainings and apps, mind-
fulness is reconceived as a behavioral technique that regenerates and
recalibrates attention. An emphasis is placed on training minds and bodies
to gain attentional and affective control under conditions of information
overload. That is, rest is co-opted to support working at or over capacity and
reframed according to the idea that the subconscious mind never rests. The
brief and intermittent periods of rest that mindfulness-based interventions
permit offer respite from or sustain attention, yet they also promise to
facilitate access to alternative modes of thinking.
Once considered an antidote to mind wandering, contemplative neu-
roscience now suggests that mindfulness can help “steer people away from
10 Science, Technology, & Human Values XX(X)
the negative biases that we see in mental illness, and instead nudge them
toward positive, constructive, and creative patterns of thinking” (Fox and
Koroma 2018, 11). A remarkable passage in Stulberg and Magness’s (2017b)
book Peak Performance captures this transformation of unproductive rest
into an “Other” mode of information processing extraordinarily well:
Our subconscious mind functions in an entirely different manner than our
conscious mind. It breaks from the pattern of linear thinking and works much
more randomly, pulling information from parts of our brain that are inacces-
sible when we’re consciously working on something. It is in these parts of the
brain, in the vast forests bordering the narrow “if-then” highway that our
conscious mind runs on, where our creative ideas lie . . . it’s only when we
turn off the conscious mind, shifting into a state of rest, that insights from the
subconscious mind surface.
Drawing on the idea of a default mode, Stulberg and Magness link focus
and undivided attention to the execution of mundane tasks and a narrow
“if-then” highway. What they carve out—metaphorically—is a space of
play that is buried below and which can only be consciously accessed
when we actively go off-line and put our brains at rest. In this context,
rest is reconceived as a mechanism that supports resilient and creative
information processing—in both brains and large-scale technical systems.
Algorithmic Modulations of Attention
Adam, the restless Google engineer who rarely replies to texts, is only one
of many examples presented in Stulberg and Magness’s (2017a) book; and
yet he is a very memorable one, since Adam was at that time wholly
immersed “in the brains and guts of a car . . . to teach an inanimate object
moving at 70 miles per hour to differentiate between a stray plastic bag and
a stray deer.” This is to say that Adam and Google’s prototype self-driving
car essentially face the same problem: neither Adam nor self-driving cars
have the opportunity to escape their informationally dense environments.
They are called upon to sustain attention to task while being confronted
with an endless stream of information that threatens to overwhelm their
cognitive capacities.
Without necessarily taking account of ongoing exchanges between cog-
nitive neuroscience and machine learning, Peak Performance draws atten-
tion to the slippages that characterize current research in both fields. For
instance, Google DeepMind speaks of a “virtuous circle” between
Bruder 11
neuroscience and AI, “whereby AI researchers use ideas from neuroscience
to build new technology, and neuroscientists learn from the behaviour of
artificial agents to better interpret biological brains” (Hassabis, Summer-
field, and Botvinick 2017).
This parallelization of human and machine in cognitive science, comput-
ing, and public discourse goes back to mid-twentieth century, North Amer-
ican social science, and the concept of information overload (Levine 2017).
Nick Levine traces it to the work of American psychologist James Grier
Miller and his article “Information Input Overload and Psychopathology,”
which was published by the American Journal of Psychiatry in 1960. Cen-
tral to Grier Miller’s theory is that living systems have limited channel
capacity to process incoming information and that pathological behavior
likely results from failures in the communication of internal subsystems.
Very much in the tradition of cybernetics, Grier Miller applied his theory to
systems from the microscopic to the sociological, from the neuron to the
corporation.
Grier Miller’s universalist understanding of information overload
became characteristic of 1960s and early 1970s complex systems theory
that was largely indifferent to the fundamental disparity of human and
machine. For instance, social scientist and artificial intelligence forerunner
Herbert Simon (1971) observed that information overload “creates a pov-
erty of attention and a need to allocate that attention efficiently among the
overabundance of information sources that might consume it” (p. 41).
Throughout the 1980s and 1990s, the concept leaked into management
science and became the dominant element of an emerging public discourse
on the dangers that accompany the data deluge as well as the proliferation of
gadgets, screens, and user interfaces.
The idea of finite cognitive bandwidth is now firmly embedded in both
the neurosciences and computing. Neuro-psychologists would frame the
attendant problem as a “stability-plasticity dilemma” that haunts artificial
and biological systems in a similar way (Mermillod, Bugaiska, and Bonin
2013). A pertinent example of a pathology that derives from the stability-
plasticity dilemma is “catastrophic forgetting.” As a concept, catastrophic
forgetting is native to the machine learning domain, but it compares to the
traumatic memory loss that humans experience under conditions of shock.
Catastrophic forgetting occurs when an artificial neural network is trained
on different tasks and “forgets” one task in favor of another. For instance,
take a network that is trained to play legacy ATARI games such as Space
Invaders and Pac Man. The network will start by trying out random strate-
gies and consecutively “learn” to master Space Invaders by memorizing the
12 Science, Technology, & Human Values XX(X)
strategies that lead to success in this very game. If the network is trained on
Pac Man thereafter, it might completely erase and overwrite its knowledge
of Space Invaders.
The problem of catastrophic forgetting has been approached as an
issue of algorithmic attention. In contrast to humans, artificial neural
networks are not good at differentiating between useful and superfluous
knowledge, which is why their attentional capacities need to be care-
fully managed. Some researchers suggest to force the network into
paying “hard attention to task” (Serra et al. 2018), others promote
machinic variants of synaptic memory consolidation (Kirkpatrick
et al. 2017). These seemingly different strategies have in common that
they seek to protect knowledge from being accidentally erased. Atten-
tion is figured “as a means of guarding against undesirable synaptic
changes” (Lindsay 2020, 16).
Yet, humans remember and forget primarily when they rest, which is
why many machine learning researchers resort to insights from resting state
and default mode neuroscience. Wakeful rest and sleep are considered to
have a double role in the learning process: if we do not need to pay attention
to our environment, our brains supposedly “take out the garbage” and
purposefully forget to make space for new knowledge. At the same time,
we replay experiences from memory to solve problems creatively and store
what is important to long term memory (Langille 2019; Lewis, Knoblich,
and Poe 2018).
Current designs for artificial neural networks involve mechanisms that
reproduce this behavior in very coarse ways—yet, without factoring actual
rest into the equation. Researchers discuss mind wandering as a principle
of resilient and creative information processing in machine learning (van
Vugt 2018) or suggest encoding artificial rest and sleep into their systems
(González et al. 2020). In artificial neural networks, rest turns into a mere
algorithmic mechanism—after all, artificial neural networks never actu-
ally rest.
In the Scientific American, physicist/neuroscientist Garrett Kenyon
accordingly describes rest in artificial intelligence in similar terms as Peak
Performance authors Stulberg and Magness’s take on mindfulness in cor-
porate settings:
Sleeplike states in neural networks are very different from the mode your PC
enters after some set period of inactivity. A conventional computer that has
gone to “sleep” is effectively in suspended animation, with all computational
activity frozen in time. And the age-old advice from the IT department to try
Bruder 13
“turning your computer off and then on again” when a PC gets glitchy is
tantamount to exposing your machine to a brief period of brain death. That
kind of sleep mode would do nothing to settle an unstable neural network.
And power cycling would simply reset the network and undo any prior
training, effectively giving the network a severe case of amnesia. In neural
networks as well as living creatures, a sleeplike state is not inactivity, but a
different kind of activity that is crucial to the proper functioning of neurons.
(Kenyon 2020)
Such posthumanist rhetorics gradually blur the differences between biolo-
gical and artificial systems—with the effect that rest is consolidated as yet
another form of cognitive labor.
It is this reframing of rest that fueled the hype around brief and inter-
mittent mindfulness exercises as a source of psychological resilience and
creativity. Corporate mindfulness trainings subsume the Buddhist practice
of mindfulness meditation to the logics of psychological resilience and thus
reflect “a disturbing utilitarianism—a partial adoption of asceticism that is
actually the antithesis of productivity’s insatiable appetite for self-
enhancement” (Gregg 2018, 122). Recent experiments in machine learning
and artificial intelligence contribute to this reframing of rest as a cognitive
technology. Against this backdrop, I would like to conclude by focusing on
the question of what it would take to escape these logics of necessity: can
machine learning systems be employed to experiment with alternative cog-
nitive subjectivities?
Conclusion
The current prominence of mindfulness trainings and apps suggests that
North Americans and Europeans increasingly think about mindfulness, and
about their lives more generally, in algorithmic terms. Technologies play an
important part in this process: as Ruckenstein and Schüll (2017) observe:
apps, trackers, and new, device-based pedagogies “bring machinic agency
to bear on human ways of defining, categorizing, and knowing life” (p.
269). At the same time, machine learning researchers experiment with
implementing coarse principles of cognition in the human brain in artificial
neural networks and thus prepare the ground for selling these as generative
models of how humans perceive and learn. Whereas the algorithmic mod-
eling of cognitive processes is conceived to augment artificial intelligence,
neural networks, or neuromorphic devices supposedly further our under-
standing of cognition in the brain.
14 Science, Technology, & Human Values XX(X)
In other words, both contemporary neuroscience and neuroscience-
inspired machine learning research appear to close in on algorithmic under-
standings of cognition in humans and machines. The related—by now
rather speculative—transpositions are not always and inherently proble-
matic. Yet, in their current form, they invite us to think about cognition
primarily as a problem of preventing pathology. This tendency is exacer-
bated in neuroscience-inspired, artificial neural networks. They provide
working models of cognitive labor under conditions of overload and lend
themselves well to experiments with technological fixes for the effects of
working at or over capacity.
Yet, Adam’s issues with work–life balance and the difficulties that
(Google’s) driverless cars have with differentiating between plastic bags
and stray deer appear comparable only within an information processing
framework that presupposes the inevitability of overload. While these tech-
niques and technologies may help alleviate its effects of working at or over
capacity, they simultaneously burden the worker, and the worker alone with
managing overload—and thus reframe rest as yet another form of labor.
Current exchanges between cognitive neuroscience and cognitive comput-
ing suggest that “there is no idle time, either for human or non-human
actors,” as media historian Markus Krajewski (2018) writes in his book
The Server. “A human servant is never physiologically inactive. The same
applies to a machine: as soon as it goes idle, it turns to maintenance tasks,
attending to its own treatment and care” (p. 346).
If we want to think beyond this reframing of rest as cognitive labor, we
need to situate and historicize the underlying epistemology. The current
interest in determining the algorithms of mindfulness is rooted in a reor-
ientation toward nonconscious cognitive processes in North American and
European cognitive neuroscience since the 1990s. It gradually drew atten-
tion to patterns of infrastructural activity in the brain and thus aligned our
understanding of biological cognition with contemporary paradigms of
information processing, exemplified in cloud computing (Bruder 2019).
Thinking about humans as information processors, however, implies
neither that we gear our entire lives toward managing overload that is
imposed nor that the search for algorithms of mindfulness needs to turn
into an endeavor of relentless optimization. The knowledge that contempo-
rary neuroscience produces, and that machine learning research selectively
perpetuates, lends itself well to unsettling old dichotomies, such as those
between attention and distraction or between task and rest. Rather than
resorting to this knowledge only to overcome pathologies that derive from
our social and informational environments, it might aid in exposing the
Bruder 15
frameworks that naturalize overload and define the inability, or unwilling-
ness, to succumb to it as pathological.
If, as I suggested earlier, the reframing of mind wandering as subcon-
scious information processing is a de-pathologizing gesture, this gesture
may also be understood as losing interest in perpetuating the notion of
psychopathology more generally. Could machine learning systems support
this process? In Cloud Ethics, Amoore (2020) writes that algorithms
“cannot be controlled via a limit point at the threshold of madness because
the essence of their logic is to generate that threshold, to adapt and to
modulate it over time” (p. 110). Machine learning algorithms internalize
and exhibit significant aspects of the rationalities that undergird them—they
“are never far from their conjoined histories with psychosis, neurosis,
trauma, and the imagination of the brain as a system” (p. 114; see also
Halpern 2014). At the same time, they generate new forms and models of
what is considered normal and abnormal, precisely because they merely
internalize some coarse principles and fragmentary sets of data that can only
ever be partial. Machine learning systems do not—in a traditional way—
understand what they are modeling and therefore potentially and acciden-
tally unsettle the very rationalities they were made to internalize.
I believe that studying and experimenting with these systems can con-
tribute to thinking beyond currently paradigmatic epistemologies of
machine learning, and toward diversifying or queering related, in North
American and European neuro-psychologies. The flipping of contrasts that
created an opening for de-pathologizing mind wandering might be a good
model for algorithmically unsettling the idea of pathological cognition. This
process demands continuous and recurring engagement with the technical-
ities of algorithmic systems and the knowledge practices they implement—
“continual, careful, collective, and always partial reinscriptions of a
cultural-technical situation in which we all find ourselves” (Philip, Irani,
and Dourish 2012, 5).
Author’s Note
Part of the research for this article was conducted during a fellowship granted by
the Institute for Advanced Studies “Media Cultures of Computer Simulation,”
Leuphana University Lüneburg.
Acknowledgments
I am extremely grateful to Rebecca Jablonsky, Nick Seaver, and Tero Karppi for
their feedback on earlier versions of this article and would like to thank the
16 Science, Technology, & Human Values XX(X)
anonymous reviewers as well as the participants of the panel “Attention,” held at the
4S Annual Meeting in New Orleans, for their very valuable comments and
suggestions.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research,
authorship, and/or publication of this article: The research that undergirds this paper
has been partly funded through the SNSF Sinergia Grant “Governing through
Design” (grant no. 189933).
ORCID iD
Johannes Bruder https://orcid.org/0000-0003-1749-8338
Notes
1. I am indebted to Michael Fisch who pointed me toward the significance of
working over capacity. In An Anthropology of the Machine, Fisch (2019) dis-
cusses the significance of “finessing the interval” for a system working over
capacity, such as the Tokyo Commuter Train network. Human brains and com-
muter trains make for unlikely bed fellows, but it is this metaphorical paralleli-
zation of human and machine that sits at the heart of this article.
2. In this regard, meditation apps fall into a class of technics that include self-
trackers that monitor and automatically intervene in the behavior of individuals
(Berg 2017; Schüll 2016), apps or gadgets that “cancel” unwanted frequencies
through adding white or pink noise (Hagood 2019), or cognitive devices that
override corporeal senses and biological functions, as Chia (2019) offers,
“through an imagined master code to program the mind” (p. 6). Further, one
might want to include pharmacological cognitive enhancement with drugs such
as Adderall and Modafinil, which have been particularly prominent among stu-
dents and academics (Coveney, Gabe, and Williams 2011; Vargo and Petróczi
2016; Vrecko 2015), and the microdosing of classic psychedelics, which has
gained traction among creative workers as well as in the traditional (white)
middle class (Johnstad 2018). An interdisciplinary group of researchers from the
University of California and the University of Alabama found that users expe-
rienced positive effects on sociability and “praised microdosing for its ability to
increase productivity and satisfaction at their work, which is often a primary
concern among the middle class. They reported being more focused, creative,
Bruder 17
and energetic, especially on the days of and after microdosing” (Webb, Copes,
and Hendricks 2019, 37). Psychologists Vince Polito and Richard Stevenson
(2019), however, observed that the positive effects reported by microdosers are
often accompanied by and occlude a tendency toward absorption and
neuroticism.
3. Following Callard and colleagues (2013), I will from now on refer to this class of
phenomena as self-generated mental activity and only use one of the alternative
terms in case I refer to a specific aspect of self-generated mental activity.
4. Experienced mindfulness practitioners appear to be able to “change the relation-
ship with the resting state and experience the stream of stimulus-independent
mental content in an adaptive way” (Vago and Zeidan 2016, 97). Their capacity
to tell when their mind begins to wander provides experimenters with a cue to
start monitoring the interactions of large-scale brain systems when mind wander-
ing is deliberate and goal-directed.
5. Unconstrained thought has therefore also been compared to film editing, with the
hippocampus—a central element of the default mode network—as editor: it
directs the cuts that essentially determine how individuals remember specific
events and conceive of experience as episodes in time (Ben-Yakov and Henson
2018). The minds of people with hippocampal damage provide a model of where
such processes fail. They appear to wander primarily in the present, which means
that mind wandering does not occur more or less often than in healthy individ-
uals, but it apparently lacks “flexible, episodic, and scene-based” qualities.
Instead, mind wandering appears “abstract, semanticized, verbal”—and thus
entirely similar to ways of thinking that many of us have been trained to enforce
on the job (McCormick et al. 2018).
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Author Biography
Johannes Bruder studies the history and present of decision-making systems and
how these encode psychological categories, sociological models, artistic practices,
and speculative designs. His first book Cognitive Code. Post-anthropocentric Intel-
ligence and the Infrastructural Brain (MQUP, 2019) is based on fieldwork in
neuroscience laboratories and provides deep insights into the bio-politics of con-
temporary machine learning. He is the interim head of the Institute of Experimental
Design and Media Cultures (IXDM), FHNW Academy of Art and Design and
affiliated with Milieux - Institute for Art, Culture, Technology at Concordia Uni-
versity, Montreal, Quebec, Canada.