The Algorithms of Mindfulness.
Johannes Bruder
Science, Technology & Human Values June 22, 2021 DOI: 10.1177/01622439211025632 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Topics | Meditation |
| Keywords | Artificial intelligence Sleep Attention Rest Cognitive neuroscience Information overload |
| Key points | Argues that corporate mindfulness programs and experimental artificial neural networks both reframe rest as a means of optimizing cognition, drawing on the same neuroscience research about resting brains. The author contends that these algorithmic techniques are mainly designed to prevent psychopathologies in which stress is treated as the driving force of success, and proposes using machine learning to unsettle the concept of pathological cognition itself. |
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 corporate 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 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.
Comparable studies
Other theoretical and historical papers on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Mindfulness: A proposed operational definition. | 2004 | Theoretical or philosophical paper | |
| Mechanisms of mindfulness | 2005 | Theoretical or philosophical paper | |
| How Does Mindfulness Meditation Work? Proposing Mechanisms of Action From a Conceptual and Neural Perspective | 2011 | Theoretical or philosophical paper | |
| Mindfulness-based cognitive therapy for depression | 2006 | Theoretical or philosophical paper | |
| Self-awareness, self-regulation, and self-transcendence (S-ART): a framework for understanding the neurobiological mechanisms of mindfulness | 2012 | Theoretical or philosophical paper |
Citations in the library
Cited by 2
Cites 16
- A default mode of brain function Proceedings of the National Academy of Sciences January 16, 2001
- Mind the Hype: A Critical Evaluation and Prescriptive Agenda for Research on Mindfulness and Meditation Perspectives on Psychological Science October 10, 2017
- Distant from input: Evidence of regions within the default mode network supporting perceptually-decoupled and conceptually-guided cognition Neuroimage January 12, 2018
- The Decoupled Mind: Mind-wandering Disrupts Cortical Phase-locking to Perceptual Events Journal of Cognitive Neuroscience April 17, 2014
- The brain on silent: mind wandering, mindful awareness, and states of mental tranquility Annals of the New York Academy of Sciences June 1, 2016
and 11 more in the library