Attention as regulation
Attention can be viewed as a stabilizing process: a way of shaping how disturbances enter, spread through, and affect a cognitive system.
Critical Attention Systems
Bridge to Contemplative Research
A careful research path connecting adaptive regulation, trained attention, disturbance recovery, and contemplative practice.
Research bridge, not doctrine
What changes when attention is treated not only as selection, but as regulation?
Critical Attention Systems explores how computational models of adaptive regulation can offer new language for studying attention, stability, disturbance, recovery, and training in contemplative practice.
A careful framing
Computational tools for trained attention
This work asks whether ideas such as attentional stability, recovery from disturbance, and reduced reactivity can be studied with transparent simulation tools while remaining modest and respectful toward contemplative traditions.
The aim is not to reduce Buddhism to artificial intelligence, and not to use artificial intelligence to validate Buddhist doctrine. The aim is to ask whether models of regulation, disturbance, stability, and adaptive attention can support a useful dialogue between contemplative studies, cognitive science, and artificial-agent research.
Three entry points
Practice as adaptive training
Repeated cycles of regulation, switching, recovery, and reduced reactivity may gradually make a system less easily disrupted.
Models, not metaphysics
The simulations are research tools. They are not claims about enlightenment, consciousness, or the metaphysical status of mind.
Questions the work can ask
Practice-relevant dynamics
- StabilityHow does an attentional system recover after disturbance?
- ReactivityCan repeated training reduce the regulatory cost of disruption?
- SwitchingWhen does moving between regulatory modes help or hurt stability?
- HindrancesCan distraction, agitation, dullness, or craving be modeled as disturbance patterns without reducing them to a single mechanism?
Next direction
Mindfulness for AI
A future line of work will examine whether artificial agents can benefit from explicit training-like dynamics: not mindfulness as a slogan, but computational analogues of attention stabilization, disturbance monitoring, recovery, and reduced reactivity.
© Veronique Ziegler · Critical Attention Systems