Bibliographic record
Abstract
The number of mindfulness-based wellness promotion programs offered by institutions, by governments, and through mobile apps has grown exponentially in the last decade. However, the scientific understanding of what mindfulness is and how it works is still evolving. Here, I focus on 2 common mindfulness practices: focused attention (FA) and open monitoring (OM). First, I summarize what is known about FA and OM meditation at the psychological level. While they share similar emotion regulation goals, they differ in terms of some of their attention regulation goals. Second, I turn to the neuroscientific literature, showing that FA meditation is associated with consistent activations of cortical control network regions and deactivations of cortical default network regions. In contrast, OM meditation seems to be most consistently associated with changes in the functional connectivity patterns of subcortical structures, including the basal ganglia and cerebellum. Finally, I present a novel account of the mental changes that occur during FA and OM meditation as understood from within the Dynamic Framework of Thought-a conceptual framework that distinguishes between deliberate and automatic constraints on thought. Although deliberate self-regulation processes are often emphasized in scientific and public discourse on mindfulness, here I argue that mindfulness may primarily involve changes in automatic constraints on thought. In particular, I argue that mindfulness reduces the occurrence of automatized sequences of mental states or habits of thought. In this way, mindfulness may increase the spontaneity of thought and reduce automatically constrained forms of thought such as rumination and obsessive thought.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".