Intrinsic neural timescales exhibit different lengths in distinct meditation techniques
Bibliographic record
Abstract
Meditation encompasses a range of practices employing diverse induction techniques, each characterized by a distinct attentional focus. In Mantra meditation, for instance, practitioners direct their attention narrowly to a given sentence that is recursively repeated, while other forms of meditation such as Shoonya meditation are induced by a wider attentional focus. Here we aimed to identify the neural underpinnings and correlates associated with this spectrum of distinct attentional foci. To accomplish this, we used EEG data to estimate the brain's intrinsic neural timescales (INTs), that is, its temporal windows of activity, by calculating the Autocorrelation Window (ACW) of the EEG signal. The autocorrelation function measures the similarity of a timeseries with a time-lagged version of itself by correlating the signal with itself on different time lags, consequently providing an estimation of INTs length. Therefore, through using the ACW metric, our objective was to explore whether there is a correspondence between the length of the brain's temporal windows of activity and the width of the attentional scope during various meditation techniques. This was performed on three groups of highly proficient practitioners belonging to different meditation traditions, as well as a meditation-naïve control group. Our results indicated that practices with a wider attentional focus, like Shoonya meditation, exhibit longer ACW durations compared to practices requiring a narrower attentional focus, such as Mantra meditation or body-scanning Vipassana meditation. Together, we demonstrated that distinct meditation techniques with varying widths of attentional foci exhibit unique durations in their brain's INTs. This may suggest that the width of the attentional scope during meditation relates and corresponds to the width of the brain's temporal windows in its neural activity. SIGNIFICANCE STATEMENT: Our research uncovered the neural mechanisms that underpin the attentional foci in various meditation techniques. We revealed that distinct meditation induction techniques, featured by their range of attentional widths, are characterized by varying lengths of intrinsic neural timescales (INTs) within the brain, as measured by the Autocorrelation Window function. This finding may bridge the gap between the width of attentional windows (subjective) and the width of the temporal windows in the brain's neural activity (objective) during different meditation techniques, offering a new understanding of how cognitive and neural processes are related to each other. This work holds significant implications, especially in the context of the increasing use of meditation in mental health and well-being interventions. By elucidating the distinct neural foundations of different meditation techniques, our research aims to pave the way for developing more tailored and effective meditation-based treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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 teacher head, 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".