ArticleCognitive, affective & behavioral neuroscience2026
When attention falters: Brain, breathing, and behavioral signals of lapses in interoceptive attention.
Article in Cognitive, affective & behavioral neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Update of
Authors and funding
7 authors.
Funding
Abstract
Mind‑body practices, such as meditation and yoga, involve paying attention to breathing sensations. During these practices, individuals report "interoceptive lapses," moments when attention drifts away from internal bodily sensations. While lapses in attention to the external world have been widely studied, little is known about the physiological and neural mechanisms of interoceptive lapses. Interoceptive lapses may share markers with exteroceptive lapses-such as reaction time variability and default-mode network (DMN) connectivity-but may also depend on distinct brain systems and breathing physiology. We examined behavioral, physiological, and neural signals preceding lapses in a sample of 93 adolescents enriched for GAD and depression symptoms. Participants performed a 20-min breath counting task in the fMRI scanner with simultaneous breath recordings. Lapses were defined as moments when counting errors occurred. The sample was split into training and validation sets to test machine learning models predicting attentional lapses. The strongest predictors were timing and variability of button responses (AUCs > 0.75). Breathing variability and breathing-behavior synchronization showed smaller but generalizable predictive value (AUCs < 0.65). Whole-brain connectivity models also predicted lapses (AUC ≈ 0.65), incorporating the DMN, dorsal and ventral attention, and somatomotor networks. Furthermore, models that included brain connectivity marginally outperformed behavior-only models. Comparisons to previous exteroceptive findings indicate some common markers (e.g., reaction time variability) and some unique markers (e.g., selective perceptual coupling with attentional networks). Although limited by the clinical sample and lack of a control task, these results highlight brain-body markers of interoceptive attention that may inform real-time monitoring during mind-body interventions.
Indexed as
Identifiers
42332286What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.