Mindfulness Meditators Show Enhanced Accuracy and Different Neural Activity During Working Memory

Bailey, Neil W.; Freedman, Gabrielle; Raj, Kavya; Spierings, Kara N; Piccoli, Lara R.; Sullivan, Caley; Chung, Sung Wook; Hill, Aron T.; Rogasch, Nigel C.; Fitzgerald, Paul B.

doi:10.1007/s12671-020-01393-8

Cited by 22 publications

(19 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is also worth noting that the difference in the Pe might overlap with our previous research using different tasks. This research demonstrated that meditators have enhanced frontal P3 amplitudes in a Go/ Nogo task (Bailey et al, 2019b), and enhanced frontal positive activity during the same time window following probe stimuli in the Sternberg working memory task (Bailey et al, 2020) and following correctly encoded stimuli in the Nback task (Wang et al, 2020). The Pe and P3 have been suggested to reflect similar underlying processes (a neural reaction to a stimulus in the case of the Go/Nogo and working memory tasks, and a neural reaction to participant response in the case of the Pe, regardless of whether it is correct or incorrect) (Ridderinkhof et al, 2009).…”

Section: Discussionmentioning

confidence: 67%

Meditators Probably Show Increased Behaviour-Monitoring Related Neural Activity

et al. 2023

View full text Add to dashboard Cite

Objectives Mindfulness meditation is associated with better attention function. Performance monitoring and error-processing are important aspects of attention. We investigated whether experienced meditators showed different neural activity related to performance monitoring and error-processing. Previous research has produced inconsistent results. This study used more rigorous analyses and a larger sample to resolve the inconsistencies. Method We used electroencephalography (EEG) to measure the error-related negativity (ERN) and error positivity (Pe) following correct and incorrect responses to a Go/Nogo task from 27 experienced meditators and 27 non-meditators. Results No differences were found in the ERN (all p > 0.05). Meditators showed larger global field potentials (GFP) in the Pe after correct responses and errors, indicating stronger neural responses (p = 0.019, FDR-p = 0.152, np2 = 0.095, BFincl = 2.691). This effect did not pass multiple comparison controls. However, single-electrode analysis of the Pe did pass multiple comparison controls (p = 0.002, FDR-p = 0.016, np2 = 0.133, BFincl = 220.659). Meditators also showed a significantly larger Pe GFP for errors, which would have passed multiple comparison controls, but was not a primary analysis (p = 0.003, np2 = 0.149, BF10 = 9.999). Conclusions Meditation may strengthen neural responses related to performance monitoring. However, these strengthened neural responses were not specific to error monitoring (although the error-related Pe may be more sensitive to group differences than the correct response Pe). These conclusions remain tentative, because the single-electrode analysis passed multiple comparison controls, but the analysis including all electrodes did not. Preregistration This study was not preregistered.

show abstract

Section: Discussionmentioning

confidence: 67%

Meditators Probably Show Increased Behaviour-Monitoring Related Neural Activity

et al. 2023

View full text Add to dashboard Cite

show abstract

“…We chose experimental manipulations that are well established to provide differentiation of neural activity in the comparison of two conditions, which assesses the real-world applicability of the cleaning pipelines (Clayson, Baldwin, et al, 2021). We assessed the variance explained by a comparison of alpha oscillatory power between the working memory delay and probe periods of the Sternberg task (Bailey et al, 2020) and alpha power between EO and EC resting-state data (computation of which is described in the Supplementary Materials, section 4, pages 36-53). Within these metrics, higher values are likely to reflect better performance, with cleaning pipelines that provided higher values providing a better chance to detect statistically significant differences within an experiment.…”

Section: Methodsmentioning

confidence: 99%

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and Application to Oscillations

Bailey

Biabani

Hill

et al. 2022

Preprint

View full text Add to dashboard Cite

Electroencephalographic (EEG) data is typically contaminated with non-neural artifacts which can confound the results of experiments. Artifact cleaning approaches are available, but often require time-consuming manual input and significant expertise. Advancements in artifact cleaning often only address a single artifact, are only compared against a small selection of pre-existing methods, and seldom assess whether a proposed advancement improves experimental outcomes. To address these issues, we developed RELAX (the Reduction of Electroencephalographic Artifacts), an automated EEG cleaning pipeline implemented within EEGLAB that reduces all artifact types. RELAX cleans continuous data using Multiple Wiener filtering [MWF] and/or wavelet enhanced independent component analysis [wICA] applied to artifacts identified by ICLabel [wICA_ICLabel]). Several versions of RELAX were tested using three datasets containing a mix of cognitive and resting recordings (N = 213, 60 and 23 respectively). RELAX was compared against six commonly used EEG cleaning approaches across a wide range of artifact cleaning quality metrics, including signal-to-error and artifact-to-residue ratios, measures of remaining blink and muscle activity, and the amount of variance explained by experimental manipulations after cleaning. RELAX with MWF and wICA_ICLabel showed amongst the best performance for cleaning blink and muscle artifacts while still preserving neural signal. RELAX with wICA_ICLabel (and no MWF) may perform better at detecting the effect of experimental manipulations on alpha oscillations in working memory tasks. The pipeline is easy to implement in MATLAB and freely available on GitHub. Given its high cleaning performance, objectivity, and ease of use, we recommend RELAX for data cleaning across EEG studies.

show abstract

“…Mindfulness practice also has positive effects on attention and working memory. Some studies have found that mindfulness training can improve children’s sustained attention ( Tarrasch, 2018 ), protect working memory capacity ( Bailey et al, 2020 ), and significantly improve working memory performance ( Chambers et al, 2008 ). It can thus be inferred that high trait mindfulness may also have positive impacts on attention and working memory.…”

Section: Introductionmentioning

confidence: 99%

The Relationship Among Trait Mindfulness, Attention, and Working Memory in Junior School Students Under Different Stressful Situations

Yang

Yan

et al. 2021

Front. Psychol.

View full text Add to dashboard Cite

Attention and working memory are important cognitive functions that affect junior school students’ learning ability and academic performance. This study aimed to explore the relationships among trait mindfulness, attention, and working memory and to explore differences in performance between a high trait mindfulness group and a low one in attention and working memory under different stressful situations. In study 1, 216 junior school students completed the Five Facet Mindfulness Questionnaire (FFMQ), and their attention and working memory were tested in a non-pressure situation. The results showed that attention had a partial mediating effect between mindfulness and working memory. In study 2, the high trait mindfulness group and the low one were tested for attention and working memory under situations with single and multiple pressures. One notable result was that the attention and working memory performances of the high mindfulness group were all significantly higher than those of the low mindfulness group in every stress situation (no stress, single stress, and multiple stresses). Other important results were that trait mindfulness moderates the relationship between stress and attention and between stress and working memory. These results suggest that trait mindfulness has a protective effect in the process by which various stresses affect attention and working memory. These findings indicate that trait mindfulness is an important psychological quality that affects the attention and working memory of junior school students, and it is also an important psychological resource for effectively coping with the impact of stress on attention and working memory. Therefore, it is possible that improving trait mindfulness may help to improve junior school students’ attention and working memory and enable them to cope better with stress, thereby helping to improve academic performance. This research is of great significance for understanding the association between key psychological qualities and cognitive functions in different stressful situations. These findings also provide insight for future studies in educational psychology.

show abstract

Mindfulness Meditators Show Enhanced Accuracy and Different Neural Activity During Working Memory

Cited by 22 publications

References 99 publications

Meditators Probably Show Increased Behaviour-Monitoring Related Neural Activity

Meditators Probably Show Increased Behaviour-Monitoring Related Neural Activity

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and Application to Oscillations

The Relationship Among Trait Mindfulness, Attention, and Working Memory in Junior School Students Under Different Stressful Situations

Contact Info

Product

Resources

About