Measuring Mindfulness: A Psychophysiological Approach

From Confound to Clinical Tool: Mindfulness and the Observer Effect in Research and Therapy

The observer effect (OE), the idea that observing a phenomenon changes it, has important implications across scientific disciplines involving measurement and observation. While often viewed as a confounding variable to control for, this paper argues that the OE should be seriously accounted for, explored, and systematically leveraged in research and clinical settings. Specifically, mindfulness practices that cultivate present-moment, nonjudgmental awareness are proposed as a platform to account for, explore, and intentionally harness the OE. In research contexts, mindfulness training may allow participants to provide more precise self-reports by minimizing reactive biases that perturb the observed phenomena. Empirical evidence suggests that mindfulness enhances interoceptive awareness and reduces automatic judgment, potentially increasing measurement sensitivity, specificity, and validity. Clinically, psychotherapies often aim to make unconscious patterns explicitly observable to the client, capitalizing on the transformative potential of observation. Mindfulness directly cultivates this capacity for meta-awareness, allowing individuals to decenter from rigid cognitive-emotional patterns fueling psychopathology. Rather than avoiding unpleasant experiences such as cravings or anxiety, mindfulness guides individuals to simply observe these phenomena, reducing identification and reactivity. Mindfulness practices may leverage components of the OE, facilitating lasting psychological change. To further study the OE, developing an OE index to code observer influence is proposed. Overall, this paper highlights the ubiquity of the OE and advocates developing methods to intentionally account for and apply observer influences across research and therapeutic contexts.

Enhancing Equanimity With Noninvasive Brain Stimulation: A Novel Framework for Mindfulness Interventions

Mindfulness has gained widespread recognition for its benefits for mental health, cognitive performance, and well-being. However, the multifaceted nature of mindfulness, which encompasses elements such as attentional focus, emotional regulation, and present-moment awareness, complicates its definition and measurement. A key component that may underlie its broad benefits is equanimity-the ability to maintain an open and nonreactive attitude toward all sensory experiences. Empirical research suggests that mindfulness works through a combination of top-down attentional control and bottom-up sensory and emotional processes and that equanimity's role in regulating those bottom-up processes drives the psychological and physiological benefits, making it a promising target for both theoretical and practical exploration. Given these findings, the development of interventions that specifically augment equanimity could improve the impact of mindfulness practices. Research into noninvasive brain stimulation (NIBS) suggests that it is a potential tool for altering neural circuits involved in mindfulness. However, most NIBS studies reported to date have focused on improving cognitive control systems and have left equanimity relatively unexplored. Preliminary findings from focused ultrasound interventions targeting the posterior cingulate cortex suggest that NIBS can directly facilitate equanimity by inhibiting self-referential processing in the default mode network to promote a more present-centered state of awareness. Future research should prioritize the integration of NIBS with well-defined mindfulness training protocols, focusing on equanimity as a core target. This approach could provide a novel framework for advancing both contemplative neuroscience and clinical applications, offering new insights into the mechanisms of mindfulness and refining NIBS methodologies to support individualized, precision wellness interventions.

Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline

This study focuses on classifying multiple sessions of loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data. This novel study focuses on using multiple sessions of EEG data from a single individual to train a machine learning pipeline, and then using a new session data from the same individual for the classification. Here, two meditation techniques, LKM-Self and LKM-Others were compared with non-meditation EEG data for 12 participants. Among many tested, three BCI pipelines we built produced promising results, successfully detecting features in meditation/ non-meditation EEG data. While testing different feature extraction algorithms, a common neural network structure was used as the classification algorithm to compare the performance of the feature extraction algorithms. For two of those pipelines, Common Spatial Patterns (CSP) and Short Time Fourier Transform (STFT) were successfully used as feature extraction algorithms where both these algorithms are significantly new for meditation EEG. As a novel concept, the third BCI pipeline used a feature extraction algorithm that fused the features of CSP and STFT, achieving the highest classification accuracies among all tested pipelines. Analyses were conducted using EEG data of 3, 4 or 5 sessions, totaling 3960 tests on the entire dataset. At the end of the study, when considering all the tests, the overall classification accuracy using SCP alone was 67.1%, and it was 67.8% for STFT alone. The algorithm combining the features of CSP and STFT achieved an overall classification accuracy of 72.9% which is more than 5% higher than the other two pipelines. At the same time, the highest mean classification accuracy for the 12 participants was achieved using the pipeline with the combination of CSP STFT algorithm, reaching 75.5% for LKM-Self/ non-meditation for the case of 5 sessions of data. Additionally, the highest individual classification accuracy of 88.9% was obtained by the participant no. 14. Furthermore, the results showed that the classification accuracies for all three pipelines increased with the number of training sessions increased from 2 to 3 and then to 4. The study was successful in classifying a new session of EEG meditation/ non-meditation data after training machine learning algorithms using a different set of session data, and this achievement will be beneficial in the development of algorithms that support meditation.

Investigating the effect of mindfulness training for stress management in military training: the relationship between the autonomic nervous system and emotional regulation

BACKGROUND

Military personnel face an increased risk of developing mental disorders owing to the stressful environments they encounter. Effective stress management strategies are crucial to mitigate this risk. Mindfulness training (MT) is promising as a stress management approach in such demanding settings. This study uses a quantitative approach to investigate the impact of MT on the relationship between the autonomic nervous system (ANS) and emotional regulation.

METHODS

The study evaluated the effectiveness of MT in reducing stress among 86 military personnel. Participants were divided into two groups: MT (n = 42) and non-MT (n = 38). The study compared the two groups using measures of heart rate variability (HRV), a reliable indicator of ANS activity.

RESULTS

The MT group exhibited a significant increase in HRV (14.4%, p = 0.001) and alpha asymmetry (AA) in the frontal lobe (45.7%, p < 0.001) compared to the non-MT group. Notably, the MT group achieved significantly higher scores on the parachute landing fall (PLF) training performance (p < 0.001). These improvements in HRV, AA, and PLF performance were strongly correlated. Furthermore, AA fully mediated the relationship between HRV and PLF training performance.

CONCLUSIONS

The findings suggest that MT has a positive impact on stress resilience, potentially by mitigating anxiety and attention deficits induced by extreme stressors. These positive effects are facilitated by concurrent modulation of the frontal cortex and autonomic nervous system. Our findings provide insight into the neural mechanisms behind MT-induced stress reduction from the perspective of neuromodulation.

Precision Psychobiotics for Gut-Brain Axis Health: Advancing the Discovery Pipelines to Deliver Mechanistic Pathways and Proven Health Efficacy

Advancing microbiome-gut-brain axis science requires systematic, rational and translational approaches to bridge the critical knowledge gaps currently preventing full exploitation of the gut microbiome as a tractable therapeutic target for gastrointestinal, mental and brain health. Current research is still marked by many open questions that undermine widespread application to humans. For example, the lack of mechanistic understanding of probiotic effects means it remains unclear why even apparently closely related strains exhibit different effects in vivo. For the therapeutic application of live microbial psychobiotics, consensus on their application as adjunct treatments to conventional neuromodulators, use in unmedicated populations or in at-risk cohorts with sub-clinical symptomatology is warranted. This missing information on both sides of the therapeutic equation when treating central nervous system (CNS) conditions makes psychobiotic research challenging, especially when compared to other pharmaceutical or functional food approaches. Expediting the transition from positive preclinical data to proven benefits in humans includes interpreting the promises and pitfalls of animal behavioural assays, as well as navigating mechanism-informed decision making to select the right microbe(s) for the job. In this review, we consider how these decisions can be supported in light of information accrued from a range of clinical studies across healthy, at-risk and pathological study populations, where specific strains have been evaluated in the context of gastrointestinal physiology, brain function and behaviour. Examples of successful, partial and unsuccessful translation from bench to bedside are considered. We also discuss the developments in in silico analyses that have enhanced our understanding of the gut microbiome and that have moved research towards pinpointing the host-microbe interactions most important for optimal gut-brain axis function. Combining this information with knowledge from functional assays across in vitro and ex vivo domains and incorporating model organisms can prime the discovery pipelines with the most promising and rationally selected psychobiotic candidates.

Electroencephalography microstates highlight specific mindfulness traits

The present study aimed to investigate the spontaneous dynamics of large-scale brain networks underlying mindfulness as a dispositional trait, through resting-state electroencephalography (EEG) microstates analysis. Eighteen participants had attended a standardized mindfulness-based stress reduction training (MBSR), and 18 matched waitlist individuals (CTRL) were recorded at rest while they were passively exposed to auditory stimuli. Participants' mindfulness traits were assessed with the Five Facet Mindfulness Questionnaire (FFMQ). To further explore the relationship between microstate dynamics at rest and mindfulness traits, participants were also asked to rate their experience according to five phenomenal dimensions. After training, MBSR participants showed a highly significant increase in FFMQ score, as well as higher observing and non-reactivity FFMQ sub-scores than CTRL participants. Microstate analysis revealed four classes of microstates (A-D) in global clustering across all subjects. The MBSR group showed lower duration, occurrence and coverage of microstate C than the control group. Moreover, these microstate C parameters were negatively correlated to non-reactivity sub-scores of FFMQ across participants, whereas the microstate A occurrence was negatively correlated to FFMQ total score. Further analysis of participants' self-reports suggested that MBSR participants showed a better sensory-affective integration of auditory interferences. In line with previous studies, our results suggest that temporal dynamics of microstate C underlie specifically the non-reactivity trait of mindfulness. These findings encourage further research into microstates in the evaluation and monitoring of the impact of mindfulness-based interventions on the mental health and well-being of individuals.

Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions

While a very few studies have been conducted on classifying loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data for a single session, there are no such studies conducted for multiple session EEG data. Thus, this study aims at classifying existing raw EEG meditation data on single and multiple sessions to come up with meaningful inferences which will be highly beneficial when developing algorithms that can support meditation practices. In this analysis, data have been collected on Pre-Resting (before-meditation), Post-Resting (after-meditation), LKM-Self and LKM-Others for 32 participants and hence allowing us to conduct six pairwise comparisons for the four mind tasks. Common Spatial Patterns (CSP) is a feature extraction method widely used in motor imaginary brain computer interface (BCI), but not in meditation EEG data. Therefore, using CSP in extracting features from meditation EEG data and classifying meditation/non-meditation instances, particularly for multiple sessions will create a new path in future meditation EEG research. The classification was done using Linear Discriminant Analysis (LDA) where both meditation techniques (LKM-Self and LKM-Others) were compared with Pre-Resting and Post-Resting instances. The results show that for a single session of 32 participants, around 99.5% accuracy was obtained for classifying meditation/Pre-Resting instances. For the 15 participants when using five sessions of EEG data, around 83.6% accuracy was obtained for classifying meditation/Pre-Resting instances. The results demonstrate the ability to classify meditation/Pre-Resting data. Most importantly, this classification is possible for multiple session data as well. In addition to this, when comparing the classification accuracies of the six mind task pairs; LKM-Self, LKM-Others and Post-Resting produced relatively lower accuracies among them than the accuracies obtained for classifying Pre-Resting with the other three. This indicates that Pre-Resting has some features giving a better classification indicating that it is different from the other three mind tasks.

Questionnaires based on natural language processing elicit immersive ruminative thinking in ruminators: Evidence from behavioral responses and EEG data

Rumination is closely related to mental disorders and can thus be used as a marker of their presence or a predictor of their development. The presence of masking and fabrication in psychological selection can lead to inaccurate detection of psychological disorders. Human language is considered crucial in eliciting specific conscious activities, and the use of natural language processing (NLP) in the development of questionnaires for psychological tests has the potential to elicit immersive ruminative thinking, leading to changes in neural activity. Electroencephalography (EEG) is commonly used to detect and record neural activity in the human brain and is sensitive to changes in brain activity. In this study, we used NLP to develop a questionnaire to induce ruminative thinking and then recorded the EEG signals in response to the questionnaire. The behavioral results revealed that ruminators exhibited higher arousal rates and longer reaction times, specifically in response to the ruminative items of the questionnaire. The EEG results showed no significant difference between the ruminators and the control group during the resting state; however, a significant alteration in the coherence of the entire brain of the ruminators existed while they were answering the ruminative items. No differences were found in the control participants while answering the two items. These behavioral and EEG results indicate that the questionnaire elicited immersive ruminative thinking, specifically in the ruminators. Therefore, the questionnaire designed using NLP is capable of eliciting ruminative thinking in ruminators, offering a promising approach for the early detection of mental disorders in psychological selection.

Linking University Students' Mindfulness to Positive Adjustment Amidst COVID-19 Pandemic: a 6-month Cross-Lagged Panel Design

Objectives

In the face of a global pandemic, research on wellness-fostering resources is urgently needed, especially with longitudinal designs and diverse samples. According to the mindfulness-to-meaning theory and broaden-and-build theory, this study examined the reciprocal associations among a group of Chinese university students' trait mindfulness, positive and negative affect, and use of positive coping strategies, including positive reappraisal, planning, and seeking of emotional support during the COVID-19 pandemic.

Methods

Participants were 247 Hong Kong university students (M _age = 20.96, SD = 2.38; female = 86%) who completed survey measures of mindfulness, positive and negative affect, and positive coping strategies at baseline and 6-month follow-up. Data were analysed using a cross-lagged panel design, controlling for participants' age.

Results

The proposed reciprocal model exhibited an excellent fit with the data. There was a reciprocal association between trait mindfulness and positive affect over time. However, no significant reciprocal effect was found among mindfulness, negative affect, and positive coping strategies.

Conclusions

Theoretically, the current findings extended the two theories to a non-Western population during a critical time and suggested a long-term reciprocal association between positive affect and mindfulness. Our study provided important insight into university students' positive well-being during COVID-19 and demonstrated the wellness-fostering effect of mindfulness.

Contemplative Practices Behavior Is Positively Associated with Well-Being in Three Global Multi-Regional Stanford WELL for Life Cohorts

Positive associations between well-being and a single contemplative practice (e.g., mindfulness meditation) are well documented, yet prior work may have underestimated the strength of the association by omitting consideration of multiple and/or alternative contemplative practices. Moreover, little is known about how contemplative practice behavior (CPB) impacts different dimensions of well-being. This study investigates the relationship of CPB, consisting of four discrete practices (embodied somatic-observing, non-reactive mindfulness, self-compassion, and compassion for others), with multiple dimensions of well-being. As with other canonical lifestyle behaviors, multiple contemplative practices can be integrated into one's daily routine. Thus, it is critical to holistically consider these behaviors, extending them beyond a simple uni-dimensional measure (e.g., daily mindfulness meditation practice). We developed an integrative measure of four types of contemplative practice and found it to be significantly associated with a multi-dimensional measure of well-being. Importantly, our findings were from three large global multi-regional cohorts and compared against better-understood lifestyle behaviors (physical activity). Data were drawn from California/San Francisco Bay Area, (n = 6442), Hangzhou City (n = 10,268), and New Taipei City (n = 3033). In all three cohorts, we found statistically significant (p < 0.05) positive associations between CPB and well-being, both overall and with all of the constituent domains of well-being, comparable to or stronger than the relationship with physical activity across most well-being outcomes. These findings provide robust and cross-cultural evidence for a positive association between CPB and well-being, illuminate dimensions of well-being that could be most influenced by CPB, and suggest CPB may be useful to include as part of fundamental lifestyle recommendations for health and well-being.

The Effectiveness of Immersive Virtual Reality (VR) Based Mindfulness Training on Improvement Mental-Health in Adults: A Narrative Systematic Review

OBJECTIVE

In recent years, digital techniques, such as virtual reality (VR) has been employed in tandem with more traditional psychological interventions. The aim of this study is to investigate whether VR-based mindfulness training can improve mental health outcomes, and notably mindfulness levels amongst adults. In addition, this review seeks to summarise the various designed VR scenarios, as well as those elements around VR that may assist people in practising mindfulness and meditation.

METHODS

The search for eligible studies for inclusion was conducted via the following databases: the Applied Social Science Index & Abstract (ASSIA), PsychINFO, Medline, EMBASE, and the Web-of-Science Core Collection. Only experimental studies were eligible for inclusion, and specifically, those that compared the effectiveness of mindfulness training using immersive VR (on the one hand) with a control condition.

RESULTS

This search generated 2523 articles published between 2016 and 2022, and of these, 106 were assessed for eligibility. This review included seven studies, with a collective total of 798 participants. VR-based mindfulness training has been shown to be more effective than conventional mindfulness - it improves levels of mindfulness and meditation experience; but also shown to reduce anxiety, depression, improve sleep quality, emotion regulation, and generate mood improvement. VR-based mindfulness training frequently contains natural 'environmentally relevant' elements, such as forest, grassland, caves, sea, etc. CONCLUSIONS: This review suggests that using VR to assist mindfulness training is an effective and innovative way to improve mental health conditions within the adult population. Further directions and limitations are discussed.

Comparing impacts of meditation training in focused attention, open monitoring, and mindfulness-based cognitive therapy on emotion reactivity and regulation: Neural and subjective evidence from a dismantling study

Commonly conducted mindfulness‐based trainings such as Mindfulness‐based Stress Reduction (MBSR) and Mindfulness‐based Cognitive Therapy (MBCT) highlight training in two key forms of meditation: focused attention (FA) and open monitoring (OM). Largely unknown is what each of these mindfulness practices contributes to emotional and other important training outcomes. This dismantling trial compared the effects of structurally equivalent trainings in MBCT, FA, and OM on neural and subjective markers of emotional reactivity and regulation among community adults, with the aim to better understand which forms of training represent active ingredients in mindfulness trainings. Participants with varying levels of depressive symptoms were randomized to one of the three trainings. Before and after each 8‐week training, N = 89 participants completed a modified version of the Emotional Reactivity and Regulation Task while electroencephalographic (EEG) and self‐reported emotional responses to negative, positive, and neutral photographic images were collected. Examination of EEG‐based frontal alpha band asymmetry during passive viewing (reactivity) and active regulation phases of the task showed that FA and MBCT trainings produced significant leftward hemispheric shifts in frontal alpha asymmetry, commonly associated with a shift toward approach‐based positive affect. Self‐reported emotional responses to negative images corroborated these results, suggesting salutary changes in both emotional reactivity and regulation. OM training had limited beneficial effects, restricted to the subjective outcomes. The findings suggest that MBCT may derive its greatest benefit from training in FA rather than OM. Discussion highlights the potential value of FA training for emotional health.

In the first report comparing emotion‐relevant impacts of focused attention meditation (FA), open awareness meditation (OM), and Mindfulness‐based Cognitive Therapy (MBCT) among those with depressive symptoms, we show that FA and MBCT produced leftward hemispheric shifts in frontal alpha asymmetry, consistent with approach‐based positive affect, during an emotion reactivity and regulation task. Self‐reported emotional responses to negative images corroborated these results. The findings highlight the potential value of FA and MBCT training for emotional health.

Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise

Mindfulness has growing empirical support for improving emotion regulation in individuals with Autism Spectrum Disorder (ASD). Mindfulness is cultivated through meditation practices. Assessing the role of mindfulness in improving emotion regulation is challenging given the reliance on self-report tools. Electroencephalography (EEG) has successfully quantified neural responses to emotional arousal and meditation in other populations, making it ideal to objectively measure neural responses before and after mindfulness (MF) practice among individuals with ASD. We performed an EEG-based analysis during a resting state paradigm in 35 youth with ASD. Specifically, we developed a machine learning classifier and a feature and channel selection approach that separates resting states preceding (Pre-MF) and following (Post-MF) a mindfulness meditation exercise within participants. Across individuals, frontal and temporal channels were most informative. Total power in the beta band (16-30 Hz), Total power (4-30 Hz), relative power in alpha band (8-12 Hz) were the most informative EEG features. A classifier using a non-linear combination of selected EEG features from selected channel locations separated Pre-MF and Post-MF resting states with an average accuracy, sensitivity, and specificity of 80.76%, 78.24%, and 82.14% respectively. Finally, we validated that separation between Pre-MF and Post-MF is due to the MF prime rather than linear-temporal drift. This work underscores machine learning as a critical tool for separating distinct resting states within youth with ASD and will enable better classification of underlying neural responses following brief MF meditation.

Mindfulness-Based Program for Anxiety and Depression Treatment in Healthcare Professionals: A Pilot Randomized Controlled Trial

In primary health care, the work environment can cause high levels of anxiety and depression, triggering relevant expert and individual change. Mindfulness-Based Stress Reduction (MBSR) programs reduce signs of anxiety and depression. The purpose of this sub-analysis of the total project, was to equate the effectiveness of the standard MBSR curriculum with the abbreviated version in minimizing anxiety and depression. This randomized controlled clinical trial enrolled 112 mentors and resident specialists from Family and Community Medicine and Nurses (FCMN), distributed across six teaching units (TU) of the Spanish National Health System (SNHS). Experimental group participants received a MBRS training (abbreviated/standard). Depression and anxiety levels were measured with the Goldberg Anxiety and Depression Scale (GADS) at three different time periods during the analysis: before (pre-test) and after (post-test) participation, as well as 3 months after the completion of intervention. Taking into account the pre-test scores as the covariate, an adjusted analysis of covariance (ANCOVA) showed significant depletion in anxiety and depression in general (F (2.91) = 4.488; p = 0.014; η² = 0.090) and depression in particular (F (2, 91) = 6.653; p = 0.002; η² = 0.128 at the post-test visit, maintaining their effects for 3 months (F (2.79) = 3.031; p = 0.050; η² = 0.071-F (2.79) = 2.874; p = 0.049; η² = 0.068, respectively), which is associated with the use of a standard training program. The abbreviated training program did not have a significant effect on the level of anxiety and depression. The standard MBSR training program had a positive effect on anxiety and depression and promotes long-lasting effects in tutors and resident practitioners. New research is needed to demonstrate the effectiveness of abbreviated versions of training programs.

The Current Evidence Levels for Biofeedback and Neurofeedback Interventions in Treating Depression: A Narrative Review

This article is aimed at showing the current level of evidence for the usage of biofeedback and neurofeedback to treat depression along with a detailed review of the studies in the field and a discussion of rationale for utilizing each protocol. La Vaque et al. criteria endorsed by the Association for Applied Psychophysiology and Biofeedback and International Society for Neuroregulation & Research were accepted as a means of study evaluation. Heart rate variability (HRV) biofeedback was found to be moderately supportable as a treatment of MDD while outcome measure was a subjective questionnaire like Beck Depression Inventory (level 3/5, “probably efficacious”). Electroencephalographic (EEG) neurofeedback protocols, namely, alpha-theta, alpha, and sensorimotor rhythm upregulation, all qualify for level 2/5, “possibly efficacious.” Frontal alpha asymmetry protocol also received limited evidence of effect in depression (level 2/5, “possibly efficacious”). Finally, the two most influential real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback protocols targeting the amygdala and the frontal cortices both demonstrate some effectiveness, though lack replications (level 2/5, “possibly efficacious”). Thus, neurofeedback specifically targeting depression is moderately supported by existing studies (all fit level 2/5, “possibly efficacious”). The greatest complication preventing certain protocols from reaching higher evidence levels is a relatively high number of uncontrolled studies and an absence of accurate replications arising from the heterogeneity in protocol details, course lengths, measures of improvement, control conditions, and sample characteristics.

Negative emotional state slows down movement speed: behavioral and neural evidence

BACKGROUND

Athletic performance is affected by emotional state. Athletes may underperform in competition due to poor emotion regulation. Movement speed plays an important role in many competition events. Flexible control of movement speed is critical for effective athletic performance. Although behavioral evidence showed that negative emotion can influence movement speed, the nature of the relationship remains controversial. Thus, the present study investigated how negative emotion affects movement speed and the neural mechanism underlying the interaction between emotion processing and movement control.

METHODS

The present study combined electroencephalography (EEG) technology with a cued-action task to investigate the effect of negative emotion on movement speed. In total, 21 undergraduate students were recruited for this study. Participants were asked to perform six consecutive action tasks after viewing an emotional picture. Pictures were presented in two blocks (one negative and one neutral). After the participants completed a set of tasks (neutral of negative), they were subjected to complete a 9-point self-assessment manikin scale. Participants underwent EEG while performing the tasks.

RESULTS

At the behavior level, there was a significant main effect of emotional valence on movement speed, with participants exhibiting significantly slower movements in the negative emotional condition than in the neutral condition. EEG data showed increased theta oscillation and larger P1 amplitude in response to negative than to neural images suggesting that more cognitive resources were required to process negative than neutral images. EEG data also showed a larger late CNV area in the neutral condition than in the negative condition, which suggested that there was a significant decrease in brain activation during action tasks in negative emotional condition than in the neural. While the early CNV did not reveal a significant main effect of emotional valence.

CONCLUSION

The present results indicate that a negative emotion can slow movement, which is largely due to negative emotional processing consuming more resources than non-emotional processing and this interference effect mainly occurred in the late movement preparation phase.