Temporal characteristics of emotional conflict processing: the modulation role of attachment styles

The neurophysiological mechanisms of emotional conflict are influenced by social associations information of varying valence

Previous research has indicated that emotional valence can influence the resolution of emotional conflicts, with this effect benefiting from the prioritized processing of negative emotions. In this study, a social learning paradigm was utilized to train participants to associate different neutral faces with distinct social meanings (e.g., stingy, generous). These learned neutral faces were then combined with emotion words of varying valence to create a novel face-word Stroop task. This task was employed to investigate whether social affective associations of different valences continue to impact emotional conflict processing. Concurrently, electroencephalogram data was recorded while participants completed the task. Behavioral results revealed that when participants were presented with neutral faces associated with negative social associations, emotional conflict resolution is facilitated, whereas when faced with neutral faces linked to positive social associations, the emotional conflict effect was significantly present. Consistency between event-related potentials and microstate results indicated that negative social associations facilitated conflict resolution, while positive social associations required participants to recruit more cognitive resources to inhibit irrelevant emotional interference. These findings further expand the factors influencing emotional conflict and relevant neurophysiological explanations.

Automatic attentional capture by food items in a visuospatial attention task - A study with event-related brain potentials

The incentive sensitization theory suggests that repeated exposure to rewarding substances or food shapes neural circuits to create an attentional bias towards these stimuli. There is ongoing debate about whether attentional capture by such stimuli is an early automatic process or a later stage in the processing cascade. Event-related brain potentials (ERPs) provide a means to pinpoint the timing and location of attentional capture. ERPs were recorded from 28 normal weight healthy women as they attended to the left or right hemifield of a visual display while fixating a central point. Stimuli comprised bars presented left and right of the fixation point simultaneously with the task being to respond to slightly smaller bars on the attended side by button press. The bars appeared superimposed on task-irrelevant distractor stimuli (either food pictures or pictures of non-food objects). The bilateral stimuli elicited a positivity that was largest as posterior sites contralateral to the attended hemifield between 75 and 250 ms. Critically, this contralateral attention effect was enhanced by food distractors on the attended side and diminished by food distractors on the unattended side, demonstrating signs of attention capture by food stimuli as early as 80 ms poststimulus.

Decoding cognition in neurodevelopmental, psychiatric and neurological conditions with multivariate pattern analysis of EEG data

Multivariate pattern analysis (MVPA) of electroencephalographic (EEG) data represents a revolutionary approach to investigate how the brain encodes information. By considering complex interactions among spatio-temporal features at the individual level, MVPA overcomes the limitations of univariate techniques, which often fail to account for the significant inter- and intra-individual neural variability. This is particularly relevant when studying clinical populations, and therefore MVPA of EEG data has recently started to be employed as a tool to study cognition in brain disorders. Here, we review the insights offered by this methodology in the study of anomalous patterns of neural activity in conditions such as autism, ADHD, schizophrenia, dyslexia, neurological and neurodegenerative disorders, within different cognitive domains (perception, attention, memory, consciousness). Despite potential drawbacks that should be attentively addressed, these studies reveal a peculiar sensitivity of MVPA in unveiling dysfunctional and compensatory neurocognitive dynamics of information processing, which often remain blind to traditional univariate approaches. Such higher sensitivity in characterizing individual neurocognitive profiles can provide unique opportunities to optimise assessment and promote personalised interventions.

Enhancing EEG-based attachment style prediction: unveiling the impact of feature domains

Introduction

Attachment styles are crucial in human relationships and have been explored through neurophysiological responses and EEG data analysis. This study investigates the potential of EEG data in predicting and differentiating secure and insecure attachment styles, contributing to the understanding of the neural basis of interpersonal dynamics.

Methods

We engaged 27 participants in our study, employing an XGBoost classifier to analyze EEG data across various feature domains, including time-domain, complexity-based, and frequency-based attributes.

Results

The study found significant differences in the precision of attachment style prediction: a high precision rate of 96.18% for predicting insecure attachment, and a lower precision of 55.34% for secure attachment. Balanced accuracy metrics indicated an overall model accuracy of approximately 84.14%, taking into account dataset imbalances.

Discussion

These results highlight the challenges in using EEG patterns for attachment style prediction due to the complex nature of attachment insecurities. Individuals with heightened perceived insecurity predominantly aligned with the insecure attachment category, suggesting a link to their increased emotional reactivity and sensitivity to social cues. The study underscores the importance of time-domain features in prediction accuracy, followed by complexity-based features, while noting the lesser impact of frequency-based features. Our findings advance the understanding of the neural correlates of attachment and pave the way for future research, including expanding demographic diversity and integrating multimodal data to refine predictive models.