Attachment style, emotional feedback, and neural processing: investigating the influence of attachment on the P200 and P400 components of event-related potentials

Attachment Style, Task Difficulty, and Feedback Type: Effects on Cognitive Load

This study examines how attachment styles influence cognitive load during a dot counting task with varying difficulty levels and feedback types. From an initial pool of 96 participants, 27 were selected based on attachment classifications from the Experiences in Close Relationships-Revised (ECR-R) questionnaire. These participants completed the task while receiving personal and group-based feedback, and EEG recordings monitored cognitive load using the theta/beta ratio (TBR). Results show that negative personal feedback consistently elevated cognitive load across all attachment styles. Avoidant and fearful-avoidant individuals did not exhibit significant differences in cognitive load between positive and negative group feedback, suggesting a relatively stable response regardless of feedback valence. In contrast, securely attached individuals showed increased cognitive load under negative feedback conditions. Furthermore, individuals with higher attachment-related anxiety or avoidance experienced more pronounced increases in cognitive load as task difficulty increased. These findings suggest that attachment-related differences shape cognitive responses to feedback and task complexity level. The study highlights the role of adaptive feedback strategies in optimizing cognitive engagement across different attachment profiles.

Which ERP components are effective in measuring cognitive load in multimedia learning? A meta-analysis based on relevant studies

The open and generative nature of multimedia learning environments tends to cause cognitive overload in learners, and cognitive load is difficult for researchers to observe objectively because of its implicit and complex nature. Event-related potentials (ERP), a method of studying potential changes associated with specific events or stimuli by recording the electroencephalogram (EEG), has become an important method of measuring cognitive load in cognitive psychology. Although many studies have relied on ERP output measurements to compare different levels of cognitive load in multimedia learning, the results of the effect of cognitive load on ERP have been inconsistent. In this study, we used a meta-analysis of evidence-based research to quantitatively analyze 17 experimental studies to quantitatively evaluate which ERP component (amplitude) is most sensitive to cognitive load. Forty five effect sizes from 26 studies involving 360 participants were calculated. (1) The results of the studies analyzed in subgroups indicated high level effect sizes for P300 and P200 (2) Analyses of moderating variables for signal acquisition did not find that different methods of signal acquisition had a significant effect on the measurement of cognitive load (3) Analyses of moderating variables for task design found that a task system with feedback was more convenient for the measurement of cognitive load, and that designing for 3 levels of cognitive load was more convenient for the measurement of cognitive load than for 2 levels of cognitive load. (4) Analyses of continuous moderating variables for subject characteristics did not find significant effects of age, gender, or sample size on the results.

Neurophysiological insights into sequential decision-making: exploring the secretary problem through ERPs and TBR dynamics

Decision-making under uncertainty, a cornerstone of human cognition, is encapsulated by the "secretary problem" in optimal stopping theory. Our study examines this decision-making challenge, where participants are required to sequentially evaluate and make irreversible choices under conditions that simulate cognitive overload. We probed neurophysiological responses by engaging 27 students in a secretary problem simulation while undergoing EEG monitoring, focusing on Event-Related Potentials (ERPs) P200 and P400, and Theta to Beta Ratio (TBR) dynamics.Results revealed a nuanced pattern: the P200 component's amplitude declined from the initial to the middle offers, suggesting a diminishing attention span as participants grew accustomed to the task. This attenuation reversed at the final offer, indicating a heightened cognitive processing as the task concluded. In contrast, the P400 component's amplitude peaked at the middle offer, hinting at increased cognitive evaluation, and tapered off at the final decision. Additionally, TBR dynamics illustrated a fluctuation in attentional control and emotional regulation throughout the decision-making sequence, enhancing our understanding of the cognitive strategies employed.The research elucidates the dynamic interplay of cognitive processes in high-stakes environments, with neurophysiological markers fluctuating significantly as participants navigated sequential choices. By correlating these fluctuations with decision-making behavior, we provide insights into the evolving strategies from heightened alertness to strategic evaluation. Our findings offer insights that could inform the use of neurophysiological data in the development of decision-making frameworks, potentially contributing to the practical application of cognitive research in real-life contexts.

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.