Emotional reactivity and its impact on neural circuitry for attention-emotion interaction through regression-based machine learning model

Abstract

Attentional paradigm can have a significant influence on the processing and experience of positive and negative emotions. Attentional mechanism refers to the tendency to selectively attend to a particular stimulus while ignoring others. In the context of emotions, individuals may exhibit attentional biases towards either positive or negative emotional stimuli. By directing attention towards a specific stimulus, individuals can modulate their emotional responses. When attention is directed towards negative or threatening stimuli, it can intensify negative emotions such as fear, sadness, anger and anxiety. Conversely, directing attention away from negative stimuli can reduce emotional reactivity and promote emotional regulation. Similarly, paying attention to positive stimuli can amplify positive emotions and facilitate positive emotional experiences. Attentional paradigms are also responsible for cognitive appraisal of emotional stimuli. The allocation of attention can shape how emotional stimuli are evaluated and categorized, influencing the subsequent emotional response. Since the relationship between attention and emotions is complex and can vary across individuals and contexts, it is important to understand the underlying cognitive neural dynamics of the same. Custom rank allocation model (CRAM) was used to decode the underlying neural dynamics of cognitive and emotional resource sharing through the non-significant EEG channels. During the main effect of global–local (GL), CRAM ranks and scores indicated that the EEG channels C4, PZ, OZ, and P4 were found to be the most non-significant channels. Similarly, CRAM ranks and scores of the interaction effects between global–local and positive emotion-negative emotion and the interaction effects between global–local and frequent-deviant-equal indicated midline central EEG channels CZ, PZ, FZ and OZ to be the main contributor of the cognitive and emotional resources to others. Understanding the dynamics of attention-emotion conflicts with reference to significant and non-significant channels is important to gain insights into the complex computational interplay between attention and emotion, leading to a deeper understanding of human cognition and emotion regulation.