Research Projects

The ability to stay focused on tasks is essential for everyday functioning. Despite this, our thoughts often drift, taking our attention away from ongoing tasks. Mind-wandering (MW)—also known as task-unrelated thinking—is a frequent experience, occurring in 30% to 50% of daily life. This common cognitive phenomenon can significantly impact attention, decision-making, and mental well-being. Understanding the brain dynamics involved in MW is therefore crucial, as it may inform the development of targeted interventions for individuals who frequently experience MW or struggle to maintain attention. To achieve this understanding, it is essential to consider individual differences, particularly personality traits, which may influence MW tendencies. Personality traits, as described by the OCEAN model (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism), could play a key role in the dynamics of MW. Current research indicates that certain traits are more susceptible to MW than others. Specifically, studies have suggested that neuroticism, a trait characterized by emotional instability, poor coping with stress, and cognitive rumination, is linked to increased MW. A significant limitation of existing studies is the incomplete understanding of how personality traits interact with MW brain dynamics, further exacerbated by the reliance on retrospective self-reports of participants to measure MW, which are often biased. Self-reports may capture only those MW episodes that participants recall, raising significant concerns about data accuracy. To address this issue, the proposed study aims to establish a relationship between personality traits and MW dynamics using electroencephalography (EEG), a non-invasive technique that captures brain activity in real time. EEG enables the objective detection of neural markers related to MW without relying on subjective self-reports. The primary goal of this research is to develop a robust signal analysis framework to automatically detect MW episodes in EEG data. This framework will first be validated on labeled EEG datasets and then extended to identify MW states in newly collected, unlabeled data. Once these states are accurately detected and labeled, machine learning models will be applied to investigate key research questions. The central hypothesis is that personality traits significantly shape brain activity patterns during MW, which can be objectively tested with EEG. Achieving these objectives will deepen our understanding of how personality traits shape cognitive processes related to MW. Moreover, as MW is linked to attention-deficit/hyperactivity disorder (ADHD), understanding its neural mechanisms may guide new intervention strategies and reveal neural markers to monitor their effectiveness. The findings could lead to the development of targeted training interventions that consider individual personality traits, thus improving focus and mental health outcomes across diverse populations.