Pleasantness of mind wandering is positively associated with focus back effort in daily life: Evidence from resting state fMRI

Working memory capacity predicts focus back effort under different task demands

According to the cognitive flexibility view, individuals with higher cognitive control ability are more flexible in experiencing on task or mind wandering during tasks with different loads. On the other hand, the resource-control theory posits that executive control is essential for allocating attentional resources between mind wandering and tasks. Focus back effort may reflect the adjustment of executive control in the resource-control theory. Here, 121 participants completed two span tasks, as well as high- and low-load tasks, while mind wandering and focus back effort were measured. Our findings indicated that mind wandering was influenced by working memory capacity (WMC) and focus back effort. Additionally, participants demonstrated a higher focus back effort during the higher load task. This effect was particularly pronounced in individuals with lower WMC, which was treated as a continuous variable. These findings integrate the cognitive flexibility view and resource-control theory to describe how individuals modulate mind wandering.

The role of focus back effort in the relationships among motivation, interest, and mind wandering: an individual difference perspective

Considerable research suggests a link between mind wandering and diminished levels of motivation and interest. During episodes of mind wandering, individuals may engage in efforts to redirect their attention back to the task at hand (known as focus back effort). Building on the resource-control hypothesis, we hypothesized that the influence of interest and motivation on mind wandering may be mediated by focus back effort. In Study 1, we employed a latent-variable approach to investigate these relationships across three tasks with varying cognitive demands. The results showed that individual differences in interest indirectly influenced mind wandering through the mediating factors of motivation and focus back effort. Furthermore, individual differences in interest indirectly predicted task performance through the mediating factors of motivation, focus back effort, and mind wandering during the high-load task. In Study 2, we replicated the relationships among these factors in a reading comprehension task. The results consistently support the role of focus back effort as an adaptive mechanism for executive control, enabling the allocation of cognitive resources to both mind wandering and task performance. These findings underscore the significance of focus back effort in elucidating the interplay between mind wandering, motivation, interest, and task performance. Importantly, our results align with the resource-control theory.

Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study

Distracted driving is one of the main causes of traffic accidents. By predicting the attentional state of drivers, it is possible to prevent distractions and promote safe driving. In this study, we developed a model that could predict the degree of distracted driving based on brain activity. Changes in oxyhemoglobin concentrations were measured in drivers while driving a real car using functional near-infrared spectroscopy (fNIRS). A regression model was constructed for each participant using functional connectivity as an explanatory variable and brake reaction time to random beeps while driving as an objective variable. As a result, we were able to construct a prediction model with the mean absolute error of 5.58 × 102 ms for the BRT of the 12 participants. Furthermore, the regression model with the highest prediction accuracy for each participant was analyzed to gain a better understanding of the neural basis of distracted driving. The 11 of 12 models that showed significant accuracy were classified into five clusters by hierarchical clustering based on their functional connectivity edges used in each cluster. The results showed that the combinations of the dorsal attention network (DAN)-sensory-motor network (SMN) and DAN-ventral attention network (VAN) connections were common in all clusters and that these networks were essential to predict the degree of distraction in complex multitask driving. They also confirmed the existence of multiple types of prediction models with different within- and between-network connectivity patterns. These results indicate that it is possible to predict the degree of distracted driving based on the driver's brain activity during actual driving. These results are expected to contribute to the development of safe driving systems and elucidate the neural basis of distracted driving.