By Ayoush Srivastava
Human brains are prone to losing focus to spontaneous and self-generated thought – this concept is termed mind-wandering. Mind-wandering (MW) can be defined as the change in attention away from an external task to self-generated internal thoughts, often unrelated to the task.1 However, the definition of MW remains highly contested due to numerous studies published regarding its associated positive and negative effects.2 Despite this division in opinion, understanding MW in a quantifiable context and pioneering techniques to reliably detect this cognitive state would provide greater insight into treating related conditions or disorders.
MW is difficult to theoretically describe due to the wide variety of cognitive behaviours grouped under the term.3 However, researchers have been able to propose three distinct methods of categorising MW.3 One method is by describing MW episodes based off the content of the individual’s thoughts; these internal processes are typically unrelated to the task being performed or to be performed soon.3 Another method is based off describing and characterising the stimuli instigating the MW episode, whether it’s a prolonged external stimulus or internal thoughts.3 Finally, the third method describes MW episodes via its dynamics, which allows MW to be distinguished from other mental episodes like creative thought, rumination and more.3 However, it is important to note that these methods have been proposed, not clinically or experimentally proven; more time is required to fully comprehend the complexities of MW and characterise the attributes of MW episodes.
Despite the ambiguity in characterising MW episodes, its causes can be described using the cognitive control system (CCS).3 This model suggests that humans consciously maintain a task list whilst monitoring a task’s progress that their attention is directed towards.3 All while doing this, the CCS also conducts cost-benefit analyses on each task, determining whether the completion of the task will bring the individual closer to completing its goals.3 These appraisals allow the CCS to better direct its attention towards tasks whose completion provides higher rewards. Furthermore, humans oftentimes divide their attention amongst multiple tasks, and whilst this does cause a decline in performance efficiency, task completion brings the overarching goal closer in reach.3 As such, the occurrence of MW can be attributed to the CCS: if a task is judged to have insufficient value towards achieving a goal, the CCS will search for a new task with higher rewards, in turn initiating a MW episode.3
However, with the lack of behavioural markers during its occurrence, the detection of MW provides a significant challenge. Current methodologies involve an overdependency on self-reporting from study participants.1 For example, the most reliable and frequently used method for studying MW is the online thought sampling method; this method relies on repeatedly interrupting a study participant who is directed to perform an external task and report whether they’re having a task-related or unrelated thought.1 Unfortunately, this method does not account for the complexity of a task or the attentive state of the participant before the exercise, thus indicating a need for a more reliable tool for detecting MW.1 Although past attempts in applying machine learning with different types of markers – like behavioural, task-related, and physiological – have been mildly successful, the use of scalp EEG neural data instead is sure to provide a more comprehensive and accurate model for predicting and determining an individual’s attentive state.1 In fact, a study performed by Dhindsa et al. found that when recording EEG activity during live lectures, they found an accuracy of 80-83%.1 Furthermore, Tasika et al. applied a support vector machine and decision tree classifier as part of a multi-part framework to detect MW with an accuracy rate of 84.49% from two individuals using EEG data.1 As MW is a common symptom for many neurodevelopmental disorders like ADHD, there is great promise in applying machine learning algorithms powered by EEG data to detect MW in real-time, potentially playing a major role in diagnosing patients.4
Although MW has come into the spotlight of cognitive neuroscience research, its startling rise is also partially due to its association with the recently discovered default-mode network (DMN), a neural network comprising multiple interacting regions of the brain.5 A DMN tends to activate at rest state – where the individual may be focused on self-generated thoughts – and deactivate during a cognitively demanding task.5 Furthermore, disorders or malfunctions in the DMN are associated with numerous neurological, psychiatric, and psychological pathologies as observed by a study from Bucker et al.5 As a result, multiple strategies using neuroimaging technology and theoretical assumptions have been applied to study the correlation between DMNs and MW.5
No matter the task at hand, MW is an unavoidable part of daily life. Though it is difficult to characterise, learning how to detect this cognitive condition could provide a road to earlier diagnosis for patients with neurodevelopmental disorders.
- Dong HW, Mills C, Knight RT, Kam JWY. Detection of mind wandering using EEG: Within and across individuals. PLoS One. 2021;16(5):e0251490.
- Zheng Y, Wang D, Zhang Y, Xu W. Detecting mind wandering: An objective method via simultaneous control of respiration and fingertip pressure. Front Psychol. 2019;10:216.
- Shepherd J. Why does the mind wander? Neurosci Conscious. 2019;2019(1):niz014.
- Bozhilova NS, Michelini G, Kuntsi J, Asherson P. Mind wandering perspective on attention-deficit/hyperactivity disorder. Neurosci Biobehav Rev. 2018;92:464–76.
- Gruberger M, Ben-Simon E, Levkovitz Y, Zangen A, Hendler T. Towards a neuroscience of mind-wandering. Front Hum Neurosci. 2011;5:56.