Self-Reported Stickiness of Mind-Wandering Affects Task Performance

When asked to perform a certain task, we typically spend a decent amount of time thinking thoughts unrelated to that task--a phenomenon referred to as 'mind-wandering.' It is thought that this mind-wandering is driven at least in part by our unfinished goals and concerns. Previous studies have shown that just after presenting a participant with their own concerns, their reports of task-unrelated thinking increased somewhat. However, effects of these concerns on task performance were somewhat inconsistent. In this study we take the opposite approach, and examine whether task performance depends on the self-reported thought content. Specifically, a particularly intriguing aspect of mind-wandering that has hitherto received little attention is the difficulty of disengaging from it, in other words, the ''stickiness'' of the thoughts. While presenting participants with their own concerns was not associated with clear effects on task performance, we showed that the reports of off-task thinking and variability of response times increased with the amount of self-reported stickiness of thoughts. This suggests that the stickiness of mind-wandering is a relevant variable, and participants are able to meaningfully report on it

Modeling the Effects of Attentional Cueing on Meditators

Training in meditation has been shown to affect functioning of several attentional subsystems, most prominently conflict monitoring, and to some extent orienting. These previous findings described the effects of cueing and manipulating stimulus congruency on response times and accuracies. However, changes in accuracy and response times can arise from several factors. Computational process models can be used to distinguish different factors underlying changes in accuracy and response times. When decomposed by means of the drift diffusion model, a general process model of decision making that has been widely used, both the congruency and cueing effects, is subserved by a change in decision thresholds. Meditators showed a modest overall increase in their decision threshold, which may reflect an ability to wait longer and collect more information before responding

Media multitasking, mind-wandering, and distractibility:A large-scale study

Previous studies suggest that frequent media multitasking - the simultaneous use of different media at the same time - may be associated with increased susceptibility to internal and external sources of distraction. At the same time, other studies found no evidence for such associations. In the current study, we report the results of a large-scale study (N=261) in which we measured media multitasking with a short media-use questionnaire and measured distraction with a change-detection task that included different numbers of distractors. To determine whether internally generated distraction affected performance, we deployed experience-sampling probes during the change-detection task. The results showed that participants with higher media multitasking scores did not perform worse as distractor set size increased, they did not perform worse in general, and their responses on the experience-sampling probes made clear that they also did not experience more lapses of attention during the task. Critically, these results were robust across different methods of analysis (i.e., Linear Mixed Modeling, Bayes factors, and extreme-groups comparison). At the same time, our use of the short version of the media-use questionnaire might limit the generalizability of our findings. In light of our results, we suggest that future studies should ensure an adequate level of statistical power and implement a more precise measure for media multitasking

Decoding study-independent mind-wandering from EEG using convolutional neural networks

Objective. Mind-wandering is a mental phenomenon where the internal thought process disengages from the external environment periodically. In the current study, we trained EEG classifiers using convolutional neural networks (CNNs) to track mind-wandering across studies. Approach. We transformed the input from raw EEG to band-frequency information (power), single-trial ERP (stERP) patterns, and connectivity matrices between channels (based on inter-site phase clustering). We trained CNN models for each input type from each EEG channel as the input model for the meta-learner. To verify the generalizability, we used leave-N-participant-out cross-validations (N = 6) and tested the meta-learner on the data from an independent study for across-study predictions. Main results. The current results show limited generalizability across participants and tasks. Nevertheless, our meta-learner trained with the stERPs performed the best among the state-of-the-art neural networks. The mapping of each input model to the output of the meta-learner indicates the importance of each EEG channel. Significance. Our study makes the first attempt to train study-independent mind-wandering classifiers. The results indicate that this remains challenging. The stacking neural network design we used allows an easy inspection of channel importance and feature maps.</p

Distinguishing Vigilance Decrement and Low Task Demands from Mind-wandering:A Machine Learning Analysis of EEG

Mind-wandering is a ubiquitous mental phenomenon that is defined as self-generated thought irrelevant to the ongoing task. Mind-wandering tends to occur when people are in a low-vigilance state or when they are performing a very easy task. In the current study, we investigated whether mind-wandering is completely dependent on vigilance and current task demands, or whether it is an independent phenomenon. To this end, we trained support vector machine (SVM) classifiers on EEG data in conditions of low and high vigilance, as well as under conditions of low and high task demands, and subsequently tested those classifiers on participants' self-reported mind-wandering. Participants' momentary mental state was measured by means of intermittent thought probes in which they reported on their current mental state. The results showed that neither the vigilance classifier nor the task demands classifier could predict mind-wandering above-chance level, while a classifier trained on self-reports of mind-wandering was able to do so. This suggests that mind-wandering is a mental state different from low vigilance or performing tasks with low demands—both which could be discriminated from the EEG above chance. Furthermore, we used dipole fitting to source-localize the neural correlates of the most import features in each of the three classifiers, indeed finding a few distinct neural structures between the three phenomena. Our study demonstrates the value of machine-learning classifiers in unveiling patterns in neural data and uncovering the associated neural structures by combining it with an EEG source analysis technique

Is There Neural Evidence for an Evidence Accumulation Process in Memory Decisions?

Models of evidence accumulation have been very successful at describing human decision making behavior. Recent years have also seen the first reports of neural correlates of this accumulation process. However, these studies have mostly focused on perceptual decision making tasks, ignoring the role of additional cognitive processes like memory retrieval that are crucial in real-world decisions. In this study, we tried to find a neural signature of evidence accumulation during a recognition memory task. To do this, we applied a method we have successfully used to localize evidence accumulation in scalp EEG during a perceptual decision making task. This time, however, we applied it to intracranial EEG recordings, which provide a much higher spatial resolution. We identified several brain areas where activity ramps up over time, but these neural patterns do not appear to be modulated by behavioral variables such as the amount of available evidence or response time. This casts doubt on the idea of evidence accumulation as a general decision-making mechanism underlying different types of decisions

Relation between centro-parietal positivity and diffusion model parameters in both perceptual and memory-based decision making

Several studies have suggested that the centro-parietal positivity (CPP), an EEG potential occurring approximately 500 ms post- stimulus, reflects the accumulation of evidence for making a decision. Yet, most previous studies of the CPP focused exclusively on perceptual decisions with very simple stimuli. In this study, we examined how the dynamics of the CPP depended on the type of decision being made, and whether its slope was related to parameters of an accumulator model of decision making. We show initial evidence that memory- and perceptual decisions about carefully-controlled face stimuli exhibit similar dynamics, but offset by a time difference in decision onset. Importantly, the individual-trial slopes of the CPP are related to the accumulator model's drift parameter. These findings help to further understand the role of the CPP across different kinds of decisions

The effects of mindfulness-based cognitive therapy on affective memory recall dynamics in depression:A mechanistic model of rumination

Objectives: converging research suggests that mindfulness training exerts its therapeutic effects on depression by reducing rumination. Theoretically, rumination is a multifaceted construct that aggregates multiple neurocognitive aspects of depression, including poor executive control, negative and overgeneral memory bias, and persistence or stickiness of negative mind states. Current measures of rumination, most-often self-reports, do not capture these different aspects of ruminative tendencies, and therefore are limited in providing detailed information about the mechanisms of mindfulness. Methods: we developed new insight into the potential mechanisms of rumination, based on three model-based metrics of free recall dynamics. These three measures reflect the patterns of memory retrieval of valenced information: the probability of first recall (Pstart) which represents initial affective bias, the probability of staying with the same valence category rather than switching, which indicates strength of positive or negative association networks (Pstay), and probability of stopping (Pstop) or ending recall within a given valence, which indicates persistence or stickiness of a mind state. We investigated the effects of Mindfulness-Based Cognitive Therapy (MBCT; N = 29) vs. wait-list control (N = 23) on these recall dynamics in a randomized controlled trial in individuals with recurrent depression. Participants completed a standard laboratory stressor, the Trier Social Stress Test, to induce negative mood and activate ruminative tendencies. Following that, participants completed a free recall task consisting of three word lists. This assessment was conducted both before and after treatment or wait-list. Results: while MBCT participant’s Pstart remained relatively stable, controls showed multiple indications of depression-related deterioration toward more negative and less positive bias. Following the intervention, MBCT participants decreased in their tendency to sustain trains of negative words and increased their tendency to sustain trains of positive words. Conversely, controls showed the opposite tendency: controls stayed in trains of negative words for longer, and stayed in trains of positive words for less time relative to pre-intervention scores. MBCT participants tended to stop recall less often with negative words, which indicates less persistence or stickiness of negatively valenced mental context. Conclusion: MBCT participants showed a decrease in patterns that may perpetuate rumination on all three types of recall dynamics (Pstart, Pstay, and Pstop), compared to controls. MBCT may weaken the strength of self-perpetuating negative associations networks that are responsible for the persistent and “sticky” negative mind states observed in depression, and increase the positive associations that are lacking in depression. This study also offers a novel, objective method of measuring several indices of ruminative tendencies indicative of the underlying mechanisms of rumination

The impact of mood-induction on maladaptive thinking in the vulnerability for depression

Background and objectivesMind-wandering, and specifically the frequency and content of mind-wandering, plays an important role in the psychological well-being of individuals. Repetitive negative thinking has been associated with a high risk to develop and maintain Major Depressive Disorder. We here combined paradigms and techniques from cognitive sciences and experimental clinical psychology to study the transdiagnostic psychiatric phenomenon of repetitive negative thinking. This allowed us to investigate the adjustability of the content and characteristics of mind-wandering in individuals varying in their susceptibility to negative affect.MethodsParticipants high (n = 42) or low (n = 40) on their vulnerability for negative affect and depression performed a Sustained Attention to Response Task (SART) after a single session of positive fantasizing and a single session of stress induction in a cross-over design. Affective states were measured before and after the interventions.ResultsAfter stress, negative affect increased, while after fantasizing both positive affect increased and negative affect decreased. Thoughts were less off-task, past-related and negative after fantasizing compared to after stress. Individuals more susceptible to negative affect showed more off-task thinking after stress than after fantasizing compared to individuals low on this.LimitationsIn this cross-over design, no baseline measurement was included, limiting comparison to ‘uninduced’ mind-wandering. Inclusion of self-related concerns in the SART could have led to negative priming.ConclusionsStress-induced negative thinking underlying vulnerability for depression could be partially countered by fantasizing in a non-clinical sample, which may inform the development of treatments for depression and other disorders characterized by maladaptive thinking

Captivated by thought:"Sticky" thinking leaves traces of perceptual decoupling in task-evoked pupil size

Throughout the day, we may sometimes catch ourselves in patterns of thought that we experience as rigid and difficult to disengage from. Such "sticky" thinking can be highly disruptive to ongoing tasks, and when it turns into rumination constitutes a vulnerability for mental disorders such as depression and anxiety. The main goal of the present study was to explore the stickiness dimension of thought, by investigating how stickiness is reflected in task performance and pupil size. To measure spontaneous thought processes, we asked participants to perform a sustained attention to response task (SART), in which we embedded the participant's concerns to potentially increase the probability of observing sticky thinking. The results indicated that sticky thinking was most frequently experienced when participants were disengaged from the task. Such episodes of sticky thought could be discriminated from neutral and non-sticky thought by an increase in errors on infrequent no-go trials. Furthermore, we found that sticky thought was associated with smaller pupil responses during correct responding. These results demonstrate that participants can report on the stickiness of their thought, and that stickiness can be investigated using pupillometry. In addition, the results suggest that sticky thought may limit attention and exertion of cognitive control to the task