Modeling Effects of Rumination on Free Recall Using ACT-R.

Ruminative thinking, characterized by a recurrent focus on negative and self-related thought, is a key cognitive vulnerability marker of depression and, therefore, a key individual difference variable. This study aimed to develop a computational cognitive model of rumination focusing on the organization and retrieval of information in memory, and how these mechanisms differ in individuals prone to rumination and individuals less prone to rumination. Adaptive Control of Thought-Rational (ACT-R) was used to develop a rumination model by adding memory chunks with negative valence to the declarative memory.

Working memory load impairs tacit coordination but not inter-brain EEG synchronization.

Coordinating actions with others is thought to require Theory of Mind (ToM): the ability to take perspective by attributing underlying intentions and beliefs to observed behavior. However, researchers have yet to establish a causal role for specific cognitive processes in coordinated action. Since working memory load impairs ToM in single-participant paradigms, we tested whether load manipulation affects two-person coordination.

Beyond kindness: a proposal for the flourishing of science and scientists alike.

We argue that many of the crises currently afflicting science can be associated with a present failure of science to sufficiently embody its own values. Here, we propose a response beyond mere crisis resolution based on the observation that an ethical framework of flourishing derived from the Buddhist tradition aligns surprisingly well with the values of science itself. This alignment, we argue, suggests a recasting of science from a competitively managed activity of knowledge production to a collaboratively organized moral practice that puts kindness and sharing at its core.

Comparing resting state and task-based EEG using machine learning to predict vulnerability to depression in a non-clinical population.

Major Depressive Disorder (MDD) affects a large portion of the population and levies a huge societal burden. It has serious consequences like decreased productivity and reduced quality of life, hence there is considerable interest in understanding and predicting it. As it is a mental disorder, neural measures like EEG are used to study and understand its underlying mechanisms.

Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and Research Agenda From a Cross-Linguistic Workshop.

This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis.

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

. 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.

Network controllability measures of subnetworks: implications for neurosciences.

Recent progress in network sciences has made it possible to apply key findings from control theory to the study of networks. Referred to as network control theory, this framework describes how the interactions between interconnected system elements and external energy sources, potentially constrained by different optimality criteria, result in complex network behavior. A typical example is the quantification of the functional role certain brain regions or symptoms play in shaping the temporal dynamics of brain activity or the clinical course of a disease, a property that is quantified in terms of the so-called controllability metrics.

Temporal Structure in Sensorimotor Variability: A Stable Trait, But What For?

Unlabelled: Human performance shows substantial endogenous variability over time, and this variability is a robust marker of individual differences. Of growing interest to psychologists is the realisation that variability is not fully random, but often exhibits temporal dependencies. However, their measurement and interpretation come with several controversies.

Decoding the cognitive states of attention and distraction in a real-life setting using EEG.

Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study.

Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model.

Biophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time.

The steady state visual evoked potential (SSVEP) tracks "sticky" thinking, but not more general mind-wandering.

For a large proportion of our daily lives, spontaneously occurring thoughts tend to disengage our minds from goal-directed thinking. Previous studies showed that EEG features such as the P3 and alpha oscillations can predict mind-wandering to some extent, but only with accuracies of around 60%. A potential candidate for improving prediction accuracy is the Steady-State Visual Evoked Potential (SSVEP), which is used frequently in single-trial contexts such as brain-computer interfaces as a marker of the direction of attention.

Thalamic bursts modulate cortical synchrony locally to switch between states of global functional connectivity in a cognitive task.

Performing a cognitive task requires going through a sequence of functionally diverse stages. Although it is typically assumed that these stages are characterized by distinct states of cortical synchrony that are triggered by sub-cortical events, little reported evidence supports this hypothesis. To test this hypothesis, we first identified cognitive stages in single-trial MEG data of an associative recognition task, showing with a novel method that each stage begins with local modulations of synchrony followed by a state of directed functional connectivity.

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.

The art of planning ahead: When do we prepare for the future and when is it effective?

Preparing for the future during ongoing activities is an essential skill. Yet it is currently unclear to what extent we can prepare for the future in parallel with another task. In two experiments, we investigated how characteristics of a present task influenced whether and when participants prepared for the future, as well as its usefulness.

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.

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.

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.

Predicting task-general mind-wandering with EEG.

Mind-wandering refers to the process of thinking task-unrelated thoughts while performing a task. The dynamics of mind-wandering remain elusive because it is difficult to track when someone's mind is wandering based only on behavior. The goal of this study is to develop a machine-learning classifier that can determine someone's mind-wandering state online using electroencephalography (EEG) in a way that generalizes across tasks.

Tibetan Buddhist monastic debate: Psychological and neuroscientific analysis of a reasoning-based analytical meditation practice.

Analytical meditation and monastic debate are contemplative practices engaged in by Tibetan Buddhist monastics that have up to now been largely unexplored in Western contemplative science. The highly physical form of contemplative debating plays an important role in the monastic curriculum. Based on discussions and recorded interviews Tibetan monastic teachers and senior students at Sera Jey Monastic University and preliminary experiments, we outline an initial theory that elucidates the psychological mechanisms underlying this practice.

Computational modelling approaches to meditation research: why should we care?

Computational modeling and meditation are not frequently mentioned in the same breath. However, in this article we argue that computational modeling can provide insights into the mechanisms by which meditation produces its effects on cognition. Moreover, computational modeling allows the researcher to make predictions about how effects of meditation will generalize to other contexts such as other tasks, which can be tested in subsequent experiments.