Victoria Romero

Functional connectivity within and between intrinsic brain networks correlates with trait mind wandering.

&NA; Individual differences across a variety of cognitive processes are functionally associated with individual differences in intrinsic networks such as the default mode network (DMN). The extent to which these networks correlate or anticorrelate has been associated with performance in a variety of circumstances. Despite the established role of the DMN in mind wandering processes, little research has investigated how large‐scale brain networks at rest relate to mind wandering tendencies outside the laboratory. Here we examine the extent to which the DMN, along with the dorsal attention network (DAN) and frontoparietal control network (FPCN) correlate with the tendency to mind wander in daily life. Participants completed the Mind Wandering Questionnaire and a 5‐min resting state fMRI scan. In addition, participants completed measures of executive function, fluid intelligence, and creativity. We observed significant positive correlations between trait mind wandering and 1) increased DMN connectivity at rest and 2) increased connectivity between the DMN and FPCN at rest. Lastly, we found significant positive correlations between trait mind wandering and fluid intelligence (Ravens) and creativity (Remote Associates Task). We interpret these findings within the context of current theories of mind wandering and executive function and discuss the possibility that certain instances of mind wandering may not be inherently harmful. Due to the controversial nature of global signal regression (GSReg) in functional connectivity analyses, we performed our analyses with and without GSReg and contrast the results from each set of analyses. HighlightsResting state functional connectivity was examined in several intrinsic networks.Connectivity between the DMN and FPCN positively correlated with trait mind wandering.Connectivity within the DMN positively correlated with trait mind wandering.Trait mind wandering positively correlated with fluid intelligence and creativity.

Physiological Synchronization Is Associated with Narrative Emotionality and Subsequent Behavioral Response

Neurophysiological compliance is a correlation of neurophysiological measures (synchronicity) between individuals. Higher compliance among team members is related to better performance, and higher synchronicity occurs during emotional moments of a stimulus. The aim of the current study is to examine whether synchrony may be observable via peripheral nervous system (PNS) activity. We used inter-subject correlation (ISC) analysis to assess whether synchronicity of PNS measures are related to stimulus emotionality or similarity in behavioral responses. Participants viewed a 100-second emotional video, followed by an appeal to donate experimental earnings to a related charity. We found high ISC for cardiac and electrodermal activity (EDA) between donors versus non-donors. For both groups, we found an association between ISC of cardiac activity and emotional moments in the stimulus. For non-donors we found an association between ISC of EDA and emotional moments. Our findings indicate that PNS measures yield similar results to neurophysiological measures.

A prototype toolkit for sensing and modeling individual and team state

Teams of individuals working together toward a common goal must be skilled at multi-tasking to perform their own work while maintaining shared attention across the team. Experimenters who study team performance can use cutting edge methods to assess physiological, neurophysiological, and behavioral underpinnings of optimal performance; however, this requires an adequate understanding of how these signals correlate with individual and team performance. We designed a toolkit to support experimenters in evaluating individual and team performance in a laboratory setting, in testing and validating models of performance, and in developing and validating augmentation strategies to improve performance. Our toolkit provides a framework that flexibly integrates current and emerging sensors. The data fusion tool fuses time-synchronized sensor data to assess performance. The model-building and execution toolset enables experimenters to choose previously entered models, adapt these models according to the current experiment, or develop new models to test. The real-time assessment tool enables experimenters to monitor the state of individual subjects and the team as a whole (e.g., stress, workload, focused attention) throughout the experiment, and how these states relate to performance. This information is then used by the real-time augmentation tool, which suggests augmentations to optimize that performance. Together, these tools provide a proof-of-concept prototype of a flexible modeling tool that would allow sensor inputs to be used to model and predict both individual and team performance.

Designing an Adaptive Approach for the Real-Time Assessment and Augmentation of Performance of Cyber Analyst Teams

Full-spectrum cyber operations, including both Cyber Network Attack and Cyber Network Defense, place enormous cognitive demands on operators and teams. When demands are too high or tasks are not properly allocated, performance degrades, and missions may fail. A thorough, real-time evaluation of the state of the individual and the team would be an effective approach to avoiding operator overload. We describe an approach that supports the real-time assessment and augmentation of team performance. First, the physiological and affective state and the behavioral performance of individual operators is measured by fusing data from individual sensors. Signals from individual operators are then fused to enable a comprehensive and holistic characterization of team performance. Advanced modeling techniques are then implemented to compare current team performance with optimal levels of performance. Finally, augmentation strategies are recommended to optimize performance of cyber teams.

Mindfulness-based training with transcranial direct current stimulation modulates neuronal resource allocation in working memory: A randomized pilot study with a nonequivalent control group

Mindfulness-based training (MBT) and transcranial electrical stimulation (TES) methods such as direct current stimulation (tDCS) have demonstrated promise for the augmentation of cognitive abilities. The current study investigated the potential compatibility of concurrent “electrical” MBT and tDCS (or eMBT) by testing its combined effects on behavioral and neurophysiological indices of working memory (WM) and attentional resource allocation. Thirty-four healthy participants were randomly assigned to either a MBT task with tDCS group (eMBT) or an active control training task with sham tDCS (Control) group. Training lasted 4-weeks, with up to twenty MBT sessions and with up to eight of those sessions that were eMBT sessions. Electroencephalography was acquired during varying WM load conditions using the n-back task (1-, 2-, 3-back), along with performance on complex WM span tasks (operation and symmetry span) and fluid intelligence measures (Ravens and Shipley) before and after training. Improved performance was observed only on the 3-back and spatial span tasks for eMBT but not the Control group. During 3-back performance in the eMBT group, an increase in P3 amplitude and theta power at electrode site Pz was also observed, along with a simultaneous decrease in frontal midline P3 amplitude and theta power compared to the Control group. These results are consistent with the neural efficiency hypothesis, where higher cognitive capacity was associated with more distributed brain activity (i.e., increase in parietal and decrease in frontal amplitudes). Future longitudinal studies are called upon to further examine the direct contributions of tDCS on MBT by assessing the differential effects of electrode montage, polarity, current strength and a direct contrast between the eMBT and MBT conditions on performance and neuroimaging outcome data. While preliminary, the current results provided evidence for the potential compatibility of using eMBT to modulate WM capacity through the allocation of attention and its neurophysiological correlates.

Correction to: Reduced interference in working memory following mindfulness training is associated with increases in hippocampal volume

The article Reduced interference in working memory following mindfulness training is associated with increases in hippocampal volume, written by Jonathan Greenberg, Victoria L. Romero, Seth Elkin-Frankston, Matthew A. Bezdek, Eric H. Schumacher, and Sara W. Lazar.

Reduced interference in working memory following mindfulness training is associated with increases in hippocampal volume

Proactive interference occurs when previously relevant information interferes with retaining newer material. Overcoming proactive interference has been linked to the hippocampus and deemed critical for cognitive functioning. However, little is known about whether and how this ability can be improved or about the neural correlates of such improvement. Mindfulness training emphasizes focusing on the present moment and minimizing distraction from competing thoughts and memories. It improves working memory and increases hippocampal density. The current study examined whether mindfulness training reduces proactive interference in working memory and whether such improvements are associated with changes in hippocampal volume. 79 participants were randomized to a 4-week web-based mindfulness training program or a similarly structured creative writing active control program. The mindfulness group exhibited lower proactive interference error rates compared to the active control group following training. No group differences were found in hippocampal volume, yet proactive interference improvements following mindfulness training were significantly associated with volume increases in the left hippocampus. These results provide the first evidence to suggest that (1) mindfulness training can protect against proactive interference, and (2) that these benefits are related to hippocampal volumetric increases. Clinical implications regarding the application of mindfulness training in conditions characterized by impairments to working memory and reduced hippocampal volume such as aging, depression, PTSD, and childhood adversity are discussed.

A Flexible Framework for the Creation of Narrative-Centered Tools

To better support the creation of narrative-centered tools, developers need a flexible framework to integrate, catalog, select, and reuse narrative models. Computational models of narrative enable the creation of software tools to aid narrative processing, analysis, and generation. Narrative-centered tools explicitly or implicitly embody one or more models of narrative by their definition. However, narrative model creation is often expensive and difficult with no guaranteed benefit to the end system. This paper describes our preliminary approach towards creating the SONNET narrative framework, a flexible framework to integrate, catalog, select, and reuse narrative models, thereby lowering development costs and improving benefits from each model. The framework includes a lightweight ontology language for the definition of key terms and interrelationships among them. The framework specifies model metadata to allow developers to discover and understand models more readily. We discuss the structure of this framework and ongoing development incorporating narrative models.

Towards Evaluating Computational Models of Intuitive Decision Making with fMRI Data

A vast array of everyday tasks require individuals to use intuition to make decisions and act effectively, including civilian and military professional tasks such as those undertaken by firefighters, police, search and rescue, small unit leaders, and information analysts. To better understand and train intuitive decision making (IDM), we envision future training systems will represent IDM through computational models and use these models to guide IDM learning. This paper presents the first steps to the problem of validating computational models of IDM. To test if these models correlate with human performance, we examine methods to analyze functional magnetic resonance imaging (fMRI) data of human participants performing intuitive tasks. In particular, we examine the use of a new deep learning representation called sum-product networks to perform model-based fMRI analysis. Sum-product networks have been shown to be simpler, faster, and more effective than previous deep learning approaches, making them ideal candidates for this computationally demanding analysis.