Project description:Nonvolatile phase-change random access memory (PCRAM) is regarded as one of the promising candidates for emerging mass storage in the era of Big Data. However, relatively high programming energy hurdles the further reduction of power consumption in PCRAM. Utilizing narrow edge-contact of graphene can effectively reduce the active volume of phase change material in each cell, and therefore realize low-power operation. Here, it demonstrates that the power consumption can be reduced to ≈53.7 fJ in a cell with ≈3 nm-wide graphene nanoribbon (GNR) as edge-contact, whose cross-sectional area is only ≈1 nm2 . It is found that the polarity of the bias pulse determines its cycle endurance in the asymmetric structure. If a positive bias is applied to the graphene electrode, the endurance can be extended at least one order longer than the case with a reversal of polarity. In addition, the introduction of the hexagonal boron nitride (h-BN) multilayer leads to a low resistance drift and a high programming speed in a memory cell. The work represents a great technological advance for the low-power PCRAM and can benefit in-memory computing in the future.
Project description:Human interactions are increasingly taking place from a distance through methods of remote interpersonal communication like video chatting and social media. While remote interpersonal communication has existed for millennia—with the first postal system arising in ∼2400 B.C.—accelerated advances in technology and the recent global COVID-19 pandemic have led to a dramatic increase in remote interpersonal communication use in daily life. Remote interpersonal communication presents a challenge to the field of social-cognitive neuroscience, as researchers seek to understand the implications of various types of remote interpersonal communication for the “social brain.” The present paper reviews our current understanding of the social-cognitive neural network and summarizes critical differences between the neural correlates of social cognition in remote vs. face-to-face interactions. In particular, empirical and theoretical work is reviewed that highlight disparities in the neural mechanisms of social perception, evaluation of social stimuli, human motivation, evaluation of social reward, and theory of mind. Potential impacts of remote interpersonal communication on the development of the brain’s social-cognitive network are also discussed. Finally, this review closes with future directions for research on social-cognitive neuroscience in our digital technology-connected world and outlines a neural model for social cognition in the context of remote interpersonal communication. For the field of social-cognitive neuroscience to advance alongside of the ever-evolving society, it is crucial for researchers to acknowledge the implications and concepts suggested for future research in this review.
Project description:Cognitive neuroscience increasingly relies on complex data analysis methods. Researchers in this field come from highly diverse scientific backgrounds, such as psychology, engineering, and medicine. This poses challenges with respect to acquisition of appropriate scientific computing and data analysis skills, as well as communication among researchers with different knowledge and skills sets. Are researchers in cognitive neuroscience adequately equipped to address these challenges? Here, we present evidence from an online survey of methods skills. Respondents (n = 307) mainly comprised students and post-doctoral researchers working in the cognitive neurosciences. Multiple choice questions addressed a variety of basic and fundamental aspects of neuroimaging data analysis, such as signal analysis, linear algebra, and statistics. We analyzed performance with respect to the following factors: undergraduate degree (grouped into Psychology, Methods, and Biology), current researcher status (undergraduate student, PhD student, and post-doctoral researcher), gender, and self-rated expertise levels. Overall accuracy was 72%. Not surprisingly, the Methods group performed best (87%), followed by Biology (73%) and Psychology (66%). Accuracy increased from undergraduate (59%) to PhD (74%) level, but not from PhD to post-doctoral (74%) level. The difference in performance for the Methods vs. non-methods (Psychology/Biology) groups was especially striking for questions related to signal analysis and linear algebra, two areas particularly relevant to neuroimaging research. Self-rated methods expertise was not strongly predictive of performance. The majority of respondents (93%) indicated they would like to receive at least some additional training on the topics covered in this survey. In conclusion, methods skills among junior researchers in cognitive neuroscience can be improved, researchers are aware of this, and there is strong demand for more skills-oriented training opportunities. We hope that this survey will provide an empirical basis for the development of bespoke skills-oriented training programs in cognitive neuroscience institutions. We will provide practical suggestions on how to achieve this.
Project description:The past decade has seen growing concern about research practices in cognitive neuroscience, and psychology more broadly, that shake our confidence in many inferences in these fields. We consider how these issues affect developmental cognitive neuroscience, with the goal of progressing our field to support strong and defensible inferences from our neurobiological data. This manuscript focuses on the importance of distinguishing between confirmatory versus exploratory data analysis approaches in developmental cognitive neuroscience. Regarding confirmatory research, we discuss problems with analytic flexibility, appropriately instantiating hypotheses, and controlling the error rate given how we threshold data and correct for multiple comparisons. To counterbalance these concerns with confirmatory analyses, we present two complementary strategies. First, we discuss the advantages of working within an exploratory analysis framework, including estimating and reporting effect sizes, using parcellations, and conducting specification curve analyses. Second, we summarize defensible approaches for null hypothesis significance testing in confirmatory analyses, focusing on transparent and reproducible practices in our field. Specific recommendations are given, and templates, scripts, or other resources are hyperlinked, whenever possible.
Project description:Although people may endorse egalitarianism and tolerance, social biases can remain operative and drive harmful actions in an unconscious manner. Here, we investigated training to reduce implicit racial and gender bias. Forty participants processed counterstereotype information paired with one sound for each type of bias. Biases were reduced immediately after training. During subsequent slow-wave sleep, one sound was unobtrusively presented to each participant, repeatedly, to reactivate one type of training. Corresponding bias reductions were fortified in comparison with the social bias not externally reactivated during sleep. This advantage remained 1 week later, the magnitude of which was associated with time in slow-wave and rapid-eye-movement sleep after training. We conclude that memory reactivation during sleep enhances counterstereotype training and that maintaining a bias reduction is sleep-dependent.
Project description:Recent evidence supports a negative association between anxiety and cognitive control. Given age-related reductions in some cognitive abilities and the relation of late life anxiety to cognitive impairment, this negative association may be particularly relevant to older adults. This critical review conceptualizes anxiety and cognitive control from cognitive neuroscience and cognitive aging theoretical perspectives and evaluates the methodological approaches and measures used to assess cognitive control. Consistent with behavioral investigations of young adults, the studies reviewed implicate specific and potentially negative effects of anxiety on cognitive control processes in older adults. Hypotheses regarding the role of both aging and anxiety on cognitive control, the bi-directionality between anxiety and cognitive control, and the potential for specific symptoms of anxiety (particularly worry) to mediate this association, are specified and discussed.
Project description:Accumulating evidence suggests that many findings in psychological science and cognitive neuroscience may prove difficult to reproduce; statistical power in brain imaging studies is low and has not improved recently; software errors in analysis tools are common and can go undetected for many years; and, a few large-scale studies notwithstanding, open sharing of data, code, and materials remain the rare exception. At the same time, there is a renewed focus on reproducibility, transparency, and openness as essential core values in cognitive neuroscience. The emergence and rapid growth of data archives, meta-analytic tools, software pipelines, and research groups devoted to improved methodology reflect this new sensibility. We review evidence that the field has begun to embrace new open research practices and illustrate how these can begin to address problems of reproducibility, statistical power, and transparency in ways that will ultimately accelerate discovery.
Project description:The nested data structure is prevalent for cognitive measure experiments due to repeatedly taken observations from different brain locations within subjects. The analysis methods used for this data type should consider the dependency structure among the repeated measurements. However, the dependency assumption is mainly ignored in the cognitive neuroscience data analysis literature. We consider both statistical, and machine learning methods extended to repeated data analysis and compare distinct algorithms in terms of their advantage and disadvantages. Unlike basic algorithm comparison studies, this article analyzes novel neuroscience data considering the dependency structure for the first time with several statistical and machine learning methods and their hybrid forms. In addition, the fitting performances of different algorithms are compared using contaminated data sets, and the cross-validation approach. One of our findings suggests that the GLMM tree, including random term indices indicating the location of functional near-infrared spectroscopy optodes nested within experimental units, shows the best predictive performance with the lowest MSE, RMSE, and MAE model performance metrics. However, there is a trade-off between accuracy and speed since this algorithm is required the highest computational time.
Project description:Psychotic experiences may be understood as altered information processing due to aberrant neural computations. A prominent example of such neural computations is the computation of prediction errors (PEs), which signal the difference between expected and experienced events. Among other areas showing PE coding, hippocampal-prefrontal-striatal neurocircuits play a prominent role in information processing. Dysregulation of dopaminergic signaling, often secondary to psychosocial stress, is thought to interfere with the processing of biologically important events (such as reward prediction errors) and result in the aberrant attribution of salience to irrelevant sensory stimuli and internal representations. Bayesian hierarchical predictive coding offers a promising framework for the identification of dysfunctional neurocomputational processes and the development of a mechanistic understanding of psychotic experience. According to this framework, mismatches between prior beliefs encoded at higher levels of the cortical hierarchy and lower-level (sensory) information can also be thought of as PEs, with important consequences for belief updating. Low levels of precision in the representation of prior beliefs relative to sensory data, as well as dysfunctional interactions between prior beliefs and sensory data in an ever-changing environment, have been suggested as a general mechanism underlying psychotic experiences. Translating the promise of the Bayesian hierarchical predictive coding into patient benefit will come from integrating this framework with existing knowledge of the etiology and pathophysiology of psychosis, especially regarding hippocampal-prefrontal-striatal network function and neural mechanisms of information processing and belief updating.
Project description:A cognitive neuroscience perspective seeks to understand behavior, in this case disruptive behavior disorders (DBD), in terms of dysfunction in cognitive processes underpinned by neural processes. While this type of approach has clear implications for clinical mental health practice, it also has implications for school-based assessment and intervention with children and adolescents who have disruptive behavior and aggression. This review articulates a cognitive neuroscience account of DBD by discussing the neurocognitive dysfunction related to emotional empathy, threat sensitivity, reinforcement-based decision-making, and response inhibition. The potential implications for current and future classroom-based assessments and interventions for students with these deficits are discussed.