scholarly journals The Malleability of Attentional Capture

2021 ◽  
Author(s):  
Han Zhang ◽  
Tessa Abagis ◽  
John Jonides
Keyword(s):  
Individual Differences ◽  
Attentional Capture ◽  
Visual Cognition ◽  
Color Singleton ◽  
Training Data ◽  
Clear Understanding ◽  
Previous Trial ◽  
Attentional State ◽  
Underlying Mechanisms ◽  
And Training

We suggest that consideration of trial-by-trial variations, individual differences, and training data will enrich the current framework in Luck et al. (2020). We consider whether attentional capture is modulated by trial-by-trial fluctuations of attentional state and experiences on the previous trial. We also consider whether individual differences may affect attentional capture while highlighting potential challenges in using the color-singleton task to measure individual differences. Finally, performance in the color-singleton task can be modified dramatically with practice but the underlying mechanisms are not entirely clear. Understanding the malleability of attentional capture may broaden the current framework and resolve outstanding questions. The version of record of this manuscript will be available in Visual Cognition (2021), https://doi.org/10.1080/13506285.2021.1915903

The Science of Wisdom in a Polarized World: Knowns and Unknowns

2020 ◽  
Author(s):  
Igor Grossmann ◽  
Nic M. Weststrate ◽  
Monika Ardelt ◽  
Justin Peter Brienza ◽  
Mengxi Dong ◽  
...  
Keyword(s):  
Individual Differences ◽  
Problem Solving ◽  
Best Practices ◽  
Cognitive Sciences ◽  
Methodological Challenges ◽  
The Common ◽  
Empirical Approaches ◽  
And Training ◽  
Contextual Differences

Interest in wisdom in the cognitive sciences, psychology, and education has been paralleled by conceptual confusions about its nature and assessment. To clarify these issues and promote consensus in the field, wisdom researchers met in Toronto in July of 2019, resolving disputes through discussion. Guided by a survey of scientists who study wisdom-related constructs, we established a common wisdom model, observing that empirical approaches to wisdom converge on the morally-grounded application of metacognition to reasoning and problem-solving. After outlining the function of relevant metacognitive and moral processes, we critically evaluate existing empirical approaches to measurement and offer recommendations for best practices. In the subsequent sections, we use the common wisdom model to selectively review evidence about the role of individual differences for development and manifestation of wisdom, approaches to wisdom development and training, as well as cultural, subcultural, and social-contextual differences. We conclude by discussing wisdom’s conceptual overlap with a host of other constructs and outline unresolved conceptual and methodological challenges.

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

scholarly journals Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics

2021 ◽  
Vol 7 (3) ◽  
pp. 59
Author(s):  
Yohanna Rodriguez-Ortega ◽  
Dora M. Ballesteros ◽  
Diego Renza
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Video Editing ◽  
Forgery Detection ◽  
Copy Move Forgery Detection ◽  
The Impact ◽  
And Training ◽  
Selection Of ◽  
Traditional Image

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

scholarly journals Covariate-Profile Similarity Weighting and Bagging Studies with the Study Strap: Multi-Study Learning for Human Neurochemical Sensing

2019 ◽  
Author(s):  
Gabriel Loewinger ◽  
Prasad Patil ◽  
Kenneth Kishida ◽  
Giovanni Parmigiani
Keyword(s):  
New Technology ◽  
R Package ◽  
Training Data ◽  
Tuning Parameter ◽  
Ensemble Classifiers ◽  
Profile Similarity ◽  
Special Cases ◽  
Study Heterogeneity ◽  
Brain Measurements ◽  
And Training

Prediction settings with multiple studies have become increasingly common. Ensembling models trained on individual studies has been shown to improve replicability in new studies. Motivated by a groundbreaking new technology in human neuroscience, we introduce two generalizations of multi-study ensemble predictions. First, while existing methods weight ensemble elements by cross-study prediction performance, we extend weighting schemes to also incorporate covariate similarity between training data and target validation studies. Second, we introduce a hierarchical resampling scheme to generate pseudo-study replicates (“study straps”) and ensemble classifiers trained on these rather than the original studies themselves. We demonstrate analytically that existing methods are special cases. Through a tuning parameter, our approach forms a continuum between merging all training data and training with existing multi-study ensembles. Leveraging this continuum helps accommodate different levels of between-study heterogeneity.Our methods are motivated by the application of Voltammetry in humans. This technique records electrical brain measurements and converts signals into neurotransmitter concentration estimates using a prediction model. Using this model in practice presents a cross-study challenge, for which we show marked improvements after application of our methods. We verify our methods in simulations and provide the studyStrap R package.

Crosstalk between Microglia and Neurons in Neurotrauma: An Overview of the Underlying Mechanisms

2021 ◽  
Vol 19 ◽  
Author(s):  
Muhammad Ali Haidar ◽  
Stanley Ibeh ◽  
Zaynab Shakkour ◽  
Mohammad Amine Reslan ◽  
Judith Nwaiwu ◽  
...  
Keyword(s):  
Brain Injury ◽  
Microglial Activation ◽  
Trauma Treatment ◽  
Cell Contact ◽  
Clear Understanding ◽  
Neuroinflammatory Response ◽  
Proinflammatory Responses ◽  
Underlying Mechanisms ◽  
The Brain ◽  
Cns Anomalies

: Microglia are the resident immune cells of the brain and play a crucial role in housekeeping and maintaining homeostasis of the brain microenvironment. Upon injury or disease, microglial cells become activated, at least partly, via signals initiated by injured neurons. Activated microglia, thereby, contribute to both neuroprotection and neuroinflammation. However, sustained microglial activation initiates a chronic neuroinflammatory response which can disturb neuronal health and disrupt communications between neurons and microglia. Thus, microglia-neuron crosstalk is critical in a healthy brain as well as during states of injury or disease. As most studies focus on how neurons and microglia act in isolation during neurotrauma, there is a need to understand the interplay between these cells in brain pathophysiology. This review highlights how neurons and microglia reciprocally communicate under physiological conditions and during brain injury and disease. Furthermore, the modes of microglia-neuron communication are exposed, focusing on cell-contact dependent signaling and communication by the secretion of soluble factors like cytokines and growth factors. In addition, how microglia-neuron interactions could exert either beneficial neurotrophic effects or pathologic proinflammatory responses are discussed. We further explore how aberrations in microglia-neuron crosstalk may be involved in central nervous system (CNS) anomalies, namely: traumatic brain injury (TBI), neurodegeneration, and ischemic stroke. A clear understanding of how the microglia-neuron crosstalk contributes to the pathogenesis of brain pathologies may offer novel therapeutic avenues of brain trauma treatment.

Sign in / Sign up

Export Citation Format

Share Document