scholarly journals Temporal accuracy of human cortico-cortical interactions

2016 ◽  
Vol 115 (4) ◽  
pp. 1810-1820 ◽  
Author(s):  
Idan Tal ◽  
Moshe Abeles
Keyword(s):  
Cognitive Processes ◽  
Template Matching ◽  
Cognitive Process ◽  
Point Processes ◽  
Time Window ◽  
Spatiotemporal Patterns ◽  
Timing Accuracy ◽  
Synthetic Aperture Magnetometry ◽  
Better Than

The precision in space and time of interactions among multiple cortical sites was evaluated by examining repeating precise spatiotemporal patterns of instances in which cortical currents showed brief amplitude undulations. The amplitudes of the cortical current dipoles were estimated by applying a variant of synthetic aperture magnetometry to magnetoencephalographic (MEG) recordings of subjects tapping to metric auditory rhythms of drum beats. Brief amplitude undulations were detected in the currents by template matching at a rate of 2–3 per second. Their timing was treated as point processes, and precise spatiotemporal patterns were searched for. By randomly teetering these point processes within a time window W, we estimated the accuracy of the timing of these brief amplitude undulations and compared the results with those obtained by applying the same analysis to traces composed of random numbers. The results demonstrated that the timing accuracy of patterns was better than 3 ms. Successful classification of two different cognitive processes based on these patterns suggests that at least some of the repeating patterns are specific to a cognitive process.

scholarly journals Serial and syntactic processing in the visual analysis of multi-digit numbers

2020 ◽  
Author(s):  
Dror Dotan ◽  
Ofir Eliahou ◽  
Sharon Cohen
Keyword(s):  
Cognitive Processes ◽  
Visual Information ◽  
Cognitive Process ◽  
Visual Analysis ◽  
Number System ◽  
Syntactic Processing ◽  
Read Aloud ◽  
Detailed Model ◽  
The Right ◽  
Better Than

The visual analysis of letter strings is a separate cognitive process from the analysis of digit strings. Recent studies have hypothesized that these processes are not only separate but also qualitatively different, in that they may encode information specific to numbers or to words. To examine this hypothesis and to shed further light on the visual analysis of numbers, we asked adults to read aloud multi-digit strings presented to them for brief durations. Their performance was better in digits on the number’s left side than in digits farther to the right, with better performance in the two outer digits than their neighbors. This indicates the digits were processed serially, from left to right. Visual similarity of digits increased the likelihood of errors, and when a digit migrated to an incorrect position, it was most often to an adjacent location. Interestingly, the positions of 0 and 1 were encoded better than the positions of 2-9, and 2-9 were identified better when they were next to 0 or 1. To accommodate these findings, we propose a detailed model for the visual analysis of digit strings. The model assumes imperfect digit detectors in which a digit’s visual information leaks to adjacent locations, and a compensation mechanism that inhibits this leakage. Crucially, the compensating inhibition is stronger for 0 and 1 than for the digits 2-9, presumably because of the importance of 0 and 1 in the number system. This sensitivity to 0 and 1 makes the visual analyzer specifically adapted to numbers, not words, and may be one of the brain’s reasons to implement the visual analysis of numbers and words in two separate cognitive processes.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Development of training method for vessel traffic service based on cognitive process

2021 ◽  
Author(s):  
Binbing Song ◽  
Hiroko Itoh ◽  
Yasumi Kawamura
Keyword(s):  
Cognitive Processes ◽  
Cognitive Process ◽  
Process Model ◽  
Situational Awareness ◽  
International Association ◽  
Tokyo Bay ◽  
Training Methods ◽  
Training Method ◽  
Oral Transmission ◽  
Maritime Traffic

AbstractVessel traffic service (VTS) is important to protect the safety of maritime traffic. Along with the expansion of monitoring area per VTS operator in Tokyo Bay, Japan, inexperienced operators must acquire the ability to quickly and accurately detect conditions that requires attention (CRAs) from a monitoring screen. In our previous study (Song B, Itoh H, Kawamura Y, Fukuto J (2018) Analysis of Cognitive Processes of Operators of Vessel Traffic Service. In: Proceedings of the 2018 International Association of Institutes of Navigation. IAIN 2018, pp 529–534, Song et al., J Jpn Inst Navig 140:48–54, 2019), we established a task analysis method based on the assumption that the cognitive process model consists of three stages: “situational awareness”, “situation judgment”, and “decision making”. A simulation experiment was conducted for VTS operators with different levels of ability and their cognitive processes were compared based on the observation of eye movements. The results showed that the inexperienced operators’ abilities to predict situation changes were lower. And it was considered that oral transmission of the knowledge is difficult, thus new training methods are needed to help the inexperienced operators to understand the prediction methods of experienced operators. In this study, based on the cognitive process of an experienced operator, we analyzed the prediction procedures of situation changes and developed an educational tool called vessel traffic routine (VTR). The training method learning VTR aims to quickly improve inexperienced VTS operators’ abilities to predict situation changes. A simulation verification experiment of the VTR effect was conducted for four inexperienced operators, who were divided into two groups with and without prior explanation of VTR. By evaluating the cognitive processes of inexperienced operators, it was confirmed that those given prior explanations of VTR were better at detecting CRAs.

Translation teams as cognitive systems

2021 ◽  
Vol 8 (2) ◽  
pp. 307-327
Author(s):  
Richard Pleijel
Keyword(s):  
Cognitive Processes ◽  
Cognitive Process ◽  
Time Perspective ◽  
Focal Point ◽  
Translation Studies ◽  
Cognitive Systems ◽  
Group Level ◽  
Cognitive Artifacts ◽  
Archival Material

Abstract This paper aims to bring research on different forms of group-level cognition into conversation with Cognitive Translation Studies (CTS), the focal point of the paper being cognitive processes in translation teams. It is argued that an analysis of cognition in translation teams, which exhibit the properties of a cognitive system, needs to be placed on group-level. A case study of a team, translating the Hebrew Bible Book of Psalms into Swedish in the 1980’s, is presented. The empirical base for the case study consists of archival material in the form of draft translations and paratexts. The methodological question is thus raised whether, and if so in what way, cognitive processes may be analyzed retrospectively, and not only from a real time perspective. By treating the archival material as cognitive artifacts which have constituted an integral part of the team’s cognitive process, the question is tentatively answered in a favourable way. This, it is finally argued, opens up interesting possibilities for joining CTS with translator archives research, Genetic Translation Studies (GTS), and cognitive archeology.

Transdiagnostic Cognitive Processes in Chronic Pain and Comorbid PTSD and Depression in Veterans

2021 ◽  
Author(s):  
Melissa A Day ◽  
Rhonda M Williams ◽  
Aaron P Turner ◽  
Dawn M Ehde ◽  
Mark P Jensen
Keyword(s):  
Chronic Pain ◽  
Depressive Symptoms ◽  
Pain Intensity ◽  
Cognitive Processes ◽  
Cognitive Process ◽  
Stress Disorder ◽  
Secondary Analysis ◽  
Lower Pain ◽  
Depression Scales ◽  
Ptsd Group

Abstract Background Chronic pain in Veterans is a major problem compounded by comorbid posttraumatic stress disorder (PTSD) and depression. Adopting a transdiagnostic framework to understanding “shared territory” among these diagnoses has the potential to inform our understanding of the underlying cognitive processes and mechanisms that transverse diagnostic boundaries. Purpose To examine the associations between pain-related cognitive processes (diversion, distancing, absorption, and openness), pain intensity, PTSD and depressive symptoms, and the extent to which Veterans with chronic pain with and without comorbid PTSD and depression engage in different/similar pain-related cognitive processes. Methods Secondary analysis of pretreatment data with a subsample (n = 147) of Veterans with chronic pain from a larger clinical trial. Pretreatment PCL-5 and PROMIS Depression scales were used to categorize participants into three groups: (a) Pain-only; (b) Pain-PTSD; and (c) Pain-PTSD-DEP. Results Compared to the Pain-only group, the Pain-PTSD and Pain-PTSD-DEP groups reported significantly greater pain intensity, PTSD and depressive symptoms, and ruminative pain absorption. The Pain-PTSD-DEP group had significantly lower pain diversion and pain openness scores. When diversion and openness were used within the Pain-PTSD-DEP group, however, they were both associated with lower pain intensity and openness was additionally associated with lower PTSD scores. However, in the Pain-PTSD group, pain openness was associated with higher depression scores. Conclusions Across increasing complexity of comorbidity profiles (i.e., one vs. two comorbid conditions), ruminative absorption with pain emerged as a cognitive process that transverses diagnoses and contributes to worse outcomes. Nonjudgmental acceptance may not be universally beneficial, potentially depending upon the nature of comorbidity profiles.

Are the Dimensions Underlying Aesthetic and Affective Judgment the Same?

1998 ◽  
Vol 16 (2) ◽  
pp. 97-113 ◽  
Author(s):  
Rüdiger Baltissen ◽  
Barbara-Maria Ostermann
Keyword(s):  
Cognitive Processes ◽  
Cognitive Process ◽  
Dominant Factor ◽  
Factor Analyses ◽  
Emotional Factor ◽  
Emotional Factors ◽  
Affective Judgment ◽  
Art Objects ◽  
Emotional Pictures ◽  
Main Factor

To investigate whether aesthetic and affective judgment are similar, ninety-six subjects rated twenty-four art pictures varying in theme and date of creation as well as twenty-three emotion inducing slides (IAPS) representing different emotional qualities on nine bipolar 8-point scales, e.g., warm-cold, meaningful-not meaningful. Factor analyses performed separately for each picture set revealed two basic dimensions, named cognitive and emotional factors, explaining about 60 percent of the variance. In the case of artworks, the dominant factor was constituted by cognitive scales (meaningful, interesting, simple); regarding the affective slides, the main factor was constituted by emotional scales (warm, emotional, arousing). ANOVAs confirmed the expected differences between themes and date of creation for the art picture as well as the differences between emotional qualities of the IAPS for both, the cognitive and the emotional factor. Proportion of variance of the ratings explained by gender, age, and education was low. Overall, results suggest that looking at art objects is a predominantly cognitive process requiring understanding whereas looking at emotional pictures evokes feelings with cognitive processes being only marginally involved.

scholarly journals Is hypochondriasis an anxiety disorder?

2009 ◽  
Vol 194 (6) ◽  
pp. 481-482 ◽  
Author(s):  
Bunmi O. Olatunji ◽  
Brett J. Deacon ◽  
Jonathan S. Abramowitz
Keyword(s):  
Anxiety Disorder ◽  
Cognitive Processes ◽  
Somatoform Disorder ◽  
Diagnostic Classification

SummaryAlthough hypochondriasis is currently classified as a somatoform disorder, the underlying cognitive processes may be more consistent with an anxiety disorder. This observation has important implications for treatment and subsequent revisions of the diagnostic classification of hypochondriasis.

Automatic Classification of ECG Using Multi-Classifiers with a New Fusion Method

2014 ◽  
Vol 687-691 ◽  
pp. 3917-3922
Author(s):  
Yi Chang Wang ◽  
Feng Qi Yan ◽  
Yu Fang
Keyword(s):  
Traditional Method ◽  
Heart Beat ◽  
Vector Model ◽  
Support Vector ◽  
Beat Classification ◽  
Computer Aided Analysis ◽  
Important Basis ◽  
Voting Method ◽  
Better Than

ECG signal contains abundant information of human heart activity. It is important basis of doctors’ diagnose. With the development of computer technology, computer aided analysis has been widely applied in the field of ECG analysis. Most of the traditional method is based on single classifier and too complex. Also, the accuracy is not high. This paper focuses on ECG heart beat classification, extracting different types of feature, training different classifiers by vector model and support vector machine (SVM), merging the result of multiple classifiers. In this paper, we used the advanced voting method (voting by weight) to fusion the result of different classifier, having compared it with the traditional voting method.It performed better than traditional method in term of accuracy

scholarly journals Nonlinear Classification of EEG recordings from patients with Alzheimer’s Disease using Gaussian Process Latent Variable Model

2020 ◽  
Author(s):  
S. Rajintha. A. S. Gunawardena ◽  
Fei He ◽  
Ptolemaios Sarrigiannis ◽  
Daniel J. Blackburn
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gaussian Process ◽  
Manifold Learning ◽  
Latent Variable ◽  
Time Window ◽  
Support Vector ◽  
Nonlinear Dimensionality Reduction ◽  
Variable Model

AbstractIn this work, nonlinear temporal features from multi-channel EEGs are used for the classification of Alzheimer’s disease patients from healthy individuals. This was achieved by temporal manifold learning using Gaussian Process Latent Variable Models (GPLVM) as a nonlinear dimensionality reduction technique. Classification of the extracted features was undertaken using a nonlinear Support Vector Machine. Comparisons were made against the linear counterpart, Principle Component Analysis while exploring the effect of the time window or EEG epoch length used. It was demonstrated that temporal manifold learning using GPLVM is better in extracting features that attain high separability and prediction accuracy. This work aims to set the significance of using GPLVM temporal manifold learning for EEG feature extraction in the classification of Alzheimer’s disease.

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